Abstract:In the era of social media，epidemic-related information is spreading across various social platforms along with the outbreak of large-scale pandemics. Therefore，assessing the impact of information diffusion on epidemic prevention and control in social networks and further guiding public opinion on the network reasonably have significant practical and economic importance for epidemic prevention and control. In recent years，revealing and understanding the influence of social information diffusion on the spread of epidemics and related prevention and control strategies have become a research hotspot in the field of network science. This article reviews the latest advances in the influence of information diffusion on the prevention and control of infectious disease transmission within single networks，double-layer networks，and higher-order networks，as well as related challenges and potential studies in the future.

Abstract:Objective To conduct a network analysis of traditional Chinese medicine（TCM） constitutions and psychological symptoms，construct a network analysis diagram of the two factors，explore the relationship between constitutions and psychological symptoms，as well as the differences in network structures between sexes，aiming to provide a basis for early prevention and treatment of clinical psychological disorders.Methods An online survey was used to collect the TCM constitutions and psychological symptoms of the participants. The data were analyzed and a network model was constructed using qgraph in R（Version 4.3.1）. The graphical lasso was used to simplify the network. The network stability was assessed using bootnet. NetworkComparisonTest was utilized to compare the network analyses between different sexes.Results Qi deficiency，Yin deficiency，and phlegm dampness were constitutions with high centrality in the network，while interpersonal sensitivity，anxiety，depression，and obsessive-compulsion were psychological symptoms with high centrality. The network demonstrated acceptable stability，with a strength centrality stability of 0.75，a closeness centrality stability of 0.67，and a betweenness centrality stability of 0.28. No significant differences were found in the network of TCM constitutions and psychological symptoms between different sexes.Conclusions The networks of TCM constitutions and psychological symptoms are similar across different sexes. Within this network，Qi deficiency，Yin deficiency，and phlegm dampness are constitutions with high centrality correlated with psychological symptoms. In the early screening for psychological problems，regulations targeting patients’ constitutions can achieve preventive treatment.

Abstract:Objective To investigate the effect of AMPA glutamate receptor subunit 1（glutamate A1，GluA1） interacting proteins GNAI2 and SRSF10 on drug-related targets，prognostic indicators，and diagnostic models of hepatocellular carcinoma（HCC）.Methods GluA1 interacting proteins were obtained by proteomic methods. Transcriptome data and the corresponding clinical data of liver hepatocellular carcinoma were obtained from the Xena database（https：//xenabrowser.net/）. Genes expressed in 75% of the samples and primary tumor and para-carcinoma tissue samples were retained for subsequent analysis. The limma package standard process in R language was used to perform variation analysis on the samples. The intersection of GluA1 interacting proteins and differentially expressed genes was obtained for expression analysis，univariate COX analysis，and survival curve analysis. The PDBePISA database was used to verify the molecular docking of the candidate protein set with GluA1. The string database was used to analyze the targets of HCC-related drugs in the candidate protein set. Prognostic indicators of patients were analyzed by the multivariate COX analysis. The logistic regression model was used to construct a diagnostic model for HCC.Results Six candidate proteins，namely KIF1A，GNAI2，RPL3，ARPC1A，EIF3H，and SRSF10，were obtained with strong affinity to GluA1. GNAI2 and SRSF10 were secondary targets of Regorafenib in HCC，and the univariate and multivariate COX analyses showed that they had significant effects on patient survival（P=0.000 339，0.0404）. GNAI2 and SRSF10 were higher in level in the disease group than in the normal group（Z=3.584，2.049； hazard ratio（HR）=2.38，1.18），and are adverse prognostic factors. In the diagnostic model of HCC，the HR coefficients were 11.8455 and -0.2037，respectively，and the area under the curve of this model on the training set was 0.971，indicating excellent performances.Conclusion GluA1 interacting proteins GNAI2 and SRSF10 are secondary targets of Regorafenib in HCC，which may become prognostic indicators independent of other factors and have certain diagnostic significance.

