OBJECTIVE To establish the an optimal prediction model for carbapenem-resistant Acinetobacter bau-mannii(CRAB)infection in ICU patients based on machine learning(ML)so as to help clinicians to diagnose and make decisions.METHODS The clinical data were collected from the patients who were hospitalized in ICUs of a three-A hospital from Jan.1,2017 to Dec.31,2024 and were randomly divided into the training set and the test set in a 7∶3 ratio.The characteristic variables were selected by means of LASSO regression analysis in combina-tion with multivariate logistic regression analysis.Five types of M L classification models were integrated,the opti-mal model was analyzed and identified.The performance of the prediction model for CRAB infection in the ICU patients was evaluated with the use of sensitivity,specificity,accuracy,areas under receiver operating characteris-tic curves(AUCs),calibration curves,Hosmer-Lemeshow test and decision curve analysis(DCA).The outputs of the ML models were interpreted by Shapley additive explanations(SHAP)and permutation importance.RESULTS A total of 2 904 patients were enrolled in the study,695(23.93％)of whom had CRAB infection.The AUC of XGBoost model was highest in the training set and the test set,respectively(0.994 and 0.907).The result of Hosmer-Lemeshow test showed that the calibration curves of the XGBoost model indicated that the predicated risk was highly con-sistent with the observed risk(x2=7.323 and 4.609,P=0.513 and 0.764,respectively).The DCA curves showed that the XGBoost model performed best within the whole range of threshold,with the highest net profit.The length of ICU stay,use of tigecycline,central venous catheterization,use of carbapenems and use of ventilator were determined as the major predictive factors by means of SHAP.CONCLUSIONS The XGBoost model is established and interpreted by SHAP.It provides bases for screening of the ICU patients at high risk of CRAB infection.

OBJECTIVE To establish the an optimal prediction model for carbapenem-resistant Acinetobacter bau-mannii(CRAB)infection in ICU patients based on machine learning(ML)so as to help clinicians to diagnose and make decisions.METHODS The clinical data were collected from the patients who were hospitalized in ICUs of a three-A hospital from Jan.1,2017 to Dec.31,2024 and were randomly divided into the training set and the test set in a 7∶3 ratio.The characteristic variables were selected by means of LASSO regression analysis in combina-tion with multivariate logistic regression analysis.Five types of M L classification models were integrated,the opti-mal model was analyzed and identified.The performance of the prediction model for CRAB infection in the ICU patients was evaluated with the use of sensitivity,specificity,accuracy,areas under receiver operating characteris-tic curves(AUCs),calibration curves,Hosmer-Lemeshow test and decision curve analysis(DCA).The outputs of the ML models were interpreted by Shapley additive explanations(SHAP)and permutation importance.RESULTS A total of 2 904 patients were enrolled in the study,695(23.93％)of whom had CRAB infection.The AUC of XGBoost model was highest in the training set and the test set,respectively(0.994 and 0.907).The result of Hosmer-Lemeshow test showed that the calibration curves of the XGBoost model indicated that the predicated risk was highly con-sistent with the observed risk(x2=7.323 and 4.609,P=0.513 and 0.764,respectively).The DCA curves showed that the XGBoost model performed best within the whole range of threshold,with the highest net profit.The length of ICU stay,use of tigecycline,central venous catheterization,use of carbapenems and use of ventilator were determined as the major predictive factors by means of SHAP.CONCLUSIONS The XGBoost model is established and interpreted by SHAP.It provides bases for screening of the ICU patients at high risk of CRAB infection.

BACKGROUND:A number of studies have shown that there are many inducible methods by which bone marrow mesenchymal stem cel s can differentiate into hepatocytes, but the specific molecular mechanism is unclear yet. OBJECTIVE:To review the programs and underlying mechanisms by which bone marrow mesenchymal stem cel s differentiate into hepatocytes. METHODS:A computer-based online search of CNKI, VIP, WanFang and PubMed databases was performed to retrieve articles about directional differentiation of bone marrow mesenchymal stem cel s into hepatocytes published between 2004 and 2015. The key words were“hepatocyte (-like) cel s, bone marrow mesenchymal stem cel s, differentiation”in Chinese and English, respectively. Final y, 62 articles were included in result analysis. RESULTS AND CONCLUSION:There are many programs for hepatic differentiation of bone marrow mesenchymal stem cel s, but the specific molecular mechanism is stil unclear. Many studies mainly focus on Notch signaling pathway, Wnt/β-catenin signaling pathway, P38 signal pathway, miR-122 and effect of calcium ions. Bone marrow mesenchymal stem cel s that can be induced to differentiate into mature hepatocytes provide an ideal cel ular source for hepatocyte transplantation and artificial liver, which is proposed to be a new strategy for clinical treatment of end-stage liver disease.

OBJECTIVETo explore the once sampling quantitation of Houttuynia cordata through its DNA polymorphic bands that carried information entropy, from other form that the expression of traditional Chinese medicine polymorphism, genetic polymorphism, of traditional Chinese medicine.

METHODThe technique of inter simple sequence repeat (ISSR) was applied to analyze genetic polymorphism of H. cordata samples from the same GAP producing area, the DNA genetic bands were transformed its into the information entropy, and the minimum once sampling quantitation with the mathematical mode was measured.

RESULTOne hundred and thirty-four DNA bands were obtained by using 9 screened ISSR primers to amplify from 46 strains DNA samples of H. cordata from the same GAP, the information entropy was H=0.365 6-0.978 6, and RSD was 14.75%. The once sampling quantitation was W=11.22 kg (863 strains).

CONCLUSIONThe "once minimum sampling quantitation" were calculated from the angle of the genetic polymorphism of H. cordata, and a great differences between this volume and the amount from the angle of fingerprint were found.

Amplified Fragment Length Polymorphism Analysis ; DNA, Plant ; Genetic Variation ; Houttuynia ; genetics ; Microsatellite Repeats ; genetics

OBJECTIVETo study the genetic polymorphisms of methylenetetrahydrofolate reductase (MTHFR) among the Han, Buyi and Miao populations in Guizhou and to provide genetic data for establishment of the genetic polymorphism bank of Guizhou Minorities.

METHODSThe technique of polymerase chain reaction and restriction fragment length polymorphism (PCR-RFLP) was used to detect the genotype and allele frequencies at two mononucleotide sites (677 and 1298) of MTHFR among the Han population in Libo county, the Buyi population in Libo county and the Miao population in Leishan county.

RESULTSAt the site of 677, the T allele frequencies were found to be 22.8%, 16.1%, 10.6%, for the Han, Buyi, Miao populations respectively. At the site of 1298, the C allele frequencies were 28.9%, 39.1%, 48.7% for the Han, Buyi, Miao populations respectively. The frequencies for the combined heterozygote of 677CT/1298AC were 16.66%, 22.7%, 11.1% for the three populations respectively. Moreover, one case with combined homozygote of 677TT/1298CC was seen in the Miao population.

CONCLUSIONThe polymorphisms of the two mononucleotide sites (677 and 1298) of MTHFR are diverse in different populations. The C allele frequencies at the site of MTHFR 1298 of the Miao population in Leishan county and the Buyi population in Libo county are high, and the C allele frequency in the Miao population is higher than those hitherto reported in literature.

Base Sequence ; China ; Female ; Gene Frequency ; Genotype ; Humans ; Male ; Methylenetetrahydrofolate Reductase (NADPH2) ; genetics ; Polymerase Chain Reaction ; Polymorphism, Genetic ; genetics ; Polymorphism, Restriction Fragment Length ; Sequence Analysis, DNA