Rongwu Xiang

G protein-coupled receptors (GPCRs) are pivotal membrane drug targets. Its modulators can be classified as agonist, antagonist and inverse agonist. However, discriminating these three functional classes using traditional drug discovery methods remains challenging and is often struggled with capturing the complex relationship between receptor-ligand interaction and post-binding function. Here we extracted interaction features from GPCRdb structures using Protein-Ligand Interaction Profiler, including interaction types and geometry. These primary features were further re-engineered to generate secondary features with more detailed interaction profiles, including interaction angles and energies. After feature selection using LASSO regression, we trained multiple classifiers with these feature sets, and finally achieved an optimal model with best performance for distinguishing GPCR modulators. Our framework demonstrates an interpretable machine learning workflow for GPCR modulator classification, offering a scalable approach for structure-based GPCR drug discovery.

Diabetic retinopathy (DR) is a very common complication of diabetes that can lead to retinal damage, affecting vision. If not detected and diagnosed in time, it may result in blindness. With the development of deep learning, various automatic diagnosis models for DR have been proposed. Most models are based on convolutional neural networks, but due to the small size of convolution kernels in shallow networks, the receptive field is limited, preventing the capture of global information. This limitation forces models to require more convolutional layers to expand the receptive field and capture global information related to DR. In this paper, we use a combined module of multiscale convolution and convolutional self-attention, and we adjust the module structure, allowing the model to have both long-range dependency modeling capability and CNN’s multiscale local feature extraction capability, while having fewer model parameters than the selected comparison models. Our proposed MSAmix-Net achieved an accuracy of 82.3% in the five-class classification task on the combined dataset of APTOS-2019 and Messidor-2, and an accuracy of 93.9% in the three-class classification task on the DRAC-2022 dataset. The experiments demonstrate that the model can effectively capture and represent the complex features of diabetic retinopathy.

Diabetic Retinopathy (DR) is a common complication of diabetes that can lead to vision loss if not detected early. Deep learning models, particularly Convolutional Neural Networks (CNN), have shown impressive results in DR detection. However, these models are susceptible to adversarial attacks, such as the Fast Gradient Sign Method (FGSM), leading to misclassification of detection data. In order to improve the robustness of the Diabetic Retinopathy (DR) detection model against FGSM attacks, we propose a new neural network in this paper that combines the Radial Basis Function (RBF) network with the modern CNN ResNet. We first trained ResNet-101 and the proposed ResNet-101-RBF on the aptos2019-blindness-detection dataset. After training, we evaluated the performance of the two models on normal DR data, and next, we evaluated the performance of the two models on adversarial data generated by FGSM attacks. Our results show that the accuracy of ResNet-101 in normal data is 78.26%, and the accuracy of ResNet-101-RBF is 75.26%, with the accuracy of ResNet-101 being 3% higher than that of ResNet-101-RBF in normal data. However, on adversarial samples generated with perturbation rates of 0.01, 0.05, and 0.1, the accuracy of ResNet-101-RBF is 6%, 5.5%, and 4.75% higher than that of ResNet-101, respectively. The ResNet-101-RBF model can maintain a high detection accuracy in normal data while having higher robustness against FGSM attacks compared to the ResNet-101 model.

Purpose Although there are many different methods of treating type 2 diabetes (T2D), it is still difficult to draw coincident conclusions concerning the efficacy and safety of different classes of new drugs, and the recommendation level of them has still kept uncertain as second anti-diabetic agents. Therefore, the aim of this study was to summarize evidence on the efficacy and safety of DPP-4is, GLP-1RAs and SGLT-2is as monotherapy or add-on to metformin (Met) for treatment of T2D. Materials and methods We searched PubMed, Embase, Cochrane library and ClinicalTrials.gov for relevant articles in keeping with established methods using terms associated with anti-diabetic agents up to February, 2020, with no start date restriction. Weighted mean difference and risk ratios with 95% confidence intervals were calculated within traditional and network meta-analysis. Primary outcomes were the mean change in hemoglobin A1c (HbA1c), fasting plasma glucose (FPG) change and the frequency of hypoglycemic events from baseline after 12 weeks of treatment. Results In total, 64 eligible studies comprising 37,780 patients and 7 treatment strategies were included. The results of primary outcomes showed that GLP-1RAs were significantly more effective than DPP-4is or SGLT-2is in reducing HbA1c when add-on to Met. For FPG, both GLP-1RAs and SGLT-2is significantly reduced FPG compared with DPP-4is whether add-on to Met or not. For hypoglycemia, monotherapy has a lower risk than combination therapy except for SGLT-2is. Ranking probability analysis indicated that GLP-1RAs and SGLT-2is, respectively, reduced HbA1c and FPG most when add-on to Met. Meanwhile, GLP-1RAs took the lowest risk to induce the hypoglycemia, whereas GLP-1RAs plus Met the highest. Conclusions Both GLP-1RAs and SGLT-2is have their own advantages in efficacy and safety. Monotherapy is beneficial for reducing the risk of hypoglycemia. The recommendation should be a patient-centered approach when selecting treatment choices.

