Objective To determine the knee joint cartilage thickness using different methods and explore the feasibility of mathematical statistical models of dataset for the prediction of cartilage thickness.Methods A total of 304 patients diagnosed as knee osteoarthritis(OA)combined with varus deformity and undergoing unilateral total knee arthroplasty at the Second Affiliated Hospital of Army Medical University from March 2023 to March 2024 were selected for the study.All patients had complete preoperative and postoperative clinical data.The healthy cartilage at four anatomical sites of patients,including the distal femur lateral condyle,lateral tibial plateau,posterior medial femoral condyle,and posterior lateral femoral condyle were selected,and the knee joint cartilage thickness was determined based on preoperative MRI analysis,robotic navigation system tracing,tissue section of surgical specimen and digital vernier caliper.The baseline indicators of demographics,disease and imaging ffor patients were collected to construct a dataset,and four models of linear regression analysis,principal component analysis,Least Absolute Shrinkage and Selection Operator(LASSO)regression analysis,and K-nearest neighbors(KNN)analysis were established for predicting the accuracy,determination coefficient(R2)and root mean square error(RMSE),and the regression equation for predicting cartilage thickness was established.Results The knee joint cartilage thicknesses determined by preoperative MRI analysis,robotic navigation system tracing,tissue section of surgical specimen had no statistically significant difference with that by digital vernier caliper(P>0.05).The predictive efficiencies of models of linear regression analysis,principal component analysis,and LASSO regression analysis for the knee joint cartilage thickness all failed to meet the expectations(R2<0.3,RMSE>0.03).The predictive effect of KNN model on the cartilage thickness of the distal femur lateral condyle and lateral tibial plateau was not ideal(R2=0.23,RMSE=0.29),while it had potential predictive value(accuracy=0.21,accuracy=0.15).Conclusion The prediction model of knee joint cartilage thickness based on individual parameters has certain scientificity,and the feasibility of KNN model is relatively high.However,due to insufficient sample size and unclear individual parameter weight,the efficiencies of the four established prediction models are not ideal,which fails to provide definite prediction equations.Therefore,the construction scheme of the prediction model still needs to be further optimized.

Objective To investigate the serum amino acid levels in patients with acute schizophrenia(SCZ)and their predictive effect on the improvement of cognitive function after treatment,so as to provide new insights into the clinical intervention of cognitive impairment in SCZ patients.Methods A total of 66 patients with acute SCZ were enrolled(case group-baseline period).Among them,36 cases completed the follow-up after 3 months of standardized treatment(case group-follow-up period);52 healthy controls(HCs)were included.The MATRICS Consensus Cognitive Battery(MCCB)was used to assess the cognitive function of all the participants.Liquid chromatography-tandem mass spectrometry(LC-MS)was employed to detect the concentrations of 18 amino acids in fasting serum of the case group-baseline period and the control group.Independent samples t-test was used to compare serum amino acid levels and cognitive function between the case group-baseline period and the control group.Paired t-test was used to compare the differences in cognitive function between the baseline period and the follow-up period of the case group.Spearman correlation analysis and multivariate linear regression model were used to investigate the correlation between serum amino acid levels at baseline in the case group and the improvement of cognitive function after 3 months of treatment.Results Compared with the control group,the cognitive function of SCZ patients in multiple dimensions at baseline was significantly reduced(P＜0.05).After treatment,the scores of Trail Making Test(TMT),Brief Assessment of Cognition in Schizophrenia:Symbol Coding(BACS),Wechsler Memory Scale-Ⅲ(WMS),and Brief Visuospatial Memory Test-Revised(BVMT)in patients were significantly improved(all P＜0.05).In addition,the levels of proline,methionine,histidine,phenylalanine,arginine,tyrosine,aspartic acid,tryptophan,lysine,and glutamic acid were significantly lower in the case group at baseline than in the control group(all P＜0.05).Among them,the baseline tyrosine level had a significant predictive value for the improvement of TMT(R2=0.136,P=0.029),Neuropsychological Assessment Battery(NAB)(R2=0.339,P＜0.001),and Mayer-Salovey-Caruso Emotional Intelligence Test(MSCEIT)test(R2=0.165,P=0.015).The baseline arginine level had a significant predictive value for the improvement rate of Fluency test(R2=0.113,P=0.048).Conclusion There is a decrease in various amino acid levels in patients with SCZ,and some amino acids can effectively predict the improvement of cognitive function after treatment.

