Objective The purpose of this study is to observe the dynamic changes of host intestinal microflora during the evolution and development of the pathogenesis of"spleen qi deficiency syndrome-spleen yin deficiency syndrome",so as to explore its possible mechanism.Methods The rat model of spleen Qi deficiency syndrome was established by exhaustive swimming and improper diet rhythm.On this basis,the rat model of spleen-yin deficiency syndrome was established by combining the method of consuming yin fluid.Immune-associated cytokines were assessed using ELISA assay and altered composition and diversity in gut microbiota were observed via 16S rRNA sequencing analysis.Results Compared with the control group,sIgA,IL-4,IL-10 and TGF-β in the small intestine of the model group decreased,while IFN-γ and IL-17 levels increased(P<0.05,P<0.01).The changes of immune-related cytokines proved that the immune function of spleen deficiency rats was low.In addition,the intestinal flora structure of rats with spleen deficiency has also changed significantly.The proportion of Firmicutes and Bacteroides changed and the abundance of Proteobacteria increased.The abundance of some probiotics like Lactobacillus and Oscillospira decreased(P<0.01,P<0.05)while some pathogens like Clostridium,SMB53,Campylobacterales,Desulfovibrionales and Enterobacteriales increased(P<0.05).The characteristic changes of intestinal flora in spleen Qi deficiency stage were the abnormal increase of Allobaculum,Bifidobacterium,Prevotella and Adlercreutzia(P<0.01),while in spleen Yin deficiency stage was characterized by a significant increase in the abundance of Ruminococcus(P<0.05).Conclusion Spleen deficiency syndrome is closely related to the changes of intestinal microflora.The abundance of probiotics in the gut microbiota of rats with spleen deficiency syndrome decreased and the abundance of pathogenic bacteria increased.The gut microbiota changes dynamically in the development of spleen deficiency syndrome,which have their own characteristics in the spleen Qi deficiency and spleen Yin deficiency stage respectively.

Objective To explore the causal association between immune cells and allergic rhi-nitis using Mendelian randomization(MR)approach.Methods GWAS data for 731 types of im-mune cells and allergic rhinitis were obtained from genome-wide association study(GWAS)databas-es.A two-sample bidirectional MR analysis was conducted,with the inverse-variance weighted(IVW)method as the primary analytical approach,and the weighted median method,MR-Egger re-gression,simple mode method,and weighted mode method as supplementary approaches.Sensitivity analyses,including heterogeneity tests,pleiotropy tests,and the leave-one-out method,were per-formed.Bonferroni correction was applied to the preliminary results to enhance their reliability and rigor.Results The two-sample forward MR analysis revealed correlations between 67 immune cell phenotypes and allergic rhinitis.After Bonferroni correction,four immune cell phenotypes were finally identified.Among them,the expression of CD3 on CD39-positive activated CD4 regulatory T cells(OR=0.953,95％CI,0.931 to 0.978,P＜0.001,Padj=0.007),the expression of herpesvirus entry mediator(HVEM)on CD45RA-negative CD4+T cells(OR=0.965,95％CI,0.948 to 0.983,P＜0.001,Padj=0.008),and the percentage of human leukocyte antigen class DR(HLA-DR)-high-expressing monocytes among leukocytes(OR=0.929,95％CI,0.885 to 0.974,P=0.002,Padj=0.157)were protective factors for allergic rhinitis.In contrast,the percentage of transitional B cells among B cells(OR=1.094,95％CI,1.032 to 1.161,P=0.003,Padj=0.183)was a risk factor for allergic rhinitis.The reverse MR analysis showed no causal relationship between allergic rhinitis and the four immune cell phenotypes.Conclusion The two-sample forward MR analysis confirms a caus-al link between immune cells and allergic rhinitis.MR analysis has the advantages of reducing con-founding factor interference and avoiding reverse causation,providing a theoretical basis for in-depth research on immune mechanisms,sensitive biomarkers,and drug treatment targets of allergic rhinitis.

