Objective To evaluate the efficacy of Puerariae lobatae radix （PLR） and Anemarrhenae Rhizoma （AR） in preventing and treating Alzheimer’s disease （AD） and explore its potential mechanism of action by LC-MS serum metabolomics strategy. Methods The AD rat model was established by administering aluminum chloride （AlCl₃） and D-galactose （D-gal） for 20 weeks. The traditional Chinese medicine intervention group was given the PLR, AR, and PLR-AR extracts for 8 weeks by gavage. The model effect and efficacy were evaluated by Morris water maze test and biochemical indicators including SOD, NO, and MDA; Metabolomics research based on the UHPLC-Q/TOF-MS method was conducted, and relevant metabolic pathways were analyzed through the MetaboAnalyst online website. Results The learning and memory abilities of AD model rats were significantly decreased compared with the control group, and the levels of oxidative stress and lipid peroxides were significantly increased （P<0.05）, while the SOD content was decreased considerably （P<0.01）. The learning and memory abilities of AD model rats were improved, oxidative stress and lipid peroxidation levels were reversed, and serum SOD content was increased significantly after the intervention of PLR-AR, with better effects than single drugs. Through metabolomics, 70 differential metabolites were identified between the AD model group and the control group, mainly involving 10 pathways, including phenylalanine, tyrosine, and tryptophan biosynthesis, phenylalanine metabolism, and unsaturated fatty acid biosynthesis, et.al. The intervention of PLR-AR could adjust 47 metabolites, with 20 metabolites showing significant differences （P<0.05）. The significantly adjusted metabolites involve 6 pathways, including phenylalanine, tyrosine, and tryptophan biosynthesis, et al. Conclusion The combination of PLR and AR could significantly improve the learning and memory abilities of AD rat models. The mechanism may be related to the improvement of oxidative stress and lipid peroxidation levels, the increase of serum SOD content, and the regulation of phenylalanine, tyrosine, and tryptophan biosynthesis pathways.

OBJECTIVE: To investigate the relationship between physical activity and genetic risk and their combined effects on the risk of developing chronic obstructive pulmonary disease. METHODS: This prospective cohort study included 318,085 biobank participants from the UK. Physical activity was assessed using the short form of the International Physical Activity Questionnaire. The participants were stratified into low-, intermediate-, and high-genetic-risk groups based on their polygenic risk scores. Multivariate Cox regression models and multiplicative interaction analyses were used. RESULTS: During a median follow-up period of 13 years, 9,209 participants were diagnosed with chronic obstructive pulmonary disease. For low genetic risk, compared to low physical activity, the hazard ratios ( HRs) for moderate and high physical activity were 0.853 (95% confidence interval [ CI]: 0.748-0.972) and 0.831 (95% CI: 0.727-0.950), respectively. For intermediate genetic risk, the HRs were 0.829 (95% CI: 0.758-0.905) and 0.835 (95% CI: 0.764-0.914), respectively. For participants with high genetic risk, the HRs were 0.809 (95% CI: 0.746-0.877) and 0.818 (95% CI: 0.754-0.888), respectively. A significant interaction was observed between genetic risk and physical activity. CONCLUSION Moderate or high levels of physical activity were associated with a lower risk of developing chronic obstructive pulmonary disease across all genetic risk groups, highlighting the need to tailor activity interventions for genetically susceptible individuals.
Humans ; Pulmonary Disease, Chronic Obstructive/epidemiology* ; Exercise ; Male ; Female ; Middle Aged ; Prospective Studies ; Aged ; Genetic Predisposition to Disease ; Risk Factors ; United Kingdom/epidemiology* ; Incidence ; Adult

