Background Air pollution exposure has a significant impact on maternal and child health. However, the research on the association between ambient ozone (O₃) exposure during pregnancy and the risk of premature birth in newborns is limited, and the conclusions are inconsistent. Objective To investigate the association of ambient O₃ exposure during pregnancy with the risk of preterm birth in Guangdong Province. Methods Data of pregnant women in Guangzhou from 2013 to 2019 and Foshan from 2018 to 2023 were collected, and O₃ concentrations during different trimesters were assessed according to maternal residential addresses. Bilinear interpolation was used to evaluate the concentrations of air pollution. A cohort study design was adopted in our study. Restricted cubic spline curves were used to evaluate the exposure-response relationship between O₃ exposure and preterm birth risk and explore potential exposure threshold of O₃. Logistic regression models were used to evaluate the association of O₃ exposure with preterm birth. Results A total of 702 924 pregnant women were included in this study, of whom 43 051 (6.12%) were preterm. The average O₃ exposure concentrations of pregnant women during the first, second, third, and whole trimesters were 95.51, 97.51, 100.60, and 97.87 μg·m⁻³, respectively. We observed J-shaped associations between O₃ exposure and preterm birth risk during the second, third, and whole trimesters of pregnancy using restricted cubic spline curves. This study found that there were threshold concentrations between O₃ exposure and preterm birth risk during different gestational periods, and the threshold concentrations in the first, second, third, and whole trimesters were 112.32, 99.83, 111.74, and 112.46 μg·m⁻³, respectively. During the second, third, and whole trimesters of pregnancy, after adjusting for maternal age, baby sex, pre-pregnancy body mass index, mode of delivery, baby birth weight, gestational diabetes, and gestational hypertension, the odds ratios (OR) of preterm birth were 1.02 (95%CI: 1.01, 1.04), 1.02 (95%CI: 1.00, 1.03), and 1.17 (95%CI: 1.13, 1.21) for each 10 μg·m⁻³ increase in O₃ concentration above the O₃ threshold. No significant association was found between O₃ exposure and the risk of preterm birth during the first trimester. Conclusion There is a nonlinear association between the risk of preterm birth and O₃ exposure during pregnancy, and higher concentrations of O₃ exposure during pregnancy are associated with the risk of preterm birth. Above the O₃ threshold concentration during pregnancy, especially during the second, third, and whole trimesters, the risk of preterm birth elevates with the increase of O₃ exposure concentrations.

Background Pesticides like organophosphorus and pyrethroids are extensively utilized, and associated potential human health risks arising from multi-route exposure, including environmental sources and dietary intake, cannot be overlooked. Conducting human exposure studies using pesticide exposure biomarkers is essential for an objective evaluation of human pesticide exposure levels. Objective To develop a rapid and precise liquid chromatography-tandem mass spectrometry method for the detection of 12 pesticide metabolites in urine, including 5 metabolites of organophosphorus pesticide, 4 metabolites of pyrethroid pesticide, 2 metabolites of herbicides, and 1 metabolite of insecticide. Methods After overnight enzymatic hydrolysis, urine samples were subjected to extraction and purification using Oasis HLB 96-well solid-phase extraction. Subsequently, the samples were analyzed by liquid chromatography-tandem mass spectrometry and quantified using the isotope internal standard method. The developed method was employed to analyze 143 urine samples from a general population to assess its effectiveness and to evaluate pesticide exposure levels. Results All 12 target compounds exhibited good linear ranges, with their correlation coefficients of calibration curves exceeding 0.999. The limits of detection (LOD) ranged from 0.02 to 0.19 μg·L⁻¹, while the limits of quantitation (LOQ) ranged from 0.06 to 0.27 μg·L⁻¹. The recoveries at three spiked levels ranged from 84% to 112%, and the inter- and intra- day precisions of targeted analystes were 0.43%-9.6% and 1.6%-9.7% respectively. Using this method, 143 urine samples from residents in Jiangsu region were analyzed, and 11 pesticides were detected except N,N-diethyl-m-toluamide (DEET). Conclusion The established method of solid-phase extraction combined with liquid chromatography-tandem mass spectrometry has the characteristics of low detection limit, good repeatability, and high throughput, which is suitable for quantitative detection of selected 12 pesticides in large batches of human urine samples, and provides technical support for pesticide internal exposure monitoring and health risk assessment.

