Objective To investigate serum metabolites and metabolic pathways alterations in patients with ischemic stroke(IS)through metabolomic analysis,and to identify reliable serum metabolic biomarkers for IS diagnosis.Methods This prospective study enrolled patients with IS admitted to the Department of Neurology at Xiangcheng People's Hospital of Suzhou from December 1,2022 to December 31,2023.Age-and sex-matched healthy individuals were recruited as controls during the same period.Baseline characteristics were collected,including age,sex,height,body mass index,and blood pressure.Venous blood samples were obtained after an 8 h fast for biochemical analysis of blood glucose,total bilirubin,serum creatinine,urea nitrogen,total cholesterol,triglycerides,high-density lipoprotein cholesterol,and low-density lipoprotein cholesterol.Serum metabolites of both groups were extracted and analyzed using ultra-high-performance liquid chromatography coupled with tandem mass spectrometry.Metabolomic data were processed using Simca-p software for unsupervised principal component analysis(PCA)and orthogonal partial least squares discriminant analysis(OPLS-DA)to evaluate group separation and experimental stability.Differential metabolites were defined by variable importance in projection(VIP)≥1.0,fold change(FC)≥2.0 or ≤0.5,and P＜0.05.Drug-derived exogenous metabolites were excluded by cross-referencing the Human Metabolome Database(HMDB,https://hmdb.ca/)and PubChem(https://pubchem.ncbi.nlm.nih.gov/).MetaboAnalyst 6.0(http://www.metaboanalyst.ca),a comprehensive web-based tool for metabolomic data analysis,was employed to map differential metabolites to the Kyoto encyclopedia of genes and genomes(KEGG)databased and to perform pathway enrichment analysis.Machine learning models were developed using Python.Least absolute shrinkage and selection operator(LASSO)regression and random forest(RF)algorithms were employed to identify diagnostic biomarkers capable of effectively distinguishing IS patients from controls.Metabolites identified by both methods were integrated into an extreme gradient boosting(XGBoost)model.Model performance was evaluated using receiver operating characteristic(ROC)curves with 5-fold cross-validation and internal validation(70％training,30％validation set).Results A total of 51 IS patients and 51 matched controls were included.(1)A total of 1 255 serum metabolites were identified(964 in positive ion mode,291 in negative ion mode).PC A and OPLS-DA demonstrated distinct metabolic separation between IS patients and controls.In IS group,260 metabolites were upregulated and 337 downregulated in positive ion mode;99 were upregulated and 34downregulated in negative ion mode.(2)Among the 1 255 metabolites,259 were identified as differential metabolites based on the criteria of VIP ≥ 1.0,FC≥2.0 or≤0.5 and P＜0.05.After excluding drug-derived metabolite through referencing HMDB and PubChem databases,a total of 220 endogenous differential metabolites were found to coexist in both positive and negative ion modes.Among them,119 metabolites were up-regulated and 101 were down-regulated in the IS group.The expression of these 220 metabolites showed significant differences between the IS and control groups.(3)KEGG pathway analysis highlighted five dysregulated pathways:upregulation of denovo triacylglycerol biosynthesis,glycerophosphate shuttle,and cardiolipin biosynthesis;downregulation of bile acid biosynthesis and methylhistidine metabolism.(4)LASSO and RF algorithms identified 24 and 30 candidate biomarkers,respectively.Four overlapping metabolites were selected:2-((3R)-3-((3R,5S,7S,9S,10S,13R,14S,17R)-3,7-dihydroxy-10,13-dimethylhexadecahydro-1H-cyclopenta[a]phenanthren-17-yl)butanamido)ethane-1-sulfonic acid(m/z 971.571 29),arginine-conjugated cholic acid(m/z 587.379 21),laccaic acid A(m/z 576.010 93)and NCGC00380235-01_C32H48O9_beta-D-xylopyranoside,3,17-dihydroxyspirosta-5,25(27)-dien-1-yl(m/z 559.326 48).The expression levels of 2-((3R)-3-((3R,5S,7S,9S,10S,13R,14S,17R)-3,7-dihydroxy-10,13-dimethylhexadecahydro-1H-cyclopenta[a]phenanthren-17-yl)butanamido)ethane-1-sulfonic acid(m/z 971.571 29),arginine-conjugated cholic acid(m/z 587.379 21),and laccaic acid A(m/z 576.010 93)were upregulated,while the expression level of NCGC00380235-01_C32H48O9_beta-D-xylopyranoside,3,17-dihydroxyspirosta-5,25(27)-dien-1-yl(m/z 559.326 48)was downregulated.An IS diagnostic model was established based on four metabolic biomarkers using the XGBoost algorithm.The area under the ROC curve was 1.000(95％CI 1.000-1.000)in the training set and 0.988 in the validation set(95％CI 0.963-1.000).Conclusions Patients with IS exhibit significant metabolic disturbance.The four identified biomarkers may serve as potential biomarkers for the effective identification of IS:2-((3 R)-3-((3R,5S,7S,9S,10S,13R,14S,17R)-3,7-dihydroxy-10,13-dimethylhexadecahydro-1H-cyclopenta[a]phenanthren-17-yl)butanamido)ethane-1-sulfonic acid(m/z971.571 29),arginine-conjugated cholic acid(m/z587.379 21),laccaic acid A(m/z 576.010 93),and NCGC00380235-01_C32H48O9_beta-D-xylopyranoside,3,17-dihydroxyspirosta-5,25(27)-dien-1-yl(m/z 559.326 48).

