OBJECTIVE To explore the effects of trichloroisocyanuric acid(TCCA)on the prolifera-tion of spermatogonia by inducing oxidative stress and ferroptosis.METHODS GC-1 cells were cultured in DMEM-F12 medium,and cell proliferation was plotted according to the growth curve.GC-1 cells were treated with TCCA at concentrations of 0(cell control),97,194,and 387 μmol·L-1 for 24 h.Cell viability was detected using the CCK-8 method,apoptosis cells were stained with Hoechst 33342,cell cycle was examined by PI staining method,RT-qPCR was performed to measure the mRNA expres-sion levels of apoptosis-related genes Bax,Fas,oxidative stress-related genes superoxide dismutase 2(SOD2),glutathione peroxidase 4(GPX4),nuclear factor erythroid 2-related factor 2(Nrf2),solute carrier family 7 member 11(SLC7A11),dihydroorotate dehydrogenase(DHODH),DNA methyltransferase 3A(DNMT3A),and DNA methyltransferase 3L(DNMT3L).The Griess method was used to determine the nitric oxide(NO)content,colorimetric method for the malondialdehyde(MDA)level,DCFH-DA fluores-cence probe method for the reactive oxygen species(ROS)level,DNTB colorimetric method for the reduced glutathione(GSH)content,and WST-8 method for the reduced coenzymeⅡ(NADPH)content.RESULTS Compared with the cell control group,the cell survival rates in the TCCA 194 and 387 μmol·L-1 groups decreased significantly(P<0.01),accompanied by nuclear condensation and fragmentation,a significant increase in apoptosis rate(P<0.01),and cell arrest in the G2/M phase(P<0.05).Additionally,in the TCCA 387 μmol·L-1 group,the levels of NO,MDA and ROS increased(P<0.01),while the levels of GSH and NADPH decreased(P<0.01).Moreover,the mRNA expressions of SOD2,GPX4,Nrf2,SLC7A11,and DHODH decreased(P<0.05,P<0.01),while the expressions of Bax,Fas,DNMT3L,and DNMT3A increased(P<0.05,P<0.01).CONCLUSION TCCA exposure reduces the viability of GC-1 cells,inhibits cell proliferation,induces apoptosis of GC-1 cells.The mechanism may be related to the ability of TCCA to enhance oxidate stress,induce ferroptosis,and interfere with the methylation of GC-1 cells.

With the emergence of high-throughput technology and massive toxicology data,toxicology research has entered the era of big data.How to efficiently integrate existingtoxicological data,clarify the toxic effects of chemicals,and use these patterns to providenew information,in order to achieve effi-cient prediction of the toxicity of new chemicalsubstances,is one of the cutting-edge issues in toxicology.In view of the high cost,low throughput and difficulty in revealing the mechanism information of tradi-tional chemical toxicity testing methods,high throughput prediction models are urgently needed.Machine learning methods have been applied to toxicity testing,such as supervised learning models,unsupervised learning models,deep learning models,reinforcement learning models,and transfer learning models.Chemical characteristic data commonly used in machine learning models include chemical structure data,text data,toxicological genome data and image data.There is huge potential for applying machine learning to toxicity testing and machine learning methods have made some prog-ress.However,current research focuses on the processing of data and development of models,which has failed to produce a widely used and accepted method.In addition,the prediction accuracy of machine learning models is not only dependent on algorithms,but also affected by data quality,and the mutual promotion and development of algorithms and data quality remains a big challenge.In short,data processing and model construction in the field of toxicology require interdisciplinary cooperation and technological innovation.With the increasing perfection of toxicology databases and the continuous optimization of various model algorithms,the toxicity prediction of new chemicals based on machine learning models will become increasingly efficient and accurate,playing an important role in ensuring human health and environmental safety.

