Objective:To investigate the incidence of osteoporosis (OP) in maintenance hemodialysis (MHD) patients at the blood purification center in Gaochun district, and analyze its influencing factors.Methods:A retrospective study was conducted on 3 622 patients who received regular MHD treatment at Nanjing Gaochun People′s Hospital and Nanjing Gaochun Traditional Chinese Medicine Hospital from January 2019 to December 2023. The demographic characteristics, comorbidities, and clinical data such as blood calcium and creatinine of patients were collected. The ultivariate Logistic regression model was applied to analyze the influencing factors of OP in MHD patients.Results:The survey revealed that 33.63% of MHD patients had decreased bone mass, and 37.24% of MHD patients experienced osteoporosis. According to the occurrence of OP, 3 622 patients were separated into the OP group (1 349 cases) and the non-OP group (2 273 cases). Univariate analysis showed that compared with the non-OP group, the albumin (ALB) level in the OP group was lower: (38.95 ± 5.17) g/L vs. (40.32 ± 5.84) g/L, there was statistical difference( P<0.05). Compared with the non-OP group, the levels of immunoreactive parathyroid hormone (iPTH) and alkaline phosphatase (ALP) in the OP group were higher: (262.29 ± 36.76) ng/L vs. (249.55 ± 32.73) ng/L, (114.74 ± 18.01) U/L vs. (109.63 ± 17.25) U/L, the proportion of patients aged≥60 years old, female and dialysis duration≥5 years was higher: 61.75%(833/1 349) vs. 47.87%(1 088/2 273), 66.35%(895/1 349) vs. 54.86%(1 247/2 273), 52.34%(706/1 349) vs. 34.36%(781/2 273), there were statistical differences( P<0.05). Multivariate Logistic regression revealed ALB ( OR = 0.724, 95% CI 0.568 - 0.920), iPTH ( OR = 1.374, 95% CI 1.095 - 1.725), ALP ( OR = 1.325, 95% CI 1.070 - 1.641), age ( OR = 2.753, 95% CI 1.664 - 4.556), gender ( OR = 2.993, 95% CI 1.611 - 5.560), and dialysis time ( OR = 4.216, 95% CI 2.365 - 7.516) were all influencing factors for the occurrence of OP in MHD patients ( P<0.05). Conclusions:The incidence of OP in MHD patients in Gaochun district is high, and its occurrence is closely related to ALB, iPTH, ALP, age, gender and dialysis time. Clinical attention should be focused on this.

OBJECTIVE:Nowadays,machine learning algorithms are gradually being applied to predict stroke and cardiovascular disease.Compared with traditional regression models,machine learning can learn from data to achieve high prediction accuracy by exploring the flexible relationship between a large number of predictive features and outcome variables,providing a new method for the formulation of individualized treatment and rehabilitation programs.This study aims to systematically evaluate stroke functional recovery and prognosis prediction models based on machine learning,comprehensively assessing their predictive performance and clinical application potential to provide references for the development,application,and promotion of related predictive models.METHODS:This review was conducted following the PRISMA(Preferred Reporting Items for Systematic Reviews and Meta-Analyses)guidelines.Relevant literature on stroke prognosis prediction using machine learning methods was selected by searching PubMed,EMbase,Web of Science Core Collection,CNKI,WanFang,and the China Biomedical Literature Database,with the search period from January 1,2014,to July 1,2024.Two researchers independently screened the literature and extracted data based on inclusion and exclusion criteria,using the Prediction model Risk Of Bias ASsessment Tool(PROBAST)to assess model quality.RESULTS:(1)A total of 3 126 articles were obtained in the preliminary search.After screening and exclusion,18 articles were finally included.150 prediction models were constructed using 13 machine learning methods.The three most frequently used methods are Logistic Regression,Random Forest,and Extreme Gradient Boosting(XGBoost).Only one study was externally validated.Eight studies reported how the missing data were handled.(2)In terms of outcome indicators,8 studies used the combination of clinical data and imaging data to build models,9 studies only used clinical data to build models,and 1 study only used imaging data to build models.(3)Each of the 18 studies gave the most important characteristics of the study,with the most mentioned being the National Institute of Health Stroke Scale and age.All studies reported area under curve values ranging from 0.74 to 0.96,with the highest area under curve being 0.96.The overall risk of bias in all models was high.The high risk of bias in the field of model analysis was the main reason for the high risk of overall bias in all models.(4)The results of meta-analysis showed that age and National Institute of Health Stroke Scale score had significant influence on stroke prognosis,with age[MD=8.49,95％CI(6.24,10.75),P＜0.01]and National Institute of Health Stroke Scale score[MD=4.78,95％CI(2.56,7.00),P＜0.01].CONCLUSION:This study systematically evaluated the predictive model of functional recovery and prognosis of stroke based on machine learning,and all the models have good predictive potential.However,future studies should increase the sample size of the included model,adopt prospective studies,and add external validation of the model to improve the stability and prediction accuracy of the model,control the risk of bias,and contribute to the validation and promotion of the model in practical clinical applications.At the same time,the interpolation of missing values is more transparent and accurate.Although existing machine learning models show good predictive performance,it is also important to focus on the functionality and usability of the model,and the inclusion of features will reduce ease of use.We should develop easy to use model interfaces and user-friendly clinical tools to enable medical staff to better apply the model for clinical decision.

