loss function literature review

Survey of the loss function in classification models: Comparative study in healthcare and medicine

• Published: 05 June 2024

Cite this article

• Sepideh Etemadi 1 &
• Mehdi Khashei 1 , 2  

The selection of an appropriate classification approach depends heavily on the classification rate, which is the most important factor in achieving the desired decision quality. While researchers have examined the impact of different features on the performance of classification approaches, cost/loss functions have received less attention in the comparative literature review, despite their theoretical significance in influencing the classification rate. This paper aims to address this gap by conducting a comparative study on the influence of different cost/loss functions on the classification rate of diverse classifiers. To achieve this objective, the study considers the five most popular and commonly utilized types of cost/loss functions: linear and nonlinear continuous, linear and nonlinear semi-continuous, and discrete cost/loss functions. Furthermore, it takes into consideration the three primary categories of classification approaches: statistical, intelligent, and deep learning classifiers. In addition, a total of 44 benchmark datasets from three distinct domains of medicine, specifically cancer and disease diagnosis, therapy, and biology science, are chosen for analysis. Based on empirical findings, it is evident that the selection of cost/loss functions has a notable impact on the classification rate. The numerical results demonstrate that the discrete cost/loss function performs the best, followed by the semi-continuous and continuous cost/loss functions, in that order. This clearly highlights the positive and direct correlation between aligning the cost/loss function with the goal function of classification approaches and achieving a higher classification rate. Moreover, the average effectiveness of the nonlinear versions of the semi-continuous and continuous cost/loss functions is comparable to that of their linear counterparts. While the choice of cost/loss function can influence the classification rate of various classifiers, the degree of improvement varies depending on the classifier type. In general, statistical classifiers demonstrate a greater degree of enhancement, followed by intelligent classifiers and deep learning models in second and third positions, respectively. Overall, the study reveals a negative correlation between the complexity of classifiers and the improvement in the classification rate when altering the cost/loss function. Furthermore, the numerical findings suggest that the variations in the degree of improvement achieved by changing the cost/loss functions are substantial and affected by the type and domain of the data.

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Department of Industrial and Systems Engineering, Isfahan University of Technology (IUT), Isfahan, 84156-83111, Iran

Sepideh Etemadi & Mehdi Khashei

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All authors have the same contribution to preparing this manuscript. (1- Sepideh Etemadi: Data curation, Writing- Original draft preparation, Visualization, Writing- Reviewing, and Editing. 2- Mehdi Khashei: Conceptualization, Methodology, Investigation, Software, Writing- Reviewing, and Editing).

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Etemadi, S., Khashei, M. Survey of the loss function in classification models: Comparative study in healthcare and medicine. Multimed Tools Appl (2024). https://doi.org/10.1007/s11042-024-19543-8

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• Classification
• Cost/Loss Functions
• Shallow and Deep intelligent Classifiers
• Disease and Cancer Diagnosis

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A review of small object and movement detection based loss function and optimized technique

The objective of this study is to supply an overview of research work based on video-based networks and tiny object identification. The identification of tiny items and video objects, as well as research on current technologies, are discussed first. The detection, loss function, and optimization techniques are classified and described in the form of a comparison table. These comparison tables are designed to help you identify differences in research utility, accuracy, and calculations. Finally, it highlights some future trends in video and small object detection (people, cars, animals, etc.), loss functions, and optimization techniques for solving new problems.

1 Introduction

Object detection is a computer technology method that is connected to object recognition and networks. It can recognize instances of specific semantic item categories (for example, people, buildings, or cars) in computer-generated pictures and videos [ 1 ]. In-depth object recognition research may be shown in the recognition of faces and pedestrians. Object recognition is used in many image-processing applications, including image search and video surveillance [ 2 ]. Each object class contains distinguishing characteristics that help in categorizing the class. For example, all circles are circular. These specialized procedures are employed in the identification of object classes. When looking for a circle, for example, you are looking for anything that is at a certain distance away from the point (i.e., the center). Items that are upright at the corners and have the same side length are also necessary while looking for a square. A similar method is used for facial recognition, which can identify the eyes, nose, and mouth as well as skin color and the distance between the eyes [ 3 , 4 ]. The challenge of anticipating the types and locations of distinct things present in a picture is known as a realm of image processing, there is a difficulty with object recognition. In contrast to classification, each instance of an object is recognized in the object recognition task, so object recognition is a measurement task for each instance. In contrast to classification, each instance of an object is recognized in the object recognition task, so object recognition is a measurement task for each instance as the scale-invariant feature transform [5] and the histogram orientation gradient [ 6 ].

A remote sensing imaging study has gained a lot of interest as the remote sensing technology has advanced. Simultaneously, the identification of ships and airplanes using optical remote sensing images [ 7 ] is significant in a wide range of applications [ 8 ]. Over the last 10 years, many regional convolutional neural network (R-CNN) techniques [ 9 ], particularly faster R-CNN [ 10 ], have been utilized in the high-resolution identification of high-resolution items in the PASCAL VOC dataset. They cannot detect extremely small things information in general because complex pictures are hard to evaluate due to their low construction and appearance [ 11 ]. Objects in optical distant sensing pictures, on the other hand, typically have smaller characteristics, which bring more problems than traditional object detection, and there are still few good solutions [ 12 , 13 ]. There have been some attempts to overcome the issue of small object detection (SOD). By simply raising the input picture resolution, it is simple to enhance the resolution of the object’s fine details, which generally results in a substantial investment in training and testing [ 14 , 15 ]. Another method aimed to create a multi-scale representation that combined multiple functions at a lower level to expand the function at a higher level, thereby naively magnifying the size of the function [ 16 , 17 ].

Video motion detection is a function of IP cameras, recording software and NVR used to trigger an alarm by detecting physical movement in a designated area. In real time, the data from the current image is compared to the data from the previous image. Every major change triggers a camera warning [ 18 ]. This alarm can be used to trigger many operations, such as sending current real-time images via email, tilt or move the camera at a certain point, control external devices (such as turning on lights or beeping) etc. and use still images for regular object recognition. etc. Use still images for regular object recognition. There is increasing recognition of video objects (VOs), autonomous driving [ 19 ], and video surveillance [ 20 , 21 ]. In 2015, the use of video for object recognition became a new challenge for the surveillance [ 22 ]. The Image Net Visual Recognition Challenge is a large-scale visual recognition competition (ILSVRC2015). With the help of ILSVRC2015, the research on VO identification has progressed. Identifying things in each frame was one of the first attempts to recognize VOs.

This work studies and analyses the abovementioned network-based small target method, video detection, loss function, and optimization methods at different stages.

The main objective of this study is to give a summary of the studies on the identification of small objects and video-based networks. The first topic covered is the detection of small objects and VOs, as well as a study on current technology. The classification and description of the detection, loss function, and optimization strategies are presented as a comparison table.

2 Literature review

There are various approaches present for small object and movement detection. Some of the important literature that covers more important object detection is discussed below.

Chen et al. [ 23 ] proposed using deep learning to identify small objects. This study starts with a short overview of the four pillars of microscopic item identification: multi-scale rendering, contextual information, super-resolution, and range. Then, it offers a range of modern datasets for detecting small objects. Furthermore, current micro-object detection systems are being studied with an emphasis on modifications and tweaks to improve detection efficiency, in comparison to conventional object recognition technologies.

Ren et al. [ 24 ] studied how to tackle the challenge of employing remote sensing technology to identify tiny objects in optical imaging, and an enhanced faster R-CNN approach was developed. As a consequence of common characteristics, the studio built a comparable architecture that used downlink and avoided the use of connections to produce a single high-resolution, high-level feature map. This is critical so that we can view all the identified items.

Huang et al. [ 25 ] created a model for recognizing prominent objects in hyperspectral pictures on wireless networks, thereby using visibility optimization to CNN characteristics. The model first uses a two-channel CNN to extract the spatial and spectral properties of the same measurement and then employs functional combinations to produce the final bump map, which optimizes the bump value of the foreground and foreground signals. The CNN function is used to compute the background.

Hua et al. [ 26 ] proposed a real-time object recognition framework for cascaded convolutional networks using visual attention mechanisms, convolutional storage network inference methods, and semantic object relevance, combined with the fast and exact functions of deep learning algorithms, and performed ablation and comparative experiments. By testing the cascade network introduced in this study, different datasets can be used and more complex detection results can be obtained.

Yundong et al. [ 27 ] proposed a new method, that is, multi-block SSDs that add sub-layers to detect and extend local context information. The test results of multiple SSDs and conventional SSDs are compared. The algorithm shown increases the detection rate of small objects to 23.2%.

Bosquet et al. [ 28 ] proposed STDnet and ConvNet to identify tiny objects with a size of less than 16 × 16 pixels based on regional ideas. STDnet relies on an additional visual attention process called RCN, which chooses the most likely candidate area, consisting of one or more tiny items and their surrounding RCN feeds are more accurate and economical, improving accuracy while conserving memory and increasing the frame rate. This study also incorporates automated k-means anchoring, which improves on traditional heuristics.

Kunfu et al. [ 29 ] proposed a fully integrated framework for identifying objects in any orientation in remote sensing pictures. The web provides a functional aggregation architecture for obtaining functional representations for ROI discovery and ROI provision. The combination of quality recommendations and ROI-O is used to process recommendations for effective implementation.

Zheng et al. [ 30 ] introduced a new framework for large-scale target recognition, namely, HyNet for MSR remote sensing imaging, which opens up a new avenue for research of the depiction of scale-invariant functions. Display zoom functions are elements with pyramid-shaped detection areas, which are used to detect objects more accurately with multiple scales in MSR remote sensing images.

Tian et al. [ 31 ] provided a 3D recognition network that can provide a wide range of local functions from images, BEV maps, to point clouds. The adaptive merging network provides an effective method to merge multi-mode data functions. Whenever a vast number of objects appear, the adaptive weighting component restricts the intensity of each signal and chooses information for further evaluation, while the spatial fusion module includes azimuth and geometry info.

Li et al. [ 32 ] reported that PDF-Net is an optical RSI-specific SOD network that may employ mapping and cross-path data, as well as multi-resolution features, to efficiently and accurately identify outgoing objects of various sizes in optical RSIs. PDF-Net has always outperformed the modern SOD method in the ORSSD dataset in terms of visual comparison and quantification. Furthermore, ablation analysis verified the efficacy of the main components.

Fadl et al. [ 33 ] proposed a system that uses spatio–temporal information and fusion of two-dimensional convolutional neural networks (2D-CNNs) to detect inter-frame operations (delete frames, insert frames, and copy frames). RBF-Gaussian support vector machine (SVM) is utilized in the classification phase before automatically extracting depth characteristics.

Zhu et al. [ 34 ] outlined the approaches that have been discovered thus far for detecting VOs. This research examines the available datasets, scoring criteria, and provides an overview of the various classes of deep learning-based methods for identifying VOs. Depending on how time and space information is used, detection methods have been developed. These categories include flow-based technology, LSTM, nursing technology, and follow-up technology.

Alhimale et al. [ 35 ] researched and successfully developed a fall detection system that can fulfill the demands of the elderly (especially indoors). As a result, our video-based fall detection system decreases the likelihood that older individuals will be concerned about falling and will limit their activities at home or in solitude. Furthermore, fall detection systems have been created to preserve people’s privacy, even when their everyday activities are dangerous, by tracking in real-time.