Objective To screen differentially expressed genes in lung adenocarcinoma by constructing Lasso-Cox model to provide potential gene targets for the research of lung adenocarcinoma and new directions for clinical diagnosis,treatment and prognosis by calculating patient risk score and constructing prediction model of lung adenocarcinoma.Methods The gene expression and clinical data of lung adenocarcinoma were downloaded from the Cancer Genome Atlas(TCGA)and Gene Expression Omnibus database(GEO).The TCGA database was used to train model,and the two databases were combined for model validation.The screened differentially expressed genes(DEGs)of lung adenocarcinoma were analyzed by univariate Cox and multivariate Lasso-Cox to construct a risk score prognosis model.Risk score from the final Cox prediction model and clinical data were combined to determine independent prognostic factors.GO enrichment analysis,KEGG pathway analysis and CIBERSORTx immunoassay were used to evaluate the biological interpretation of differentially expressed genes in the risk model.Results The analysis using univarate Cox and Lasso-Cox regreesion identified 9 differentially expressed genes associated with the prognosis of lung adenocarcinoma.Multivariate Cox regression analysis,incorporating clinical data,revealed that a history of malignant tumors,N stage,T stage,and the risk score were independent prognostic factors.Conclusion The prognositic model of lung adenocarcinoma can effectively predict the prognosis risk and provide a theoretical basis for clinical decision-making and personalized treatment.

Objective To compare the effects of SARIMA,LSTM and EDM in predicting the incidence of HFRS in Weifang under different circumstances,and explore the best prediction model.Methods The monthly incidence of HFRS in Weifang from January 2011 to December 2017 was selected to construct the SARIMA model,univariate LSTM model,univariate EDM model,and SARIMAX model,multivariate LSTM model,and multivariate EDM model including meteorological factors.The monthly incidence from January 2018 to December 2018 was predicted,and the prediction effects of each model were compared.Results The MAPE of SARIMA model,univariate LSTM model,multivariate LSTM model,univariate EDM model,multivariate EDM model were 42.17%,48.40%,16.19%,55.00%,51.79%,respectively.Conclusion The multivariate LSTM model including meteorological factors had a good prediction effect on the incidence of HFRS in Weifang,and the prediction results could provide reference for the prevention and control of HFRS.

Objectives To explore the causal relationship between hypothyroidism and nonalcoholic fatty liver disease(NAFLD)by two-sample Mendelian randomization.Methods Genome-wide association study(GWAS)data was used to identify genetic variation data associated with hypothyroidism as instrumental variables(IVs).MR-Egger regression,inverse variance weighting method(IVW)and weighted median method(WME)were used to examine the causal effects between hypothyroidism and NAFLD.The causal association was evaluated with OR.Results A total of 334 SNP loci were included as Ⅳ,and there was no heterogeneity and pleiotropy in the Ivs.Based on IVW estimation mryhod,the OR with 95%CI of two different sets were 21.30(95%CI:93.61～125.50)]and 5.79(95%CI:1.03～32.47),which both indicated a significant causal association between the hypothyroidism and NAFLD(P＜0.05).What′s more,the results of Mendelian randomization were robust.Conclusion Hypothyroidism is potentially associated with NAFLD.

Objective To explore the prognostic factors of colorectal cancer patients in gene data using random survival forest model.Method The differentially expressed genes were screened using the gene expression data of colorectal cancer in TCGA database,and combined with clinical and survival information.The RSF model is constructed and compared with the traditional Lasso-Cox regression model.Results The RSF model obtained 13 important factors affecting the prognosis of colorectal cancer patients,including HAND1(VIMP=0.090)and PCOLCE2(VIMP=0.075)genes,and analyzed the interaction between pathological N,PCOLCE2 gene and IGSF9 gene variables.Compared with Lasso-Cox model,the RSF model has better model calibration(IBS:training set:0.205 vs.0.214;test set:0.210 vs.0.221)although its prediction error rate is slightly higher(1-C-index:training set:0.296 vs.0.213;test set:0.369 vs.0.332).Conclusion RSF model has a good performance in processing the analysis of right censored survival data,can find important influencing factors and the interaction between variables,and provide scientific basis for the improvement of prognosis and quality of life of colorectal cancer patients.

