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.

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.

This paper proposes a mathematical model based on probability graphs and an algorithm of iterative reasoning,which is an automatic interactive question-and-answer mathematical model based on the classic TCM syndrome differentiation system and the theory of prescription and syndrome correspondence.It is used for TCM online interactive consultation and automatic syndrome differentiation analysis to improve the effectiveness of remote TCM clinical consultation and help TCM artificial intelligence assist syndrome differentiation.This model can express the clinician's experience in syndrome differentiation,and reflect his ability to accumulate and apply knowledge of ancient Chinese books,which is conducive to the inheritance and development of TCM physicians'personal experience.The model is scalable and configurable,and can continuously accumulate experience in dialectics and knowledge of traditional Chinese medicine.The use of iterative reasoning algorithm can realize the automatic analysis and reasoning of more optimized syndrome analysis results through brief interactive question and answer,providing more efficient and convenient practical assistance for clinical diagnosis and treatment of traditional Chinese medicine,which is conducive to accelerating the inheritance and promotion of traditional Chinese medicine,and is conducive to the expansion of Chinese medicine.The mass basis and market supply of medical diagnosis and treatment services have far-reaching social benefits.

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

Glycolytic metabolism enzymes have been implicated in the immunometabolism field through changes in metabolic status. PGK1 is a catalytic enzyme in the glycolytic pathway. Here, we set up a high-throughput screen platform to identify PGK1 inhibitors. DC-PGKI is an ATP-competitive inhibitor of PGK1 with an affinity of K _d = 99.08 nmol/L. DC-PGKI stabilizes PGK1 in vitro and in vivo, and suppresses both glycolytic activity and the kinase function of PGK1. In addition, DC-PGKI unveils that PGK1 regulates production of IL-1β and IL-6 in LPS-stimulated macrophages. Mechanistically, inhibition of PGK1 with DC-PGKI results in NRF2 (nuclear factor-erythroid factor 2-related factor 2, NFE2L2) accumulation, then NRF2 translocates to the nucleus and binds to the proximity region of Il-1β and Il-6 genes, and inhibits LPS-induced expression of these genes. DC-PGKI ameliorates colitis in the dextran sulfate sodium (DSS)-induced colitis mouse model. These data support PGK1 as a regulator of macrophages and suggest potential utility of PGK1 inhibitors in the treatment of inflammatory bowel disease.

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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Objective:To ascertain the endemic status of paragonimiasis in Zhejiang Province.Methods:From 2005 to 2020, 2-3 villages in 1-2 counties (cities, districts, hereinafter refferred to as counties) in historical endemic areas of paragonimiasis in Zhejiang Province were selected for monitoring each year. In each village, 50 to 150 local residents were selected as monitoring subjects, venous blood samples were collected, and serum Paragonimus antibody was detected by enzyme-linked immunosorbent assay (ELISA). Fifty to 100 intermediate hosts crabs or crayfish were collected in each village, and the infection of Paragonimus metacercaria was detected by crushing precipitation. Results:The positive rate of Paragonimus antibody was 2.9% (94/3 297); 3 929 crabs or crayfish were divided into 2 749 groups, 790 of which were found to have Paragonimus metacercaria infection, with a Paragonimus metacercaria infection rate of 28.7%. Conclusions:Paragonimus transmission chain exists in some counties of Zhejiang Province, which still has the potential risk of Paragonimus epidemic. Therefore, it is necessary to strengthen monitoring and carry out extensive health education to improve residents' self-protection awareness.

We conducted a randomized, open-label, parallel-controlled, multicenter trial on the use of Shuanghuanglian (SHL), a traditional Chinese patent medicine, in treating cases of COVID-19. A total of 176 patients received SHL by three doses (56 in low dose, 61 in middle dose, and 59 in high dose) in addition to standard care. The control group was composed of 59 patients who received standard therapy alone. Treatment with SHL was not associated with a difference from standard care in the time to disease recovery. Patients with 14-day SHL treatment had significantly higher rate in negative conversion of SARS-CoV-2 in nucleic acid swab tests than the patients from the control group (93.4% vs. 73.9%, P = 0.006). Analysis of chest computed tomography images showed that treatment with high-dose SHL significantly promoted absorption of inflammatory focus of pneumonia, which was evaluated by density reduction of inflammatory focus from baseline, at day 7 (mean difference (95% CI), -46.39 (-86.83 to -5.94) HU; P = 0.025) and day 14 (mean difference (95% CI), -74.21 (-133.35 to -15.08) HU; P = 0.014). No serious adverse events occurred in the SHL groups. This study illustrated that SHL in combination with standard care was safe and partially effective for the treatment of COVID-19.
COVID-19 ; Humans ; Medicine, Chinese Traditional ; Research ; SARS-CoV-2 ; Treatment Outcome