Lee et al. [ 36 ] proposed a new method using advanced neural network ART2 to detect scene changes. To capture the smooth interval, the suggested technique extracts the CC sequence from the video and then generates a gray-scale variance sequence. A typical progressively shifting local minimum sequence will develop during this procedure. It will be deleted from the softbox after being recovered by our local minimum detection method. Then, the resulting smooth intervals are combined to form a new sequence. From the new sequence, feature components such as pixel differences, histogram differences, and correlation coefficients can be extracted.

Kousik et al. [ 37 ] developed a deep learning problem-solving model that uses a new framework to combine CNNs with repetitive neural networks to discover the value of videos. By using recursive convolutional neural network (CRNN) to record time, space, and local restricted features to complete the task of finding obvious objects in the dynamic reference video dataset. Compared with conventional video recognition methods, the evaluation based on the reference dataset has advantages in accuracy, F -measure, mean absolute error, and calculation amount.

Xu et al. [ 38 ] presented a unique video smoke detection system based on a deep distribution network. The goal of bump detection is to emphasize the most important parts of things in a photograph. To generate realistic smoke highlights, outbound CNNs at the pixel and object levels are merged. For use in video smoke detection, an end-to-end architecture for recording departing smoke and predicting the existence of smoke is given.

Yang et al. [ 39 ] described a narrowband Internet of Things (NB-IoT) based digital video intrusion detection method, and an NB network-based digital video intrusion detection system was constructed. Intelligent categorization is accomplished through the usage of IoT and the SVM algorithm. The classification time, accuracy, and false alarm rate of the model were examined. The classification time is 40.80 s, the shortest is 27 s, the recognition rate is 87.60%, and the worst is 83.70%. The false detection rate may reach 15%, but it is always less than 20%, demonstrating that the classification system is reliable and accurate.

Yamazaki et al. [ 40 ] proposed a method for autonomously identifying surgical tools from video footage during laparoscopic gastrectomy. Validation has been performed on a unique automated approach based on the open-source neural network framework YOLOv3 for detecting surgical instrument operation in laparoscopic gastrostomy videotapes.

Yue et al. [ 41 ] used YOLO-GD (Ghost Net and Depth wise convolution) to detect the images of cups, chopsticks, bowls etc., and capture the different types of dishes ( Table 1 ).

Comparative study of SOD as well as movement detection technique

The above comparison table represents some small objects as well as movement detection techniques. Compared to the above techniques the Multi-block SSD approach achieves 96.6% percent overall accuracy, while CNN spatiotemporal features and fusion for surveillance video forgery detection yields excellent accuracy.

3 Studies related to SOD

Increasing picture capture resolution.

Increasing the input resolution of the model.

Using tiling on the pictures.

Increasing data generation through augmentation.

Model anchoring for self-learning.

Eliminating superfluous classifications.

Figure 1 specifies the simplest way of detecting small objects.

Structure of SOD.

Zhang et al. [ 44 ] proposed the boundary-aware high-resolution network (BHNet), which is a novel protruding item-detecting technique. BHNet is intended to be a parallel architecture. It allows for high-resolution information extraction from low-level functions, which is reinforced by various semantics, using a parallel architecture with a low resolution. There are also several multipath channel estimators and region extenders that capture more precise context-sensitive layer functionalities. To track the borders of visible objects, a loss function is given, which can assist us in determining precise detection bounds. BHNet is a specialist at locating exceptional items with powerful functions for extracting numerous characteristics.

Liang et al. [ 45 ] provided a context-sensitive network for identifying outgoing RGB-D objects. The suggested approach is divided into three components: feature extraction, multi-mode context fusion, and context-sensitive expansion. The first component is in charge of determining hierarchical functions based on color and depth. CNN was used in each photograph. The second component employs an LSTM version to include additional characteristics to represent multimodal spatial correlation in context. Experiment findings with two publicly accessible reference datasets demonstrate that the suggested technique is capable of providing the most recent performance for recognizing significant stereo RGB-D objects.

Kumar and Srivastava [ 46 ] developed an object identification method that recognizes things in pictures using deep learning neural networks. To obtain high target detection accuracy in real-time, this study integrates the Single Shot Multi-Block Detection method with faster CNN. This method is appropriate for both still pictures and videos. The proposed model’s accuracy is greater than 75%. This model takes around 5–6 h to train. To extract information from visual characteristics, this model employs a CNN. The class names are then classified using function mapping. This technique, by default, employs distinct filters with various frames to remove aspect ratio discrepancies, as well as multi-scale feature maps for object recognition.

Jiao et al. [ 47 ] developed a new network for object identification, RFP-Net. RFP-Net was the first to apply the RF and eRF concepts to generate bids based on regions. The RF from each sliding window is used as a reference frame in this technique, and the eRF range is used to filter out low-quality phrases. In addition, we developed an eRF-based matching technique to identify positive and negative samples trained by RFP-Net, therefore addressing the imbalance between positive and negative samples as well as the scaling problem in object recognition.

Liang et al. [ 48 ] proposed a multi-style attention fusion network (MAFNet). MAFNet, in particular, is made up of a dual signal spatial attention (DSA) module, an attention middle presentation module, and a dual service module (DAIR). He used a multi-level service function merging module and advanced channel attention module (HCA and MLFF). DSA seeks to increase low-level performance while filtering out background noise. DAIR utilizes two branches to adaptively integrate spatial and semantic information from intermediate layer functions. HCA reserves the block’s high-level semantic characteristics via two distinct channel operations. The abovementioned multi-level functions are successfully integrated in a trainable manner by MLFF.

Liu et al. [ 49 ] presented image processing-based integrated traffic sign recognition. Color-based techniques, shape-based methods, color and shape-based methods, LIDAR, and machine learning are the five primary inspection methods studied in this study. To comprehend and summarize the mechanics of different techniques, the methods in each category are also split into distinct sub-categories. Some of the comparison techniques have been implemented in some updated methods that are not compared in public records.

Pollara et al. [ 50 ] described different ways of detecting and monitoring low-cost, low-power devices using certain hydrophones. The ship’s acoustic properties were thoroughly examined to establish its physical specifications. These variables can be used to categorize ships. The Stevens Acoustic Library is a collection of acoustic instruments.

Wang et al’s. [ 51 ] study is broken into two sections: A data collection based on the drone’s point of view is developed and a variety of approaches are utilized to detect tiny objects. Through a series of comparative experiments, a machine learning technique based on SVM and a deep learning method based on the YOLO network were effectively constructed. We can see that the SVM-based machine learning method uses less computer resources and saves time. However, due to the selection of the region of interest, it is impossible to enhance accuracy and dependability in some particular scenarios. Deep learning based on neural networks, on the other hand, can give more accuracy.

Xue et al. [ 52 ] presented an improved approach for identifying small things, which improves the performance of different scales and integrates contextual semantic information across them. The results of tests on the large MS COCO dataset show that this method can improve the accuracy of small object identification while staying reasonably quick.

Zhiqiang and Jun [ 53 ] introduced CNN-based object recognition, CNN structure, features of CNN-based object recognition structure, and methods to improve recognition efficiency. CNN has a powerful feature extraction function, which can make up for the inconvenience caused by using it. Compared with traditional real-time methods, CNN also has more advantages, accuracy, and adaptability, but there is still room for improvement. This can reduce the loss of functional information, make full use of object relationships, and context and fuzzy inference can help computers deal better with issues such as occlusion and low resolution.

Elakkiya et al. [ 54 ] gave an idea of how the cervical lesions can be found and categorized. The proposed method used the tiny object identification mechanism to identify the cervical closure from the colposcopy pictures because the cervical cells are much smaller than the uterine cells. The proposed strategy also used Bayesian optimization to optimize the SOD-GAN’s hyper parameters, which reduced time complexity and improved performance in terms of efficient classification. The proposed improved SOD-GAN uses eight alternative colposcopy images as inputs and eight randomly generated noise images as outputs to produce the right colposcopy image.

Ji et al. [ 55 ] combined the YOLOv4 with two other approaches which are multi-scale contextual information and Soft-CIOU, and called it as MCS-YOLOv4. Extra scales were added to the approach to gain definite data. The authors also encompassed the perception block within the structure of the model.

Sun et al. [ 56 ] talked about real time detection of small objects especially for the moving vehicles. The approach was to gain better results from less deeper networks and by assigning the weights to the feature gained in a such a way so as to have better quantifying results ( Table 2 ).

Comparative study of SOD

The table above compares several approaches for tiny item identification. In comparison to the preceding approaches, RFP-Net, the object detection technique, employs a receptive field-based proposal generation network, which results in significantly improved accuracy.

4 Studies related to moving object detection

VO detection [ 57 ] is the task of detecting VOs instead of images. VO are free-format video clips with semantic meaning. A two-dimensional snapshot of a VO at a certain point in time is called the video object plane (VOP). VOP is determined by its texture (luminance and chroma values) and shape.

4.1 Methods for detecting objects in videos

As seen in Figure 2 , VO detectors may be categorized as streaming based on how they use temporal dependencies and aggregate attributes generated from video clips, LSTM [ 58 ], due diligence [ 59 ], and subsequent detectors. These methods of VO detection are shown schematically in Figure 2 [ 60 ].

Categories of VO detection.

4.2 Video forgery detection

Activity removal: removing the frames in question using frame deletion.

Activity addition: to introduce a foreign video from some other video, frame insertion is used.

Activity replication: the process of repeating an event by using frame duplication.

Inter-frame forgeries.

Salvadori et al. [ 62 ] reduced the transmission capacity of uncompressed video streams and thereby boosted frame rate using a low-complexity approach based on background removal and error recovery technologies. JPEG is a modern solution. The findings of this study will be taken into account while designing next-generation smart cameras for 6LoWPAN.

Amosov et al. [ 63 ] proposed to employ a set of deep neural networks (DNNs) to develop an intelligent context classifier that can recognize and discriminate between regular and critical occurrences in the security service system’s continuous video feed. Their artworks are examined by utilizing cutting-edge technologies. A probability score for each video segment is the outcome of computer vision and software technologies. To identify and detect normal and abnormal situations, a Python software module was built.

El Kaid et al. [ 64 ] proposed a CNN model, which can be used to minimize the false alarm rate, because we can delete 98% of images of someone in a wheelchair, and can more or less reduce false alarms by 17%. However, there are numerous false positives in the blank space image, and none of the evaluated CNN models can identify them owing to the image’s complexity. As a result, another concept should be considered in this study to increase the accuracy of the fall detection system.

Najva and Bijoy [ 65 ] presented a unique method for detecting and categorizing objects in movies, which uses a tensor function and SIFT to categorize items detected by a DNN. DNN, like the human brain, is capable of analyzing massive quantities of high-dimensional data with billions of variables. The results of this study show that the proposed classifier and most of the existing techniques for feature extraction and classification combine SIFT and tensor features.

Yan and Xu [ 66 ] proposed a straight-through pipeline for video caption detection. To recognize video subtitles, the Connected Text Proposal Network (CTPN) is utilized, while the residual network (ResNet), gated recurrent unit (GRU), and connected time classification (CTC) are used to detect Chinese and English subtitles in video pictures. First, use the CTPN technique to determine the subtitle region in the video picture. The identified subtitle range should then be pasted into ResNet to extract the function sequence. Then, add a bidirectional GRU layer to represent the feature sequence.

Wu et al. [ 67 ] proposed a straight-through pipeline to detect video captions. To recognize video subtitles, the CTPN is utilized, while the ResNet, GRU, and CTC are used to detect Chinese and English subtitles in video pictures. To begin, identify the subtitle region in the video picture using the CTPN technique. After determining the subtitle range, use ResNet to extract the function sequence. After that, add a bidirectional GRU layer to represent the feature sequence.

