Progress in small-molecule inhibitors targeting PD-L1.

PD-L1 is a transmembrane protein overexpressed by tumor cells. It binds to PD-1 on the surface of T-cells, suppresses T-cell activity and hinders the immune response against cancer. Clinically, several monoclonal antibodies targeting PD-1/PD-L1 have achieved significant success in cancer immunotherapy. Nevertheless, their disadvantages, such as unchecked immune responses, high cost and long half-life, stimulated pharmacologists to develop small-molecule inhibitors targeting PD-1/PD-L1. After a batch of excellent inhibitors with a biphenyl core structure were firstly reported by BMS, more and more researchers focused on small-molecule inhibitors targeting PD-L1 rather than PD-1. Numerous small-molecule inhibitors were extensively designed and synthesized in the past few years. In this paper, the structural characteristics of PD-L1 and complexes of PD-L1 with its inhibitors are elaborated and small molecule inhibitors developed in the last decade are summarized as well. This paper aims to provide insights into further designing and synthesis of small molecule inhibitors targeting PD-L1.

Synergistic material modification-induced optimization of interfacial charge transfer and surface hydrogen adsorption.

Being chemically stable, low cost and made from abundant resources, titanium dioxide (TiO2) possesses the most desired advantages for photocatalytic applications. However, the intrinsic limits of high surface hydrogen adsorption energy, wide band gap, low separation rate and rapid recombination of the photogenerated charge carriers greatly hamper its utilization. To address these issues, the present work combines density functional theory (DFT) calculations with rational modifications of TiO2 with nickel doping and an ultra-thin shield of fluorinated carbon (FNT) for application in the photocatalytic hydrogen evolution reaction (HER). Comprehensive studies imply that the synergistic modifications not only optimize the surface H adsorption, but also facilitate the interfacial charge transfer and simultaneously prevent the photochemical and chemical corrosion of the catalysts. In good agreement with the theoretical predictions, the resulting FNT photocatalysts demonstrate an optimal HER efficiency of 13.0 mmol g-1 h-1, nearly 33-times and over three-times beyond that of the pristine TiO2 (0.4 mmol g-1 h-1) and the Ni-doped TiO2 (4.2 mmol g-1 h-1), respectively. Moreover, the composite also exhibits excellent stability with a well-reproducible HER performance over a 66-hour cyclic HER test of 15 cycles.

Deep Sc-RNA sequencing decoding the molecular dynamic architecture of the human retina.

The human retina serves as a light detector and signals transmission tissue. Advanced insights into retinal disease mechanisms and therapeutic strategies require a deep understanding of healthy retina molecular events. Here, we sequenced the mRNA of over 0.6 million single cells from human retinas across six regions at nine different ages. Sixty cell sub-types have been identified from the human mature retinas with unique markers. We revealed regional and age differences of gene expression profiles within the human retina. Cell-cell interaction analysis indicated a rich synaptic connection within the retinal cells. Gene expression regulon analysis revealed the specific expression of transcription factors and their regulated genes in human retina cell types. Some of the gene's expression, such as DKK3, are elevated in aged retinas. A further functional investigation suggested that over expression of DKK3 could impact mitochondrial stability. Overall, decoding the molecular dynamic architecture of the human retina improves our understanding of the vision system.

A novel multi-scale 2D CNN with weighted focal loss for arrhythmias detection on varying-dimensional ECGs.

Objective. The ECG is a standard diagnostic tool for identifying many arrhythmias. Accurate diagnosis and early intervention for arrhythmias are of great significance to the prevention and treatment of cardiovascular disease. Our objective is to develop an algorithm that can automatically identify 30 arrhythmias by using varying-dimensional ECG signals.Approach. In this paper, we firstly proposed a novel multi-scale 2D CNN that can effectively capture pathological information from small-scale to large-scale from ECG signals to identify 30 arrhythmias from 12-lead, 6-lead, 4-lead, 3-lead, and 2-lead ECGs. Secondly, we explored the effects of varying convolution kernels sizes and branch subnetworks on the model's performance for each arrhythmia. Thirdly, we introduced the weighted focal loss to alleviate the positive-negative class imbalance problem in the multi-label arrhythmias classification. Fourthly, we explored the utility of reduced-lead ECGs in detecting arrhythmias by comparing the performances of models on varying-dimensional ECGs.Main results. As a follow-up entry after the PhysioNet/Computing in Cardiology Challenge (2021), our proposed approach achieved the official test scores of 0.52, 0.47, 0.53, 0.51, and 0.50 for the 12-lead, 6-lead, 4-lead, 3-lead, and 2-lead ECGs on the hidden test set (comparable to that of 6th, 11th, 4th, 5th, and 7th out of 39 teams in the Challenge).Significance. A multi-scale framework capable of detecting 30 arrhythmias from varying-dimensional ECGs was proposed in our work. We preliminarily verified that the multi-scale perception fields may be necessary to capture more comprehensive pathological information for arrhythmias detection. Besides, we also verified that the weighted focal loss may alleviate the positive-negative class imbalance and improve the model's generalization performance on the cross-dataset. In addition, we observed that some reduced-lead models, such as the 4-lead and 3-lead models, can even achieve performance that is almost comparable to that of the 12-lead model. The excellent performance of our proposed framework demonstrates its great potential in detecting a wide range of arrhythmias.

EEG-Based Emotion Recognition Using a 2D CNN with Different Kernels.