Objective:To investigate the awareness of Chinese gastroenterologists regarding the treatment targets of inflammatory bowel disease (IBD) as defined by the STRIDE-Ⅱ consensus and to identify challenges encountered in clinical practice.Methods:A nationwide cross-sectional survey was conducted via an online questionnaire distributed to gastroenterologists between February 16 and March 31, 2024. The survey encompassed demographic data, recognition and adherence to the STRIDE-Ⅱ consensus, perceived importance of IBD treatment targets, and clinical challenges.Results:A total of 203 valid questionnaires were collected from 28 provinces (autonomous regions/municipalities). The majority of participants (177/203, 87.2%) reported routinely referencing the STRIDE-Ⅱ consensus in IBD management. Over 90% of physicians identified endoscopic mucosal healing as the core treatment target. Additionally, approximately 80% emphasized the importance of clinical remission, endoscopic remission, quality-of-life improvement, disability prevention, and normalization of growth in pediatric patients. The challenges reported by more than half of physicians in acheiving the goals of the treatment process included lack of a standardized definition for mucosal healing (69.5%), management of psychological comorbidities (89.2%), drug failure (74.4%), penetrating Crohn's disease (72.4%), perianal fistulizing Crohn's disease (65.0%), and IBD management during pregnancy (61.1%).Conclusions:Chinese gastroenterologists demonstrate high awareness and adherence to the STRIDE-Ⅱ consensus, prioritizing mucosal healing as the cornerstone of IBD management. However, numerous challenges remain in clinical practice, particularly regarding operational definitions of therapeutic endpoints and complex disease phenotypes.

Objective:To investigate the effects of ascites grading and the application of non-selective beta-blockers (NSBBs) on the 1-year prognosis of acute-on-chronic liver failure (ACLF).Methods:1 386 ascitic cases with ACLF were graded and followed up for one year. The 1-year prognostic effect of ascites grade and NSBBs was analyzed on ACLF by the Kaplan Meier Log-rank test, Cox stepwise regression, and multivariate regression.The t-test, Mann-Whitney U, or Kruskal-Wallis test were used for intergroup comparison of measurement data. The χ2 test was used for intergroup comparison of numerical data. Results:The incidence rate of ascites at admission was 77.56% in 1 386 ACLF cases. The Log-rank (Mantel-Cox) of the 1-year survival curve test for 1 386 ACLF patients with ascites grade was 21.384, P<0.01. Multivariate regression and Cox stepwise regression analysis showed that ascites grade, age, gastrointestinal bleeding, pulmonary infection, acute kidney injury, prothrombin activity (PTA), urea, MELD-Na score, and the use of NSBBs were closely related to the 1-year prognosis of ACLF. The log rank (Mantel-Cox) of NSBBs treatment in the grade 2/3 ascites group was 6.113, P=0.013, and the difference was statistically significant, suggesting that NSBBs treatment can help improve the 1-year survival rate in ACLF patients with grade 2 and 3 ascites. Conclusions:Ascites grading and the use of NSBBs affect the prognostic factor of ACLF at one year. NSBBs may be beneficial for the long-term prognosis of ACLF, and treatment can be continued in patients who have already received NSBBs prior to the onset of ACLF.

Objective To observe the value of spectral CT material separation technology for diagnosing traumatic bone marrow edema in limbs.Methods Totally 51 patients with limb traumatic bone marrow edema were retrospectively enrolled and divided into young group(n=26,18-43 years)and middle-aged group(n=25,46-74 years).Taken MRI as reference standard,the efficacy of spectral CT Water-hydroxyapatite(HAP)image for diagnosing bone marrow edema in trauma area was analyzed,and the Water-HAP density values were compared between groups.Results No significant difference of diagnosing bone marrow edema was found between spectral CT and MRI(x2=0.201,P=0.654),and the consistency was high(Kappa=0.774).Water-HAP density value in bone marrow edema area was higher than that in non bone marrow edema area(t=24.634,P＜0.05),and no significant difference of Water-HAP density values in bone marrow edema area nor non bone marrow edema area was found between young group and middle-aged group(both P＞0.05).Conclusion Spectral CT material separation technology was helpful for diagnosing traumatic bone marrow edema in limbs.

With the rapid development of competitive sports, the incidence of anterior cruciate ligament (ACL) injury is on the rise. Such injuries may shorten athletes′ career and lead to other long-term adverse consequences. Although athletes generally recover well after ACL reconstruction, many still struggle to return to their pre-injury performance levels. Advances in the understanding of ACL anatomy and injury mechanisms, along with the evolution of surgical techniques and rehabilitation methods, have provided more individualized and tailored options for athletes following ACL injuries. However, there is currently no consensus in China regarding surgical and rehabilitation strategies for competitive athletes aiming to return to sports after ACL injuries. To this end, the Sports Medicine Committee of the Chinese Research Hospital Association and the Editorial Board of the Chinese Journal of Trauma jointly formulated the Expert consensus on surgical treatment and rehabilitation for competitive sports athletes returning to sports after anterior cruciate ligament injury ( version 2025), and presented 14 recommendations covering surgical indications, preoperative rehabilitation, surgical timing, surgical strategies and postoperative rehabilitation strategies, aiming to improve the surgical treatment and rehabilitation system for ACL injuries in competitive athletes and facilitate their return to high-level sports performance after injury.

