OBJECTIVE To investigate the causal relationships between gut microbiota， blood metabolites and antidepressant treatment response from a genetic perspective， and to assess the potential mediating role of blood metabolites. METHODS This study utilized a two-sample Mendelian randomization （MR） design. Exposure data were derived from four large-scale gut microbiome genome-wide association study （GWAS） datasets and two blood metabolite GWAS datasets. The inverse variance weighted method was used as the primary method to evaluate the causal relationships between gut microbiota， blood metabolites and antidepressant effects. The robustness， heterogeneity and horizontal pleiotropy of the results were evaluated through various sensitivity analyses. Additionally， the false discovery rate （FDR） was applied to correct type Ⅰ errors caused by multiple hypothesis testing. Finally， MR mediation analysis was conducted to test the potential mediating effect of blood metabolites. RESULTS The s_ Bilophila was negatively associated with the effectiveness of antidepressant treatment （ P ＝8.030×10 －5 ， then P ＝0.033 after FDR correction）， and the f_Bacteroidales was positively associated with the effectiveness of antidepressant treatment （ P ＝3.275×10 －4 ， then P ＝0.034 after FDR correction）. Over a hundred blood metabolites were also screened out as being associated with antidepressant response， but after FDR correction， no significant causal relationship was observed. The P value of the mediation effect proportion of blood metabolites in the “gut microbiota-blood metabolites-antidepressant efficacy” pathway was greater than 0.05. CONCLUSIONS The s_ Bilophila may represent a risk factor for antidepressant effects， whereas the f_Bacteroidales may serve as a protective factor for antidepressant effects. The correlation between blood metabolites and antidepressant efficacy is not strong， and no genetic evidence is found to support that the investigated blood metabolites play a key mediating role between the gut microbiota and antidepressant response.

This paper reports a 45-year-old female patient with lung squamous cell carcinoma who received chemotherapy for multiple systemic metastases,and then 171 mg of the immune checkpoint inhibitor serplulimab was added,ivd,d1(21 d as a cycle).After 2 cycles of treatment,the patient developed dizziness and nausea,and tumor brain metastasis was considered.The lung CT showed that the irregular mass shadow in the anterior segment of the upper lobe of the right lung was enlarged compared with the previous one,and MRI of the liver showed patchy abnormal signal in the liver segment Ⅳ.PET-CT showed that the lung,liver,adrenal gland,left groin and multiple bones were all progressed compared with the previous progress.It was considered to be tumor hyperprogression caused by serplulimab.Serplulimab was immediately discontinued and methylprednisolone was given for symptomatic treatment,but the patient still died due to overprogression.The Naranjo's Assessment Scale was used to evaluate the correlation between the tumor progression and serplulimab in this case,and the result was' likely to be related'.This case suggested that,the prognosis of tumor hyperprogression caused by immune checkpoint inhibitors has a poor prognosis,the clinical use of immune checkpoint inhibitors should be alert to this situation,and pay attention to early differential diagnosis and timely treatment to avoid serious consequences.

This paper reports a 45-year-old female patient with lung squamous cell carcinoma who received chemotherapy for multiple systemic metastases,and then 171 mg of the immune checkpoint inhibitor serplulimab was added,ivd,d1(21 d as a cycle).After 2 cycles of treatment,the patient developed dizziness and nausea,and tumor brain metastasis was considered.The lung CT showed that the irregular mass shadow in the anterior segment of the upper lobe of the right lung was enlarged compared with the previous one,and MRI of the liver showed patchy abnormal signal in the liver segment Ⅳ.PET-CT showed that the lung,liver,adrenal gland,left groin and multiple bones were all progressed compared with the previous progress.It was considered to be tumor hyperprogression caused by serplulimab.Serplulimab was immediately discontinued and methylprednisolone was given for symptomatic treatment,but the patient still died due to overprogression.The Naranjo's Assessment Scale was used to evaluate the correlation between the tumor progression and serplulimab in this case,and the result was' likely to be related'.This case suggested that,the prognosis of tumor hyperprogression caused by immune checkpoint inhibitors has a poor prognosis,the clinical use of immune checkpoint inhibitors should be alert to this situation,and pay attention to early differential diagnosis and timely treatment to avoid serious consequences.