Abstract:Objective To investigate the risk predictors of high-risk papillary thyroid microcarcinoma（PTMC） and to construct and validate a reliable diagnostic nomogram model.Methods A retrospective analysis was performed on the clinicopathologic and ultrasound imaging data of patients with PTMC from the SEER database who underwent surgical treatment from 2004 to 2015（training set） and patients with thyroid micronodules treated in the Thyroid Treatment Center of Sichuan Provincial People’s Hospital from 2020 to 2022（external validation set）. In the validation set，the risk predictors of high-risk PTMC were analyzed using logistic regression； a diagnostic nomogram model was constructed，and an internal validation set and an external validation set were used for internal and external validation，respectively. An indirect assessment was performed according to the preoperative ultrasound imaging characteristics to examine the feasibility and reliability of preoperative ultrasound imaging characteristics in predicting high-risk PTMC.Results The training set included 1552 patients，and the external validation set included 516 patients. The independent risk factors for high-risk PTMC were sex（male），age（no more than 55），number of nodules（multikitchen），capsular invasion，and abnormal cervical lymph nodes（P<0.05）. The C-index of the constructed nomogram was 0.946. In the training set and the external validation set，the prediction results of the nomogram model were in good agreement with the actual results. The area under the ROC curve for diagnosing high-risk PTMC based on the ultrasound imaging characteristics was 0.931（95%CI=0.910-0.953），which had a high consistency with the diagnosis based on pathological characteristics（κ=0.611，P<0.05）.Conclusions The diagnostic model of high-risk PTMC constructed in this study has good predictive validity. The prediction of high-risk PTMC based on preoperative ultrasound imaging characteristics is clinically feasible and holds promise for clinical application.

Abstract:Objective To investigate the trend of influenza prevalence by constructing an Autoregressive Integrated Moving Average （ARIMA） model for influenza and making predictions on the validation set，and to provide a scientific basis for the prevention and control of influenza in Chongqing，China.Methods In this study，R software was used for the ARIMA model fitting of influenza data in Chongqing from January 2010 to June 2021，and the data from July to December 2021 were used to evaluate the fitting performance of the model.Results The prevalence of this influenza disease presented with a noticeable seasonal pattern，with a yearly cycle and a peak in winter and spring. The overall prevalence rate tended to increase first and then decrease，and the best-fitting model was ARIMA（0，1，2）×（0，1，2）₁₂，which had a root mean square error of 10.70 and a mean absolute percentage error of 70.04% in predicting the attack rate in July to December 2021，suggesting that the model had good predictive efficacy.Conclusion The ARIMA model has a certain effect in predicting the onset and prevalence trend of influenza in Chongqing and can estimate the attack rate of influenza in the future，which can provide a reference for the prevention and control of influenza in the future.

Abstract:Objective To investigate the possible causal association between tea intake and digestive system malignancies by using the two-sample Mendelian randomization method.Methods In the genome-wide association studies（GWAS） of the European population，the single nucleotide polymorphisms（SNPs） that are strongly associated with the exposure factor of tea intake were selected as instrumental variables. The summary statistics of digestive system tumors provided by The United Kingdom Biobank were used as outcome variables，including esophageal cancer，gastric cancer，small intestine cancer，colon cancer，rectal cancer，liver cancer，and intrahepatic cholangiocarcinoma. Inverse-variance weighting（IVW） was used as the primary analytic method，and a series of analyses were performed to evaluate the reliability of the study，including heterogeneity test，pleiotropic analysis，and sensitivity analysis.Results At the genome-wide significance level（P<5×10^-8），32 independent SNPs associated with tea intake were included as instrumental variables. The IVW analysis showed that tea intake was associated with an increased risk of liver cancer or intrahepatic cholangiocarcinoma（odds ratio=1.0019，95%CI=1.0003-1.0035，P=0.020），while there was no significant association between tea intake and the development of other digestive system tumors. The research findings were not affected by pleiotropy or heterogeneity，and the sensitivity analysis verified the reliability of results.Conclusion This Mendelian randomization study shows that tea intake might be a risk factor for the increased risk of liver cancer and intrahepatic cholangiocarcinoma；however，further Mendelian randomization studies with larger sample sizes of GWAS data are still needed to verify such association.

