Objective To construct a machine learning algorithm using biomarkers to predict the risk of sepsis-induced respiratory tract infection in order to assist clinicians in making decisions.Methods Based on the diagnostic criteria of the research subjects,and the basic clinical data of the participants were collected.The data set was randomly split into a training set(80％)and a validation set(20％).Use feature filtering algorithms to select the best subset of variables from the training set,and use this subset to construct random forest(RF),extreme gradient boosting(XGBoost),adaptive boosting(AdaBoost),Logistic regression(LR),ridge regression(Ridge),and support vector machine(SVM)classifiers.Then,evaluate the model's generalization ability using a validation dataset.Evaluate the performance of the model comprehensively through accuracy,precision,recall,and area under the curve.Results A total of 377 patients with sepsis-induced respiratory tract infection(case group)and 564 patients with respiratory tract infection(control group)were included,and 17 variables were found to be suitable for the initial model construction.Using feature screening algorithm,we found that the predictive performance of tree models(RF,XGboost,and AdaBoost)was better than that of linear models(LR,SVM,and Ridge).The AdaBoost model included 14 biomarkers,and its prediction accuracy was better than RF,XGBoost,LR,SVM,Ridge models,its precision,recall,accuracy and area under the curve were 0.90,0.84,91.75％and 0.950,respectively.The Ridge model had the worst prediction performance,with an accuracy of 82.97％,its precision,recall and area under the curve were 0.90,0.72 and 0.835 respectively.Conclusion In this study,six predictive models of sepsis-induced respiratory tract infection were developed,among which AdaBoost model could more accurately predict the risk of sepsis-induced respiratory tract infection and help to assist clinical decision-making.

Objective To construct a machine learning algorithm using biomarkers to predict the risk of sepsis-induced respiratory tract infection in order to assist clinicians in making decisions.Methods Based on the diagnostic criteria of the research subjects,and the basic clinical data of the participants were collected.The data set was randomly split into a training set(80％)and a validation set(20％).Use feature filtering algorithms to select the best subset of variables from the training set,and use this subset to construct random forest(RF),extreme gradient boosting(XGBoost),adaptive boosting(AdaBoost),Logistic regression(LR),ridge regression(Ridge),and support vector machine(SVM)classifiers.Then,evaluate the model's generalization ability using a validation dataset.Evaluate the performance of the model comprehensively through accuracy,precision,recall,and area under the curve.Results A total of 377 patients with sepsis-induced respiratory tract infection(case group)and 564 patients with respiratory tract infection(control group)were included,and 17 variables were found to be suitable for the initial model construction.Using feature screening algorithm,we found that the predictive performance of tree models(RF,XGboost,and AdaBoost)was better than that of linear models(LR,SVM,and Ridge).The AdaBoost model included 14 biomarkers,and its prediction accuracy was better than RF,XGBoost,LR,SVM,Ridge models,its precision,recall,accuracy and area under the curve were 0.90,0.84,91.75％and 0.950,respectively.The Ridge model had the worst prediction performance,with an accuracy of 82.97％,its precision,recall and area under the curve were 0.90,0.72 and 0.835 respectively.Conclusion In this study,six predictive models of sepsis-induced respiratory tract infection were developed,among which AdaBoost model could more accurately predict the risk of sepsis-induced respiratory tract infection and help to assist clinical decision-making.

Background and aim:A genome-wide association study has indicated the association of numerous genes in the 6p21.3 region with chronic hepatitis B virus(HBV)infection.In this study,we screened 12 representative single-nucleotide polymorphisms(SNPs)from the 6p21.3 region and investigated their association with the risk of chronic hepatitis B(CHB)to better understand the molecular etiology un-derlying CHB risk in the Han Chinese population. Methods:Between March 2021 and November 2022,we included 183 patients with CHB(case group)and 196 with natural HBV clearance(control group).Allele typing of the selected SNPs was performed using snapshot technology.The correlation between the 12 chosen SNPs and the risk of chronic HBV infection was examined using binary logistic regression analysis.Interacting genes of the variants were identified,and expression quantitative trait loci(eQTL)were analyzed using the 3DSNP database. Results:We validated 12 previously reported CHB susceptibility sites,including rs1419881 of tran-scription factor 19(TCF19),rs3130542 and rs2853953 of human leukocyte antigen(HLA)-C,rs652888 of euchromatic histone-lysine-methyltransferase 2(EHMT2),rs2856718,rs9276370,rs7756516,and rs7453920 of HLA-DQ,rs378352 of HLA-DOA,and rs3077,rs9277535,and rs9366816 of HLA-DP.Logistic regression analyses revealed that polymorphisms such as rs9276370,rs7756516,rs7453920,rs3077,rs9277535,and rs9366816 were positively correlated with natural HBV clearance in the dominant model.Conversely,rs3130542 and rs378352 were identified as risk factors for CHB.Haplotype analysis revealed that rs9276370,rs7756516,and rs7453920 in HLA-DQ were TTG and GCA haplotypes.Although the TTG haplotype was positively correlated with a higher risk of CHB,the GCA haplotype significantly influenced the natural clearance of HBV.Bioinformatics analysis demonstrated that rs378352,rs3077,and rs9366816 were located within enhancer states;rs3077 and rs9366816 overlapped with nine tran-scription factor-binding sites,whereas rs378352 altered five sequence motifs.Furthermore,eQTL analysis demonstrated the functional tendencies of eight statistically significant SNPs(rs3130542,rs9276370,rs7756516,rs7453920,rs378352,rs3077,rs9277535,and rs9366816). Conclusions:Genetic variations within the 6p21.3 region were associated with chronic HBV infection in the Han Chinese population in southern China.Furthermore,the GCA haplotype including rs9276370,rs7756516,and rs7453920 of HLA-DQ contributed significantly to natural HBV clearance,implying that multiple SNPs exert a cumulative allelic effect on HBV infection.