Objective To investigate serum metabolites and metabolic pathways alterations in patients with ischemic stroke(IS)through metabolomic analysis,and to identify reliable serum metabolic biomarkers for IS diagnosis.Methods This prospective study enrolled patients with IS admitted to the Department of Neurology at Xiangcheng People's Hospital of Suzhou from December 1,2022 to December 31,2023.Age-and sex-matched healthy individuals were recruited as controls during the same period.Baseline characteristics were collected,including age,sex,height,body mass index,and blood pressure.Venous blood samples were obtained after an 8 h fast for biochemical analysis of blood glucose,total bilirubin,serum creatinine,urea nitrogen,total cholesterol,triglycerides,high-density lipoprotein cholesterol,and low-density lipoprotein cholesterol.Serum metabolites of both groups were extracted and analyzed using ultra-high-performance liquid chromatography coupled with tandem mass spectrometry.Metabolomic data were processed using Simca-p software for unsupervised principal component analysis(PCA)and orthogonal partial least squares discriminant analysis(OPLS-DA)to evaluate group separation and experimental stability.Differential metabolites were defined by variable importance in projection(VIP)≥1.0,fold change(FC)≥2.0 or ≤0.5,and P＜0.05.Drug-derived exogenous metabolites were excluded by cross-referencing the Human Metabolome Database(HMDB,https://hmdb.ca/)and PubChem(https://pubchem.ncbi.nlm.nih.gov/).MetaboAnalyst 6.0(http://www.metaboanalyst.ca),a comprehensive web-based tool for metabolomic data analysis,was employed to map differential metabolites to the Kyoto encyclopedia of genes and genomes(KEGG)databased and to perform pathway enrichment analysis.Machine learning models were developed using Python.Least absolute shrinkage and selection operator(LASSO)regression and random forest(RF)algorithms were employed to identify diagnostic biomarkers capable of effectively distinguishing IS patients from controls.Metabolites identified by both methods were integrated into an extreme gradient boosting(XGBoost)model.Model performance was evaluated using receiver operating characteristic(ROC)curves with 5-fold cross-validation and internal validation(70％training,30％validation set).Results A total of 51 IS patients and 51 matched controls were included.(1)A total of 1 255 serum metabolites were identified(964 in positive ion mode,291 in negative ion mode).PC A and OPLS-DA demonstrated distinct metabolic separation between IS patients and controls.In IS group,260 metabolites were upregulated and 337 downregulated in positive ion mode;99 were upregulated and 34downregulated in negative ion mode.(2)Among the 1 255 metabolites,259 were identified as differential metabolites based on the criteria of VIP ≥ 1.0,FC≥2.0 or≤0.5 and P＜0.05.After excluding drug-derived metabolite through referencing HMDB and PubChem databases,a total of 220 endogenous differential metabolites were found to coexist in both positive and negative ion modes.Among them,119 metabolites were up-regulated and 101 were down-regulated in the IS group.The expression of these 220 metabolites showed significant differences between the IS and control groups.(3)KEGG pathway analysis highlighted five dysregulated pathways:upregulation of denovo triacylglycerol biosynthesis,glycerophosphate shuttle,and cardiolipin biosynthesis;downregulation of bile acid biosynthesis and methylhistidine metabolism.