Abstract:Objective To investigate the causal relationship between gastroesophageal reflux disease（GERD） and chronic obstructive pulmonary disease（COPD） using two-sample bidirectional Mendelian randomization（MR）.Methods Based on the pooled data from large-scale genome-wide association studies（GWAS），single nucleotide polymorphisms（SNPs） which were independent of each other and highly correlated with GERD and COPD were selected as instrumental variables. The inverse-variance weighted fixed-effects（IVW-FE） model was used as the main analysis method，while simple median，weighted median，and MR-Egger regression methods were used to validate the results and test the stability. The F-statistic，Cochran’s Q test，MR-Egger intercept test，Mendelian randomization pleiotropy residual sum and outlier（MR-PRESSO） test，and leave-one-out method were used for sensitivity analysis. The odds ratio（OR） and 95% confidence interval（CI） were used as effect sizes for the bidirectional causal relationship between GERD and COPD.Results The IVW-FE analysis showed that GERD increased the risk of COPD（OR=1.757，95%CI=1.425-2.166，P=0.000），which was confirmed by the simple median and weighted median methods. However，no causal relationship between the two factors was shown by the MR-Egger method. In the inverse MR analysis，there was no evidence of increased risk of GERD caused by COPD using IVW-FE（OR=0.999，95%CI=0.962-1.037，P=0.962）. Similarly，no association between the two factors was found using the simple median，weighted median，and MR-Egger regression methods. The F-statistic of the instrumental variable was greater than 10，which showed that there was no weak instrumental variable. The Cochran’s Q test，MR-Egger intercept test，and MR-PRESSO test did not show heterogeneity or horizontal pleiotropy between instrumental variables. The leave-one-out analyses indicated that no single SNP had a significant effect on the overall outcome.Conclusion GERD is significantly and causally associated with an increased risk of COPD. However，there is no evidence suggesting that COPD causes an increased risk of GERD.

Abstract:Objective To explore the causality between thyroid dysfunction and lacunar stroke using two-sample Mendelian randomization（MR） analysis.Methods The study extracted summary data from published genome-wide association studies（GWAS） for hypothyroidism，hyperthyroidism，and lacunar stroke，comprising 494 577，172 938，and 254 459 samples，respectively. In the forward MR analysis，hypothyroidism and hyperthyroidism were used as the exposure，with 103 and 5 single nucleotide polymorphisms（SNPs） selected as instrumental variables（IVs），respectively，and lacunar stroke as the outcome. In the reverse MR analysis，lacunar stroke was used as the exposure，with 3 SNPs selected as IVs，and hypothyroidism and hyperthyroidism as the outcomes. Bidirectional MR analyses were performed using the inverse variance weighted（IVW） method as the primary analysis method，and weighted median（WM） and MR-Egger methods were used as supplementary analysis methods to evaluate causal effects. Additionally，heterogeneity and pleiotropy tests were conducted，and the leave-one-out analysis was employed to assess the stability of the results.Results Genetic predisposition to hypothyroidism was significantly associated with an increased risk of lacunar stroke（IVW：odds ratio［OR］=1.118，95%CI=1.030-1.214）. No causal association was found between hyperthyroidism and lacunar stroke（IVW：OR=1.011，95%CI=0.958-1.067），lacunar stroke and hypothyroidism（IVW：OR=1.093，95%CI=0.996-1.200），and lacunar stroke and hyperthyroidism（IVW：OR=0.857，95%CI=0.556-1.320）.Conclusion Hypothyroidism is causally related to an increased risk of lacunar stroke. However，no causal associations were found for hyperthyroidism and lacunar stroke or in the reverse study. These results require further verification through laboratory investigation and evidence from future studies with larger sample sizes.