Objective:To document the effectiveness of ultrasound-guided genicular nerve block (GNB) in treating knee osteoarthritis (KOA).Methods:A total of 92 KOA patients were randomly divided into an observation group and a control group with 46 in each. Those in both groups were treated conventionally, including with non-steroidal anti-inflammatory drugs, acupuncture, ultrasound, laser irradiation and manipulation therapy. The observation group additionally underwent ultrasound-guided genicular nerve block treatment, once a week for 2 weeks. Pain scoring on a visual analog scale (VAS), the Western Ontario and McMaster University Osteoarthritis Index (WOMAC) and a 6-minute walk test (6MWT) were used to evaluate everyone before and after the treatment, and then 8 weeks later.Results:In the observation group the average VAS rating [(3.54±2.00) at week 2 and (4.13±2.04) at week 8] and the average WOMAC subscale and total scores [(36.91±16.91) at week 8] had improved significantly right after the experiment and 8 weeks later. But in the control group this was true only right after the treatment. The observation group also demonstrated superior improvements in 6MWT distance at week 2 [(434.22±125.19)m] and week 8 [(446.35±126.45)m] compared to both its own baseline and the control group.Conclusions:Ultrasound-guided genicular nerve block is a rapid, precise, effective, and long-lasting intervention for alleviating pain, improving knee function and enhancing walking endurance in KOA patients.

Objective:To investigate the feasibility of the bortezomib, lenalidomide, and dexamethasone (VRD) regimen combined with autologous hematopoietic stem cell transplantation (auto-HSCT) in patients with multiple myeloma (MM) and renal impairment, analyze treatment efficacy and renal responses stratified based on renal dysfunction severity, and explore the prognostic significance of early renal response and its affecting factors.Methods:This retrospective study, conducted at the First Affiliated Hospital of Soochow University, categorized 316 patients with newly diagnosed MM (NDMM) from August 2018 to October 2022 based on renal function for analysis of clinical characteristics, treatment response, and prognosis. Continuous variables were compared using t-tests or Mann-Whitney U tests, categorical variables utilizing Chi-square tests, survival outcomes employing Kaplan-Meier and Log-rank tests, and renal response predictors with logistic regression.Results:Patients were stratified based on baseline estimated glomerular filtration rate (eGFR) : normal [≥90 ml·min -1· (1.73 m 2) -1, n=160], mild [≥60 ml·min -1· (1.73 m 2) -1 to <90 ml·min -1· (1.73 m 2) -1, n=55], moderate [≥30 ml·min -1· (1.73 m 2) -1 to <60 ml·min -1· (1.73 m 2) -1, n=39], and severe impairment [<30 ml·min -1· (1.73 m 2) -1, n=62]. Moderate and severe renal impairment correlated with advanced International Staging System/Revised International Staging System classification, lower hemoglobin levels, frailty, and higher light-chain/IgD subtype prevalence ( P<0.05). Despite younger age ( P=0.001) and higher transplant rates ( P=0.041) in severe cases, overall response rates ( ORR: 93.7% ; ≥VGPR: 82.9% ) were comparable across groups ( P>0.05). Among 24 dialysis-dependent patients at diagnosis, 11 (45.8% ) achieved dialysis independence after induction [median: 3.0 (0.5–4.0) months], including 10 undergoing auto-HSCT. In 89 evaluable patients [baseline eGFR <50 ml·min -1· (1.73 m 2) -1], renal ORR (RORR) was 70.8% [rapid complete response: 31.5% ; rapid partial response: 11.2% ; rapid minimal response (RMR) : 28.1% ]. Renal response predicted better survival (overall survival: HR=0.36, 95% CI: 0.13–0.99, P=0.049). Moderate-to-severe renal impairment was associated with increased transplant-related adverse events and delayed engraftment ( P<0.05) ; however, auto-HSCT significantly improved outcomes after 33.5-month median follow-up (range: 2–65 months). Multivariate analysis identified 1q21+ ( OR=3.58, 95% CI: 1.17–11.02, P=0.026) and light-chain subtype ( OR=2.86, 95% CI: 1.08–7.69, P=0.036) as independent predictors of poor renal response. Conclusion:VRD regimen plus auto-HSCT demonstrates robust efficacy in NDMM, including patients with renal impairment, with a 70.8% RORR and manageable toxicity. Achieving ≥RMR correlates with superior prognosis, whereas 1q21+ and light-chain subtype independently predict inferior renal response.