Fang et al. [ 68 ] introduced a Deep Video Saliency Network (DevsNet), a new deep learning platform with which the meaning of video streams can be determined. DevsNet is primarily made up of two parts: 3D convolutional network (3D-ConvNet) and bidirectional long-term and short-term memory convolutional networks. (BConvLSTM). 3D-ConvNet aims to examine short-term spatio–temporal information, while B-ConvLSTM examines long-term spatio–temporal attributes.

Wang et al. [ 69 ] proposed a completely scalable network with a communication structure for high-precision VO recognition and cost-effective computation. The scale recognition module, in particular, is added to acquire characteristics with bigger alterations. The ROI structure module retrieves and combines RoI’s location and context functions. Feature aggregation is also used to improve the performance of the reference frame by deforming the flow. SCNet’s efficacy has been demonstrated through several trials. In our RoI module, you may add another auxiliary branch with a paired structure for invoking RoI functions, similar to the local function block in BConvLSTM. In addition, SCNet now mainly controls accuracy, so there is still a lot of room for speed improvement.

Zhu and Yan [ 70 ] proposed traffic sign recognition using YOLOv5 and compared with SSD with some extended features ( Table 3 ).

Comparative study of moving object detection

The above comparison table represents some moving object detection techniques.

5 Studies related to loss function

In object recognition tasks, the loss function is the most important element in determining identification accuracy. First, the connection between location and classification is established by multiplying the factor based on IoU by the classification loss function’s typical cross-entropy loss [ 71 ]. The square mistake represented by the root (MSE) [ 72 ] is the main force of the basic loss function. It is simple to comprehend and apply, and it works effectively in most cases. Take the difference between the forecast and the ground truth, blockage, and the average of the whole dataset to compute the MSE. In statistics, the loss function is frequently used to estimate parameters, and the event in question is a function of the difference between the estimated and true values of the data instance. Abraham Wald reintroduced statistics in the middle of the 20th century, reintroducing this concept is as old as Laplace [ 73 , 74 ]. For example, in an economic context, this is usually economic loss or regret. In classification, this is the penalty for misclassifying the example. In actuarial science, especially after Harald Kramer’s work in the 1920s, it is used in the insurance industry to model premium payment models. The model manages the Loss which is the price of not meeting expectations, in the best way. Loss is the price of not meeting expectations. In financial risk management, this function is allocated to monetary loss [ 75 – 77 ]. Some important studies covering the more important objective-based loss function research are discussed below.

Fang et al. [ 78 ] proposed a hostile network based on conditional patches, which uses a generator network based on sampled data patches and a conditional discriminator network with additional loss functions to check fine blood vessels and coarse data. Experiments will be conducted on the public STARE and DRIVE datasets, showing that the proposed model is superior to more advanced methods.

Fan and Liu [ 79 ] investigated GAN training with various combination techniques and discovered that synchronization of the discriminator and generator between clients offers the best outcomes for two distinct challenges. The study also discovered empirical results indicating that federated learning is typically resilient for the number of consumers having IID learning data and modest non-IID learning data. However, if the data distribution is significantly skewed, the existing compound learning scheme (such as FedAvg) would be anomalous owing to the weight difference.

Liu et al. [ 80 ] proposed a model based on a two-layer backbone architecture, it provides end-to-end pose estimation at the 6D category level to detect bounding boxes. In this scenario, the 6D posture is created straight from the network and ensures that no further steps or post-processing are needed, such as Perspective-n-point. Our loss function and CNN’s two-layer architecture make collaborative multi-task learning quick and effective. This study increases posture estimation accuracy by substituting completely linked layers with fully folded layers. Transform your pose estimation challenge into a classification and regression problem with the aid of our network, which are termed as Pose-cls and Pose-reg.

Sharma and Mir [ 81 ] developed a unique technique for segmenting VOs using unsupervised learning. The process is divided into two stages, each of which considers the basic frame and the current frame for segmentation. We build dense region clauses, bounding boxes, and scores in the first step. Following that, we develop a feature extraction technique that utilizes the attention network for feature encoding. Finally, using the Softmax technique, these functions are scaled and combined to generate object segmentation.

Liu et al. [ 82 ] proposed a continuous deep network based on mixed sampling and mixed loss computation to detect salient items. Not only the hybrid sampling may integrate original and sample features but it can also acquire a wider receiving field using horrible convolution. The hybrid loss function, which combines cross-entropy loss and area loss, can further minimize the gap between the salient map and the terrain’s realism. A fully linked CRF model might be used to increase spatial coherence and contour placement even further.

Steno et al. [ 83 ] attempted to enhance the accuracy of threat localization and minimize detection time by employing a quicker and better R-CNN (with a suggested network divided by region). The planned network by area has been modified to make it simpler to discover things using the new docking box design. Improved RPN can give a more comprehensive summary of characteristics. Furthermore, by including sample weights into the classification loss function, an enhanced cross-entropy function is created, which improves the classification deficit and the multi-task loss function’s performance. In MATLAB, the average accuracy is improved to 0.27, the average processing time is lowered, and the average processing time is increased by 0.27.

Gu et al. [ 84 ] proposed better lightweight detection using Context Aware Dense Feature Distillation. And use rich contextual feature for SOD ( Table 4 ).

Comparative study of loss function

The above comparison table represents some loss functions and their calculation techniques. Compared to the above techniques federated generative adversarial learning produces a higher accuracy and has the advantage of accurate trajectory prediction with few attempts.

6 Studies related to optimization technique

In the network, optimization methods are employed to minimize a function known as the loss function or error function. The optimization approach may generate the smallest difference between the actual output and the predicted output by minimizing the loss function, allowing our model to accomplish the task more correctly.

Dumitru et al. [ 85 ] suggested an edge detector, which was compared against one of the most sophisticated techniques, the “Tricky Edge” detector. Our edge detection methodology combines particle swarm optimization with monitored optimization of cellular machine rules. We developed transferable rules that may be used for a variety of pictures with comparable features. On average, the recommended approach outperforms Canny in our advanced dataset.

Huang et al. [ 25 ] proposed a model for detecting prominent items in hyperspectral pictures on wireless networks, which employs visibility optimization to the characteristics of CNN. To define the ultimate melting behavior, to extract spatial and spectral characteristics of the same size, we first use a CNN with two channels. By maximizing the bump values of the foreground and background signals from the CNN characteristics, the final bump map is generated. The findings of this study show that the approach is effective and performs well in the creation of hyper spectral pictures.

Sasikala et al. [ 86 ] used a classifier in conjunction with an optimal model. Even with hundreds of blood vessel pictures, this experimental model outperforms previous detection techniques. This hybrid and adaptive optimization approach based on rhododendron search produces the greatest results in dynamic regions affected by the ocean, and the findings indicate a reduction in the false alarm rate of ports and other coastal surveillance locations.

Jain et al. [ 87 ] presented a novel social media-based whale optimization algorithm for identifying N thought leaders by analyzing user reputation using various popular Internet optimization functions. The approach is effective for identifying opinion leaders since it is based on humpback whale hunting behavior with bubble nets. As the number of users on the network grew, the algorithm determined the optimal option. As a consequence, the method’s total complexity remains constant. We also offered a novel community classification method based on the similarity index, which contains the clustering coefficient and the similarity of neighbors as important components. Local and worldwide opinion leaders were identified by using priorities and recommended methods and optimization features. We applied the suggested method to real-world and large-scale datasets and compared the outcomes in terms of precision, accuracy, recall, and F 1 score.

Rammurthy and Mahesh [ 88 ] recommended the Whale Harris Hawks Optimization (WHHO) technique to identify brain cancers using magnetic resonance imaging. For segmentation, we employed cellular automata and approximation set theory. Furthermore, characteristics such as tumor size, local optical orientation pattern, mean, variance, and kurtosis are retrieved from sections. Furthermore, brain tumor identification is performed using a deep CNN, while training is performed utilizing the suggested WHHO. The Whale Optimization Algorithm and the Harris Hawks Optimization Algorithm were combined (HHO). According to WHHO, deep CNN recommends utilizing alternative techniques with a maximum accuracy of 0.816, a maximum specificity of 0.791, and a maximum sensitivity of 0.974.

Zhang et al. [ 89 ] proposed the community detection based on whale optimization (WOCDA) method as a novel community discovery technique. WOCDA’s initialization strategy and three optimization operations simulate humpback whale hunting behavior and determine the community in experiments of synthetic and real networks, demonstrating that the community ratio algorithm identified by WOCDA can be detected in modern meta-heuristics in most cases. WOCDA’s efficacy, however, declines as the number of nodes in the network grows, because the random search process takes a long time until a big search space is reached.

Luo et al. [ 90 ] suggested a unique multi-scale and target vehicle recognition approach for identifying complex vehicles in natural situations. We improve the image of the dataset by utilizing the Retinex-based adaptive image correction approach to reduce the influence of shadows and highlights. This study describes a multi-layer feature extraction approach that explores the neural architecture for the best connection between layers, increasing the representation of the fundamental properties of the quicker R-CNN model and aims to analyze performance of multi-scale vehicles. We provide a target feature enhancement approach that integrates multi-layer feature information and context information from the final layer after the layers are connected to enrich the target information and improve the model’s reliability in recognizing big and small targets ( Table 5 ).

Comparative study of optimization technique

The above comparison table represents some optimization techniques. Compared to the above optimization techniques, salient object identification on hyperspectral pictures in wireless networks utilizing CNN and saliency optimization results in improved accuracy and efficiency, as well as the benefit of fewer noise.

7 Conclusion

This study reviewed different small object and movement detection, loss functions, and optimization techniques. This approach is used to increase the small object in addition to movement detection with new ideas. In this study, there are 84 research articles with the same background as this article. Articles were selected from various journals. Through the overview and reference section of the previous research articles, individual articles were selected to study the previous literature. The selected research supports the detection of smaller moving objects through performance analysis, loss functions, and optimization techniques. After careful analysis of the previous work, some landmark articles were selected for research, which may be useful for this research.

8 Future scope

Over the past few years, the communities of computer vision and pattern recognition have paid a lot of attention to object detection in images and videos. Although we have created numerous ways for detecting objects, deep learning applications promise greater accuracy for a wider range of object types. In future, we would like to implement and compare models for aerial images and video frames. Also, there is a need for certain methods which would not only detect the objects but also analyze them for further investigations. It will be crucial to use this remarkable computer technology, which is related to computer vision and image processing that recognizes and characterizes items from digital images and videos, such as people, cars, and animals.

Author contributions: Ravi Prakash Chaturvedi collected, filtered, organized, compared and worked upon the data. Udayan Ghose validated and analyzed the results. He also audited the approach and results.

Conflict of interest: The authors declare no conflict of interest.

Data availability statement: Data was collected from various research papers that are already mentioned as references in paper.

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Genes Whose Gain or Loss-of-Function Increases Endurance Performance in Mice: A Systematic Literature Review

Fakhreddin yaghoob nezhad.

1 Exercise Biology Group, Department of Sport and Health Sciences, Technical University of Munich, Munich, Germany

Sander A. J. Verbrugge

Martin schönfelder, lore becker.

2 German Mouse Clinic, Institute of Experimental Genetics, Helmholtz Zentrum München, Neuherberg, Germany

Martin Hrabě de Angelis

3 Chair of Experimental Genetics, School of Life Sciences Weihenstephan, Technical University of Munich, Freising, Germany

4 German Center for Diabetes Research, Neuherberg, Germany

Henning Wackerhage

Associated data.