(4)LASSO and RF algorithms identified 24 and 30 candidate biomarkers,respectively.Four overlapping metabolites were selected:2-((3R)-3-((3R,5S,7S,9S,10S,13R,14S,17R)-3,7-dihydroxy-10,13-dimethylhexadecahydro-1H-cyclopenta[a]phenanthren-17-yl)butanamido)ethane-1-sulfonic acid(m/z 971.571 29),arginine-conjugated cholic acid(m/z 587.379 21),laccaic acid A(m/z 576.010 93)and NCGC00380235-01_C32H48O9_beta-D-xylopyranoside,3,17-dihydroxyspirosta-5,25(27)-dien-1-yl(m/z 559.326 48).The expression levels of 2-((3R)-3-((3R,5S,7S,9S,10S,13R,14S,17R)-3,7-dihydroxy-10,13-dimethylhexadecahydro-1H-cyclopenta[a]phenanthren-17-yl)butanamido)ethane-1-sulfonic acid(m/z 971.571 29),arginine-conjugated cholic acid(m/z 587.379 21),and laccaic acid A(m/z 576.010 93)were upregulated,while the expression level of NCGC00380235-01_C32H48O9_beta-D-xylopyranoside,3,17-dihydroxyspirosta-5,25(27)-dien-1-yl(m/z 559.326 48)was downregulated.An IS diagnostic model was established based on four metabolic biomarkers using the XGBoost algorithm.The area under the ROC curve was 1.000(95％CI 1.000-1.000)in the training set and 0.988 in the validation set(95％CI 0.963-1.000).Conclusions Patients with IS exhibit significant metabolic disturbance.The four identified biomarkers may serve as potential biomarkers for the effective identification of IS:2-((3 R)-3-((3R,5S,7S,9S,10S,13R,14S,17R)-3,7-dihydroxy-10,13-dimethylhexadecahydro-1H-cyclopenta[a]phenanthren-17-yl)butanamido)ethane-1-sulfonic acid(m/z971.571 29),arginine-conjugated cholic acid(m/z587.379 21),laccaic acid A(m/z 576.010 93),and NCGC00380235-01_C32H48O9_beta-D-xylopyranoside,3,17-dihydroxyspirosta-5,25(27)-dien-1-yl(m/z 559.326 48).

Objective To investigate the prevalence of hypertension in high temperature,noise operators and related factors.Methods A total of 1 263 workers from a steel enterprise were selected,among them,583 operators contacting with high temperature and noise served as the group A,267 operators only contacting with noise as the group B,249 operators only contacting with high temperature as the group C and 164 operators without contacting with high temperature and noise as group D.The prevalence of hypertension and related factors were analyzed.Results In the analysis of hypertension prevalence rate,the group D (12.8％) ＜ group C (25.7％)＜ group B (34.6％)＜group A(43.9％).In the analysis of blood pressure(systolic pressure/diastolic pressure),the group D[(115.8± 11.5)/(73.6±5.4) mm Hg]＜ group C[(124.1±10.7)/(81.9±7.3) mm Hg]＜ group B[(132.9±11.3)/(86.7±5.2) rnm Hg]＜ group A [(143.7 ± 12.8)/(92.4 ± 6.5) mm Hg].The onset age of hypertension in the group A was earlier than that in the group B,C,D (P＜0.05).The lower educational level,the more working years,the bigger body mass index(BMI),themore smoking and drinking were,the higher the hypertension prevalence rate was.Hypertension had significantly negative correlation with the educational level and significantly positive correlation with age,working years,BMI,smoking and drinking (P＜ 0.05).Conclusion The prevalence of hypertension in high temperature and noise operators is related to personal constitution and living habits.

Objective To investigate the influence of supplemental probiotics on the changes of immunity and microecology in asthmatic children. Methods One hundred and seventy?six asthmatic children treated in the Affiliated Hospital of North China University of Science and Technology from October 2015 to October 2016 were selected in the study and were randomly divided into two groups, 88 cases in each group. Patients in the control group were given routine treatment, and the observation group was treated with routine treatment combined with probiotics. The changes in immune index and microecological index before and after the treatment were compared between the two groups. Results After treatment, the observation showed CD3+ was(65. 8±2. 6)％,CD4+was(39. 2±1. 3)％,CD8+ was(24. 5±1. 0)％,CD4+/CD8+ was(1. 6±0. 2),NK cells was(15.2±0.4)％,Th1/ Th2 was(5.7±1.3),interferon γ was(56.3±1.8)ng/L,bifidobacterium was (9. 3±0. 7)lgCFU/g,lactobacillus was(9. 5±0. 6)lgCFU/g,yeast was(6. 6±0. 8)lgCFU/g,compared with those before treatment ((52. 5±1. 7)％,(23. 6±0. 8)％,(19. 7±0. 9)％,(1. 2±0. 