Objective:To explore the interrelationship between brain functional networks and features in functional magnetic resonance imaging(fMRI)of patients with Alzheimer's disease(AD),and to construct mixed-function networks(MFN),and apply them in machine learning classification models to improve the accuracy of AD classification.Methods:102 AD patients and 227 healthy subjects in the Alzheimer's Neuroimaging Initiative(ADNI)dataset were retrospectively analyzed.The partial correlation brain network of the blood oxygen level dependent(BOLD)signal was calculated and fused with low-frequency wave amplitude(ALFF),fractional low-frequency wave amplitude(fALFF)and local consistency(ReHo)features to construct MFN.Network topology parameters were extracted,and a variety of machine learning classification models were constructed based on MFN topological parameters,accuracy,precision,recall and area under the curve(AUC)were used to evaluate the predictive efficiency of the models.Results:By constructed MFN and calculated intra group to inter group ratio(IIGR),35 features could be obtained from ALFF,fALFF and ReHo feature topological parameter analysis,after rank sum test and FDR correction,there were statistical differences among 28 features(P＜0.05).The classification results show that,all the five classifiers have high classification performance on the test data set.The accuracy,precision and recall rates of random forest(RF),adaptive lifting algorithm(AdaBoost),guided aggregation algorithm(Bagging)and support vector machine(SVM)were all 99.7％,and the AUC values were up to 100％,99.5％,99.1％and 99.5％,respectively.The accuracy(98.5％),precision(98.5％),recall(98.5％),and AUC(99.1％)of the multi-layer perceptron(MLP)were slightly lower than other models,but remained excellent.It was worth noting that RF has the highest AUC value of all models at 100.0％,while Bagging has the lowest AUC value(99.1％)in the integrated approach.The results of performance comparison show that,MFN classification model can significantly improve the recognition and classification of AD disease,and greatly improve the performance of various indicators of the classifier.The results showed that,MFN classification model was superior to intelligent classification based fusion,DBN-based multitask learning,PVT-TSVM,unsupervised learning and clustering,SVM and SVM of degree 3 polynomial kernel function in key indicators such as accuracy(99.13％),AUC(99.42％),recall rate(99.46％)and specificity(99.42％)with plasma proteins,machine learning algorithms.It was further proved that MFN classification model has good generalization ability and robustness in AD disease classification.Conclusion:The AD classification model constructed based on brain mixed function network topology parameters and machine learning can improve the accuracy of AD classification.

Background: and Purpose This study aimed to investigate the association between residual inflammatory risk (RIR) and vulnerable plaques using high-resolution magnetic resonance imaging (HRMRI) in symptomatic intracranial atherosclerotic stenosis (ICAS). Methods: This retrospective study included 70%–99% symptomatic ICAS patients hospitalized from January 2016 to December 2022. Patients were classified into four groups based on high-sensitivity C-reactive protein (hs-CRP) and low-density lipoprotein cholesterol (LDL-C): residual cholesterol inflammatory risk (RCIR, hs-CRP ≥3 mg/L and LDL-C ≥2.6 mmol/L), RIR (hs-CRP ≥3 mg/L and LDL-C <2.6 mmol/L), residual cholesterol risk (RCR, hs-CRP <3 mg/L and LDL-C ≥2.6 mmol/L), and no residual risk (NRR, hs-CRP <3 mg/L and LDL-C <2.6 mmol/L). Vulnerable plaque features on HRMRI included positive remodeling, diffuse distribution, intraplaque hemorrhage, and strong enhancement. Results: Among 336 included patients, 21, 60, 58, and 197 were assigned to the RCIR, RIR, RCR, and NRR groups, respectively. Patients with RCIR (adjusted odds ratio [aOR], 3.606; 95% confidence interval [CI], 1.346–9.662; P=0.011) and RIR (aOR, 3.361; 95% CI, 1.774–6.368, P<0.001) had higher risks of strong enhancement than those with NRR. Additionally, patients with RCIR (aOR, 2.965; 95% CI, 1.060–8.297; P=0.038) were more likely to have intraplaque hemorrhage compared with those with NRR. In the sensitivity analysis, RCR (aOR, 2.595; 95% CI, 1.201–5.608; P=0.015) exhibited an additional correlation with an increased risk of intraplaque hemorrhage. Conclusion In patients with symptomatic ICAS, RIR is associated with a higher risk of intraplaque hemorrhage and strong enhancement, indicating an increased vulnerability to atherosclerotic plaques.

In August 2025, the American Heart Association, American College of Cardiology, and more than ten other academic organizations jointly released an updated guideline for the prevention, detection, evaluation, and management of high blood pressure in adults. Integrating the latest clinical evidence, the new guideline reflects a risk-based approach centered on "comprehensive blood pressure control" and "lifespan management." It provides more proactive recommendations on hypertension diagnosis criteria, intensive blood pressure-lowering strategies, secondary hypertension screening, antihypertensive pharmacotherapy and device-based therapies. This updated guideline holds significant implications for refining hypertension prevention and management strategies in China.