Endurance is not only a key factor in many sports but endurance-related variables are also associated with good health and low mortality. Twin and family studies suggest that several endurance-associated traits are ≈50% inherited. However, we still poorly understand what DNA sequence variants contribute to endurance heritability. To address this issue, we conducted a systematic review to identify genes whose experimental loss or gain-of-function increases endurance capacity in mice. We found 31 genes including two isoforms of Ppargc1a whose manipulation increases running or swimming endurance performance by up to 1800%. Genes whose gain-of-function increases endurance are Adcy5, Adcy8 , Hk2, Il15, Mef2c, Nr4a3, Pck1 (Pepck) , Ppard, Ppargc1a (both the a and b isoforms of the protein Pgc-1α), Ppargc1b, Ppp3ca (calcineurin) , Scd1, Slc5a7, Tfe3, Tfeb, Trib3 & Trpv1 . Genes whose loss-of-function increases endurance in mice are Actn3, Adrb2, Bdkrb2, Cd47, Crym, Hif1a, Myoz1, Pappa, Pknox1, Pten, Sirt4, Thbs1, Thra, and Tnfsf12 . Of these genes, human DNA sequence variants of ACTN3 , ADCY5 , ADRB2 , BDKRB2 , HIF1A , PPARD , PPARGC1A , PPARGC1B , and PPP3CA are also associated with endurance capacity and/or VO 2 max trainability suggesting evolutionary conservation between mice and humans. Bioinformatical analyses show that there are numerous amino acid or copy number-changing DNA variants of endurance genes in humans, suggesting that genetic variation of endurance genes contributes to the variation of human endurance capacity, too. Moreover, several of these genes/proteins change their expression or phosphorylation in skeletal muscle or the heart after endurance exercise, suggesting a role in the adaptation to endurance exercise.

Introduction

Endurance is a key trait in many sports such as marathon running and triathlon. Endurance is also associated with health as a high endurance capacity is associated with fewer cardiovascular events and reduced all-cause mortality ( Kodama et al., 2009 ). In rats, selection for low endurance capacity is associated with more cardiovascular risk factors than selection for high endurance capacity suggesting a direct link between endurance capacity and disease risk ( Wisloff et al., 2005 ).

Endurance capacity is a multi-factorial trait that depends on several sub-traits and organ systems:

• simple (1) Aerobic capacity (VO 2 max) is influenced by the maximal cardiac output and by the oxygen transport capacity of the blood, and blood volume ( Bergh et al., 2000 ; Lundby et al., 2017 ).
• simple (2) Skeletal muscle endurance has been linked to type I and II subtype muscle fiber proportions ( Costill et al., 1976 ), muscle capillary density ( Brodal et al., 1977 ), mitochondrial and other metabolic enzyme activities ( Gollnick and Saltin, 1983 ) as well as the glycogen concentration of the exercising muscles ( Bergstrom et al., 1967 ).
• simple (2) Mechanical efficiency describes how much chemical energy is converted into mechanical power ( Bassett and Howley, 2000 ). Efficiency depends on many factors including body weight and height ( Maldonado et al., 2002 ).
• simple (4) Mental endurance depends on the nervous system and is defined as fatigue resistance during prolonged periods of demanding cognitive activity ( Van Cutsem et al., 2017 ).

In relation to human endurance, two important questions are: How much is endurance inherited? What DNA sequence variants affect endurance capacity? Classical genetic studies suggest that maximal aerobic performance variables (i.e., VO 2 max, physical working capacity or threshold values) are between 38 and 94% inherited ( Peeters et al., 2009 ). In the Heritage Study, Bouchard et al. estimated that the VO 2 max was 50% inherited ( Bouchard et al., 1998 ), and that the VO 2 max trainability was 47% inherited ( Bouchard et al., 1999 ). Similarly, the muscle fiber distribution was estimated to be ≈45% inherited ( Simoneau and Bouchard, 1995 ). Collectively, especially the Heritage study data suggest that the variation of major human endurance-related traits depends probably to ≈50% on DNA sequence variation [i.e., genetics, Simoneau and Bouchard, 1995 ; Bouchard et al., 1998 , 1999 )] implying that ≈50% is dependent on environmental factors such as endurance training and nutrition.

We still incompletely understand the genetics of human endurance and some researchers are even skeptical about the importance of genetics especially in relation to the VO 2 max. A recent review for endurance-related DNA variants in humans highlighted 93 endurance-associated DNA variants ( Ahmetov et al., 2016 ). Furthermore, a systematic search identified 97 DNA variants that are associated with VO 2 max/peak trainability ( Williams et al., 2017 ). Whilst the effect size of many endurance-associated polymorphisms is small, the effect of a rare EPOR DNA variant on the haematocrit and presumably VO 2 max is large, given that one carrier was an Olympic gold medalist in cross country skiing ( de la Chapelle et al., 1993 ). More recently, genome-wide association studies (GWAS) have added to our knowledge of the genetics of human endurance. Here, Rankinen et al. (2016) found in 1520 elite endurance athletes and 2760 controls, no common single nucleotide polymorphism (SNP) profile that distinguishes elite endurance athletes from ethnicity-matched controls (only one SNP near the GALNTL6 locus was significant across all studies) ( Rankinen et al., 2016 ). Another study in ≈40,000 individuals, and replication in ≈27,000, identified 30 loci that associated with heart rate change at the onset and recovery after exercise. Many of the loci included genes linked to the autonomic nervous system, a known regulator of heart rate ( Ramírez et al., 2018 ). Finally, a study of ≈91,000 individuals identified 14 loci that associated with device-measured physical activity and sleep duration of which several are linked to the central nervous system ( Doherty et al., 2018 ). Given that few physiologically plausible genetic associations have been discovered for endurance, Lundby et al. (2017) question whether “ DNA variants in the key physiological pathways for VO 2 max […] will be identified for both average individuals and also elite endurance athletes. ” Finally, in his bestselling science book Malcolm Gladwell concludes that 10,000 h is all it takes to achieve expert performance in various fields including sports, leaving little room for genetics or talent ( Gladwell, 2008 ). Thus whilst humans seem to have genetically evolved as an endurance running species ( Bramble and Lieberman, 2004 ), the genetic contribution to the large variation of endurance traits in current human populations is still unclear.

Insights into the genetics of endurance come from studies with good statistical power into the genetics of body height. Body height is not only a ≈80% inherited trait ( Silventoinen et al., 2003 ) but also influences endurance performance, e.g., in rowing, which is an endurance sport ( Maldonado et al., 2002 ). GWAS involving hundreds of thousands of individuals have revealed that the variation of body height is influenced by thousands of SNPs. For example, ≈9,500 SNPs are estimated to explain 29% of human body height variation ( Wood et al., 2014 ). Human body height is additionally influenced by rare, large-effect size mutations of genes such as mutations of AIP that cause gigantism ( Chahal et al., 2011 ) or mutations of genes such as FGFR3 that can cause dwarfism ( Foldynova-Trantirkova et al., 2012 ). Because body height is only one of many endurance performance-limiting factors and already influenced by thousands of DNA variants, it seems likely that even more common DNA sequence variants with small effect sizes and some rarer DNA variants with larger effect sizes combine to explain the effect of DNA sequence variation on human endurance capacity.

The current challenge for molecular exercise physiologists is to start to draw an overall picture of the genetics of human endurance capacity. This picture should both give an idea of the likely number of endurance capacity-influencing DNA variants and identify especially those DNA variants that have a major effect on endurance capacity. A special challenge is to explain the genetics of highly talented elite endurance athletes such as East African runners. Do these athletes share otherwise rare DNA variants with a high effect size as is the case in populations that live at high altitude ( Bigham, 2016 )? Or do they carry thousands of endurance-promoting DNA variants with small effect sizes? Do the DNA variants mainly affect exons or do they cause variation of regulatory DNA elements?

To identify genes and alleles that can have a major effect on endurance it is especially useful to review the data of transgenic mouse studies ( Garton et al., 2016 ). In transgenic mouse studies, genes are manipulated to produce a gain or loss-of-function of a gene. If this results in a measurable increase of endurance, then the gene is a candidate gene for human endurance capacity, too. The aim of this study was therefore to systematically search the literature for published studies where a gain or loss-of-function mutation of a gene increases endurance capacity in mice. A second aim of the study was to study the identified endurance genes further through bioinformatical analyses.

Materials and Methods

Systematic literature search.

We conducted a systematic review using the PRISMA guidelines ( Moher et al., 2009 ) and included all studies according to the participants, interventions, comparators and outcomes (PICO) process ( Schardt et al., 2007 ). Its main aim was to identify genes whose gain or loss-of-function significantly increases endurance capacity in mice. We first searched the six English-language databases (Google Scholar, Bio Med, Scopus, PubMed, Science Direct, and Web of Science) using our systematic search strategy and used the following combination of search terms: (“mouse” OR “murine” OR “mouse model” OR “mice” OR “mice transgenic”) AND (“overexpression” OR “knock out” OR “knock in” OR “gene transfer techniques” OR “mutagenesis” OR “gene deletion” OR “gene manipulation”) AND [“endurance exercise” OR “swimming” OR “wheel running” OR “endurance capacity” OR “mPXT” (speed progress until exhaustion test in mice) OR “mGXT” (graded maximal exercise in mice)].

Inclusion and Exclusion Criteria

After eliminating duplicates, we examined the published studies in two stages: First, we reviewed results by title and abstract and then by full-text. At each step, we deleted studies that did not match with the review’s inclusion and exclusion criteria. We included studies in this review if they met the following criteria:

• simple (1) The study needs to be published in a paper or in online peer-reviewed journal;
• simple (2) Publication language must be English;
• simple (3) Each study must show original empirical, primary data/evidence;
• simple (4) Mice must be healthy, and gene manipulation is the only intervention;
• simple (5) Increased endurance capacity must be reported as the distance or time achieved during an endurance exercise test (treadmill running, swimming, wheel-running).
• simple (6) In case an endurance capacity-influencing gene was mentioned more than once, we only analyzed the paper where it was mentioned first.

We excluded studies based on the following criteria:

• simple (1) Rat or in vitro study;
• simple (2) No transgenesis, double mutation or long non-coding RNAs manipulation;
• simple (3) Major pathological abnormalities result from the gene manipulation;
• simple (4) Mice are older than 24 months;
• simple (5) No statistically significant effect or no outcome measures;
• simple (6) No wild type mice as controls;
• simple (7) Use of an additional drug treatment or dietary supplement (we included studies where a transgene was induced, e.g., through doxycycline injection).

We illustrate our literature search in Supplementary Data S1 .

Data Extraction

We extracted the following information from each relevant study: author(s), gene name, protein name, method of gene manipulation, acclimated, exercise testing protocols, output measure, endurance capacity for control and transgenic mice, difference between control and transgenic mice as a percentage, age of the mice, mouse strain, additional measurements, and remarks ( Supplementary Table S2 ). Sometimes, the output measure values were presented only in a bar graph and not as a number. In these cases, we manually measured and estimated the relative difference of mean values between controls and transgenic mice by using the bar height. Moreover, we adopted official gene names from the Universal Protein Resource (UniProt, NCBI) and the official gene names may differ from the gene or protein names that are used in the original papers.

Bioinformatical Analyses

To obtain more information about the identified endurance genes and the proteins that they encode, we asked several research questions and performed bioinformatical analyses to answer these questions. This information is summarized in Supplementary Data S3–S11 .

Initially, we identified 2315 manuscripts with publication dates until January 2018. Based on the title or abstract we excluded 2171 studies. After that, 263 articles remained which we read fully for eligibility. We identified another 43 articles by reviewing the reference lists of the full-text articles or other sources. Finally, we read 144 full-text articles and analyzed 32 articles quantitatively. A PRISMA flowchart of our search and reading strategy is in the Supplementary Data S1 . We used the information of this systematic literature search and several bioinformatical analyses to answer several research questions which are stated as headers below.