Objective:This study utilizes a hybrid research design,integrating interviews with experimental surveys,to investigate the variances in patient trust models between tertiary hospitals and primary healthcare institutions.Methods:Thirty-eight residents participated in semi-structured interviews,which were then analyzed using content analysis.The experimental survey segment divided participants into two groups:"Tertiary Hospitals"and"Primary Healthcare Institutions,"resulting in 648 valid questionnaires.These questionnaires were subjected to variance analysis to assess differences.Results:Patients exhibit greater systemic trust in tertiary hospitals than in primary healthcare facilities,while interpersonal trust is stronger in primary healthcare settings.Suggestions:Therefore,strategies should be developed to bolster systemic trust at the primary level and emphasize the advantages of interpersonal trust.Moreover,specific measures are required to reshape patients'interpersonal trust in tertiary hospitals.

Objective:To investigate the effect of selenium on cyclophosphamide(CTX)-induced spermatogenic impairment(SI)in mice and its underlying mechanism.Methods:We equally randomized 36 male KM mice into 3 SI model and 3 control groups,the first 3 treated by intraperitoneal injection of CTX at 100 mg/kg(the SI model control group),CTX plus SI model control group,selenium deficient model group(-Se SI),selenium supplemented model group(+Se SI),while latter 3 by intraperitoneal injection of normal saline(the normal control),selenium deficiency control group(-Se control),selenium addition control group(+Se control),respectively,all once a week for 6 successive weeks.Then we observed the histopathological changes in the testes of all the mice by HE staining,obtained the sperm count in the epididymides,determined the expressions of glutathione peroxidase 4(GPx4)and SLC7A11 proteins by Western blot and ferroptosis-related genes by RT-qPCR,and examined the changes in the expres-sions of ferroptosis-related proteins and genes in the GC2-spd cells treated with ferroptosis inhibitors and inducers in combination with different concentrations of inorganic sodium selenite(SeS)and organic selenomethionine(SeM).Results:Compared with the nor-mal controls,the SI model mice showed significantly decreased testicular and prostatic organ coefficients,reduced spermatogenic lay-ers,increased voids,decreased serum ferritin concentration(P<0.05),and elevated transferrin concentration(P<0.05).The or-gan coefficients were significantly higher in the+Se SI and+Se control than in the-Se SI and-Se control groups(P<0.05,P<0.01),with evident pathological improvement of the testis tissue in the+Se controls.The expressions of the GPx4 and solute carrier family 7 members 11(SLC7A11)genes in the testis were dramatically down-regulated in the SI model controls(P<0.01),but up-reg-ulated in the+Se SI and+Se control compared with those in the-Se SI and-Se control group(P<0.01 and P<0.05),but there were no statistically significant differences between their protein expressions.The results of in vitro GC2 spd cell experiments indicated that the GPx4 gene and GPx4 protein levels in the-Se group were significantly lower than those in the normal control group(P<0.05),while the SLC7A11 gene level decreased(P<0.01).Different doses of SeS and SeM significantly increased the GPx4 protein expression compared to the average Se group.Low doses of SeM promoted a significant increase in GPx4 gene levels,while high doses of SeS increased the expression levels of SLC7A11 gene and SLC7A11 protein(P<0.05,P<0.01).The Se group showed a signifi-cant decrease in the levels of acsl4 and ptgs2 genes compared to the normal control group.SeM promoted the expression of acsl4,while SeS promoted the expression of ptgs2 and fth1(P<0.01,P<0.05).The intervention results of GC2 spd showed that the Erastin group had a decrease in ptgs2 compared to the normal control group,while the SeS+Erastin and SeM+Erastin groups had an increase in ptgs2 gene expression compared to the Erastin group.However,the ptgs2 expression of Fer-1 was lower than that of the normal con-trol group,and the ptgs2 gene level of SeS+Fer-1 and SeM+Fer-1 groups was lower than that of Fer-1 group(P<0.05);The gene quantity of GPx4 in the SeM+Erastin and SeM+Fer-1 groups increased compared to the Erastin and Fer-1 groups(P<0.01,P<0.05);SeM+Erastin and SeS+Erastin showed a decrease in SLC7A11 compared to the Erastin group,as well as SeM+Fer-1 and SeS+Fer-1 groups compared to the Fer-1 group,accompanied by an increase in acsl4 and fth1(P<0.01).Conclusion:Selenium deficiency causes the reduction of the SLC7A11 and GPx4 gene levels,disorder of ferroptosis-related genes and down-regulation of the GPx4 protein expression in the mouse testis and spermatocytes.Selenium can promote the expression of GPx4,up-regulate the level of SLC7A11,and improve spermatogenesis in the testis of the mouse with SI.There are differences between organic SeM and inorganic SeS in regulating the ferroptosis pathway-related genes.