Objective To investigate the serum amino acid levels in patients with acute schizophrenia(SCZ)and their predictive effect on the improvement of cognitive function after treatment,so as to provide new insights into the clinical intervention of cognitive impairment in SCZ patients.Methods A total of 66 patients with acute SCZ were enrolled(case group-baseline period).Among them,36 cases completed the follow-up after 3 months of standardized treatment(case group-follow-up period);52 healthy controls(HCs)were included.The MATRICS Consensus Cognitive Battery(MCCB)was used to assess the cognitive function of all the participants.Liquid chromatography-tandem mass spectrometry(LC-MS)was employed to detect the concentrations of 18 amino acids in fasting serum of the case group-baseline period and the control group.Independent samples t-test was used to compare serum amino acid levels and cognitive function between the case group-baseline period and the control group.Paired t-test was used to compare the differences in cognitive function between the baseline period and the follow-up period of the case group.Spearman correlation analysis and multivariate linear regression model were used to investigate the correlation between serum amino acid levels at baseline in the case group and the improvement of cognitive function after 3 months of treatment.Results Compared with the control group,the cognitive function of SCZ patients in multiple dimensions at baseline was significantly reduced(P＜0.05).After treatment,the scores of Trail Making Test(TMT),Brief Assessment of Cognition in Schizophrenia:Symbol Coding(BACS),Wechsler Memory Scale-Ⅲ(WMS),and Brief Visuospatial Memory Test-Revised(BVMT)in patients were significantly improved(all P＜0.05).In addition,the levels of proline,methionine,histidine,phenylalanine,arginine,tyrosine,aspartic acid,tryptophan,lysine,and glutamic acid were significantly lower in the case group at baseline than in the control group(all P＜0.05).Among them,the baseline tyrosine level had a significant predictive value for the improvement of TMT(R2=0.136,P=0.029),Neuropsychological Assessment Battery(NAB)(R2=0.339,P＜0.001),and Mayer-Salovey-Caruso Emotional Intelligence Test(MSCEIT)test(R2=0.165,P=0.015).The baseline arginine level had a significant predictive value for the improvement rate of Fluency test(R2=0.113,P=0.048).Conclusion There is a decrease in various amino acid levels in patients with SCZ,and some amino acids can effectively predict the improvement of cognitive function after treatment.

Objective To determine the knee joint cartilage thickness using different methods and explore the feasibility of mathematical statistical models of dataset for the prediction of cartilage thickness.Methods A total of 304 patients diagnosed as knee osteoarthritis(OA)combined with varus deformity and undergoing unilateral total knee arthroplasty at the Second Affiliated Hospital of Army Medical University from March 2023 to March 2024 were selected for the study.All patients had complete preoperative and postoperative clinical data.The healthy cartilage at four anatomical sites of patients,including the distal femur lateral condyle,lateral tibial plateau,posterior medial femoral condyle,and posterior lateral femoral condyle were selected,and the knee joint cartilage thickness was determined based on preoperative MRI analysis,robotic navigation system tracing,tissue section of surgical specimen and digital vernier caliper.The baseline indicators of demographics,disease and imaging ffor patients were collected to construct a dataset,and four models of linear regression analysis,principal component analysis,Least Absolute Shrinkage and Selection Operator(LASSO)regression analysis,and K-nearest neighbors(KNN)analysis were established for predicting the accuracy,determination coefficient(R2)and root mean square error(RMSE),and the regression equation for predicting cartilage thickness was established.Results The knee joint cartilage thicknesses determined by preoperative MRI analysis,robotic navigation system tracing,tissue section of surgical specimen had no statistically significant difference with that by digital vernier caliper(P>0.05).The predictive efficiencies of models of linear regression analysis,principal component analysis,and LASSO regression analysis for the knee joint cartilage thickness all failed to meet the expectations(R2<0.3,RMSE>0.03).The predictive effect of KNN model on the cartilage thickness of the distal femur lateral condyle and lateral tibial plateau was not ideal(R2=0.23,RMSE=0.29),while it had potential predictive value(accuracy=0.21,accuracy=0.15).Conclusion The prediction model of knee joint cartilage thickness based on individual parameters has certain scientificity,and the feasibility of KNN model is relatively high.However,due to insufficient sample size and unclear individual parameter weight,the efficiencies of the four established prediction models are not ideal,which fails to provide definite prediction equations.Therefore,the construction scheme of the prediction model still needs to be further optimized.

