The continuous emergence of SARS-CoV-2 variants as well as other potential future coronavirus has challenged the effectiveness of current COVID-19 vaccines. Therefore, there remains a need for alternative antivirals that target processes less susceptible to mutations, such as the formation of six-helix bundle (6-HB) during the viral fusion step of host cell entry. In this study, a novel high-throughput screening (HTS) assay employing a yeast-two-hybrid (Y2H) system was established to identify inhibitors of HR1/HR2 interaction. The compound IMB-9C, which achieved single-digit micromolar inhibition of SARS-CoV-2 and its Omicron variants with low cytotoxicity, was selected. IMB-9C effectively blocks the HR1/HR2 interaction in vitro and inhibits SARS-CoV-2-S-mediated cell-cell fusion. It binds to both HR1 and HR2 through non-covalent interaction and influences the secondary structure of HR1/HR2 complex. In addition, virtual docking and site-mutagenesis results suggest that amino acid residues A930, I931, K933, T941, and L945 are critical for IMB-9C binding to HR1. Collectively, in this study, we have developed a novel screening method for HR1/HR2 interaction inhibitors and identified IMB-9C as a potential antiviral small molecule against COVID-19 and its variants.

The outer membrane composed predominantly of lipopolysaccharide (LPS) is an essential biological barrier for most Gram-negative (G^-) bacteria. Lipopolysaccharide transport protein (Lpt) complex LptDE is responsible for the critical final stage of LPS transport and outer membrane assembly. The structure and function of LptDE are highly conserved in most G^- bacteria but absent in mammalian cells, and thus LptDE complex is regarded as an attractive antibacterial target. In recent 10 years, the deciphering of the three-dimensional structure of LptDE protein facilities the drug discovery based on such "non-enzyme" proteins. Murepavadin, a peptidomimetic compound, was reported to be the first compound able to target LptD, enlightening a new class of antibacterial molecules with novel mechanisms of action. This article is devoted to summarize the molecular characteristics, structure-function of LptDE protein complex and review the development of murepavadin and related peptidomimetic compounds, in order to provide references for relevant researches.

Objective To analyze the combined effect of body mass index(BMI)and age with cancer occurrence among a hypertensive population in Minhang District,Shanghai.Methods Participants of this study were 212 394 hypertensive patients without cancer in Minhang District,Shanghai,registered in the electronic health information system from 2007 to 2015.Age and BMI were included as smoothing functions in the generalized additive Cox proportional risk model.The bivariate response model was constructed to visualize results using surface plots and to comprehensively analyze the association of BMI and age with the risk of cancer occurrence.Results A total of 22 141 participants developed cancer by Dec 31,2018.The association between age and the risk of cancer incidence showed an overall linear trend while the association between BMI and the risk of cancer incidence showed an overall"U"shape.BMI at about 26 kg/m2 showed the lowest risk of cancer incidence.The risk of cancer occurrence increased with increasing age in people with different BMIs.The associations between BMI and the risk of cancer incidence were different at different age groups:there was no significant association between BMI and the risk of cancer incidence in the young people(20-44 years).While in the middle-aged and older people aged over 45 years,BMI was associated with the risk of cancer incidence in a"U"shape.The lowest risk of cancer incidence was around the BMI of 26 kg/m2.Conclusion BMI among the population with hypertension should be controlled in a reasonable range,especially in the middle-aged and older population,to prevent cancer occurrence.

This article introduced the basic theory of quantile regression and its application in medical and public health research for methodological reference.We fit the quantile regression model to cross-sectional data from the National Health and Nutrition Examination Survey to estimate the association between sleep duration and depression levels at different quantiles of depression levels.The method showed that among people with low levels of depression,sleep duration was not significantly associated with depression level.While among people with higher levels of depression,the association of sleep duration with depression level became much more pronounced.The real data analysis shows that quantile regression can provide a comprehensive analysis of association between exposure and continuous outcome,and identify subgroups that are more sensitive to exposure,providing recommendations for targeted interventions.The method showed promising application value in medical and public health research.