The Gain or Loss-of-Function of What Genes Increases Endurance Performance?

Our analysis revealed 31 genes/isoforms including two isoforms of Ppargc1a whose gain or loss-of-function increased endurance performance in mice. Specifically, we identified 19 genes [ Adcy5, Adcy8 , Hk2, Il15, Mef2c, Nr4a3, Pck1 (Pepck), Ppard, Ppargc1a (both the a and b isoforms of the mitochondrial biogenesis regulator Pgc-1α), Ppargc1b, Ppp3ca (calcineurin) , Scd1, Slc5a7, Tfe3, Tfeb, Trib3, and Trpv1; Figure 1 ] whose gain-of-function increased endurance capacity in mice. We also found 14 genes ( Actn3, Adrb2, Bdkrb2, Cd47, Crym, Hif1a, Myoz1, Pappa, Pknox1, Pten, Sirt4, Thbs1, Thra , Tnfsf12 ; Figure 2 ) whose loss-of-function increases endurance capacity. Collectively, we will refer to these genes as endurance genes for simplicity. The relative increase ranged from 12% for Pten to 1800% for Pck1 ( Figure 1 , ​ ,2). 2 ). To explain, if the mean value for the wildtype mouse was e.g., 100 units in an endurance test, then the transgenic mice achieved on average between 112 units (i.e., an increase of 12 units or %) or 1900 units (i.e., an increase by 1800 units or %), respectively. The detailed experimental design and endurance performance data for each transgenic and wildtype mouse pair are summarized in Table 1 and in full detail in Supplementary Data S2 .

Genes whose gain-of-function increases running or swimming endurance in mice. Percentage increase in % were calculated by direct comparison the control animals. Finally, genes are plotted from high to low effect size.

Genes whose loss-of-function increases running or swimming endurance in mice. Percentage increase in % were calculated by direct comparison the control animals. Finally, genes are plotted from high to low effect size.

Genes whose transgenesis in mice increases endurance capacity.

Do Endurance Genes Overlap With Human Genes Where DNA Variants Are Associated With Human Endurance, VO 2 max Trainability and Other Endurance-Related Traits?

To study whether endurance genes also play a role in the variation of human endurance, we overlapped our set of endurance genes with a list of human genes where DNA variants associate with endurance ( Ahmetov et al., 2016 ) and genes where DNA variants associate with VO 2 max trainability ( Williams et al., 2017 ). Moreover, we searched for phenotypes for endurance genes reported in human GWAS by searching the human GWAS catalog. The first overlap analysis we screened the human homologs of the mouse genes. Here, we revealed that human endurance gene variants of ACTN3 , ADRB2 , BDKRB2 , HIF1A , PPARD , PPARGC1A , PPARGC1B , and PPP3CA are also associated with human endurance ( Ahmetov et al., 2016 ). Moreover, DNA variants linked to ADCY5 , PPARD , and HIF1A are associated with VO 2 max trainability in humans ( Williams et al., 2017 ). In a second step, we investigated whether the identified endurance genes were linked in GWAS to phenotypes that are potentially relevant for endurance performance. To do so, we performed a GWAS catalog search for each gene. This search revealed several associations between endurance genes and a several physiological and pathological human phenotypes (Supplementary Data S3 ). Associations of potential relevance for endurance performance include an association of PCK1 with the hemoglobin concentration ( Astle et al., 2016 ), an association of PPARGC1A with the resting heart rate ( Eppinga et al., 2016 ), an association of SCD with metabolic traits ( Suhre et al., 2011 ) specifically blood levels of myristate (14:0)/myristoleate (14:1n5) ( Shin et al., 2014 ), and an association of TFEB with left ventricular wall thickness ( Wild et al., 2017 ). Collectively, these analyses demonstrate that some endurance genes are assocuated with human endurance-associated traits, too.

How Much Does the DNA Sequence of Human Endurance Gene Exomes Vary in 60,706 Humans?

Next we used the ExAC browser to explore the extent to which the DNA sequence of human endurance gene exoms varies ( Lek et al., 2016 ). This analysis revealed extensive genetic variation of endurance genes in humans. On average, each human homologue of an endurance gene had 174 missense DNA variants, 5 loss-of-function DNA variants and 11 copy number DNA variants. Additionally, for ACTN3, CRYM, TFE3 , and THRA homozygous loss-of-function DNA variants were reported. For ACTN 3 it is already known that a loss-of-function can be tolerated as ≈20% of the population are homozygous for a ACTN3 R577X variant ( Yang et al., 2003 ). This and the results of GWAS s suggest a large amount of common or rare, functionally relevant DNA sequence variation in the human homologues of mouse endurance genes.

In What Human Tissues Are Endurance Genes Expressed?

We have already mentioned that endurance is a trait that is determined by the function and interplay of several organ systems. These include skeletal muscle as the key force-generating and energy converting organ, the liver for glycogen storage and gluconeogenesis, the oxygen-delivering organs lung, heart, vasculature and blood as well as the brain due to its role in mental fatigue. To study the expression in resting human organs, we retrieved gene expression data from the GTEx Portal database (GTEx Consortium, 2015) and plotted this as a heat map ( Figure 3 and Supplementary Data S5 ). This reveals that Myoz1 and Actn3 are selectively expressed in skeletal muscle whereas Pck1 is selectively expressed in the liver, at least at rest. In addition, several other genes, such as Sirt4, Ppargc1a, Il15Adcy8Bdkrb2, Pappa , and Slc5a7 , are not expressed in these selected organs.

Heatmap illustrating the expression levels of endurance genes in transcripts per million (TPM) in some endurance exercise organs. If no expression value was given in GTEx Portal, we recorded the expression as “0” which signifies either no expression or no data.

Do Endurance Genes Change Their Expression in Muscle After Human Endurance and Resistance (Strength) Exercise? What Endurance Genes/Proteins Are Detected as Phosphorylated Proteins at Rest or After High Intensity Exercise in Human Muscle?

Several genes may be expressed at low levels in resting human organs but may increase their expression in response to endurance exercise. One example is the gene Ppargc1a which encodes various isoforms of the mitochondrial biogenesis regulator Pgc-1α. The expression of Ppargc1a increases in response to endurance exercise both in mouse ( Baar et al., 2002 ) and human skeletal muscle ( Pilegaard et al., 2003 ). Moreover, proteins encoded by endurance genes may become phosphorylated after a bout of endurance exercise as can be demonstrated by phosphoproteomics ( Hoffman et al., 2015 ). To test whether endurance genes/proteins change their expression or phosphorylation after a bout of endurance exercise, we re-analyzed published datasets ( Vissing and Schjerling, 2014 ; Hoffman et al., 2015 ). These analyses reveal that PPARGC1A (encoding PGC-1α), NR4A3 which encodes a nuclear hormone receptor and THSB1 , which encodes thrombospondin 1, are examples for genes that increase their expression 2.5 and 5 h in the vastus lateralis after cycling for 120 min at 60% of the VO 2 peak ( Figure 4 and Supplementary Data S6 ; Vissing and Schjerling, 2014 ). In contrast, ACTN3 decreases its expression after human endurance exercise ( Figure 4 ). Whilst the direction of the expression changes of PPARGC1A , NR4A3 , and ACTN3 is consistent with the respective mouse phenotypes, THSB1 expression increases after endurance exercise but a global deletion of Thsp1 increases capillarity and exercise capacity in mice ( Malek and Olfert, 2009 ). Thus, THSB1 expression after endurance exercise suggests that it promotes a reduced adaptation to endurance exercise.

Effect of human endurance exercise on the expression of PPARGC1A (A) , NR4A3 (B) , THSB1 (C) , and ACTN3 (D) in the vastus lateralis 2.5 and 5 h after exercise ( Vissing and Schjerling, 2014 ).

In addition, proteins encoded by ACTN3, ADRB2, MEF2C and TFEB were detected as phosphorylated proteins in vastus lateralis samples after a single bout of high-intensity cycle exercise at ≈90% of the maximal power output (Wmax) for ≈10 min (Supplementary Data S7 ). Of these proteins, TFEB phosphorylation significantly decreased from pre to post exercise ( Hoffman et al., 2015 ). Collectively these expression and phosphoproteomics data show that some endurance genes change their expression or phosphorylation in human skeletal muscle after exercise and are therefore potential regulators of skeletal muscle endurance adaptations.

As we have mentioned, skeletal muscle is not the only organ whose function limits endurance performance. The heart is a particularly important endurance organ. Endurance training induces the athlete’s heart or physiological cardiac hypertrophy. Such a heart has a hypertrophied left ventricle, which increases stroke volume, cardiac output and the VO 2 max ( Ekblom, 1968 ; Ellison et al., 2012 ). To test whether endurance genes change their expression in a mouse model of physiological cardiac hypertrophy induced through 8 weeks of swimming versus a pathologically hypertrophied heart achieved through isoproterenol treatment versus sedentary controls, we re-analyzed the dataset of Galindo et al. (2009) . This analysis revealed that depending on the measured probe, Ppargc1a increased by 4.27 and 6.96-fold, whereas Thra decreased -2.50-fold, Ppard by -2.86-fold and Tnfsf12 by -1.80 fold, in the physiologically hypertrophied heart specifically when compared to sedentary control, respectively (Supplementary Data S8 ). Some endurance genes alter their expression in response to both physiological and pathological hypertrophy of the heart. Hif1a increases by 1.99-fold, Mef2c by 1.99 – 2.90-fold and Pten by 2.05-fold in during physiological hypertrophy, while Hif1a decreases -1.87-fold, Mef2c -2.16-fold, and Pten by -1.74 -1.84-fold during pathological hypertrophy. Ppargc1b and Hk2 specifically decrease their expression in pathological hypertrophy by -2.80-fold, and -1.95-fold, respectively, and not during physiological hypertrophy. These data demonstrate that endurance exercise can induce or repress the expression of some endurance genes in the heart of mice.

Do Endurance Genes Interact With Each Other? Do Endurance Genes Share Common Features?

Next, we investigated whether endurance genes are functionally linked and whether these genes are enriched among specific classes of genes such as genes that share a common domain or molecular function. First, we performed a STRING analysis that predicts direct physical and other associations for a group of proteins (Supplementary Data S9 ; Szklarczyk et al., 2017 ). Figure 5 illustrates the results of this analysis. Clusters in this figure are linked to the mitochondrial biogenesis regulator Pgc1a (encoded by Ppargc1a ), the calcium/calmodulin-stimulated phosphatase calcineurin encoded by PPP3CA and the β2-adrenoceptor encoded by Adrb2 . This suggests some functional interaction between endurance genes.

String analysis for endurance genes. Lines between proteins indicate evidence of association.

We then performed a functional enrichment analysis to see whether the endurance performance-increasing genes share common features. It has been pointed out that such enrichment analyses may be erroneous if the enrichment is not adjusted to the transcriptome of the tissue of interest ( Timmons et al., 2015 ). However, the endurance effect of our gene list could be due to expression in several tissues and so we are unable to use one tissue-specific background list. Instead, we have conducted a general meta-enrichment analysis using ToppGene ( Chen et al., 2009 ). This analysis has revealed potentially relevant enrichments that include the mouse phenotype “abnormal heart rate” (MP:0001629) for 8 of the genes and “abnormal metabolism” (MP:0005266) for 13 of the endurance genes. All significant enrichments are listed in Supplementary Data S10 .

How Many Endurance Genes/Proteins Are Predicted to Be Secreted?