Abstract:Objective Clinical observations and epidemiological investigations have found an association of gastroesophageal reflux disease（GERD） with interstitial lung disease（ILD）. We aimed to investigate whether GERD and ILD are causally associated with each other and the direction of the association through Mendelian randomization（MR） analysis.Methods Eligible single nucleotide poly-morphisms（SNPs）were selected as instrumental variables（IVs） from the pooled data of genome-wide association studies（GWAS）.Two-sample Mendelian randomization analyses were performed using the inverse-variance weighted method（IVW），the MR-Egger regression method，and the weighted median estimator（WME）. The IVW method was used as the main causal analysis. The MR-PRESSO test and MR-Egger regression method were used in sensitivity analysis to detect and correct for horizontal pleiotropy. The leave-one-out method，Cochran’s Q test，and funnel plot were used to assess the stability and reliability of MR results. The odds ratio（OR） was used to evaluate the causality between GERD and ILD.Results The IVW results showed that GERD increased the risk of ILD，and each standard deviation increase in log-transformed GERD resulted in a 22% increase in the risk of ILD（OR=1.22，95%CI=1.11-1.33，P=3.52e-05）. Conversely，ILD also increased the risk of GERD.Conclusion GERD and ILD have a bidirectional causal relationship.

Abstract:Objective To investigate the possible causal link between ankylosing spondylitis（AS） and mental disorders through a two-sample Mendelian randomization（MR） analysis，and to offer genetic insights into the early prevention of the development and progression of mental disorders in patients with AS.Methods Using the public data from non-overlapping genome-wide association studies，AS was considered as the exposure variable，and mental disorders （bipolar affective disorder and major depression，anorexia nervosa，Alzheimer’s disease，and anxiety disorder） as the outcome variables. We used inverse variance weighting（IVW） as the main analysis to assess the causal effect，with the MR-Egger method，weighted median method，weighted mode，and simple mode for supplementary causal analyses. The Cochran’s Q test and MR-Egger intercept test were used for sensitivity analysis.Results The IVW results revealed that genetically predicted AS had positive causal associations with bipolar affective disorder and major depression（odds ratio ［OR］=1.055，95%CI=1.019-1.093，P=0.003） and anorexia nervosa（OR=1.370，95%CI=1.068-1.759，P=0.013） and a negative causal association with Alzheimer’s disease（OR=0.976，95%CI=0.955-0.997，P=0.029），but with no causal association with anxiety disorder（OR=1.034，95%CI=0.906-1.179，P=0.620）. The sensitivity analysis detected no heterogeneity or horizontal pleiotropy（P>0.05），which indicated no bias in the results. The leave-one-out analysis suggested that the results were robust.Conclusion This study provides new evidence for causal relationships between AS and mental disorders. AS may increase the risk of bipolar affective disorder and major depression and anorexia nervosa. These findings offer new insights into the genetic research of mental disorders. However，the conclusions need further MR-based validation with larger-sample genome-wide association data.

Abstract:Objective To investigate the causality between nasal diseases and chronic obstructive pulmonary disease（COPD） and to enhance understanding of the traditional Chinese medicine theory—the nose is the portal to the lungs.Methods This study was based on pooled statistical data from genome-wide association studies（GWAS）. A two-sample Mendelian randomization（MR） method was used to investigate the causality between allergic rhinitis/acute sinusitis/chronic sinusitis/nasal polyps and COPD，using genetic factors as instrumental variables.Results The study revealed that genetic factors-dependent chronic sinusitis was significantly correlated with an increased risk of COPD after excluding the pleiotropy and heterogeneity（IVW：P=0.008 6）. Genetic factors-dependent nasal polyps were significantly correlated with an elevated risk of COPD（IVW：P=0.003 2）. No associations were found between allergic rhinitis/acute sinusitis and COPD.Conclusion Chronic sinusitis and nasal polyps are correlated with an increased risk of COPD. Nonetheless，high-level randomized controlled trials are imperative to validate these findings.

Abstract:Cervical cancer ranks fourth in both the incidence and mortality of malignant tumors among women，and has been increasing in young people. The prevention and treatment of cervical cancer differs greatly due to the uneven distribution of medical resources in different regions. Therefore，it is necessary to explore new diagnosis and treatment methods suitable for regions with different resources to promote the prevention and treatment of cervical cancer. Artificial intelligence（AI） is a science of developing computer programs to simulate，extend，and expand the behavior of Homo sapiens. In recent years，with excellence in image analysis，AI has shown great potential in the precise screening，diagnosis，and treatment of cancer. However，AI still faces great problems and challenges in the clinical diagnosis of cervical cancer，which cannot completely replace doctors. This paper summarizes the research progress on AI in the early screening and precise diagnosis and treatment of cervical cancer，in order to provide a reference for personalized diagnosis and treatment and improve the clinical outcome of the patients.