BACKGROUND: Ischemic heart disease (IHD) represents the most significant disease burden among all cardiovascular diseases (CVDs). The increasing prevalence of metabolic risks in the 21^st century has a profound impact on the disease burden associated with IHD. We analyzed the global, regional, and national burdens of IHD attributable to metabolic risks from 1990 to 2021. METHODS: The data were taken from Global Burden of Disease (GBD) study 2021. Deaths, disability-adjusted life years (DALYs), the average annual percent change (AAPC), age-standardized death rates per 100,000 persons (ASDR) and age-standardized rate per 100,000 persons (ASR) of DALYs ranging from 1990 to 2021, were extracted and stratified according to region, nationality, socio-demographic index (SDI), sex, and age. Additionally, the global future trends were predicted using Nordpred prediction model. RESULTS: Compared to 1990, in 2021, the number of death and DALYs from metabolic risk-attributed IHD increased globally by 67.35% and 59.91%, respectively; whereas ASDR and ASR of DALYs showed a decreasing trend and the most severe impact was observed in male and elderly populations. In addition, the burden of disease showed an inverted V-shaped relationship with SDI from 1990 to 2021. AAPC showed a significant increase in developing countries and a decrease in developed countries. We also analyzed the effects of different risk factors including metabolic risk factors on IHD in different SDI regions and genders. The prediction of future disease burden showed that the number of death and DALYs will keep rising, while ASDR and ASR of DALYs will maintain a certain downward trend. CONCLUSIONS The results of this study highlighted the need for screening and intervention for metabolic risk factors in specific regions and populations, this should call for increased collaboration between developing and developed countries to reduce the burden of disease and improve the prognosis of patients with IHD.

Metabolic dysfunction-associated steatohepatitis (MASH), a severe type of metabolic dysfunction-associated steatotic liver disease (MASLD), is a leading etiology of end-stage liver disease worldwide, posing significant health and economic burdens. microRNA-320 (miR-320), a ubiquitously expressed and evolutionarily conserved miRNA, has been reported to regulate lipid metabolism; however, whether and how miR-320 affects MASH development remains unclear. By performing miR-320 in situ hybridization with RNAscope, we observed a notable downregulation of miR-320 in hepatocytes during MASH, correlating with disease severity. Most importantly, miR-320 downregulation in hepatocytes exacerbated MASH progression as demonstrated that hepatocyte-specific miR-320 deficient mice were more susceptible to high-fat, high-fructose, high-cholesterol diet (HFHC) or choline-deficient, amino acid-defined, high-fat diet (CDAHFD)-induced MASH compared with control littermates. Conversely, restoration of miR-320 in hepatocytes ameliorated MASH-related steatosis and fibrosis by injection of adeno-associated virus 8 (AAV8) carrying miR-320 in different types of diet-induced MASH models. Mechanistic studies revealed that miR-320 specifically regulated fibroblast growth factor 1 (FGF1) production in hepatocytes by inhibiting regulator factor X1 (RFX1) expression. Notably, knockdown of Rfx1 in hepatocytes mitigated MASH by enhancing FGF1-mediated AMPK activation. Our findings underscore the therapeutic potential of hepatic miR-320 supplementation in MASH treatment by inhibiting RFX1-mediated FGF1 suppression.

Objective:To investigate the predictive value of platelet/lymphocyte ratio (PLR) for conversion surgery in patients with small bowel obstruction treated with intestinal obstruction catheter. Methods:The clinical data of patients with intestinal obstruction treated by intestinal obstruction catheter in the 900TH Hospital of Joint Logistics Support the Force were retrospectively collected. Logistic regression analyses were performed to identify independent predictors of conversion surgery. Restricted cubic spline (RCS) curves assessed the dose-response relationship between PLR and conversion surgery. Receiver operating characteristic (ROC) curves assessed the validity of the predictive model. Results:A total of 73 patients with small bowel obstruction treated conservatively with intestinal obstruction catheter were included and 25 (34.2％) patients were referred for surgical intervention after poor outcome of conservative treatment. Multivariate logistic regression analysis showed that PLR was an independent predictor of conversion surgery (P=0.032). The RCS analysis showed a non-linear correlation between a progressively higher risk of conversion surgery as the PLR increased. The ROC curves show that the incorporation of PLR into the prediction model significantly enhances its predictive efficacy (All P<0.05). Conclusion:PLR was found to be significantly associated with conversion surgery in small bowel obstruction patients treated with intestinal obstruction catheter,with a non-linear correlation. Incorporating PLR into the prediction model can significantly enhance the predictive efficacy,thus providing reference value for clinicians' decision-making.