Finally, ≈3000 proteins are predicted to be secreted from cells. To find out how many endurance genes are predicted to encode secreted proteins, we retrieved the list of secreted genes/proteins from Protein Atlas 1 and compared these genes with the list of endurance genes. We identified three endurance genes, Pappa ( Pappalysin 1), Thbs1 (Thrombospondin 1) and Tnfsf12 (TNF superfamily member 12) that encode proteins that are predicted to be secreted (Supplementary Data S11 ). Together, these secreted proteins could play possible roles in inter-organ signaling in relation to acute exercise or adaptation to chronic exercise.

The main finding of this systematic review is the identification of 31 genes whose gain or loss-of-function increases endurance performance in mice by up to 1800% when compared to wildtype control mice. Further bioinformatical analyses reveal the DNA sequence variability of these genes in humans, their organ-specific expression pattern, functional links in-between these genes and the proteins they encode, and a role for some endurance genes during adaptation to endurance exercise. This endurance gene list also provides an up-to-date candidate list for more targeted human genetic analyses for endurance performance or trainability.

Relevance of the Identified Endurance Genes for Explaining Human Endurance

A key question is whether the mouse endurance genes are relevant for the genetics of human endurance. Our analyses suggest that this is probably the case. First, human variants of ACTN3 , ADRB2 , BDKRB2 , HIF1A , PPARD , PPARGC1A , PPARGC1B , and PPP3CA are associated with human endurance ( Ahmetov et al., 2016 ) and human variants of ADCY5 , PPARD and HIF1A are associated with VO 2 max trainability ( Williams et al., 2017 ). Second, GWAS studies identified SNPs linked to the human homologues of endurance genes to the hemoglobin concentration ( Astle et al., 2016 ), resting heart rate ( Eppinga et al., 2016 ), metabolic traits ( Suhre et al., 2011 ; Shin et al., 2014 ), and left ventricular wall thickness ( Wild et al., 2017 ). Finally, we found that the exon DNA sequence of human endurance genes varies considerably. On average, each endurance gene has 174 missense DNA variants, 5 loss-of-function DNA variants and 11 copy number DNA variants in 60,706 individuals ( Lek et al., 2016 ; note that this data refers to the number of alleles and that the number of carriers is much higher). However, associations or DNA variants of endurance genes does not mean that they actually increase endurance in humans ( Houweling et al., 2018 ). Associations need to be replicated and supported by functional analysis, such as in mouse models, to find mechanisms responsible for endurance phenotypes ( Eynon et al., 2017 ). So far, only for ACTN3 there is consistent data showing that a common DNA variant in humans influences muscle performance, which is similar in the ACTN3 mouse model ( Houweling et al., 2018 ). Collectively, this suggests that variants of the human homologues of mouse endurance genes could contribute to the variation of endurance-related traits seen in the human population. This has to be replicated in future studies.

Several Endurance Genes Affect Mitochondrial Biogenesis and Energy Metabolism

The majority of the endurance genes identified in our study are linked to skeletal muscle metabolism and mitochondrial biogenesis. Here, the transcriptional co-factor Pgc-1α plays a key role ( Lin et al., 2002 ). The authors of the original Ppargc1a (which encodes Pgc-1α) overexpression study did not test the endurance capacity of the transgenic mice. However, subsequent studies demonstrated the effect of the overexpression of Ppargc1a ( Calvo et al., 2008 ), of the b-isoform of Ppargc1a ( Tadaishi et al., 2011 ) and of Ppargc1b ( Arany et al., 2007 ) on endurance capacity, skeletal muscle mitochondrial biogenesis and muscle fiber-related gene expression. A related factor is Ppard whose overexpression has similar effects on mitochondrial biogenesis, muscle fiber-related gene expression and endurance capacity ( Wang et al., 2004 ). Many of the other endurance genes regulate the expression of Ppargc1a isoforms or of Ppard or the activity of the proteins that these genes encode which explains their effect on endurance capacity. Ppargc1a expression also increases after endurance exercise in mouse ( Baar et al., 2002 ) and human skeletal muscle ( Pilegaard et al., 2003 ; Figure 4 ) as well as the heart during swimming-induced cardiac hypertrophy (Supplementary Data S8 ) suggesting that it is a mediator of muscle and heart adaptations to endurance exercise ( Holloszy, 1967 ).

Two of the endurance genes haven been linked to thyroid hormone signaling. They are Crym , which encodes a thyroid hormone-binding crystallin ( Seko et al., 2016 ) and Thra which encodes a nuclear thyroid hormone receptor ( Pessemesse et al., 2012 ). The mechanisms are probably linked to the effect of thyroid hormones on mitochondrial biogenesis via Pgc-1α and related factors ( Weitzel and Alexander Iwen, 2011 ). Interestingly, some endurance athletes have been reported to take thyroid medication as a treatment ( Hart, 2017 ) which is a concern as the real purpose might be to enhance endurance capacity through thyroid hormone treatment.

Pgc-1α and related factors are, however, not the only regulators of mitochondrial biogenesis, muscle metabolism and fiber type-specific gene expression. A different group includes Ppp3ca which encodes a subunit the Ca 2+ -activated phosphatase calcineurin ( Jiang et al., 2010 ), the calcineurin regulator calsarcin-2 encoded by Myoz1 ( Frey et al., 2008 ) and the calcineurin-regulated transcription factor Tfeb which promotes mitochondrial biogenesis and other metabolic adaptations ( Mansueto et al., 2017 ). A genome-wide association study has also linked TFEB to left ventricular wall thickness and TFEB phosphorylation decreased significantly from pre to post exercise ( Hoffman et al., 2015 ), suggesting that TFEB may regulate skeletal muscle and heart adaptations to endurance exercise.

Some Endurance Genes Have an Effect on the Oxygen-Delivery System

In humans, a key determinant of a high endurance capacity is the VO 2 max, which depends on the maximal oxygen transport capacity. This in turn depends on the maximal cardiac output, which is increased in the athlete’s heart, and on the oxygen transport capacity of the blood ( Bergh et al., 2000 ; Lundby et al., 2017 ). Earlier studies reported that cardiac-specific expression of the kinase Mek1 increased cardiac function ( Bueno et al., 2000 ) and that the expression of a dominant negative form of Pi3k in the heart prevented physiological cardiac hypertrophy (i.e., the development of an athlete’s heart) after swimming in mice ( McMullen et al., 2003 ). Unfortunately, whether the heart-specific overexpression of these two genes increased exercise capacity in mice was not tested. In another study, researchers overexpressed the catecholamine-related, adenylyl cyclase-encoding genes Adcy5 and Adcy8 in the heart. They found that this overexpression increased cardiac contractility and endurance capacity in the transgenic mice when compared to wildtype controls ( Esposito et al., 2008 ). Also, ADCY5 gene variants are associated with VO 2 max trainability in humans ( Williams et al., 2017 ) and for ADCY5 and ADCY8 together 659 different missense DNA variants, 17 loss-of-function DNA variants and 13 DNA copy number variants have been reported for 60,706 humans in the Exac study ( Lek et al., 2016 ). Collectively, this suggests that numerous DNA variants of ADCY5 and ADCY8 might contribute to the variation of VO 2 max trainability and perhaps VO 2 max seen in humans. In a different model, the knockout of Thbs1 (encoding thrombospondin-1) increased skeletal muscle and cardiac capillary density, left ventricular size and endurance capacity ( Malek and Olfert, 2009 ). Together this demonstrates changed activity of some endurance genes may contribute to the development of an Athlete’s heart.

Other endurance genes change their expression in the heart in endurance-exercising mice ( Figure 3 ; especially Ppargc1a appears to increase during physiological cardiac hypertrophy) or are associated with cardiac phenotypes in GWAS studies (Supplementary Data S3 ). Here, the SNP near PCK1 was associated with the hemoglobin concentration ( Astle et al., 2016 ). However, it is unclear whether the overexpression of Pck1 (encoding Pepck) in skeletal muscle can explain an increased hemoglobin concentration ( Hakimi et al., 2007 ).

Endurance Genes, Neural and Behavioral Mechanisms

Mental fatigue has recently been highlighted as an endurance-influencing factor ( Van Cutsem et al., 2017 ) but we know little about the molecular mechanisms that influence mental fatigue, neural function and behavior in relation to endurance. Two of the endurance genes are linked to the nervous system. Acetylcholine is synthesized from acetyl-CoA and choline and released from motor endplates to cause muscle fibers to contract. Interestingly, the overexpression of the sodium-choline channel gene Slc5a7 increased choline transport, endurance capacity but not strength and physical activity in mice ( Holmstrand et al., 2014 ). The overexpression of Pck1 in skeletal muscle not only increased the endurance capacity most ( Figure 1 ) but made these mice hyperactive in their home cages ( Hakimi et al., 2007 ). How the high expression of a gluconeogenetic enzyme in skeletal muscle can increase spontaneous activity in mice is unclear.

Limitations

Like in our earlier review on hypertrophy-causing genes ( Verbrugge et al., 2018 ), a limitation of this review is that the manipulated genes are subjectively chosen by the researchers of each study. Moreover, many researchers do not test whether a manipulated gene changes endurance exercise capacity. The consequence is that the list of manipulated genes and the mice that are tested in an endurance test is subjective, resulting in a biased list of endurance genes. Currently, the International Mouse Phenotyping Consortium (IMPC) generates and phenotypically analyses 20,000 mouse lines 2 but measurement of endurance capacity is not included in the first-line analysis ( Brown and Moore, 2012 ). Here a triage system might be useful, so that mice that have increased cardiac function, a higher haematocrit or other endurance-associated phenotypes are then also tested in an endurance test.

Another limitation of the study is that the variation of endurance performance also depends on the endurance tests used in the individual studies. For example, if the endurance performance of the same wildtype and transgenic animals is measured in a graded exercise test versus a time trial test then the results will differ. For example, in the Pck1 mouse study the researchers found that wildtype mice ran ≈200 m at a speed of 20 m/min whereas Pck1 -overexpressing mice ran in-between 2000 and 6000 m, which is on average ≈1800% more than the wildtype mice. In contrast, when the same mice were compared in a graded exercise test with an increase of 1 m/min every minute then the Pck1 -overexpressing mice achieved a maximum speed of ≈50 m/min whereas the wildtype controls ran up to a speed of ≈20 m/min. This is a much smaller increase of 150% ( Hakimi et al., 2007 ). Together this demonstrates that the type of endurance performance test can greatly affect the outcome and differences in percent between wildtype and transgenic mouse strains. Generally, researchers should aim for standardized protocols for running tests to exhaustion or for graded exercise tests. Booth et al. give some specific recommendations for such exercise tests in mice ( Booth et al., 2010 ).

A final limitation of this study is that we do not include mouse models where the gain or loss-of-function of a gene reduces endurance performance. Such mouse models can provide important insights into genes that influence endurance performance ( Garton et al., 2016 ). However, the major problem with such mouse models is that it is difficult to judge whether the decrease in performance is because of the reduction of a true endurance-increasing variable or whether endurance is decreased because of a disease. For example, we would expect that almost all tumor-bearing mice have a reduced endurance capacity even though the effect of the genetic manipulation causes tumor and does not directly affect endurance-influencing variables.