This article introduced the basic theory of non-parametric regression and its application in medical and public health research for methodological reference.We conducted Cox proportional hazard models with restricted cubic splines using chronic disease management data from a Center for Disease Control and Prevention.We aimed to explore the separate and combined effects of mean fasting blood glucose level and glucose variability on all-cause mortality among individuals with type 2 diabetes.A non-linear association was observed between glucose variability and the risk of all-cause mortality.The association between glucose variability and all-cause mortality was stronger at higher mean fasting blood glucose levels compared to lower levels.The non-parametric regression methods comprehensively explored dose-response relationships between continuous exposure and outcome,revealing the combined effects of continuous exposures,which provided recommendations for targeted interventions.The method showed promising application value in medical and public health research.

This paper briefly introduced the varying coefficient model and used the varying coefficient Cox model as an example to demonstrate its application in the fields of medicine and public health through real data analysis,thereby provided methodological references for related research.The example was based on chronic disease management data from a Center for Disease Control and Prevention,fitting a varying coefficient Cox model to explore the time-varying association between body mass index(BMI)and mortality risk among a hypertensive population.The results showed that being underweight(BMI＜18.5 kg/m2)was associated with a higher risk of mortality,but this association weakened over time;being overweight(23 kg/m2≤BMI＜25 kg/m2)was associated with a lower risk of mortality,and this association also weakened over time.The varying coefficient model captured how the impact of exposure factors on outcomes changed with other variables,helping to comprehensively understand the complex relationships between variables,and had significant application and promotion value in medical and public health research.

Propensity score weighting methods are crucial tools for adjusting confounding and conducting causal inference in observational studies.This paper introduces various propensity score weighting methods,and details the key features of each to aid researchers in selecting the most suitable method for their analyses.Additionally,the application of these methods in the fields of medicine and public health is demonstrated through an example data analysis.The example is based on data from 996 patients who underwent percutaneous coronary intervention(PCI).Different propensity score weighting methods are used to explore the effect of PCI combined with abciximab on six-month post-operative survival rates.The results indicate that different propensity score weighting methods provide treatment effect estimates tailored to different inference goals,highlighting the importance of selecting the appropriate method based on the specific inference objective.Propensity score weighting methods enhance the balance of covariate distribution between treatment and control groups,making them powerful tools for adjusting confounding factors in observational studies.Their applicability and value in medical and public health research are significant.

This paper introduces a transfer learning approach based on regression models and demonstrates its application in the medical field through an example.Using data from the 2013-2014 U.S.National Health and Nutrition Examination Survey,the study investigates the association of sleep duration with depression levels and depressive disorder.It employs demographic characteristics and lifestyle factors as predictor variables to predict depression levels and depressive disorder across different racial groups.Compared to models built solely on target racial groups,transfer learning enhances the accuracy of estimating the effect of sleep duration in the target group and improves the prediction accuracy for depression levels and depressive disorder.The results illustrate that transfer learning effectively integrates source data to significantly improve estimation and prediction capabilities of target models,especially in situations with limited target data and heterogeneous data sources.