Emotion recognition is receiving significant attention in research on health care and Human-Computer Interaction (HCI). Due to the high correlation with emotion and the capability to affect deceptive external expressions such as voices and faces, Electroencephalogram (EEG) based emotion recognition methods have been globally accepted and widely applied. Recently, great improvements have been made in the development of machine learning for EEG-based emotion detection. However, there are still some major disadvantages in previous studies. Firstly, traditional machine learning methods require extracting features manually which is time-consuming and rely heavily on human experts. Secondly, to improve the model accuracies, many researchers used user-dependent models that lack generalization and universality. Moreover, there is still room for improvement in the recognition accuracies in most studies. Therefore, to overcome these shortcomings, an EEG-based novel deep neural network is proposed for emotion classification in this article. The proposed 2D CNN uses two convolutional kernels of different sizes to extract emotion-related features along both the time direction and the spatial direction. To verify the feasibility of the proposed model, the pubic emotion dataset DEAP is used in experiments. The results show accuracies of up to 99.99% and 99.98 for arousal and valence binary classification, respectively, which are encouraging for research and applications in the emotion recognition field.

Age-related macular degeneration: Epidemiology, genetics, pathophysiology, diagnosis, and targeted therapy.

Age-related macular degeneration (AMD) is a complex eye disorder and is the leading cause of incurable blindness worldwide in the elderly. Clinically, AMD initially affects the central area of retina known as the macula and it is classified as early stage to late stage (advanced AMD). The advanced AMD is classified into the nonexudative or atrophic form (dry AMD) and the exudative or neovascular form (wet AMD). More severe vision loss is typically associated with the wet form. Multiple genetic factors, lipid metabolism, oxidative stress and aging, play a role in the etiology of AMD. Dysregulation in genetic to AMD is established to 46%-71% of disease contribution, with CFH and ARMS2/HTRA1 to be the two most notable risk loci among the 103 identified AMD associated loci so far. Chronic cigarette smoking is the most proven consistently risk living habits for AMD. Deep learning algorithm has been developed based on image recognition to distinguish wet AMD and normal macula with high accuracy. Currently, anti-vascular endothelial growth factor (VEGF) therapy is highly effective at treating wet AMD. Several new generation AMD drugs and iPSC-derived RPE cell therapy are in the clinical trial stage and are promising to improve AMD treatment in the near future.

Untargeted metabolomics for uncovering plasma biological markers of wet age-related macular degeneration.

Wet age-related macular degeneration (wAMD) causes central vision loss and represents a major health problem in elderly people. Here we have used untargeted metabolomics using UHPLC-MS to profile plasma from 127 patients with wAMD (67 choroidal neovascularization (CNV) and 60 polypoidal choroidal vasculopathy (PCV)) and 50 controls. A total of 545 biochemicals were detected. Among them, 17 metabolites presented difference between patients with wAMD and controls. Most of them were oxidized lipids (N=6, 35.29%). Comparing to controls, 28 and 18 differential metabolites were identified in patients with CNV and PCV, respectively. Two metabolites, hyodeoxycholic acid and L-tryptophanamide, were differently distributed between PCV and CNV. We first investigated the genetic association with metabolites in wet AMD (CFH rs800292 and HTRA1 rs10490924). We identified six differential metabolites between the GG and AA genotypes of CFH rs800292, five differential metabolites between the GG and AA genotypes of HTRA1 rs10490924, and four differential metabolites between the GG and GA genotypes of rs10490924. We selected four metabolites (cyclamic acid, hyodeoxycholic acid, L-tryptophanamide and O-phosphorylethanolamine) for in vitro experiments. Among them, cyclamic acid reduced the activity, inhibited the proliferation, increased the apoptosis and necrosis in human retinal pigment epithelial cells (HRPECs). L-tryptophanamide affected the proliferation, apoptosis and necrosis in HRPECs, and promoted the tube formation and migration in primary human retinal endothelial cells (HRECs). Hyodeoxycholic acid and O-phosphorylethanolamine inhibited the tube formation and migration in HRECs. The results suggested that differential metabolites have certain effects on wAMD pathogenesis-related HRPECs and HRECs.

ECG-based biometric under different psychological stress states.

BACKGROUND AND OBJECTIVE: In recent years, people have been exploring methods for biometric identification through electrocardiogram (ECG) signals. Under the same psychological pressure state, biometric identification through ECG signals is a traditional verification method. However, ECG signals are affected by changes in psychological stress, and ECG-Based biometric under different psychological stress states are still challenging. In this paper, we propose a method combining manual and automatic features for ECG-based biometric under different psychological stress states. And propose a new indicator Stress Classification Coefficient (SCC) that assesses the effect of different psychological stress on heart rate variability (HRV) features. METHODS: In our method, we obtain manual features to be a three-step process: first, HRV features obtained from the ECG signals. Second, based on HRV features, the mental state of the experimental subjects is assessed by using the Gaussian mixture model (GMM). Finally, use cluster centers to process the original HRV features to reduce the Stress Classification Coefficient (SCC). Also, the one-dimensional convolutional neural network is constructed to automatically extract the implied features of ECG signals. Finally, the manual feature and the automatic feature are combined, and the final recognition result is obtained through the support vector machine (SVM) model. The major attribute of the proposed method is that it can perform ECG biometric under different psychological stress states. The combination of manual and automatic features expands the application scenarios of ECG-based biometric. RESULTS: Based on this method, we used the Montreal stress model with calculation experiment in the laboratory to induce stress on 23 healthy students (10 women and 13 men, aged 20-37), and obtain their ECG signals under different stress conditions. Through this method to recognize the above data, an average recognition rate of more than 95% can be achieved, the average F1 score is 0.97. CONCLUSIONS: The proposed method in this article is a promising approach to deal with the effects of different psychological stresses on ECG-Based biometric. It provides the possibility of ECG-Based biometric under different psychological stress.