Objective To analyze the clinical characteristics associated with prenatal diagnosis of FBN 1 gene mutations in a family.This study explores the correlation between gene mutations and their corresponding clini-cal phenotypes,emphasizing the significance of prenatal diagnosis in providing a foundation for subsequent follow-up and intervention.Methods Genomic DNA was extracted from the amniotic fluid of the fetus and the peripheral blood of the parents for trio-whole exome sequencing.The candidate variant identified was subsequently validated using Sanger sequencing.Results The pedigree comprised four generations and nine family members,with four individuals exhibiting slender limbs and toes.Among these,three showed congenital lens dislocation or subluxation.No abnormalities in the cardiovascular system were observed.Genetic testing of symptomatic individuals revealed a heterozygous mutation(c.6158G>T)in the FBN 1 gene.Conclusions The FBN 1 c.6158G>T(p.C2053F)muta-tion was identified as the pathogenic variant responsible for the condition in this family,exhibiting autosomal domi-nant inheritance.To our knowledge,this is the first reported case of the FBN 1 c.6158G>T(p.C2053F)mutation in China.Prenatal diagnosis can facilitate early confirmation of the condition and provide a foundation for subsequent in-terventions and follow-up care.

Objective To analyze the clinical characteristics associated with prenatal diagnosis of FBN 1 gene mutations in a family.This study explores the correlation between gene mutations and their corresponding clini-cal phenotypes,emphasizing the significance of prenatal diagnosis in providing a foundation for subsequent follow-up and intervention.Methods Genomic DNA was extracted from the amniotic fluid of the fetus and the peripheral blood of the parents for trio-whole exome sequencing.The candidate variant identified was subsequently validated using Sanger sequencing.Results The pedigree comprised four generations and nine family members,with four individuals exhibiting slender limbs and toes.Among these,three showed congenital lens dislocation or subluxation.No abnormalities in the cardiovascular system were observed.Genetic testing of symptomatic individuals revealed a heterozygous mutation(c.6158G>T)in the FBN 1 gene.Conclusions The FBN 1 c.6158G>T(p.C2053F)muta-tion was identified as the pathogenic variant responsible for the condition in this family,exhibiting autosomal domi-nant inheritance.To our knowledge,this is the first reported case of the FBN 1 c.6158G>T(p.C2053F)mutation in China.Prenatal diagnosis can facilitate early confirmation of the condition and provide a foundation for subsequent in-terventions and follow-up care.

