BackgroundSchizophrenia， as a common chronic mental disorder， although second-generation antipsychotic drugs have shown significant efficacy in alleviating positive symptoms， the widespread cognitive dysfunction among patients remains a challenge in clinical treatment. Traditional Chinese medicine has unique advantages in the treatment of mental disorders. However， the current clinical research on the combination of Wendan Decoction and antipsychotic drugs for schizophrenia varies in quality， and there is a lack of systematic reviews evaluating its effects on cognitive improvement and safety. ObjectiveTo systematically evaluate the effects of modified Wendan Decoction combined with antipsychotic drugs on schizophrenia symptoms and cognitive improvement， providing evidence-based support for the clinical application of Wendan Decoction. MethodsLiterature searches were conducted in China National Knowledge Infrastructure （CNKI）， Wanfang Data， VIP Information， China Biomedical Literature Service System， China Clinical Trial Registry， PubMed， Web of Science， the Cochrane Library， and Embase to collect randomized controlled trials （RCTs） of modified Wendan Decoction combined with antipsychotic drugs for the treatment of schizophrenia. The search period was from the establishment of the databases to March 19， 2026. The quality of the included literature was evaluated using the Cochrane 6.3. Meta-analysis was performed using RevMan 5.3 software. ResultsA total of 11 studies were included， involving 1 130 patients with schizophrenia. Among them， 566 cases were in the study group （receiving modified Wendan Decoction combined with antipsychotic drugs）， and 564 cases were in the control group （receiving antipsychotic drugs only）. Meta-analysis showed that the effective rate of improvement in psychotic symptoms in the study group was higher than that in the control group （RR=1.21，95% CI： 1.15–1.27， P<0.01）. In terms of psychotic symptoms， the Positive and Negative Symptom Scale （PANSS） positive symptom score （MD=-3.69， 95% CI： -5.87–-1.51， P<0.01） and PANSS total score （MD=-9.20， 95% CI： -11.80–-6.59， P<0.01） of the study group were lower than those of the control group. In cognitive function assessments， the Mini-Mental State Examination （MMSE） score （MD=2.51， 95% CI： 1.33–3.68， P<0.01） and the Loewenstein Occupational Therapy Cognitive Assessment （LOTCA） score （MD=11.85， 95% CI： 2.55–21.15， P=0.010） of the study group were higher than those of the control group， and the Wisconsin Card Sorting Test （WCST） score was lower than that of the control group （MD=-9.34， 95% CI： -12.57–-6.11， P<0.01）. The levels of brain-derived neurotrophic factor （BDNF） （SMD=1.34， 95% CI： 0.63–2.05， P<0.01） and nerve growth factor （NGF） （MD=6.94， 95% CI： 4.00–9.89， P<0.01） of the study group were higher than those of the control group. In terms of safety， there was no statistically significant difference in the incidence of adverse reactions between the two groups （RR=0.60， 95% CI： 0.31–1.18， P=0.14）. ConclusionThe modified Wendan Decoction combined with antipsychotic drugs may be more effective than antipsychotic drugs alone in improving positive symptoms and cognitive function in patients with schizophrenia， and it also exerts a favorable neurotrophic regulatory effect. ［Funded by Postgraduate Education Reform and Quality Improvement Project of Henan Province （number， YJS2023AL060）； Key Scientific Research Projects of Higher Education Institutions in Henan Province （number， 24B320018， 25B310004）］

As the core vehicle for preserving and transmitting traditional Chinese medicine（TCM） academic thought and clinical experience， the establishment of a robust quality evaluation system for TCM clinical case reports is a crucial component in the current standardization and modernization of TCM. Based on the practical experience of constructing the China Clinical Cases Library of Traditional Chinese Medicine by the China Association of Chinese Medicine， this study conducted a comprehensive analysis of critical challenges， including insufficient authenticity and unfocused evaluation criteria. It proposed a three-dimensional evaluation framework grounded in the structure-process-outcome logic， encompassing three dimensions of authenticity and standardization， characteristics and advantages， application and translational impact. This framework integrated 12 key evaluation indicators in a systematic manner. The model preserved the academic characteristics of TCM syndrome differentiation and treatment， while aligning with modern scientific research standards， achieving a balance between individualized TCM experience and standardized evaluation. Concurrently， this study provided theoretical foundations and methodological guidance for evaluating the quality of TCM clinical cases， contributing significantly to the inheritance of TCM knowledge， evidence-based practice， and the reform of talent evaluation mechanisms.