Summary and Conclusion

A high endurance capacity is important for many sports, is associated with good health, low mortality and many endurance-associated traits are ≈50% inherited. However, how DNA variants contribute to the variation of human endurance and especially of VO 2 max and VO 2 max trainability is still incompletely understood and some aspects are controversial ( Lundby et al., 2017 ). The contribution of this study to our understanding of endurance genetics is a list of 31 genes whose gain or loss-of-function increases endurance performance by up to 1800% in mice. Many of the identified endurance genes are linked to biological pathways that are relevant for endurance, especially mitochondrial biogenesis and muscle metabolism. Moreover, exome sequencing data for 60,706 individuals contain a large number of amino acid sequence and/or function-changing DNA variants for these genes ( Lek et al., 2016 ), suggesting that human variants of these genes partially explain the variation of endurance capacity. In contrast, few endurance genes are linked to the oxygen-transporting systems that limit the VO 2 max. Here the best VO 2 max-influencing candidate genes are the ADCY5 and ADCY8 adenylyl cyclase genes that increase cardiac contractility ( Esposito et al., 2008 ). ADCY5 gene variants are also associated with VO 2 max trainability in humans ( Williams et al., 2017 ) and more than 600 types of function-altering DNA variants have been reported for ADCY5 and ADCY8 in 60,706 humans ( Lek et al., 2016 ). Still, this leaves much of the known genetic variability of the VO 2 max and VO 2 max trainability unexplained. Practically, this endurance gene list and our earlier list of hypertrophy-promoting list may be useful for more targeted and in depth genetic analyses of elite endurance athletes such as East African runners.

Author Contributions

FYN and SV conducted the systematic paper analysis. FYN, SV, MS, and HW did the bioinformatical analyses. LB and MH revised the manuscript. HW drafted the manuscript and all authors contributed to writing the manuscript.

Conflict of Interest Statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Acknowledgments

We thank Dr. Kenneth Dyar for commenting on the manuscript.

Funding. This work was supported by the German Research Foundation (DFG) and the Technical University of Munich within the Open Access Publishing Funding Program and the German Federal Ministry of Education and Research (Infrafrontier grant 01KX1012).

1 https://www.proteinatlas.org/humanproteome/secretome

2 http://www.mousephenotype.org/

Supplementary Material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fphys.2019.00262/full#supplementary-material

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How Does Dehydration Affect The Body's Temperature Regulation, Metabolism, And Organ Functions? A Review By Doctors

Expert opinion from dr. anuvitha kamath, mbbs · 3 years of experience · india.

Dehydration is defined as the loss of body fluids hampering the normal body function. It is mainly caused by hot climate. The signs and symptoms are dry skin , dry tongue, wrinkled skin, sunken eyes , loss of skin elasticity, absence of tears, cold clammy skin, rapid breathing, headache , fatigue , dizziness , syncope , flushed skin, low blood pressure , dry mouth , muscle cramps , dark urine , constipation , loss of appetite, and intolerance to heat. The risks and complications of dehydration in the elderly include an increased risk of falls due to giddiness, hypotension , weakness , skin sores, or skin irritation, increased risk of urinary tract infection due to less body water, constipation , heat stroke , seizure due to low potassium and sodium levels, kidney failure , hypotensive shock , coma , brain swelling, and death. It can slow down the metabolism, reduce organ function, and exhaustion.

→ See more questions and expert answers related to Dehydration.

Expert opinion from Dr. Alan Thomas Charly

Mbbs · 1 years of experience · india.

Dehydration has a substantial impact on the body's temperature regulation, metabolism, and organ functions. Dehydration impairs the body's capacity to regulate temperature effectively, increasing the risk of overheating. Metabolism slows, making it more difficult to burn calories and keep energy levels stable. Vital organs such as the kidneys and heart may be less efficient, potentially leading to difficulties. Dehydration can also have an impact on cognition, emotions, and physical performance. It is crucial to stay hydrated to support these critical body processes and general well-being.

Expert opinion from Dr. Himabindu Sreenivasulu

Dehydration disrupts the body's ability to regulate temperature, which can lead to overheating. It reduces sweat production, a crucial cooling mechanism. Metabolism slows down as the body tries to conserve water, leading to decreased energy levels. Organ functions suffer as dehydration affects blood flow and can lead to electrolyte imbalances, impacting the heart, kidneys, and brain. It's essential to stay hydrated to maintain proper temperature regulation, metabolism, and organ function for overall health.

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Physical Review B

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Gallium defects in α − Al 2 O 3 : A density functional theory study

Glen r. jenness, manoj k. shukla, and benjamin c. masters, phys. rev. b 109 , 235204 – published 4 june 2024.

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Aluminum is an important component in many consumer goods, such as automobiles. Ore refinement and the production of end products incurs a significant energy cost. An alternative is to leverage liquid metal embrittlement (LME); here, a liquid metal alloys with a solid matrix to form a brittle alloy. By outlining the desired shape, LME can produce low-cost machined parts and take advantage of recycled stock. A famous example is the LME of aluminum via gallium. An issue that arises is the formation of aluminum oxide ( Al 2 O 3 ) , either as a passivation layer or along the aluminum grain boundaries. It is not clear from the existing literature how this oxide layer would impact the LME through the application of gallium. In the current study, we examine the defect formation energies of gallium in α − Al 2 O 3 and its diffusion barriers as a function of gallium charge state. We find that while gallium in the + 3 charge state is thermodynamically favored to insert into α − Al 2 O 3 , there exists a high diffusion barrier that prevents it from traveling through the Al 2 O 3 matrix.

• Received 7 September 2023
• Revised 4 April 2024
• Accepted 14 May 2024

DOI: https://doi.org/10.1103/PhysRevB.109.235204

Published by the American Physical Society

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• Environmental Laboratory, US Army Engineer Research and Development Center , 3909 Halls Ferry Road, Vicksburg, Mississippi 39180, USA
• Construction Engineering Research Laboratory, US Army Engineer Research and Development Center , 2902 Newmark Drive, Champaign, Illinois 62822, USA
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• † [email protected]

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Vol. 109, Iss. 23 — 15 June 2024

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Location of the various Wyckoff sites in α − Al 2 O 3 . Aluminum atoms (gray spheres) are located on the 12c sites and oxygen atoms (red spheres) are located at the 18e site. The green sphere denotes the 6a site, violet the 6b site, blue the 18d site, and yellow the 36f site. The solid black line denotes the unit cell boundary.

Optimized Ga defect at the 6a Wyckoff site. Red, gray, and green spheres denote oxygen, aluminum, and gallium, respectively. The solid black line denotes the unit cell boundary.

Charge density differences for a Ga interstitial at the 6a Wyckoff site for charge states 0–3. Red, gray, and green spheres denote oxygen, aluminum, and gallium, respectively. The solid black line denotes the unit cell boundary. The atoms and density grid were shifted by 0.5 of the unit cell in the x and z direction for ease of visualization around the periodic boundaries. Isosurface shown is the 1 × 10 − 3 e − Å − 3 . Yellow regions denotes areas of charge accumulation whereas blue denotes areas of charge depletion.

Optimized Ga defect at the 6b Wyckoff site. Red, gray, and green spheres denote oxygen, aluminum, and gallium, respectively. The solid black line denotes the unit cell boundary.

Optimized Ga defect at the 18d Wyckoff site. Red, gray, and green spheres denote oxygen, aluminum, and gallium, respectively. The solid black line denotes the unit cell boundary.

Optimized Ga defect at the 36f Wyckoff site. Red, gray, and green spheres denote oxygen, aluminum, and gallium, respectively. The solid black line denotes the unit cell boundary.

Optimized substitutional Ga defects. Red, gray, and green spheres denote oxygen, aluminum, and gallium, respectively. The solid black line denotes the unit cell boundary.

Log plot of defect concentration for the Ga@6a interstitial compared to the concentration of the Ga@Al substitution under oxygen-rich conditions with an O 2 reference as a function of Fermi level. Concentrations are calculated according to Eq. ( 9 ).

Reaction pathway for the diffusion of Ga as a function of charge state in the z direction.

Reaction pathway for the diffusion of Ga for the neutral crystal and the + 3 charge state in the x and y directions.

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A rare incidence of severe dermatological toxicities triggered by concomitant administration of all-trans retinoic acid and triazole antifungal in patients with acute promyelocytic leukemia: a case series and review of the literature

• Aisha Jamal   ORCID: orcid.org/0000-0001-5022-7498 1 , 3 ,
• Rafia Hassam 1 ,
• Qurratulain Rizvi 1 ,
• Ali Saleem 1 ,
• Anum Khalid 2 &
• Nida Anwar 1  

Journal of Medical Case Reports volume  18 , Article number:  261 ( 2024 ) Cite this article

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All-trans retinoic acid (ATRA) is an indispensable part of the treatment of acute promyelocytic leukemia (APL). Although, mild cutaneous toxicities like mucocutaneous xerosis, rash, and pruritus are well reported, ATRA associated severe dermatological toxicities are extremely rare. ATRA is primary metabolized by cytochrome P450 (CYP450) enzyme system, and triazole antifungals are notorious for their strong inhibitory effect on CYP450.

Case presentation

Three Asian APL patients experienced rare ATRA-induced severe dermatological toxicities: exfoliative dermatitis (ED) in cases 1 and 2, and necrotic scrotal ulceration in case 3. Both case 1 (33-year-old female), and case 2 (28-year-old male) landed in emergency department with dehydration, generalized skin erythema and xerosis during their induction chemotherapy. Both of these patients also developed invasive aspergillosis and required concomitant triazole antifungals during their chemotherapy. For ED, intravenous fluids and broad-spectrum antibiotics were started along with application of local emollients to prevent transdermal water loss. Although their general condition improved but skin exfoliation continued with complete desquamation of palms and soles. Dermatology was consulted, and clinical diagnosis of ED was established. Discontinuation of ATRA resulted in complete resolution of ED. Case 3 (15-year-old boy) reported two blackish mildly tender scrotal lesions during induction chemotherapy. He also had mucocutaneous candidiasis at presentation and was kept on triazole antifungal. Local bacterial & fungal cultures, and serological testing for herpes simplex virus were reported negative. Despite adequate local care and optimal antibiotic support, his lesions persisted, and improved only after temporary discontinuation of ATRA. After a thorough literature review and considering the temporal association of cutaneous toxicities with triazole antifungals, we speculate that the concomitant use of triazole antifungals inhibited the hepatic metabolism of ATRA, resulting in higher serum ATRA concentration, and markedly accentuated cutaneous toxicities in our patients.

By highlighting this crucial pharmacokinetic interaction, we want to caution the fellow oncologists to be mindful of the inhibitory effect of triazole antifungals on CYP450. We propose using a non-myelosuppressive combination of ATRA and arsenic trioxide for management of APL hence, obliterating the need of prophylactic antifungals. However, in the event of invasive fungal infection (IFI), we suggest using alternative class of antifungals.

Peer Review reports

Acute promyelocytic leukemia (APL) is a rare and potentially curable subtype of acute myeloid leukemia (AML), accounting for 5–8% of AML cases [ 1 ]. Genetically, APL is characterized by reciprocal translocation t(15:17) (q22;q11–12), with consequent fusion of promyelocytic (PML) gene on chromosome 15q22 to retinoic acid receptor-alpha (RAR-alpha) gene on chromosome 17q21. The resultant fusion oncoprotein, PML-RARA, induces transcriptional repression, chromatin condensation, maturation arrest, and accumulation of abnormal promyelocytes [ 2 ]. Advent of all-trans retinoic acid (ATRA) has revolutionized the treatment landscape of APL, and along with the backbone of anthracycline based chemotherapy, it is considered to be the standard of care for APL patients. Combination treatment with ATRA plus anthracycline based chemotherapy achieves an overall complete remission and cure rate of 95% and 80% respectively, rendering ATRA indispensable in the management of APL [ 3 ].