Objective:To investigate the incidence of osteoporosis (OP) in maintenance hemodialysis (MHD) patients at the blood purification center in Gaochun district, and analyze its influencing factors.Methods:A retrospective study was conducted on 3 622 patients who received regular MHD treatment at Nanjing Gaochun People′s Hospital and Nanjing Gaochun Traditional Chinese Medicine Hospital from January 2019 to December 2023. The demographic characteristics, comorbidities, and clinical data such as blood calcium and creatinine of patients were collected. The ultivariate Logistic regression model was applied to analyze the influencing factors of OP in MHD patients.Results:The survey revealed that 33.63% of MHD patients had decreased bone mass, and 37.24% of MHD patients experienced osteoporosis. According to the occurrence of OP, 3 622 patients were separated into the OP group (1 349 cases) and the non-OP group (2 273 cases). Univariate analysis showed that compared with the non-OP group, the albumin (ALB) level in the OP group was lower: (38.95 ± 5.17) g/L vs. (40.32 ± 5.84) g/L, there was statistical difference( P<0.05). Compared with the non-OP group, the levels of immunoreactive parathyroid hormone (iPTH) and alkaline phosphatase (ALP) in the OP group were higher: (262.29 ± 36.76) ng/L vs. (249.55 ± 32.73) ng/L, (114.74 ± 18.01) U/L vs. (109.63 ± 17.25) U/L, the proportion of patients aged≥60 years old, female and dialysis duration≥5 years was higher: 61.75%(833/1 349) vs. 47.87%(1 088/2 273), 66.35%(895/1 349) vs. 54.86%(1 247/2 273), 52.34%(706/1 349) vs. 34.36%(781/2 273), there were statistical differences( P<0.05). Multivariate Logistic regression revealed ALB ( OR = 0.724, 95% CI 0.568 - 0.920), iPTH ( OR = 1.374, 95% CI 1.095 - 1.725), ALP ( OR = 1.325, 95% CI 1.070 - 1.641), age ( OR = 2.753, 95% CI 1.664 - 4.556), gender ( OR = 2.993, 95% CI 1.611 - 5.560), and dialysis time ( OR = 4.216, 95% CI 2.365 - 7.516) were all influencing factors for the occurrence of OP in MHD patients ( P<0.05). Conclusions:The incidence of OP in MHD patients in Gaochun district is high, and its occurrence is closely related to ALB, iPTH, ALP, age, gender and dialysis time. Clinical attention should be focused on this.

OBJECTIVE:Nowadays,machine learning algorithms are gradually being applied to predict stroke and cardiovascular disease.Compared with traditional regression models,machine learning can learn from data to achieve high prediction accuracy by exploring the flexible relationship between a large number of predictive features and outcome variables,providing a new method for the formulation of individualized treatment and rehabilitation programs.This study aims to systematically evaluate stroke functional recovery and prognosis prediction models based on machine learning,comprehensively assessing their predictive performance and clinical application potential to provide references for the development,application,and promotion of related predictive models.METHODS:This review was conducted following the PRISMA(Preferred Reporting Items for Systematic Reviews and Meta-Analyses)guidelines.Relevant literature on stroke prognosis prediction using machine learning methods was selected by searching PubMed,EMbase,Web of Science Core Collection,CNKI,WanFang,and the China Biomedical Literature Database,with the search period from January 1,2014,to July 1,2024.Two researchers independently screened the literature and extracted data based on inclusion and exclusion criteria,using the Prediction model Risk Of Bias ASsessment Tool(PROBAST)to assess model quality.RESULTS:(1)A total of 3 126 articles were obtained in the preliminary search.After screening and exclusion,18 articles were finally included.150 prediction models were constructed using 13 machine learning methods.The three most frequently used methods are Logistic Regression,Random Forest,and Extreme Gradient Boosting(XGBoost).Only one study was externally validated.Eight studies reported how the missing data were handled.(2)In terms of outcome indicators,8 studies used the combination of clinical data and imaging data to build models,9 studies only used clinical data to build models,and 1 study only used imaging data to build models.(3)Each of the 18 studies gave the most important characteristics of the study,with the most mentioned being the National Institute of Health Stroke Scale and age.All studies reported area under curve values ranging from 0.74 to 0.96,with the highest area under curve being 0.96.The overall risk of bias in all models was high.The high risk of bias in the field of model analysis was the main reason for the high risk of overall bias in all models.(4)The results of meta-analysis showed that age and National Institute of Health Stroke Scale score had significant influence on stroke prognosis,with age[MD=8.49,95％CI(6.24,10.75),P＜0.01]and National Institute of Health Stroke Scale score[MD=4.78,95％CI(2.56,7.00),P＜0.01].CONCLUSION:This study systematically evaluated the predictive model of functional recovery and prognosis of stroke based on machine learning,and all the models have good predictive potential.However,future studies should increase the sample size of the included model,adopt prospective studies,and add external validation of the model to improve the stability and prediction accuracy of the model,control the risk of bias,and contribute to the validation and promotion of the model in practical clinical applications.At the same time,the interpolation of missing values is more transparent and accurate.Although existing machine learning models show good predictive performance,it is also important to focus on the functionality and usability of the model,and the inclusion of features will reduce ease of use.We should develop easy to use model interfaces and user-friendly clinical tools to enable medical staff to better apply the model for clinical decision.

