The main protease (M^pro) of SARS-CoV-2 is an attractive target in anti-COVID-19 therapy for its high conservation and major role in the virus life cycle. The covalent M^pro inhibitor nirmatrelvir (in combination with ritonavir, a pharmacokinetic enhancer) and the non-covalent inhibitor ensitrelvir have shown efficacy in clinical trials and have been approved for therapeutic use. Effective antiviral drugs are needed to fight the pandemic, while non-covalent M^pro inhibitors could be promising alternatives due to their high selectivity and favorable druggability. Numerous non-covalent M^pro inhibitors with desirable properties have been developed based on available crystal structures of M^pro. In this article, we describe medicinal chemistry strategies applied for the discovery and optimization of non-covalent M^pro inhibitors, followed by a general overview and critical analysis of the available information. Prospective viewpoints and insights into current strategies for the development of non-covalent M^pro inhibitors are also discussed.

Objective     To construct a radiomics model for identifying clinical high-risk carotid plaques. Methods     A retrospective analysis was conducted on patients with carotid artery stenosis in China-Japan Friendship Hospital from December 2016 to June 2022. The patients were classified as a clinical high-risk carotid plaque group and a clinical low-risk carotid plaque group according to the occurrence of stroke, transient ischemic attack and other cerebrovascular clinical symptoms within six months. Six machine learning models including eXtreme Gradient Boosting, support vector machine, Gaussian Naive Bayesian, logical regression, K-nearest neighbors and artificial neural network were established. We also constructed a joint predictive model combined with logistic regression analysis of clinical risk factors. Results    Finally 652 patients were collected, including 427 males and 225 females, with an average age of 68.2 years. The results showed that the prediction ability of eXtreme Gradient Boosting was the best among the six machine learning models, and the area under the curve (AUC) in validation dataset was 0.751. At the same time, the AUC of eXtreme Gradient Boosting joint prediction model established by clinical data and carotid artery imaging data validation dataset was 0.823. Conclusion     Radiomics features combined with clinical feature model can effectively identify clinical high-risk carotid plaques.

Objective:To construct a diabetes foot prediction model for adult patients with type 2 diabetes based on retrospective cohort study using data from a regional health data platform.Methods:Using Yinzhou Health Information Platform of Ningbo, adult patients with newly diagnosed type 2 diabetes from January 1, 2015 to December 31, 2022 were included in this study and divided randomly the train and test sets according to the ratio of 7∶3. LASSO regression model and bidirectional stepwise regression model were used to identify risk factors, and model comparisons were conducted with net reclassification index, integrated discrimination improvement and concordance index. Univariate and multivariate Cox proportional hazard regression models were constructed, and a nomogram plot was drawn. Area under the curve (AUC) was calculated as a discriminant evaluation indicator for model validation test its calibration ability, and calibration curves were drawn to test its calibration ability.Results:No significant difference existed between LASSO regression model and bidirectional stepwise regression model, but the better bidirectional stepwise regression model was selected as the final model. The risk factors included age of onset, gender, hemoglobin A1c, estimated glomerular filtration rate, taking angiotensin receptor blocker and smoking history. AUC values (95% CI) of risk outcome prediction at year 5 and 7 were 0.700 (0.650-0.749) and 0.715(0.668-0.762) for the train set and 0.738 (0.667-0.801) and 0.723 (0.663-0.783) for the test set, respectively. The calibration curves were close to the ideal curve, and the model discrimination and calibration powers were both good. Conclusions:This study established a convenient prediction model for diabetic foot and classified the risk levels. The model has strong interpretability, good discrimination power, and satisfactory calibration and can be used to predict the incidence of diabetes foot in adult patients with type 2 diabetes to provide a basis for self-assessment and clinical prediction of diabetic foot disease risk.