Background Per- and poly-fluoroalkyl substances (PFAS) are a class of emerging persistent organic pollutants, and their negative health impacts have been widely concerned. There is a lack of epidemiological studies on the associations of PFAS exposure with lipid homeostasis. Objective To investigate the associations of perfluorooctanesulfonic acid (PFOS) and perfluorooctanoic acid (PFOA) exposure with blood lipid levels and dyslipidemia in middle-aged and elderly women. Methods This study was based on 795 middle-aged and elderly women from a female sub-cohort of the Dongfeng-Tongji cohort study, excluding the participants without blood lipid measurements and/or reported use of lipid-lowering drugs at baseline. The concentrations of plasma PFOS and PFOA were measured by ultra-high performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS). The concentrations of serum total cholesterol (TC), triglyceride (TG), high-density lipoprotein cholesterol (HDL-C), and low-density lipoprotein cholesterol (LDL-C) were measured using an automatic analyzer. The normally distributed continuous variables were presented as mean ± standard deviation (\begin{document}$ \bar{x}\pm s) $\end{document}, while the skewedly distributed continuous variables were presented as median (M) and the 25^th and 75^th percentiles (P₂₅, P₇₅). Generalized linear models (GLM), generalized additive models (GAM), and logistic regression models were applied to evaluate the associations of PFOS and PFOA exposure with blood lipid levels and the risk of dyslipidemia. Stratified analyses were also implemented to explore potential modification effects of sociodemographic characteristics, lifestyles, and menopause on associations of PFOS and PFOA exposure and blood lipids. Results The \begin{document}$ \bar{x}\pm s $\end{document} of baseline age for the study participants was (59.4±8.6) years old, and their baseline body mass index (BMI) was (24.3±3.4) kg·m⁻². The M (P₂₅, P₇₅) of baseline plasma concentrations for PFOS and PFOA were 9.96 (6.24, 15.09) μg·L⁻¹ and 1.20 (0.84, 1.80) μg·L⁻¹ respectively. The GLM analysis showed that plasma concentration of PFOS was positively associated with serum HDL-C [b (95%CI): 0.04 (0.01, 0.07)]. The plasma concentration of PFOA was also positively associated with serum TC [b (95%CI): 0.05 (0.02, 0.08)] and serum LDL-C [b (95%CI): 0.05 (0.01, 0.09)]. No significant association was observed between plasma PFOS and serum TC, TG, or LDL-C, nor between plasma PFOA and serum TG or HDL-C. The stratified analyses showed that the association between PFOA and LDL-C was significant among the participants aged <60 years old [b (95%CI): 0.06 (0.01, 0.11), P=0.014]. A modification effect was observed for age on the association of plasma PFOA with serum LDL-C, with P_interaction=0.046. The analysis of the associations between PFOS/PFOA exposure and the risk of dyslipidemia showed that an increased plasma PFOA was significantly associated with an increased risk of hypercholesterolemia, with an OR (95%CI) of 1.69 (1.23, 2.15). No association was observed between PFOS exposure and the risk of dyslipidemia. Conclusion This cross-sectional study reveals that common PFAS exposure could affect the homeostasis of blood lipids based on the female sub-cohort of the Dongfeng-Tongji cohort, which provides new evidence for the negative health impact of PFAS.

OBJECTIVE: To observe the protective effect and mechanism of hydroxyl safflower yellow A (HSYA) from myocardial ischemia-reperfusion injury on human umbilical vein endothelial cells (HUVECs). METHODS: HUVECs were treated with oxygen-glucose deprivation reperfusion (OGD/R) to simulate the ischemia reperfusion model, and cell counting kit-8 was used to detect the protective effect of different concentrations (1.25-160 µ mol/L) of HSYA on HUVECs after OGD/R. HSYA 80 µ mol/L was used for follow-up experiments. The contents of inflammatory cytokines interleukin (IL)-18, IL-1 β, monocyte chemotactic protein 1 (MCP-1), tumor necrosis factor α (TNF-α) and IL-6 before and after administration were measured by enzyme-linked immunosorbent assay. The protein expressions of toll-like receptor, NOD-like receptor containing pyrin domain 3 (NLRP3), gasdermin D (GSDMD) and GSDMD-N-terminal domain (GSDMD-N) before and after administration were detected by Western blot. NLRP3 inflammasome inhibitor cytokine release inhibitory drug 3 sodium salt (CRID3 sodium salt, also known as MCC950) and agonist were added, and the changes of NLRP3, cysteine-aspartic acid protease 1 (Caspase-1), GSDMD and GSDMD-N protein expressions were detected by Western blot. RESULTS: HSYA inhibited OGD/R-induced inflammation and significantly decreased the contents of inflammatory cytokines IL-18, IL-1 β, MCP-1, TNF-α and IL-6 (P<0.01 or P<0.05). At the same time, by inhibiting NLRP3/Caspase-1/GSDMD pathway, HSYA can reduce the occurrence of pyroptosis after OGD/R and reduce the expression of NLRP3, Caspase-1, GSDMD and GSDMD-N proteins (P<0.01). CONCLUSIONS The protective effect of HSYA on HUVECs after OGD/R is related to down-regulating the expression of NLRP3 inflammasome and inhibiting pyroptosis.
NLR Family, Pyrin Domain-Containing 3 Protein/metabolism* ; Human Umbilical Vein Endothelial Cells/metabolism* ; Humans ; Chalcone/analogs & derivatives* ; Quinones/pharmacology* ; Pyroptosis/drug effects* ; Caspase 1/metabolism* ; Glucose ; Phosphate-Binding Proteins/metabolism* ; Signal Transduction/drug effects* ; Intracellular Signaling Peptides and Proteins/metabolism* ; Oxygen/metabolism* ; Cytokines/metabolism* ; Gasdermins