Objective To investigate the anti-inflammatory effects of Bupi Yichang Pills on mice with experimental colitis and its potential mechanism of action.Methods Dextran sulfate sodium(DSS)was used to model the experimental colitis,and low-,medium-and high-doses of Bupi Yichang Pills(1.5,3.0,6.0 g·kg-1·d-1)and Mesalazine(300 mg·kg-1·d-1)were fed at the same time.Mice were observed for general behavior and weighed.Hematoxylin-eosin staining was used to observe the pathological injury of colonic tissues.qPCR and ELISA were used to detect the levels of inflammatory cytokines(TNF-α,IL-1β,IL-6,IL-10,IL-35 and TGF-β1),qPCR and Western Blot were used to detect the mRNA and protein levels of glucose transporters and glycolytic kinases.Results Low-,medium-and high-doses of Bupi Yichang Pills significantly down-regulated disease activity index in colitis mice(P＜0.05,P＜0.01).The body mass and colon length were significantly increased,while colon mass,colon mass index and unit colon mass index were significantly reduced(P＜0.05,P＜0.01),and ulcer formation and inflammatory cell infiltration in colonic tissue were significantly improved.In addition,medium-and high-doses of Bupi Yichang Pills significantly down-regulated the mRNA levels and concentrations of pro-inflammatory cytokines including TNF-α,IL-1β and IL-6(P＜0.01),while significantly up-regulated the mRNA levels and concentrations of anti-inflammatory cytokines such as IL-10,IL-35 and TGF-β1(P＜0.01).We further found that high-dose of Bupi Yichang Pills significantly down-regulated the mRNA and protein expressions of glucose transporters(Glut1,Glut2,Glut4)and glycolytic kinases(HK2,Aldolase A,PKM2)in colonic tissue(P＜0.01).Conclusions Bupi Yichang Pills effectively alleviates DSS-induced experimental colitis,and its specific mechanism of action is related to the improvement of glycolytic metabolic pathways and the regulation of inflammatory cytokine expression.