Objective:To develop a prediction model for the risk of diabetic retinopathy (DR) in patients with newly diagnosed type 2 diabetes mellitus (T2DM).Methods:Patients with new diagnosis of T2DM recorded in Yinzhou Regional Health Information Platform between January 1, 2015 and December 31, 2022 were included in the study. The predictor variables were selected by using Lasso-Cox proportional hazards regression model. Cox proportional hazards regression models were used to establish the prediction model for the risk of DR. Bootstrap method (500 resamples) was used for internal validation, and the performance of the model was assessed by C-index, the receiver operating characteristic curve and area under the curve (AUC), and calibration curve.Results:The predictor variables included in the final model were age of T2DM onset, education level, fasting plasma glucose, glycated hemoglobin A1c, urinary albumin, estimated glomerular filtration rate, and history of lipid-lowering agent and angiotensin converting enzyme inhibitor uses. The C-index of the final model was 0.622, and the mean corrected C-index was 0.623 (95% CI: 0.607-0.634). The AUC values for predicting the risk of DR after 3, 5, and 7 years were 0.631, 0.620, and 0.624, respectively, with a high degree of overlap of the calibration curves with the ideal curves. Conclusion:In this study, a simple and practical risk prediction model for DR risk prediction was developed, which could be used as a reference for individualized DR screening and intervention in newly diagnosed T2DM patients.

Objective:To construct a risk prediction model for diabetes kidney disease (DKD).Methods:Patients newly diagnosed with type 2 diabetes mellitus (T2DM) between January 1, 2015, and December 31, 2022, were selected as study subjects from the Yinzhou Regional Health Information Platform in Ningbo City. The Lasso method was used to screen the risk factors, and the DKD risk prediction model was established using Cox proportional hazard regression models. Bootstrap 500 resampling was applied for internal validation.Results:The study included 49 706 subjects, with an median ( Q1, Q3) age of 60.00 (50.00, 68.00) years old, and 55% were male. A total of 4 405 subjects eventually developed DKD. Age at first diagnosis of T2DM, BMI, education level, fasting plasma glucose, glycated hemoglobin A1c, urinary albumin, past medical history (hyperuricemia, rheumatic diseases), triglycerides, and estimated glomerular filtration rate were included in the final model. The final model's C-index was 0.653, with an average of 0.654 after Bootstrap correction. The final model's area under the receiver operating characteristic curve for predicting 4-year, 5-year, and 6-year was 0.657, 0.659, and 0.664, respectively. The calibration curve was closely aligned with the ideal curve. Conclusions:This study constructed a DKD risk prediction model for newly diagnosed T2DM patients based on real-world data that is simple, easy to use, and highly practical. It provides a reliable basis for screening high-risk groups for DKD.

Objective To develop a prediction tool for post-endoscopic retrograde cholangiopancreatography(ER-CP)early biliary tract infection(PEEBI)in patients with choledocholithiasis,and assist clinical decision-making be-fore ERCP and early personalized intervention after ERCP.Methods An observational bidirectional cohort study was adopted to select inpatients with choledocholithiasis who underwent ERCP in a hospital.Directed acyclic graph(DAGs)and the least absolute shrinkage and selection operator(LASSO)were used to predict PEEBI based on lo-gistic regression,and the models were compared and validated internally and externally.Results From January 1,2020 to September 30,2023,a total of 2 121 patients with choledocholithiasis underwent ERCP were enrolled,of whom 77(3.6％)developed PEEBI,mostly in the first 2 days after surgery(66.2％).The major influencing fac-tors for PEEBI were non-iatrogenic patient-related factors,namely diabetes mellitus(OR=2.43,95％CI:1.14-4.85),bile duct malignancy(OR=3.95,95％CI:1.74-8.31)and duodenal papillary diverticulum(OR=4.39,95％CI:1.86-9.52).Compared with the LASSO model,the DAGs model showed higher ability(3.0％)in com-prehensive discrimination(P=0.007),as well as good differentiation performance(D=0.133,P=0.894)and cal-ibration performance(x2=5.499,P=0.703)in external validation.Conclusion The DAGs model constructed in this study has good predictive performance.With the help of this tool,targeted early preventive measures in clinical practice can be taken to reduce the occurrence of PEEBI.

Objective To assess the association between Metformin use and the risk of dementia in patients with type 2 diabetes mellitus(T2DM).Methods The research data came from the big medical data platform of Yinzhou District,and we constructed a cohort of T2DM patients who had initiated treatment of Metformin or sulfonylurea since January 1,2009.The inverse probability of treatment weighting(IPTW)was used to control the baseline confounding factors,and the Cox regression model was used to estimate the HR(95%CI)of the association between Metformin use and dementia risk.Results The incidence rate of dementia in new users of Metformin(41181 persons)and sulfonylureas(38092 persons)was 128.4 per 100000 person years and 142.3 per 100000 person years respectively.Compared with sulfonylureas,the crude analysis with no adjustment for confounding factors showed that there was a negative association between the use of Metformin and the incidence of dementia,with an HR(95%CI)0.930(0.800～1.090).After adjusting for potential confounders with IPTW,Metformin was not significantly associated with the risk of dementia HR(95%CI)1.040(0.890～1.220).The subgroup analysis results for different baseline characteristics were consistent with the primary analysis results,and there were no statistically significant associations between Metformin and dementia incidence risk in all subgroups.Conclusions There is no significant association between the use of Metformin and the risk of dementia in T2DM patients in the Yinzhou District.