Objective:To analyze the association between air pollution, genetic susceptibility, and the risk of all-cause mortality and cardiovascular outcomes in patients with atrial fibrillation (AF).Methods:AF patients aged between 40-69 years old registered in the United Kingdom Biobank from 2006 to 2010 were included. After excluding those lost to follow-up or with incomplete data during follow-up, 5 814 subjects were analyzed. Long-term exposure to air pollution was estimated at the geocoded residential address of each participant. Genetic risk scores for all-cause mortality, cardiovascular disease, heart failure, myocardial infarction, and stroke were constructed separately for each object to assess the corresponding genetic susceptibility. The Cox proportional hazards model was used to analyze the association between air pollution, genetic susceptibility, and the risk of all-cause mortality and cardiovascular outcomes in AF patients.Results:During a median follow-up of 12.4 years, there were 929 of all-cause mortality (15.98%) and 1 772 of cardiovascular events (30.48%). Multivariable-adjusted analyses revealed that higher exposure to PM 2.5, PM 10, NO x, and NO 2 was associated with an increased risk of cardiovascular disease mortality, heart failure, myocardial infarction, and stroke, with hazard ratios ( HRs) ranging from 1.26 to 1.48. Specifically, for each interquartile range ( IQR) increase in PM 2.5 exposure, the HRs for the outcomes mentioned above were 1.33 (95% CI: 1.14-1.54), 1.42 (95% CI: 1.31-1.54), 1.46 (95% CI: 1.30-1.64), and 1.43 (95% CI: 1.27-1.61), respectively. Both NO x and NO 2 exposures were associated with a 9% increased risk of all-cause mortality per IQR increment, with corresponding HRs of 1.09 (95% CI: 1.02-1.17) and 1.09 (95% CI: 1.01-1.17), respectively. Individuals with high genetic susceptibility to AF had a higher risk of myocardial infarction and stroke compared to those with low genetic susceptibility, with corresponding HRs of 1.39 (95% CI: 1.04-1.87) and 1.46 (95% CI: 1.09-1.95), respectively. Compared to AF patients with low air pollution exposure, those with high air pollution exposure have adjusted population attributable fractions of up to 33.57% (95% CI: 17.87%-46.26%) for cardiovascular mortality, 28.61% (95% CI: 20.67%-35.75%) for heart failure, 33.35% (95% CI: 20.97%-43.79%) for myocardial infarction, and 42.29% (95% CI: 30.05%-52.71%) for stroke. Furthermore, there was an additive interaction between PM 2.5, NO x, and NO 2 exposure and high genetic susceptibility on the incidence of myocardial infarction. An additive interaction was also observed between NO x, NO 2 exposure, and high genetic susceptibility on the incidence of heart failure (all P<0.05). Conclusions:Both air pollution and genetic susceptibility increase the risk of all-cause mortality and cardiovascular outcomes in AF patients.

Relative Risk ( RR), Hazard Ratio ( HR), and Odds Ratio ( OR) are commonly used statistical measures in the field of public health to assess the magnitude of the effect of exposure factors on outcomes. These indicators have different calculation principles and implications in public health. However, a few researchers misused or misinterpreted RR, HR, and OR values when interpreting study results. Therefore, this article explores the relationships and differences among these measures, as well as the correct selection and application of RR, HR, and OR in both cohort study and case-control study.

Relative Risk ( RR), Hazard Ratio ( HR), and Odds Ratio ( OR) are commonly used statistical measures in the field of public health to assess the magnitude of the effect of exposure factors on outcomes. These indicators have different calculation principles and implications in public health. However, a few researchers misused or misinterpreted RR, HR, and OR values when interpreting study results. Therefore, this article explores the relationships and differences among these measures, as well as the correct selection and application of RR, HR, and OR in both cohort study and case-control study.

The global COVID-19 coronavirus pandemic has infected over 109 million people, leading to over 2 million deaths up to date and still lacking of effective drugs for patient treatment. Here, we screened about 1.8 million small molecules against the main protease (M^pro) and papain like protease (PL^pro), two major proteases in severe acute respiratory syndrome-coronavirus 2 genome, and identified 1851M^pro inhibitors and 205 PL^pro inhibitors with low nmol/l activity of the best hits. Among these inhibitors, eight small molecules showed dual inhibition effects on both M^pro and PL^pro, exhibiting potential as better candidates for COVID-19 treatment. The best inhibitors of each protease were tested in antiviral assay, with over 40% of M^pro inhibitors and over 20% of PL^pro inhibitors showing high potency in viral inhibition with low cytotoxicity. The X-ray crystal structure of SARS-CoV-2 M^pro in complex with its potent inhibitor 4a was determined at 1.8 Å resolution. Together with docking assays, our results provide a comprehensive resource for future research on anti-SARS-CoV-2 drug development.
Humans ; Antiviral Agents/chemistry* ; COVID-19 ; COVID-19 Drug Treatment ; High-Throughput Screening Assays ; Molecular Docking Simulation ; Protease Inhibitors/chemistry* ; SARS-CoV-2/enzymology* ; Viral Nonstructural Proteins