BACKGROUND/AIMS: The association between the kinesin family member 1B (KIF1B) gene polymorphism and the risk of hepatitis B virus-related hepatocellular carcinoma (HCC) has been investigated in many peer-reviewed studies. However, scholars have failed to replicate these results in validation tests. The purpose of the present study was to explore whether the KIF1B rs17401966 polymorphism was associated with susceptibility to HCC. METHODS: The results of case-controlled studies on the correlation between the KIF1B rs17401966 polymorphism and HCC susceptibility were collected using Google Scholar and the EMBASE, PubMed and CNKI databases. Based on inclusion and exclusion criteria, 5 papers with a total of 12 cohorts were included in this study. RESULTS: The 12 cohorts were integrated, and the results showed that the rs17401966 polymorphism reduced the risk for HCC under the allele, heterozygous, homozygous, and dominant models but not under the additive or recessive models. Moreover, the merged results showed strong heterogeneity, and the cumulative meta-analysis results were unreliable. A genetic differentiation analysis of the 12 cohorts found different degrees of genetic differentiation between the 5 cohorts in Zhang et al.’s study and the cohorts in the other studies. We further divided the 12 study cohorts into 2 subgroups based on fixation index value; however, the results of that analysis were inconsistent. CONCLUSIONS: The results of this meta-analysis were not able to verify the association between the KIF1B rs1740199 polymorphism and HCC risk. Therefore, a well-designed, large-scale, multicenter validation study is needed to confirm the relationship.
Alleles ; Carcinoma, Hepatocellular* ; Case-Control Studies ; Cohort Studies ; Hepatitis B virus* ; Hepatitis B* ; Hepatitis* ; Humans ; Kinesin* ; Polymorphism, Single Nucleotide ; Population Characteristics

Objective To adopt Fst statistical method to assess the heterogeneity sources of meta‐analysis by dichotomous varia‐ble .Methods The case‐control studies on the relationship between KIF1B gene rs17401966 polymorphism and hepatocellular carci‐noma(HCC) risk were collected by retrieving the databases including Google Scholar ,EMBASE ,PubMed ,ISI Web of Knowledge and CNKI .The meta analysis was performed by using the Stata12 .0 software .The genetic differentiation degree among populations was analyzed and researched by using the Arlequin 3 .5 software .Results A total of 5 case‐control studies were finally included ,in‐volving 12 research populations .The meta analysis on 12 populations showed that KIF1B gene rs17401966G allele was negatively correlated with HCC risk (OR=0 .77 ,95% CI:0 .65-0 .93 ;P=0 .005) .However ,the strong heterogeneity existed in this pooled re‐sults .The genetic differentiation test in the included 12 populations found that Zhang′s five research populations had varying de‐grees of genetic differentiation compared to other populations .Then the proper subgroup analysis was further conducted based on Fst value ,and then the I2 value of the heterogeneity test in the group 8 and 9 was descended to less than 25% .However ,the meta analysis results of the group 8 and 9 were inconsistent .Conclusion This study showed that conducting the meta‐analysis of KIF1B gene rs17401966 polymorphism and the HCC risk can find the heterogeneity sources of meta‐analysis by conducting the genetic dif‐ferentiation test in the included population .

OBJECTIVETo investigate the association of single nucleotide polymorphisms in the HLA-DP and DQ genes with the outcome of chronic hepatitis B virus infection.

METHODSTwo hundred and four healthy subjects, 255 clearance subjects, 204 asymptomatic HBV carriers (AsC), 136 chronic hepatitis B (CHB), 68 liver cirrhosis (LC) and hepatocellular carcinoma (HCC) were enrolled. Genotypes of rs3077, rs9277535 and rs2647050 were determined by sequence specific primers-PCR (PCR-SSP).

RESULTSBy using healthy subjects and clearance subjects as the control groups, rs3077 and rs9277535 were significantly associated with chronic HBV infection under additive and dominant models (P< 0.05). Meanwhile, haplotypes GGA, AGA, AAA appeared to be protective factors against chronic HBV infection (P < 0.05). By using AsC as the control group, comparison with the CHB, LC and HCC groups showed no association of the 3 SNPs or haplotypes with the clinical outcome (P > 0.05).

CONCLUSIONHLA-DP gene polymorphisms are strongly associated with chronic HBV infection. The presence of A allele at rs3077 and rs9277535 of the HLA-DP gene may decreased the risk for chronic HBV infection.

Adult ; Asian Continental Ancestry Group ; ethnology ; genetics ; Case-Control Studies ; China ; ethnology ; Female ; Genotype ; HLA-DP Antigens ; genetics ; HLA-DQ Antigens ; genetics ; Hepatitis B virus ; physiology ; Hepatitis B, Chronic ; ethnology ; genetics ; virology ; Humans ; Male ; Middle Aged ; Polymorphism, Single Nucleotide