Objective: To construct and validate a prognostic model for lung cancer based on acrolein-related genes using bioinformatics methods . Methods: Lung cancer datasets GSE30219 and GSE68465 were obtained from the GEO database , and acrolein-related gene sets were retrieved from the CTD database . Differentially expressed genes (DEGs) between cancer and adjacent tissues were identified in the GSE30219 dataset. The intersection of these DEGs and acrolein-related genes was then used to identify candidate genes . Gene set variation analysis ( GSVA) was performed to assess functional alterations based on the intersection genes . A protein-protein interaction (PPI) network was constructed based on the STRING database to identify core hub genes . Subsequently , support vector machine recursive feature elimination (SVM-RFE) and LASSO-Cox regression analyses were employed to develop a prognostic model based on acrolein-related genes , which was independently validated using the GSE68465 dataset. The CIBERSORT algorithm was applied to evaluate the immune cell infiltration characteristics between high- and low-risk groups , and functional enrichment analysis of DEGs between the two groups was conducted to further ex- plore the potential molecular mechanisms underlying the prognostic model . Results : A total of 361 acrolein-related DEGs were identified in lung cancer , and 7 key genes were selected for model construction . Kaplan-Meier survival analysis revealed that patients in the high-risk group had significantly lower survival rates compared to those in the low-risk group (P < 0. 000 1) . Receiver operating characteristic (ROC) curve analysis demonstrated that the mod- el possessed good predictive performance . Moreover , immune infiltration analysis indicated that the risk score was closely associated with multiple immune cell subsets , suggesting a potential role of acrolein-related genes in modula- ting the lung cancer immune microenvironment. Conclusion The prognostic model for lung cancer based on acro- lein-related genes demonstrates significant application value in predicting the prognosis of lung cancer , providing new insights into the potential mechanisms of acrolein in the onset and progression of lung cancer.

Background: and Purpose This study aimed to investigate the association between residual inflammatory risk (RIR) and vulnerable plaques using high-resolution magnetic resonance imaging (HRMRI) in symptomatic intracranial atherosclerotic stenosis (ICAS). Methods: This retrospective study included 70%–99% symptomatic ICAS patients hospitalized from January 2016 to December 2022. Patients were classified into four groups based on high-sensitivity C-reactive protein (hs-CRP) and low-density lipoprotein cholesterol (LDL-C): residual cholesterol inflammatory risk (RCIR, hs-CRP ≥3 mg/L and LDL-C ≥2.6 mmol/L), RIR (hs-CRP ≥3 mg/L and LDL-C <2.6 mmol/L), residual cholesterol risk (RCR, hs-CRP <3 mg/L and LDL-C ≥2.6 mmol/L), and no residual risk (NRR, hs-CRP <3 mg/L and LDL-C <2.6 mmol/L). Vulnerable plaque features on HRMRI included positive remodeling, diffuse distribution, intraplaque hemorrhage, and strong enhancement. Results: Among 336 included patients, 21, 60, 58, and 197 were assigned to the RCIR, RIR, RCR, and NRR groups, respectively. Patients with RCIR (adjusted odds ratio [aOR], 3.606; 95% confidence interval [CI], 1.346–9.662; P=0.011) and RIR (aOR, 3.361; 95% CI, 1.774–6.368, P<0.001) had higher risks of strong enhancement than those with NRR. Additionally, patients with RCIR (aOR, 2.965; 95% CI, 1.060–8.297; P=0.038) were more likely to have intraplaque hemorrhage compared with those with NRR. In the sensitivity analysis, RCR (aOR, 2.595; 95% CI, 1.201–5.608; P=0.015) exhibited an additional correlation with an increased risk of intraplaque hemorrhage. Conclusion In patients with symptomatic ICAS, RIR is associated with a higher risk of intraplaque hemorrhage and strong enhancement, indicating an increased vulnerability to atherosclerotic plaques.

Background: and Purpose This study aimed to investigate the association between residual inflammatory risk (RIR) and vulnerable plaques using high-resolution magnetic resonance imaging (HRMRI) in symptomatic intracranial atherosclerotic stenosis (ICAS). Methods: This retrospective study included 70%–99% symptomatic ICAS patients hospitalized from January 2016 to December 2022. Patients were classified into four groups based on high-sensitivity C-reactive protein (hs-CRP) and low-density lipoprotein cholesterol (LDL-C): residual cholesterol inflammatory risk (RCIR, hs-CRP ≥3 mg/L and LDL-C ≥2.6 mmol/L), RIR (hs-CRP ≥3 mg/L and LDL-C <2.6 mmol/L), residual cholesterol risk (RCR, hs-CRP <3 mg/L and LDL-C ≥2.6 mmol/L), and no residual risk (NRR, hs-CRP <3 mg/L and LDL-C <2.6 mmol/L). Vulnerable plaque features on HRMRI included positive remodeling, diffuse distribution, intraplaque hemorrhage, and strong enhancement. Results: Among 336 included patients, 21, 60, 58, and 197 were assigned to the RCIR, RIR, RCR, and NRR groups, respectively. Patients with RCIR (adjusted odds ratio [aOR], 3.606; 95% confidence interval [CI], 1.346–9.662; P=0.011) and RIR (aOR, 3.361; 95% CI, 1.774–6.368, P<0.001) had higher risks of strong enhancement than those with NRR. Additionally, patients with RCIR (aOR, 2.965; 95% CI, 1.060–8.297; P=0.038) were more likely to have intraplaque hemorrhage compared with those with NRR. In the sensitivity analysis, RCR (aOR, 2.595; 95% CI, 1.201–5.608; P=0.015) exhibited an additional correlation with an increased risk of intraplaque hemorrhage. Conclusion In patients with symptomatic ICAS, RIR is associated with a higher risk of intraplaque hemorrhage and strong enhancement, indicating an increased vulnerability to atherosclerotic plaques.