Ulcerative colitis （UC） is a commonly seen digestive system disease with unclear pathogenesis. The condition is complex and variable， often chronic， and has a long treatment period with no specific cure. Currently， the treatment of UC often involves the use of corticosteroids， aminosalicylates， and biologics in western medicine， which provide fast-acting and definite efficacy in the short term. However， with prolonged medication， some patients may develop drug resistance and worsening of the disease， leading to the occurrence of colon cancer. Research has found that oxidative stress is one of the important pathogenic factors in UC and influences its onset and development. Oxidative stress is a state of imbalance between oxidative products and the antioxidant system in the body， characterized by overexpression of oxidative products such as malondialdehyde （MDA）， reactive oxygen species （ROS）， nitric oxide （NO）， or deficiency of antioxidant enzymes such as superoxide dismutase （SOD）， catalase （CAT）， and glutathione （GSH）. It is worth noting that traditional Chinese medicine （TCM）， as a unique characteristic medicine of China， has achieved significant efficacy in the treatment of UC. Studies have shown that TCM effectively inhibits the occurrence of UC by suppressing the accumulation of metabolites and antagonizes the development of UC by enhancing the antioxidant system. Therefore， using TCM to regulate the oxidative balance as a diagnostic and therapeutic approach may be a new method and direction for the treatment of UC in the future. Based on the above research， this article summarized the mechanisms of key pathogenic proteins in oxidative stress and the occurrence and development of UC， and compiled the effective ingredients of Chinese medicine， single drugs， prescriptions， and acupuncture and moxibustion in regulating upstream and downstream target proteins of oxidative stress. These interventions can reduce pathological damage to the intestinal mucosa， lower the colon injury index， enrich the intestinal microbiota， increase colon length， and improve clinical symptoms of UC. The article is expected to expand the application of TCM in the treatment of UC and provide a reliable scientific theoretical basis.

Objective:To investigate the value of CHA2DS2-VASc score for predicting percutaneous coronary intervention (PCI) prognosis in patients with coronary chronic total occlusion.Methods:The clinical data of 139 patients with coronary chronic total occlusion who underwent PCI at the Second Hospital of Anhui Medical University from January 2019 to December 2020 were retrospectively analyzed. These patients were divided into good prognosis and poor prognosis groups according to prognosis outcomes. Univariate and multivariate Cox regression analysis was performed to evaluate the influential factors of adverse events after PCI in patients with coronary chronic total occlusion. The receiver operating characteristic curves were plotted to evaluate the value of the CHA2DS2-VASc score for predicting the occurrence of adverse events in patients with coronary chronic total occlusion undergoing PCI.Results:Among the 139 patients with coronary chronic total occlusion, the average follow-up time was (19.88 ± 7.90) months, 19 (13.7%) patients had a poor prognosis, and 120 (86.3%) patients had a good prognosis. The age, uric acid, and CHA2DS2-VASc score in the poor prognosis group were 73 (65.0, 77.0) years, (383.26 ± 120.60) μmol/L, and 4 (3, 5) points, respectively, which were significantly higher than 66 (57.0, 71.8) years, (322.68 ± 91.88) μmol/L, and 3 (2, 4) points in the good prognosis group ( U = -2.74, t = 2.24, U = -3.09, all P < 0.05). However, the proportion of patients with the successful opening of the occluded coronary artery and albumin, hemoglobin, and triacylglycerol levels in the poor prognosis group were 11 (57.9%), 36.7 (34.4, 38.3) g/L, (120.26±19.74) g/L, and 1.03 (0.85, 1.49) mmol/L, respectively, which were significantly lower than 98 (81.7%)( χ2 = 4.16, P = 0.041), 39.3 (36.78, 42.1) g/L ( U = -2.85, P = 0.004), (133.62 ± 16.84) g/L ( t = 1.52, P = 0.002), and 1.52 (1.09, 2.25) mmol/L ( U =-2.13, P = 0.033) in the good prognosis group. Multivariate Cox regression analysis showed that CHA2DS2-VASc score was a risk factor for poor prognosis of patients with coronary chronic total occlusion undergoing PCI (95% CI: 1.137-2.274, P = 0.007). The receiver operating characteristic curve analysis results showed that the area under the receiver operating characteristic curve plotted for CHA2DS2-VASc score predicting poor prognosis in patients with coronary chronic total occlusion undergoing PCI was 0.716 (95% CI: 0.588-0.844, P = 0.003), with the cut-off point of 3.5, the sensitivity of 68.4%, and specificity of 66.7%, respectively. Conclusion:Compared with those with a good prognosis, patients with a poor prognosis have a higher CHA2DS2-VASc score. The CHA2DS2-VASc score has a certain application value for predicting mid- and long-term prognosis in patients with coronary chronic total occlusion who undergo PCI.

A fundamental challenge that arises in biomedicine is the need to characterize compounds in a relevant cellular context in order to reveal potential on-target or off-target effects. Recently, the fast accumulation of gene transcriptional profiling data provides us an unprecedented opportunity to explore the protein targets of chemical compounds from the perspective of cell transcriptomics and RNA biology. Here, we propose a novel Siamese spectral-based graph convolutional network (SSGCN) model for inferring the protein targets of chemical compounds from gene transcriptional profiles. Although the gene signature of a compound perturbation only provides indirect clues of the interacting targets, and the biological networks under different experiment conditions further complicate the situation, the SSGCN model was successfully trained to learn from known compound-target pairs by uncovering the hidden correlations between compound perturbation profiles and gene knockdown profiles. On a benchmark set and a large time-split validation dataset, the model achieved higher target inference accuracy as compared to previous methods such as Connectivity Map. Further experimental validations of prediction results highlight the practical usefulness of SSGCN in either inferring the interacting targets of compound, or reversely, in finding novel inhibitors of a given target of interest.
Drug Delivery Systems ; Proteins ; Transcriptome

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