Background: COVID-19 has become one of the most serious global epidemics in the 21st Century. This study aims to explore the distribution of research capabilities of countries, institutions, and researchers, and the hotspots and frontiers of coronavirus research in the past two decades. In it, references for funding support of urgent projects and international cooperation among research institutions are provided. Method: the Web of Science core collection database was used to retrieve the documents related to coronavirus published from 2003 to 2020. Citespace.5.6.R2, VOSviewer1.6.12, and Excel 2016 were used for bibliometric analysis. Results: 11,036 documents were retrieved, of which China and the United States have contributed the most coronavirus studies, Hong Kong University being the top contributor. Regarding journals, the Journal of Virology has contributed the most, while in terms of researchers, Yuen Kwok Yung has made the most contributions. The proportion of documents published by international cooperation has been rising for decades. Vaccines for SARS-CoV-2 are under development, and clinical trials of several drugs are ongoing. Conclusions: international cooperation is an important way to accelerate research progress and achieve success. Developing corresponding vaccines and drugs are the current hotspots and research directions.

Aim: To characterize two plasmids p13294-KPC and pA1966-NR from clinical Klebsiella pneumoniae strains. Materials & methods: Plasmids p13294-KPC and pA1966-NR were fully sequenced and then detailed...

Hyponatremia, defined as a nonartifactual serum sodium level <135 mmol/L, is the most common fluid and electrolyte abnormality in clinical practice. Traditional managements (fluid restriction, hypertonic saline and loop diuretics, etc.) are difficult to maintain or ineffective. Recently, vasopressin receptor antagonists (VRAs) have shown promise for the treatment of hyponatremia. We aimed to conduct a meta-analysis to evaluate the efficacy and safety of VRAs in patients with euvolemic or hypervolemic hyponatremia. We searched Pubmed, Cochrane Library, Web of Science and Springer, etc. (latest search on June 4, 2015) for English publications with randomized controlled trials. Two authors independently screened the citations and extracted data. We calculated pooled relative risk (RR), risk difference (RD), weighted mean difference (WMD) or standard mean difference (SMD), and 95% confidence intervals (CIs) by using random and fixed effect models. We collected data from 18 trials involving 1806 patients. Both random and fixed effect meta-analyses showed that VRAs significantly increased the net change of serum sodium concentration (WMD(random) = 4.89 mEq/L, 95%CIs = 4.35-5.43 and WMD(fixed) = 4.70 mEq/L, 95%CIs = 4.45-4.95), response rate (RR(random )= 2.77, 95%CIs = 2.29-3.36 and RR(fixed) = 2.95, 95%CIs = 2.56-3.41), and 24-hour urine output (SMD(random) = 0.82, 95%CIs = 0.65-1.00 and SMD(fixed) = 0.79, 95%CIs = 0.66-0.93) compared to placebo. Furthermore, VRAs significantly decreased body weight (WMD(random) = -0.87 kg, 95%CIs = -1.24 to -0.49 and WMD(fixed) = -0.91 kg, 95%CIs = -1.22 to -0.59). In terms of safety, rates of drug-related adverse events (AEs), rapid sodium level correction, constipation, dry mouth, thirst, and phlebitis in the VRA-treated group were greater than those in control group. However, there was no difference in the total number of AEs, discontinuations due to AEs, serious AEs, death, headache, hypotension, nausea, anemia, hypernatremia, urinary tract infection, renal failure, pyrexia, upper gastrointestinal bleeding, diarrhea, vomiting, peripheral edema, and dizziness between the 2 groups. Random effect meta-analyses showed that post treatment urine osmolality, supine systolic blood pressure, and diastolic blood pressure were lowered (WMD(random) = -233.07 mOsmol/kg, 95%CIs = -298.20-147.94; WMD(random) = -6.11 mmHg, 95%CIs = -9.810 to -2.41; WMD(random )= -2.59 mmHg, 95%CIs = -4.06 to -1.11, respectively), but serum osmolality was increased (WMD(random) = 9.29 mOsmol/kg, 95%CIs = 5.56-13.03). There was no significant change from baseline in serum potassium concentration between the 2 groups (WMD(fixed) = 0.00 mmHg, 95%CIs = -0.07-0.06). VRAs are relatively effective and safe for the treatment of hypervolemic and euvolemic hyponatremia.