Identifying drug-drug interactions(DDIs)is essential to prevent adverse effects from polypharmacy.Although deep learning has advanced DDI identification,the gap between powerful models and their lack of clinical application and evaluation has hindered clinical benefits.Here,we developed a Multi-Dimensional Feature Fusion model named MDFF,which integrates one-dimensional simplified molec-ular input line entry system sequence features,two-dimensional molecular graph features,and three-dimensional geometric features to enhance drug representations for predicting DDIs.MDFF was trained and validated on two DDI datasets,evaluated across three distinct scenarios,and compared with advanced DDI prediction models using accuracy,precision,recall,area under the curve,and F1 score metrics.MDFF achieved state-of-the-art performance across all metrics.Ablation experiments showed that integrating multi-dimensional drug features yielded the best results.More importantly,we obtained adverse drug reaction reports uploaded by Xiangya Hospital of Central South University from 2021 to 2023 and used MDFF to identify potential adverse DDIs.Among 12 real-world adverse drug reaction reports,the predictions of 9 reports were supported by relevant evidence.Additionally,MDFF demon-strated the ability to explain adverse DDI mechanisms,providing insights into the mechanisms behind one specific report and highlighting its potential to assist practitioners in improving medical practice.

Amyotrophic lateral sclerosis (ALS) is an irreversible, fatal neurodegenerative disorder whose incidence is positively correlated with the aging population. ALS is characterized by the progressive loss of motor neurons, leading to muscle weakness, atrophy, and ultimately respiratory failure. The pathogenesis of ALS involves multiple factors, including genetic and environmental influences, with genetic factors playing a particularly significant role. To date, several causative genes have been identified in ALS, such as the Cu/Zn superoxide dismutase 1 (Cu/Zn SOD1, also known as SOD1) gene, transactive response DNA-binding protein 43 (TDP-43) gene, fused in sarcoma (FUS) gene, and chromosome open reading frame 72 (C9orf72). Mutations in these genes have been found not only in familial ALS but also in sporadic ALS. Based on the identified ALS risk genes, various ALS animal models have been established through multiple approaches, including transgenic models, gene knockout/knock-in models, and adeno-associated virus-mediated overexpression models. These models simulate some typical pathological features of human ALS, such as motor neuron loss, ubiquitinated inclusions, and neuromuscular junction degeneration. However, these models still have limitations: (1) single-gene mutation models are insufficient to fully replicate the complex multi-factorial pathogenesis of sporadic ALS; (2) significant differences in microenvironmental regulation mechanisms and the rate of neurodegeneration between model organisms and humans may affect the accurate reproduction of disease phenotypes and the reliable evaluation of drug efficacy. To better understand the pathogenesis of ALS and promote the development of effective therapies, constructing and optimizing ALS animal models is crucial. This review aims to summarize commonly used ALS gene mutation mouse models, analyze their phenotypes and pathological characteristics, including transgenic mouse models, gene knockout/knock-in mouse models, and adeno-associated virus-mediated overexpression mouse models, and further discuss their specific applications in ALS pathogenesis research and drug development by comparing the advantages and limitations of each model.

Amyotrophic lateral sclerosis (ALS) is an irreversible, fatal neurodegenerative disorder whose incidence is positively correlated with the aging population. ALS is characterized by the progressive loss of motor neurons, leading to muscle weakness, atrophy, and ultimately respiratory failure. The pathogenesis of ALS involves multiple factors, including genetic and environmental influences, with genetic factors playing a particularly significant role. To date, several causative genes have been identified in ALS, such as the Cu/Zn superoxide dismutase 1 (Cu/Zn SOD1, also known as SOD1) gene, transactive response DNA-binding protein 43 (TDP-43) gene, fused in sarcoma (FUS) gene, and chromosome open reading frame 72 (C9orf72). Mutations in these genes have been found not only in familial ALS but also in sporadic ALS. Based on the identified ALS risk genes, various ALS animal models have been established through multiple approaches, including transgenic models, gene knockout/knock-in models, and adeno-associated virus-mediated overexpression models. These models simulate some typical pathological features of human ALS, such as motor neuron loss, ubiquitinated inclusions, and neuromuscular junction degeneration. However, these models still have limitations: (1) single-gene mutation models are insufficient to fully replicate the complex multi-factorial pathogenesis of sporadic ALS; (2) significant differences in microenvironmental regulation mechanisms and the rate of neurodegeneration between model organisms and humans may affect the accurate reproduction of disease phenotypes and the reliable evaluation of drug efficacy. To better understand the pathogenesis of ALS and promote the development of effective therapies, constructing and optimizing ALS animal models is crucial. This review aims to summarize commonly used ALS gene mutation mouse models, analyze their phenotypes and pathological characteristics, including transgenic mouse models, gene knockout/knock-in mouse models, and adeno-associated virus-mediated overexpression mouse models, and further discuss their specific applications in ALS pathogenesis research and drug development by comparing the advantages and limitations of each model.