Objective:To develop a risk prediction model for identifying bronchopulmonary dysplasia (BPD) associated pulmonary hypertension (PH) in very premature infants.Methods:This was a retrospective cohort study. The clinical data of 626 very premature infants whose gestational age <32 weeks and who suffered from BPD were collected from October 1 st, 2015 to December 31 st, 2021 of the Seventh Medical Center of the People′s Liberation Army General Hospital as a modeling set. The clinical data of 229 very premature infants with BPD of Hunan Children′s Hospital from January 1 st, 2020 to December 31 st, 2021 were collected as a validation set for external verification. The very premature infants with BPD were divided into PH group and non PH group based on the echocardiogram after 36 weeks′ corrected age in the modeling set and validation set, respectively. Univariate analysis was used to compare the basic clinical characteristics between groups, and collinearity exclusion was carried out between variables. The risk factors of BPD associated PH were further screened out by multivariate Logistic regression, and the risk assessment model was established based on these variables. The receiver operating characteristic (ROC) area under curve (AUC) and Hosmer-Lemeshow goodness-of-fit test were used to evaluate the model′s discrimination and calibration power, respectively. And the calibration curve was used to evaluate the accuracy of the model and draw the nomogram. The bootstrap repeated sampling method was used for internal verification. Finally, decision curve analysis (DCA) to evaluate the clinical practicability of the model was used. Results:A total of 626 very premature infants with BPD were included for modeling set, including 85 very premature infants in the PH group and 541 very premature infants in the non PH group. A total of 229 very premature infants with BPD were included for validation set, including 24 very premature infants in the PH group and 205 very premature infants in the non PH group. Univariate analysis of the modeling set found that 22 variables, such as artificial conception, fetal distress, gestational age, birth weight, small for gestational age, 1 minute Apgar score ≤7, antenatal corticosteroids, placental abruption, oligohydramnios, multiple pulmonary surfactant, neonatal respiratory distress syndrome (NRDS)>stage Ⅱ, early pulmonary hypertension, moderate-severe BPD, and hemodynamically significant patent ductus arteriosus (hsPDA) all had statistically significant influence between the PH group and the non PH group (all P<0.05). Antenatal corticosteroids, fetal distress, NRDS >stage Ⅱ, hsPDA, pneumonia and days of invasive mechanical ventilation were identified as predictive variables and finally included to establish the Logistic regression model. The AUC of this model was 0.86 (95% CI 0.82-0.90), the cut-off value was 0.17, the sensitivity was 0.77, and the specificity was 0.84. Hosmer-Lemeshow goodness-of-fit test showed that P>0.05. The AUC for external validation was 0.88, and the Hosmer-Lemeshow goodness-of-fit test suggested P>0.05. Conclusions:A high sensitivity and specificity risk prediction model of PBD associated PH in very premature infants was established. This predictive model is useful for early clinical identification of infants at high risk of BPD associated PH.

Background::Growth differentiation factor-15 (GDF-15) is a stress response protein and is related to cardiovascular diseases (CVD). This study aimed to investigate the association between GDF-15 and pre-eclampsia (PE).Method::The study involved 299 pregnant women, out of which 236 had normal pregnancies, while 63 participants had PE. Maternal serum levels of GDF-15 were measured by using enzyme-linked immunosorbent assay kits and then translated into multiple of median (MOM) to avoid the influence of gestational week at blood sampling. Logistic models were performed to estimate the association between GDF-15 MOM and PE, presenting as odd ratios (ORs) and 95% confidence intervals (CIs).Results::MOM of GDF-15 in PE participants was higher compared with controls (1.588 vs. 1.000, p < 0.001). In the logistic model, pregnant women with higher MOM of GDF-15 (>1) had a 4.74-fold (95% CI= 2.23-10.08, p < 0.001) increased risk of PE, adjusted by age, preconceptional body mass index, gravidity, and parity. Conclusions::These results demonstrated that higher levels of serum GDF-15 were associated with PE. GDF-15 may serve as a biomarker for diagnosing PE.

Background::Growth differentiation factor-15 (GDF-15) is a stress response protein and is related to cardiovascular diseases (CVD). This study aimed to investigate the association between GDF-15 and pre-eclampsia (PE).Method::The study involved 299 pregnant women, out of which 236 had normal pregnancies, while 63 participants had PE. Maternal serum levels of GDF-15 were measured by using enzyme-linked immunosorbent assay kits and then translated into multiple of median (MOM) to avoid the influence of gestational week at blood sampling. Logistic models were performed to estimate the association between GDF-15 MOM and PE, presenting as odd ratios (ORs) and 95% confidence intervals (CIs).Results::MOM of GDF-15 in PE participants was higher compared with controls (1.588 vs. 1.000, p < 0.001). In the logistic model, pregnant women with higher MOM of GDF-15 (>1) had a 4.74-fold (95% CI= 2.23-10.08, p < 0.001) increased risk of PE, adjusted by age, preconceptional body mass index, gravidity, and parity. Conclusions::These results demonstrated that higher levels of serum GDF-15 were associated with PE. GDF-15 may serve as a biomarker for diagnosing PE.