ATRA, an active metabolite of vitamin A, belongs to a class of retinoids. Although retinoids are well known for their dermatological side effects like xerosis, xerostomia, erythema, pruritis, and exfoliation; severe dermatological side effects of ATRA, especially in the dosage pertinent to APL (45 mg/m 2 ), are rare. So far, only a single case of exfoliative dermatitis (ED) and a few cases of scrotal ulceration have been reported in literature [ 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 ]. We, here in, report a case series of three patients with serious and rare ATRA associated dermatological complications. We have also discussed upon the potentially precipitating pharmacokinetic interactions, as well as the detailed clinical course and management of our patients as simply withholding ATRA can jeopardize the outcome of this potentially curable malignant disorder.

In all three patients, ATRA was started as soon as abnormal promyelocytes were documented on peripheral smear/bone marrow aspirate examination (Figs.  1 , 2 , 3 ). Diagnosis was further confirmed through cytogenetic analysis as well as PML-RARA detection by polymerase chain reaction. Additionally, in all three patients, chemotherapeutic treatment was instituted according to European APL protocol, based on their risk-group classification.

Exfoliative dermatitis &Onychomadesis (CASE 1). a Peripheral smear. b Bone marrow aspirate. c Desquamation of soles. d Desquamation of palms. e Dry exfoliation of feet and shins. f Onychomadesis

Exfoliative dermatitis (CASE 2). a Peripheral smear. b Bone marrow aspirate. c and d Erythema and scaling of hands. e Cutaneous desquamation of soles

Scrotal lesions (CASE 3). a Peripheral smear. b Bone marrow aspirate. c and d Necrotic scrotal lesions with black eschar

Case 1: A 33-year-old Asian female presented in ER with history of fever, heavy menstrual bleeding and rash all over body. Induction chemotherapy and steroid prophylaxis was promptly started to prevent differentiation syndrome (DS). On Day-10 of induction chemotherapy, she developed high grade fever, cough and shortness of breath. High-resolution computerized tomography (HRCT) showed randomly scattered discrete nodular opacities with surrounding ground glass haze in both lung fields, suggestive of invasive fungal infection (IFI). Voriconazole was immediately started along with broad-spectrum antibiotics. She improved over the following 72 hour, and was discharged from hospital on Day-17. Subsequently, she landed in emergency department on Day-23 with severe dehydration, shivering, tachycardia, generalized skin erythema and discoloration of nail beds. Intravenous fluids and broad-spectrum antibiotics were started along with application of local emollients to prevent transdermal water loss. Over the next 24-36 hour, her general condition was stabilized however; skin exfoliation continued with complete desquamation of palms and soles (Fig.  1 ). Dermatology was consulted, and a clinical diagnosis of onychomadesis and exfoliative dermatitis (ED) was made. A review of her clinical case demonstrated no apparent cause for ED except for a rare association with ATRA. However, considering the curative potential of ATRA, it was continued till Day-28 as per protocol. Her skin condition gradually resolved over next 10–14 days after discontinuation of ATRA. She had recurrence of similar skin condition upon re-exposure to ATRA in her consolidation chemotherapeutic cycles, however, the exfoliation was mild and patchy that responded well to good oral hydration and local skin emollients.

Case 2: A 28-year-old Asian male presented in the out-patient clinic with the history of generalized weakness, high-grade-fever, productive cough and bruises over body. On examination, he had multiple ecchymosis and petechiae with coarse crepitations involving right-middle and left-lower lung fields. He was promptly started on broad-spectrum antibiotics. Additionally, as per protocol, induction chemotherapy and dexamethasone prophylaxis was also instituted. His fever and cough remained unresponsive despite broad-spectrum antibiotics. Voriconazole was instituted upon the identification of IFI on HRCT findings. By day-10, coagulopathy was normalized, and clearance of abnormal promyelocytes was documented by Day-18. On Day-20, he complained of skin dryness, itching and scaling; physical examination revealed generalized xerosis and erythema (Fig.  2 ). Despite aggressive skin care, generalized skin exfoliation, most pronounced on palms and soles, ensued. Clinical diagnosis of ED was established after obtaining dermatological consultation. However, in view of his clinical stability, ATRA was continued. Bone marrow aspirate on Day-28 showed morphological remission. Recurrence of erythema and exfoliation was documented during consolidation phase of chemotherapy, but the condition was responsive to local emollients and oral hydration.

Case 3: A 15-year-old Asian male presented in the out-patient clinic with complains of high-grade-fever, muco-cutaneous bleeding and pancytopenia. On presentation, patient was febrile and had oral thrush. After sending his baseline tests he was taken on broad-spectrum antibiotics and triazole antifungal (itraconazole). After completion of induction chemotherapy, patient was discharged with bi-weekly follow-ups.On Day15, he reported two blackish, mildly tender scrotal lesions with minimal serous discharge (Fig.  3 ). Antibiotic cover for soft tissue infection was commenced along with local wound care with topical steroids and antibiotics. He had no sign of systemic infection/sepsis. Local bacterial & fungal cultures and serological testing for herpes simplex virus were reported negative. Despite adequate local care and optimal antibiotic support, his lesions showed no sign of healing, and two new lesions were developed. Lesion biopsy for histopathological evaluation was declined by the patient. Keeping the rare but reported occurrence of ATRA-induced scrotal ulceration and fournier's gangrene; ATRA was transiently withheld for ten days and the lesions started to regress. However, considering the indispensable role of ATRA in APL, it was reinstituted. Scrotal lesions persisted without any worsening. ATRA was stopped after completion of protocol. Complete resolution of scrotal lesions was documented over the following two weeks. Afterwards, he received two cycles of consolidation chemotherapy, but no recurrence was reported.

Discussion and conclusion

The antineoplastic role of ATRA remains indispensable in the curative management of APL. It is considered a relatively safe drug with a well-known toxicity profile. Commonly reported adverse events include DS, pseudotumor-cerebri, hypertriglyceridemia, transaminitis, and headache. Although, mild cutaneous toxicities like muco-cutaneous xerosis, photosensitivity, rash, pruritus and sweet’s syndrome are well reported, severe dermatological toxicities are rarely reported in literature [ 18 , 19 ]. In this case series, we have discussed three cases of ATRA-induced rare dermatological complications in APL.

Case 1 and 2 developed ED during remission induction phase of chemotherapy. Literature review revealed only a single reported occurrence of ATRA-induced ED in APL by YonelIpek et al. [ 4 ]. ED is a potentially life-threatening cutaneous manifestation that is characterized by diffuse skin erythema and scaling. Various underlying disorders can trigger its onset through a complex interplay of inflammatory cytokines and phagocytes. In contrast to our cases, the case reported by Yonel Ipek et al. developed xerosis in consolidation phase, which akin to our cases started after two weeks of ATRA exposure and rapidly deteriorated to generalized erythroderma and scaling. In both cases, discontinuation of ATRA resulted in complete resolution of ED.

In case 3, we have reported ATRA-induced necrotic scrotal ulceration. Literature review revealed that over the last two decades, a total of twenty cases of ATRA-induced scrotal ulceration have been reported. Histopathological evaluation of these lesions revealed atypical granulocytic infiltration, pointing towards the possible etiological role of differentiated APL cells in the pathogenesis. Most of these cases, including ours, developed genital-lesions almost after two weeks of ATRA exposure and remained unresponsive to local and systemic antibiotics. ATRA had to be halted in most of the cases to prevent progression to fournier’s gangrene [ 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 ].

Scattered over the span of three years and considered in isolation, it was not initially apparent to us that all three cases had one striking similarity: concomitant use of ATRA and triazole antifungals. ATRA is primary metabolized by cytochrome P450 enzyme system. Triazole antifungals are notorious for their strong inhibitory effect on CYP450 enzyme system, resulting in supra-therapeutic drug levels and toxicity [ 20 , 21 , 22 ].

Potentiation of serum ATRA levels by inhibition of CYP450 system was first explored by Rigas et al. [ 23 ]. This study reported 1.8 times higher serum concentration of ATRA with concomitant use of ketoconazole. Since then a number of cases have reported the augmentation of ATRA-induced toxicities due to this pharmacokinetic interaction. Concomitant use of ATRA and triazole antifungals that is voriconazole and posaconazole has been implicated to cause severe hypercalcemia [ 24 , 25 , 26 , 27 ]. Similarly, combination with fluconazole has been reported to cause severe neurotoxicity and nephrotoxicity [ 28 , 29 ].

Considering the temporal association of dermatological complications with triazole antifungals in our patients, we speculate that the concomitant use of triazole antifungals inhibited the metabolism of ATRA, resulting in higher serum concentrations and markedly accentuated cutaneous toxicities. A study further strengthening our hypothesis was conducted by Kurzrock et al. to evaluate the maximum tolerable dose of ATRA in myelodysplastic syndrome. The study reported severe dose-limiting cutaneous toxicities, such as generalized desquamation and genital ulceration, at doses > 150 mg/m 2 /day, compared to mild xerosis and erythema in the dose range of 45–100 mg/m 2 /day. Akin to our cases, the study reported complete resolution of cutaneous toxicities within 1–2 weeks of ATRA discontinuation [ 30 ].

Another important point is the recurrence of ED in both case 1 and 2 during their consolidation chemotherapy cycles, whereas recurrent scrotal ulceration was not documented in case 3. The most likely explanation is the continuation of voriconazole as secondary prophylaxis in patients with invasive fungal infections (IFI) (case 1 and 2), whereas itraconazole was discontinued after remission induction in case 3. This once again underscores the pharmacokinetic potentiation of ATRA-induced cutaneous toxicities by triazole antifungals. An important limitation of our study is that, due to the unavailability of serum voriconazole testing, we couldn’t document serum voriconazole levels, something that could provide valuable insights into the effect of serum azole levels on the severity of cutaneous manifestations.

By highlighting this crucial pharmacokinetic interaction and its potentially severe implications, we urge our fellow oncologists to remain vigilant regarding the inhibitory effects of triazole antifungals on the metabolism of ATRA. We propose the use of a non-myelosuppressive combination of ATRA and arsenic trioxide for APL, thereby eliminating the need for prophylactic antifungals. In the case of invasive fungal infections (IFI), we recommend considering alternative classes of antifungals. However, if triazole antifungals are deemed unavoidable, we suggest close monitoring for potential side effects and implementing prophylactic measures as clinically necessary.

Availability of data and materials

Data sharing is not applicable to this manuscript as no datasets were generated or analyzed during the current study.

Abbreviations

Acute promyelocytic leukemia

All-trans retinoic acid

Cytochrome P450

Differentiation syndrome

• Exfoliative dermatitis

High-resolution computerized tomography

Invasive fungal infections

Promyelocytic leukemia-retinoic acid receptor alpha

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Aisha Jamal, Rafia Hassam, Qurratulain Rizvi, Ali Saleem & Nida Anwar

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Aisha Jamal: Conceptualization, writing-original draft, writing-review & editing. Rafia Hassam: Conceptualization, writing-original draft. Qurratulain Rizvi: Writing-review & editing. Ali Saleem: Data curation, writing—review & editing. Anum Khalid: Writing—review &editing. Nida Anwar: Writing—review & editing.

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Jamal, A., Hassam, R., Rizvi, Q. et al. A rare incidence of severe dermatological toxicities triggered by concomitant administration of all-trans retinoic acid and triazole antifungal in patients with acute promyelocytic leukemia: a case series and review of the literature. J Med Case Reports 18 , 261 (2024). https://doi.org/10.1186/s13256-024-04577-1

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Received : 19 December 2023

Accepted : 01 May 2024

Published : 27 May 2024

DOI : https://doi.org/10.1186/s13256-024-04577-1

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