Objective To develop and validate a machine learning-based predictive model to assess the in-hospital mortality risk of patients with myocardial infarction(MI)complicated by heart failure(HF)undergoing percutaneous coronary intervention(PCI).Methods This retrospective study analyzed MI patients with HF who underwent PCI at The First Affiliated Hospital of Xinjiang Medical University from January 2019 to January 2023.Patient data,including demographic characteristics,vital signs,laboratory test results,imaging parameters and medication use,were collected and randomly divided into a training set(70％)and a validation set(30％).The extreme gradient boosting(XGBoost)model was used to identify variables significantly associated with in-hospital mortality,and the Shapley additive explanations(SHAP)model was applied to assess feature importance.A predictive model was then constructed using univariate and multivariate Logistic regression analyses.Model performance was evaluated using receiver operating characteristic(ROC)curves,area under the curve(AUC)values,calibration curves,and decision curve analysis.Finally,a nomogram was developed for intuitive risk assessment.Results A total of 1 214 MI patients with HF were included in the study,with a median age of 64 years.The in-hospital mortality rate was 7.41％(90 deaths).XGBoost feature selection identified ten key predictive variables:age,myoglobin,albumin,fasting blood glucose,N-terminal pro-B-type natriuretic peptide(NT-proBNP),diabetes mellitus,creatinine,cystatin C,procalcitonin,and left ventricular ejection fraction.Based on these variables,a Logistic regression model was developed,with seven final predictors:age,diabetes mellitus,creatinine,fasting blood glucose,cystatin C,NT-proBNP,and albumin.The model demonstrated high predictive accuracy,with AUC value of 0.869(95％CI:0.84-0.89)in the training set and 0.827(95％CI:0.79-0.85)in the validation set.The calibration curve indicated that the predicted probabilities were consistent with the actual observed outcomes,and decision curve analysis showed that the model had a high net benefit across various decision thresholds.Conclusion This study developed a machine learning-based predictive model incorporating Logistic regression to assess the in-hospital mortality risk of MI patients with HF undergoing PCI.The model demonstrated high predictive performance and clinical utility.The nomogram derived from this model provides an intuitive tool for individualized risk assessment,aiding clinicians in the early identification of high-risk patients,optimizing intervention strategies,and improving patient outcomes.

Objective To construct and validate machine learning-based models to predict the risk of acute kidney injury(AKI)following percutaneous coronary intervention(PCI)in patients with acute ST-segment elevation myocardial infarction(STEMI).Methods A total of 2 315 STEMI patients who underwent PCI between January 2020 and June 2023 were included;306(13.2％)of them developed AKI.Baseline variables were screened using LASSO regression,with the optimal λ value selected via 10-fold cross-validation to identify AKI-associated features.Subsequently,eight distinct machine learning models were constructed and evaluated for their predictive performance.SHAP value analysis was employed to assess the impact of key variables on model predictions.Results LASSO regression identified seven variables significantly associated with AKI,including age,multivessel disease,preoperative creatinine,heart failure,white blood cell count,hemoglobin,and albumin levels.Among all the models,the light gradient boosting machine(LGBM)and extreme gradient boosting(XGB)demonstrated the best predictive performance,with training set AUCs being 0.899(95％CI:0.877-0.921)and 0.893(95％CI:0.868-0.918),and validation set AUCs being 0.809(95％CI:0.763-0.856)and 0.871(95％CI:0.833-0.909),respectively.SHAP analysis revealed that albumin,age,preoperative creatinine,and white blood cell count were the primary contributors to AKI risk.Conclusion This study successfully developed and validated machine learning-based predictive models capable of effectively identifying the risk of AKI following PCI in STEMI patients,thus providing valuable support for clinical decision-making.