ObjectiveTo assess the current status of medical waste management in Jinshan District of Shanghai, China, to identify existing issues, and to provide a scientific basis for formulating targeted strategies. MethodsData were collected from the routine supervision and inspection records of the Jinshan District Health Commission Supervision Institute from 2017 to 2021, covering all aspects of medical waste management, including collection, classification, transportation, storage, and administrative penalties. ResultsThe compliance rates for the establishment of institutional frameworks, staffing, internal handover, and registration in medical and healthcare institutions all exceeded 95.00%. However, only 2.31% of the medical and healthcare institutions met the 48-hour storage limit requirement for medical waste. Private institutions had significantly lower compliance rates (P<0.05) in aspects such as proper classification and collection, maintaining records for three years, adhering to the 48-hour storage limit, refraining from commercial transactions, timely disinfection and cleaning, and implementing emergency measures for waste loss. Compliance rates also varied among different types of institutions regarding the establishment of temporary storage facilities and the implementation of the transfer manifest system, with community healthcare institutions exhibiting relatively lower compliance rates (P<0.05). Over the past five years, private medical and healthcare institutions accounted for 63.33% of administrative penalty cases. ConclusionWhile medical waste management in Jinshan District, Shanghai, has gradually become more standardized, challenges remain. To address the issue of medical waste being stored for over 48 hours, medical waste transfer stations should be established to improve transfer efficiency and ensure complete waste collection. Additionally, for private and community healthcare institutions, weak links in management should be addressed by establishing medical waste quality control teams, enhancing supervision through digital tools, and optimizing management processes to comprehensively elevate medical waste management.

Identifying drug-drug interactions (DDIs) is essential to prevent adverse effects from polypharmacy. Although deep learning has advanced DDI identification, the gap between powerful models and their lack of clinical application and evaluation has hindered clinical benefits. Here, we developed a Multi-Dimensional Feature Fusion model named MDFF, which integrates one-dimensional simplified molecular input line entry system sequence features, two-dimensional molecular graph features, and three-dimensional geometric features to enhance drug representations for predicting DDIs. MDFF was trained and validated on two DDI datasets, evaluated across three distinct scenarios, and compared with advanced DDI prediction models using accuracy, precision, recall, area under the curve, and F1 score metrics. MDFF achieved state-of-the-art performance across all metrics. Ablation experiments showed that integrating multi-dimensional drug features yielded the best results. More importantly, we obtained adverse drug reaction reports uploaded by Xiangya Hospital of Central South University from 2021 to 2023 and used MDFF to identify potential adverse DDIs. Among 12 real-world adverse drug reaction reports, the predictions of 9 reports were supported by relevant evidence. Additionally, MDFF demonstrated the ability to explain adverse DDI mechanisms, providing insights into the mechanisms behind one specific report and highlighting its potential to assist practitioners in improving medical practice.

Objective:To analyze differences in detection rates for gastric precancerous lesions and neoplasms before versus after implementing a computer-aided detection (CADe) system in real-world clinical practice.Methods:Clinical data of patients who underwent gastroscopic examinations in two examination rooms (Room 1 and 2) in the Digestive Endoscopy Center of Renmin Hospital of Wuhan University were retrospectively collected during two periods: from January to June 2018 and from January to June 2021. Patients were stratified into four groups: CADe group (Room 2, 2021, using CADe), Pre-CADe group (Room 2, 2018, without CADe), 18-Con group (Room 1, 2018, without CADe), and 21-Con group (Room 1, 2021, without CADe). The differences in the detection rates of intestinal metaplasia and neoplasms between different groups were compared.Results:The detection rate of intestinal metaplasia in the CADe group was significantly higher than that in the Pre-CADe group [5.76% (198/3 437) VS 3.23% (100/3 092), χ2=23.856, P<0.001]. It was also significantly higher than that in the 21-Con group [5.76% (198/3 437) VS 2.73% (131/4 796), χ2=47.895, P<0.001]. The detection rate of neoplasms in the CADe group was significantly higher than that in the Pre-CADe group [3.23% (111/3 437) VS 1.58% (49/3 092), χ2=18.421, P<0.001] and the 21-Con group [3.23% (111/3 437) VS 1.79% (86/4 796), χ2=17.687, P<0.001]. Conclusion:The CADe system can significantly improve the detection rates of gastric intestinal metaplasia and neoplasms in clinical settings, potentially facilitating early diagnosis and treatment.