Malignant tumors， with the increasing crude morbidity and mortality year by year， have become the major diseases threatening human health. The conventional therapeutic drugs against tumors have serious adverse reactions， which can cause a heavy burden on patients. The active components of Chinese medicine can effectively inhibit tumor growth， improve the quality of life of patients， and have few toxic and side effects. Alkaloids of Chinese medicine are natural organic compounds widely existing in a variety of Chinese herbal medicines. In recent years， they have attracted more and more attention because of their anti-tumor effect. The anti-tumor mechanisms of alkaloids of Chinese medicine mainly include the induction of apoptosis， inhibition of tumor cell migration and invasion， suppression of proliferation， induction of autophagy of tumor cells， cell cycle arrest， inhibition of tumor angiogenesis， regulation of microRNA， and modulation of immunity. In addition， Chinese medicine alkaloids can also reverse tumor drug resistance and reduce the stemness of tumor stem cells. Alkaloids of Chinese medicine can regulate the phosphatidylinositol 3-kinase/protein kinase B （PI3K/Akt）， c-Jun N-terminal kinase （JNK）， p38 mitogen-activated protein kinase （p38 MAPK）， mammalian target of rapamycin （mTOR）， Notch， Hedgehog， Wnt/β-catenin， and other signaling pathways to participate in the processes of tumor proliferation， invasion and metastasis， autophagy and apoptosis， and affect the occurrence and development of tumors in multiple links and ways. The derivatives and nano-preparations of alkaloids can improve the solubility， utilization， and anti-tumor activity of alkaloids， bringing a broader prospect for the clinical application of alkaloids. This review summarized the recent anti-tumor research on alkaloids， their representative derivatives， and nano-preparations to provide references for the in-depth research on the anti-tumor effect of alkaloids.

From the process of human immunodeficiency virus-1 (HIV-1) invading cells, the combination of gp120 and CD4 is the first step for HIV-1 to invade cells. Interfering with this process can prevent HIV from recognizing target cells and inhibit virus replication. Therefore, HIV-1 gp120 is an important part of the HIV-1 life cycle. Fostesavir, a phosphatate prodrug derived from the gp120 inhibitor BMS-626529 modified by the prodrug strategy, was approved for the treatment of adult patients with multidrug resistant HIV-1 infection by the US FDA and the European Medicines Agency in 2020 and 2021, respectively. In this review, we focus on the research progress of small molecule inhibitors targeting the interaction of gp120-CD4 from the perspective of medicinal chemistry, in order to provide reference for the subsequent research of gp120 inhibitors.

Radiotherapy is widely used in the management of advanced colorectal cancer (CRC). However, the clinical efficacy is limited by the safe irradiated dose. Sensitizing tumor cells to radiotherapy via interrupting DNA repair is a promising approach to conquering the limitation. The BRCA1-BARD1 complex has been demonstrated to play a critical role in homologous recombination (HR) DSB repair, and its functions may be affected by HERC2 or BAP1. Accumulated evidence illustrates that the ubiquitination-deubiquitination balance is involved in these processes; however, the precise mechanism for the cross-talk among these proteins in HR repair following radiation hasn't been defined. Through activity-based profiling, we identified PT33 as an active entity for HR repair suppression. Subsequently, we revealed that BAP1 serves as a novel molecular target of PT33 via a CRISPR-based deubiquitinase screen. Mechanistically, pharmacological covalent inhibition of BAP1 with PT33 recruits HERC2 to compete with BARD1 for BRCA1 interaction, interrupting HR repair. Consequently, PT33 treatment can substantially enhance the sensitivity of CRC cells to radiotherapy in vitro and in vivo. Overall, these findings provide a mechanistic basis for PT33-induced HR suppression and may guide an effective strategy to improve therapeutic gain.