The demand for glucose uptake and the accompanying enhanced glycolytic energy metabolism is one of the most important features of cancer cells. Unlike the aerobic metabolic pathway in normal cells, the large amount of pyruvate produced by the dramatic increase of glycolysis in cancer cells needs to be converted to lactate in the cytoplasm, which cannot be done without a large amount of lactate dehydrogenase (LDH). This explains why elevated serum LDH concentrations are usually seen in cancer patient populations. LDH not only correlates with clinical prognostic survival indicators, but also guides subsequent drug therapy. Besides their role in cancers, LDH is also a biomarker for malaria and other diseases. Therefore, it is urgent to develop methods for sensitive and convenient LDH detection. Here, this review systematically summarizes the clinical impact of lactate dehydrogenase detection and principles for LDH detection. The advantages as well as limitations of different detection methods and the future trends for LDH detection were also discussed.

Deep learning for image denoising has recently attracted considerable attentions due to its excellent performance. Since most of current deep learning based denoising models require a large number of clean images for training, it is difficult to extend them to the denoising problems when the reference clean images are hard to acquire (e.g., optical coherence tomography (OCT) images). In this paper, we propose a novel unsupervised deep learning model called as nonlocal-generative adversarial network (Nonlocal-GAN) for OCT image denoising, where the deep model can be trained without reference clean images. Specifically, considering that the background areas of OCT images mainly contain pure real noise samples, we creatively train a discriminator to distinguish background real noise samples from the fake noise samples generated by the denoiser, i.e., the generator, and then the discriminator will guide the generator for denoising. To further enhance denoising performance, we introduce a nonlocal means layer into the generator of the Nonlocal-GAN model. Furthermore, since nearby several OCT Bscans have strong correlations, we also propose a Nonlocal-GANM model to utilize the high correlations within nearby B-scans. Extensive experimental results on clinical retinal OCT images demonstrate the effectiveness and efficiency of the proposed method.

Background: Diabetes mellitus (DM) is a chronic disease with hyperglycemia. If not treated in time, it may lead to lower limb amputation. At the initial stage, the detection of diabetes-related foot ulcer (DFU) is very difficult. Deep learning has demonstrated state-of-the-art performance in various fields and has been used to analyze images of DFUs.Objective: This article reviewed current applications of deep learning to the early detection of DFU to avoid limb amputation or infection.Methods: Relevant literature on deep learning models, including in the classification, object detection, and semantic segmentation for images of DFU, published during the past 10 years, were analyzed.Results: Currently, the primary uses of deep learning in early DFU detection are related to different algorithms. For classification tasks, improved classification models were all based on convolutional neural networks (CNNs). The model with parallel convolutional layers based on GoogLeNet and the ensemble model outperformed the other models in classification accuracy. For object detection tasks, the models were based on architectures such as faster R-CNN, You-Only-Look-Once (YOLO) v3, YOLO v5, or EfficientDet. The refinements on YOLO v3 models achieved an accuracy of 91.95% and the model with an adaptive faster R-CNN architecture achieved a mean average precision (mAP) of 91.4%, which outperformed the other models. For semantic segmentation tasks, the models were based on architectures such as fully convolutional networks (FCNs), U-Net, V-Net, or SegNet. The model with U-Net outperformed the other models with an accuracy of 94.96%. Taking segmentation tasks as an example, the models were based on architectures such as mask R-CNN. The model with mask R-CNN obtained a precision value of 0.8632 and a mAP of 0.5084.Conclusion: Although current research is promising in the ability of deep learning to improve a patient’s quality of life, further research is required to better understand the mechanisms of deep learning for DFUs.