Abstract:Objective To develop a novel marker for predicting the response to neoadjuvant chemotherapy（NAC） in patients with breast cancer（BC） using the pathomics approach.Methods A retrospective analysis was performed for 211 patients with non-specific invasive BC in The Affiliated Hospital of Southwest Medical University，among whom 155 were enrolled as training group and 56 were enrolled as validation group. CellProfiler software was used to extract high-dimensional pathomics signature from the digital pathological sections of patients，and then the Mann-Whitney U test，the Spearman correlation coefficient，and the least absolute shrinkage and selection operator（LASSO） algorithm were used for the stepwise screening of features. The optimal features after screening were used to develop pathomics signature（PS） by the support vector machine（SVM） method in the training set and validate in the independent validation set. PS and significant clinicopathological factors（P<0.05） identified in the univariate analysis were included in the multivariate logistic regression analysis for further validation.Results PS had an area under the ROC curve of 0.749（95%CI=0.672-0.827） in the training set and 0.737（95%CI=0.604-0.870） in the validation set. The multivariate logistic regression analysis showed that PS ［odds ratio（OR）=2.317］ and human epidermal growth factor receptor 2（OR=4.018） were independent predictive factors for response to NAC in BC patients.Conclusion PS can help clinicians accurately predict the response to NAC before treatment and improve the personalized treatment for BC patients.

Abstract:Objective To establish a diabetes prediction model based on four classifiers of extreme gradient boosting（XGBoost），light gradient boosting machine（LightGBM），adaptive boosting（AdaBoost），and multilayer perceptron（MLP） according to clinical indicators，and to evaluate the screening effect.Methods According to the case-control study design，99 attributes of clinical data from the study group and the control group were collected，and analyzed by python 3.8. Then the linear interpolation method and an inherent non-negative latent feature（INLF） model were used to predict the feature missing value，and the classification model was constructed using four classifiers to detect diabetes.Results Through analyses of 3 241 patients with hypertension combined with diabetes（study group） and 4 181 patients with hypertension（control group） in the model，99 features were included. The accuracy rates of the diabetes classification model based on XGBoost，LightGBM，AdaBoost，and MLP classifiers were 0.894 9，0.887 5，0.862 0，and 0.856 6，respectively.Conclusion Our proposed classifier model framework based on INLF prediction has a good screening effect，and preliminarily solves the problem of early diabetes screening through machine learning，which has certain practical significance for clinical diagnosis and can be used as a simple and effective screening method for diabetes and its complications.

Abstract:Objective To identify potential diagnostic markers for aortic valve calcification using machine learning algorithms from the three aspects of immune response，stem cell activity，and osteogenesis.Methods R language was used for the homogenization，integration，and analysis of the data from GSE51472，GSE12644，and GSE55492 datasets in the GEO database to obtain differentially expressed genes. The gene sets associated with immune，stem cell，and osteogenesis were obtained from the Amigo database，which were intersected with the differentially expressed genes，respectively，to obtain the abnormally expressed genes associated with immune，stem cell，and osteogenesis in calcified valve tissue. Machine learning techniques were used to identify potential diagnostic biomarkers among the differentially expressed genes. In addition，Gene Set Enrichment Analysis（GSEA） was performed，and the diagnostic markers were validated using clinical specimens and cytology tests.Results A total of 102 differentially expressed genes were identified through the integration and analysis of the three datasets，among which there were 51 genes associated with immune response，1 gene associated with stem cells，and 2 genes associated with osteogenesis. The GSEA analysis showed that the genes associated with immune response，stem cells，and osteogenesis were all involved in immune regulatory pathways. The analysis based on multiple machine learning techniques showed that interleukin 7 receptor（IL7R） had the highest value in the diagnosis of calcific aortic valve disease （CAVD），but the results of bioinformatics analysis were not supported by clinical specimens. Cell experiments showed that IL7R could promote the proliferation of valvular interstitial cells and induce osteogenic calcification.Conclusion IL7R may not be a peripheral blood marker for diagnosing CAVD，but it may play an important role in the progression of CAVD，and therefore，it is necessary to further investigate the role of IL7R in CAVD.