1),(12. 8±0. 3)％,(3. 4±0. 7), (44.0±1.5)ng/L,(4.2±1.1)lgCFU/g,(4.9±0.4)lgCFU/g,(3.7±0.4)lgCFU/g),the differences were statistically significant ( t= 5. 533, 9. 957, 5. 436, 6. 332, 4. 875, 9. 764, 5. 727, 15. 143, 12. 387, 10. 837, P<0. 05). After treatment,in the control group,CD3+ was(60. 1±3. 4)％,CD4+ was(30. 7±1. 2)％,CD8+ was (21.9±1.1)％,CD4+/ CD8+ was(1.4±0.3),NK cells was(14.0±0.3)％,Th1/ Th2 was(4.6±0.9), interferon γ was ( 50. 2 ± 1. 4 ) ng/L, bifidobacterium was ( 7. 6 ± 0. 8 ) lgCFU/g, lactobacillus was ( 8. 1 ± 0. 7 ) lgCFU/g, yeast was ( 4. 9 ± 0. 8 ) lgCFU/g, compared with those before treatment ( ( 52. 4 ± 2. 0 )％, ( 23. 8 ±0. 7)％,(19. 8±0. 6)％,(1. 2±0. 2),(12. 7±0. 2)％,(3. 5±1. 1),(44. 1±1. 3)ng/L,(4. 3±0. 9)lgCFU/g, (5.0±0.5)lgCFU/g,(3.8±0.6)lgCFU/g),the differences were statistically significant(t=4.469,5.899, 4. 061,4. 667,4. 023,6. 143,4. 363,10. 674,9. 201,5. 894,P<0. 05) . The above indexes in observation group were higher than those in the control group ( t=3. 948, 3. 162, 4. 187, 4. 428, 3. 857, 5. 391, 4. 202, 5. 236, 4. 728,6. 469,P<0. 05). After treatment,the observation group showed IgE(139. 4±21. 0)was kU/L,IL?4(30. 2 ±1. 7)was ng/L,IL?10 was(6. 3±0. 8)ng/L,escherichia coli was(4. 8±0. 6)lgCFU/g,streptococcus was(6. 1 ±0.9)lgCFU/g,bacillus was(4.6±0.2)lgCFU/g,staphylococcus was(1.9±0.3)lgCFU/g,enterococcus was (5.2±0.4)lgCFU/g,compared with those before treatment((381.2±49.6)kU/L,(59.4±3.5)ng/L,(13.9 ±1.1)ng/L,(7.1±0.5)lgCFU/g,(8.4±0.6)lgCFU/g,(8.0±0.6)lgCFU/g,(4.0±0.8)lgCFU/g,(7.4 ±0. 8)lgCFU/g),while the differences were statictically significant (t=22. 231,12. 667,15. 063,7. 791,6. 770, 10. 392,16. 523,7. 232,P<0. 05). After treatment,in control group showed IgE was (230. 8±31. 7) kU/L,IL?4 was (41. 3±2. 3)ng/L,IL?10 was (9. 8±0. 7)ng/L,escherichia coli was (5. 9±0. 7)lgCFU/g,streptococcus was (7. 2±1. 0)lgCFU/g,bacillus was (6. 4±0. 8)lgCFU/g,staphylococcus was(2. 7±0. 7)lgCFU/g,enterococcus was (6.1±0.6)lgCFU/g,compared with those before treatment((387.9±54.3)kU/L,(59.6±3.4)ng/L, (13. 7±1. 2)ng/L,(7. 0±0. 4)lgCFU/g,(8. 3±0. 5)lgCFU/g,(8. 1±0. 7)lgCFU/g,(4. 1±1. 0)lgCFU/g,(7. 3 ± 0. 7 ) lgCFU/g ) , while there were significant differences ( t= 9. 826, 7. 390, 6. 979, 4. 864, 4. 527, 5. 656、8. 185,4. 967,P<0. 05). The above indexes in observation group were lower than those in the control group(t=9. 618,6. 713,8. 556,5. 290,4. 803,6. 913,7. 215,4. 731,P<0. 05) . The intestinal flora time was ( 5. 6 ± 1) d,hospitalization time was (10. 2 ± 1. 3) d,hospitalization expenses (3527. 1 ± 403. 2) RMB in the observation group,compared with (10. 7±1. 8)d,(14. 6±2. 1)d,(4689. 4±526. 7)RMB in the control group,the differences between the two groups were statistically significant ( t= 12. 107, 7. 314, 6. 295, P<0. 05 ) . Conclusion Probiotic supplement can improve immune status and microecology status in asthmatic children,which is worthy of clinical use.

Objective To discuss microvessel density (MVD) and expression of vascular endothelial growth factor(VEGF), androgen receptor(AR) in the prostates of men who received re-operation after TURP. Methods Fifty cases were performed re-TURP (re-TURP group) and the remaining 50 cases served as controls. 150 specimens were collected. Sections were stained for CD34 and VEGF, AR by immuno-histo-chemistry(S-P). Statistical analysis of the results was performed using t-test or Pearson Chi-Square test Results The expression of VEGF, AR and MVD were significantly higher in the re-TURP group compared to controls(P<0. 05),but in re-TURP group, difference in VEGF and AR expression as well as MVD were not found to be significantly different between the first and the second TURP(P>0.05). Conclusion Over expression of VEGF and AR as well as high MVD in prostatic tissue might play an important role in the pathological process of BPH after TURP.