Objective:To characterize and analyze the genetic variation of hemagglutinin (HA) of influenza A (H1N1) pdm09 subtype virus in Shandong Province, and explore the genetic variation patterns for providing reference for influenza monitoring, epidemic prevention and control, and vaccine strain selection.Methods:HA gene sequences of the recommended strains of influenza vaccine from 2009 to 2024 and the representative strains of each branch were downloaded from the GISAID Influenza Data Platform, and were phylogenetically analyzed and characterized in terms of amino acid site variation with the HA gene sequences of 298 influenza A (H1N1) virus strains isolated from Shandong Province. A phylogenetic tree was constructed using the maximum likelihood (ML) method of the IQ-TREE online tool, and the amino acid site variants were viewed using MegAlign software. The potential glycosylation sites of the HA gene were predicted using the NetNGlyc 1.0 online software.Results:The HA gene homology of the 298 influenza A (H1N1) viruses isolated in Shandong Province ranged from 91.2% to 100.0%. The evolutionary branches were gradually distantly related over time, but the direction of evolution was roughly the same as that in other provinces. Amino acid mutations in the HA occurred every year and most were found in the antigenic determinants.Conclusions:The HA genes of influenza viruses isolated in Shandong Province from 2009 to 2024 are still in the process of continuous evolution, and continuous monitoring of the epidemiological trends and the evolutionary directions of influenza viruses is essential for early warning of influenza virus pandemics.

Objective To study the distribution characteristics of gut microbiota in young children infected with rotavirus and adenovirus,providing new ideas for treatment and improving prognosis.Methods Fecal samples of 22 young children clinically diagnosed with acute diarrhea were collected,including 12 confirmed cases of rotavirus infection and 10 adenovirus infections,and 12 fecal samples of healthy children during the same period were collected as controls.Using 16S rDNA targeted amplification to detect the diversity and composition structure of gut microbiota in three groups of young children,analyzing their microbial community structure,abundance,and differences.Results There were significant differences in the alpha and beta diversity analyses of the gut microbiota among the three groups of observing objects.At the family level,the healthy control group and adenovirus group were mainly dominated by the Trichospiridae family,while the rotavirus group is mainly dominated by the Enterobacteriaceae family;At the genus level,the healthy control group,rotavirus group,and adenovirus group have the highest abundance of Bacteroides,Escherichia/Shigella,and Bifidobacterium,respectively;LEfSe differential analysis showed that compared with the healthy control group,the abundance of probiotics such as Bifidobacterium,Ackermann's bacteria,Ruminococcus,and Eubacterium in the rotavirus group was significantly reduced,but the abundance of Gram negative bacteria such as Escherichia/Shigella was increased;The abundance of conditional pathogenic bacteria such as Clostridium and Staphylococcus in the adenovirus group was significantly increased,while the abundance of Bacillus was lowered,and Bifidobacterium did not decrease compared to the control group.Conclusion Both rotavirus and adenovirus infections cause dysbiosis of the gut microbiota in young children,and there are significant differences in the types and distribution of microbiota in their bodies after infection,indicating differences in the use and selection of probiotics in the treatment of diarrhea caused by the two viruses.