Abstract:Objective To compare the effectiveness of TruScreen（TS） integrated with a graph neural network and human papillomavirus（HPV） DNA quantification in cervical precancer and cancer screening in high-risk HPV-positive patients.Methods We included 400 patients whotested positive for high-risk HPV subtypes at the Department of Gynecology，Qianjiang Hospital，Chongqing University from January 2022 to May 2023. All the patients underwent TS，HPV DNA quantification in cervical cells，and a colposcopy-directed biopsy. The biopsy pathology results were used as the gold standard. The TS system was combined with graph neural network-based representation learning and feature extraction and classification with support vector machine and random forest classifiers. We calculated and compared the sensitivity，specificity，area under the receiver operating characteristic curve（AUC），negative predictive value，positive predictive value，agreement with biopsy pathology （kappa value） of the modified TS system and HPV DNA quantification in cervical precancer and cancer screening.Results The sensitivity，specificity，AUC，negative predictive value，and positive predictive value of TS with graph neural network-based classification for cervical precancer and cancer screening were 93.75%，95.45%，0.97，98.18%，and 87.50%，respectively，which were higher than those of HPV DNA quantitative testing （81.25%，90.91%，0.92，96.15%，and 64.29%，respectively）. The kappa coefficient of agreement of TS with graph neural network-based classification with biopsy pathology was 0.89，higher than 0.72 of HPV DNA quantitative testing with biopsy pathology. There was a significant difference in the AUC of TS with graph neural network-based classification and HPV DNA quantitative testing （P<0.05）.Conclusion TS with graph neural network-based classification has better performance in screening for cervical precancer and cancer in high-risk HPV-positive patients than HPV DNA quantitative testing，with high accuracy and reliability. It can be used as a simple and effective cervical cancer screening method.

Abstract:Objective To determine differentially co-expressed genes in the peripheral blood and hippocampal tissue of patients with Alzheimer’s disease（AD） by bioinformatics analysis，and to provide new ideas for AD diagnosis and therapeutic target selection.Methods We downloaded the AD-associated datasets GSE97760（9 cases of AD and 10 healthy controls） and GSE5281（10 cases of AD and 13 healthy controls） from the Gene Expression Omnibus（GEO） database for bioinformatics analysis. We selected differentially expressed genes（DEGs） in the peripheral blood and hippocampal tissues of patients with AD separately；identified the co-expressed DEGs in the two datasets； then performed Gene Ontology（GO） analysis and Kyoto Encyclopedia of Genes and Genomes（KEGG） pathway analysis on the co-expressed DEGs；and constructed a protein-protein interaction（PPI） network to further determine the key genes，followed by validation.Results Through the comprehensive analysis of the two GEO datasets，a total of 669 co-expressed DEGs were selected，including 64 up-regulated DEGs and 605 down-regulated DEGs. The GO and KEGG analyses selected 174 key items and 40 key pathways，respectively，which were significantly enriched mainly in the transcriptional regulation of RNA polymerase and glioma pathway. The PPI network determined 10 key AD-related genes：TP53，PTEN，HNRNPC，EIF4G1，SF3B1，SRSF11，PIK3R1，RBM39，LUC7L3，and RBM25，of which PTEN，HNRNPC，SF3B1，PIK3R1，and LUC7L3 were validated in the dataset GSE48350.Conclusion The five key genes（PTEN，HNRNPC，SF3B1，PIK3R1，and LUC7L3） selected in this study may serve as potential biomarkers for AD diagnosis，which deserve further research.