Objective The purpose of this study is to observe the dynamic changes of host intestinal microflora during the evolution and development of the pathogenesis of"spleen qi deficiency syndrome-spleen yin deficiency syndrome",so as to explore its possible mechanism.Methods The rat model of spleen Qi deficiency syndrome was established by exhaustive swimming and improper diet rhythm.On this basis,the rat model of spleen-yin deficiency syndrome was established by combining the method of consuming yin fluid.Immune-associated cytokines were assessed using ELISA assay and altered composition and diversity in gut microbiota were observed via 16S rRNA sequencing analysis.Results Compared with the control group,sIgA,IL-4,IL-10 and TGF-β in the small intestine of the model group decreased,while IFN-γ and IL-17 levels increased(P<0.05,P<0.01).The changes of immune-related cytokines proved that the immune function of spleen deficiency rats was low.In addition,the intestinal flora structure of rats with spleen deficiency has also changed significantly.The proportion of Firmicutes and Bacteroides changed and the abundance of Proteobacteria increased.The abundance of some probiotics like Lactobacillus and Oscillospira decreased(P<0.01,P<0.05)while some pathogens like Clostridium,SMB53,Campylobacterales,Desulfovibrionales and Enterobacteriales increased(P<0.05).The characteristic changes of intestinal flora in spleen Qi deficiency stage were the abnormal increase of Allobaculum,Bifidobacterium,Prevotella and Adlercreutzia(P<0.01),while in spleen Yin deficiency stage was characterized by a significant increase in the abundance of Ruminococcus(P<0.05).Conclusion Spleen deficiency syndrome is closely related to the changes of intestinal microflora.The abundance of probiotics in the gut microbiota of rats with spleen deficiency syndrome decreased and the abundance of pathogenic bacteria increased.The gut microbiota changes dynamically in the development of spleen deficiency syndrome,which have their own characteristics in the spleen Qi deficiency and spleen Yin deficiency stage respectively.

Objective:To analyze differences in detection rates for gastric precancerous lesions and neoplasms before versus after implementing a computer-aided detection (CADe) system in real-world clinical practice.Methods:Clinical data of patients who underwent gastroscopic examinations in two examination rooms (Room 1 and 2) in the Digestive Endoscopy Center of Renmin Hospital of Wuhan University were retrospectively collected during two periods: from January to June 2018 and from January to June 2021. Patients were stratified into four groups: CADe group (Room 2, 2021, using CADe), Pre-CADe group (Room 2, 2018, without CADe), 18-Con group (Room 1, 2018, without CADe), and 21-Con group (Room 1, 2021, without CADe). The differences in the detection rates of intestinal metaplasia and neoplasms between different groups were compared.Results:The detection rate of intestinal metaplasia in the CADe group was significantly higher than that in the Pre-CADe group [5.76% (198/3 437) VS 3.23% (100/3 092), χ2=23.856, P<0.001]. It was also significantly higher than that in the 21-Con group [5.76% (198/3 437) VS 2.73% (131/4 796), χ2=47.895, P<0.001]. The detection rate of neoplasms in the CADe group was significantly higher than that in the Pre-CADe group [3.23% (111/3 437) VS 1.58% (49/3 092), χ2=18.421, P<0.001] and the 21-Con group [3.23% (111/3 437) VS 1.79% (86/4 796), χ2=17.687, P<0.001]. Conclusion:The CADe system can significantly improve the detection rates of gastric intestinal metaplasia and neoplasms in clinical settings, potentially facilitating early diagnosis and treatment.