Abstract:Objective To identify shared genetic features and biological pathways between metabolic syndrome（MetS） and colorectal cancer（CRC），and to screen for prognostic biomarkers associated with MetS in CRC.Methods First，differential analysis was performed on MetS，CRC，and their corresponding control samples to identify genes with differential expression，which were used as disease-related genes. Subsequently，functional enrichment analysis was performed based on the expression characteristics of these genes to identify the regulated biological functions. Then，single-factor Cox regression was used to identify MetS genes associated with CRC prognosis，and LASSO and multivariate Cox regression analysis were used to construct a model for prediction of CRC prognosis. Finally，the causal relationship between MetS genes and CRC prognosis was further confirmed through Summary-data-based Mendelian Randomization.Results A total of 325 genes were upregulated and 281 genes were downregulated in both MetS and CRC. Apelin signaling and endocytosis pathways were inhibited and the nucleotide excision repair pathway was activated in both diseases. Among them，60 genes shared between MetS and CRC were associated with CRC prognosis. Eighteen genes were employed to construct a prediction model. In the CRC cohort of the TCGA database，the model demonstrated robust prediction performance with the area under the curve exceeding 0.75 for the 1-5 year period. Summary-data-based Mendelian Randomization analysis confirmed the causal relationship of P4HA1 and LARS2 with CRC prognosis.Conclusion MetS and CRC share genetic features and pathways，with inflammation as a possible link between these two diseases. Shared genes can influence the prognosis of CRC.

Abstract:Objective To analyze the influence of antibiotic therapy on tumor metastasis in patients with colorectal cancer（CRC） in Chongqing，who had undergone 5-fluorouracil（5-FU） treatment，aiming to provide novel insights and strategies regarding antibiotic usage in anticancer treatment.Methods The electronic medical records of 489 patients with CRC who underwent 5-FU treatment in seven hospitals in Chongqing from January 1，2012 to December 31，2021 were retrospectively collected. Both univariate and multivariate logistic regression analyses were utilized to discern the association between antibiotic treatment and tumor metastasis in patients with CRC who underwent 5-FU treatment.Results In patients with CRC who underwent 5-FU treatment and those who underwent 5-FU treatment without being infected，the risk for tumor metastasis was reduced in patients receiving antibiotic treatment（odds ratio ［OR］=0.410，95%CI=0.201-0.857，P<0.001；OR=0.277，95%CI=0.129-0.607，P=0.001）. The possibility of tumor metastasis was higher in patients with CRC undergoing penicillin treatment than in those receiving cephalosporins treatment alone（P<0.05）.Conclusion Antibiotic treatment is a protective factor against tumor metastasis in patients with CRC. Penicillin usage during anticancer treatment may introduce potential risks. Clinicians should exercise caution when prescribing penicillin in conjunction with 5-FU treatment.

Abstract:Objective To investigate the mechanism of action of Xiaoyao Yiji decoction in the treatment of Diarrhea-predominant irritable bowel syndrome（IBS-D） by 16s rRNA sequencing combined with metabolomics.Methods A total of 10 IBS-D patients who met the inclusion criteria in the outpatient clinic of the Department of Shaanxi Hospital of Chinese Medicine were enrolled，feces samples were collected before and after treatment by Xiaoyao Yiji decoction，the differences of intestinal flora composition and community structure，and screened differential flora were analyzed by the 16S rRNA. The differential expressed metabolites and pathways were screened and analyzed by fecal metabolomics analysis.Results Xiaoyao Yiji decoction can significantly regulate the abundance of intestinal firmicutes，proteobacteria，actinomycetes，Bacteroides，Megamonas，Escherichia shigella，Bifidobacterium，P. pristrilla，and other flora in IBS-D patients and identify 21 disease-related differential metabolites involving 16 metabolism-related pathways，among which the pathway affects phenylalanine metabolism the most and histidine metabolism followed. With the Xiaoyao Yiji decoction treatment，the gut microbiota in IBS-D patients had been changed as Firmicutes，Proteobacteria，Actinobacteria，Bacteroidetes in the phylum level and Firmicutes increased significantly. Monelensis，Escherichia-Shigella，Bifidobacterium，Faecalibacterium，Blautia were the dominant flora. The diversity analysis showed that the species richness of the intestinal flora group decreased after the treatment，but the evenness and diversity increased，and the community structure and composition were basically consistentdiversity. Negativicutes，Veillonellales_Selenomonadales is specific flora with statistical differences between groups. Twenty-one disease-related differential metabolites were selected，involving in 16 metabolism-related pathways，with the most prominent effects on phenylalanine metabolism.Conclusion Xiaoyao Yiji decoction may achieve the therapeutic effect of IBS-D by regulating the intestinal flora of Firmicutes and Veillonellales，regulating phenylalanine metabolism and histidine metabolism pathways.