Chronic heart failure （CHF） is a clinical syndrome characterized by impaired ventricular ejection function due to cardiac abnormalities， representing the terminal stage of most cardiovascular diseases. With its rising prevalence and significant impact on patients' quality of life， CHF has emerged as a major global public health concern. Current Western medicine treatments mainly involve the oral administration of the "new quadruple therapy" drugs and diuretics. Despite substantial progress in pathological research and clinical treatment， challenges persist， including considerable side effects， drug resistance， and marked interindividual variability in therapeutic response. Therefore， exploring and leveraging the advantages of traditional Chinese medicine （TCM） in treating CHF has become an urgent research priority. TCM has a millennia-long history in the prevention and treatment of heart failure， accumulating extensive clinical experience. Characterized by its multi-component and multi-target properties， TCM enables holistic regulation of multiple systemic functions and intervention in the progression of heart failure， demonstrating significant clinical relevance in its management. By employing therapeutic strategies such as warming Yang， promoting diuresis， replenishing Qi， nourishing Yin， and activating blood circulation， TCM effectively improves myocardial fibrosis， inhibits oxidative stress responses， enhances myocardial contractility， and ameliorates ventricular remodeling. Modern proprietary Chinese medicines derived from classic formulas， based on the theoretical foundation of traditional prescriptions， not only exhibit favorable clinical efficacy but also offer notable advantages in convenience， stability， and safety. This review systematically examined the significant therapeutic effects and underlying mechanisms of classical TCM formulas in the treatment of CHF， and provided an overview of the clinical application of modern proprietary Chinese medicines. It aims to provide new strategies for the clinical diagnosis and treatment of CHF.

OBJECTIVE To evaluate the efficacy and safety of amoxicillin combined with proton pump inhibitor （PPI） or potassium-competitive acid blocker （P-CAB） for Helicobacter pylori （Hp） eradication. METHODS Randomized controlled trial （RCTs） on amoxicillin combined with PPI or P-CAB for Hp eradication were retrieved from PubMed， Embase， the Cochrane Library， Web of Science， CNKI， Wanfang， and VIP data. The search time frame was from database inception to September 5， 2025. After literature screening， data extraction， and quality assessment， a network meta-analysis was performed using Stata 17.0 software. RESULTS A total of 12 RCTs involving 5 515 patients were included， encompassing 8 therapeutic regimens： PPI combined with high-dose amoxicillin for 14 days （TR1）， PPI combined with low-dose amoxicillin for 14 days （TR2）， P-CAB combined with high-dose amoxicillin for 7 days （TR3）， P-CAB combined with high-dose amoxicillin for 14 days （TR4）， P-CAB combined with high-dose amoxicillin for 10 days （TR5）， P-CAB combined with low-dose amoxicillin for 7 days （TR6）， P-CAB combined with low-dose amoxicillin for 14 days （TR7）， and P-CAB combined with low-dose amoxicillin for 10 days （TR8）. The network meta-analysis results showed that， in terms of intention-to-treat Hp eradication rates， the eradication rates of TR5 and TR4 were significantly higher than those of TR3， TR8， TR6 and TR1 （ P ＜0.05）. The surface under the cumulative ranking curve （SUCRA） values from highest to lowest were： TR4 （89.7%）＞TR5 （82.3%）＞TR7 （71.5%）＞ TR2 （48.6%）＞TR1 （43.9%）＞TR8 （28.7%）＞TR3 （22.7%）＞TR6 （12.6%）. Regarding safety， the incidence of adverse reactions in TR3 and TR5 was significantly lower than that in TR1 （ P ＜0.05）. The SUCRA values from highest to lowest were： TR1 （91.3%）＞TR4 （79.8%）＞TR5 （55.0%）＞TR7 （50.9%）＞TR8 （41.3%）＞TR2 （36.4%）＞TR3 （27.6%） ＞TR6 （17.7%）. CONCLUSIONS Although the regimen of P-CAB combined with high-dose amoxicillin for 14 days demonstrates the best efficacy， the combination of P-CAB with high-dose amoxicillin for 10 days exhibits a better balanced profile in terms of both efficacy and safety.

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.

Aim To study the therapeutic effect of al-licin on senna-induced diarrhea in mice and to explore the underlying mechanism.Methods Forty-eight C57BL/6J mice were randomly divided into six groups:control,model,loperamide positive control group(2 mg·kg-1),allicin low-dose group(6 mg·kg-1),allicin medium-dose group(12 mg·kg-1)and allicin high-dose group(18 mg·kg-1).Except for the con-trol group,the diarrhea model was induced in the other groups by intragastric administration of senna leaf ex-tract.After drug administration,several diarrhea indi-ces were measured:the rate of loose stools,diarrhea index,accumulated frequency of loose stools at differ-ent time points within 5 hours,and small intestine pro-pelling rate.Serum levels of TNF-α and IL-6 were de-tected by ELISA.Serum NO content was determined u-sing the Griess method.The activities of SOD and CAT,as well as MDA content in the ileum and colon,were measured.The pathological changes and the ex-pression of mRNA related to intestinal barrier proteins in the ileum and colon were evaluated using HE stai-ning and RT-qPCR.Results Allicin improved diar-rhea symptoms in mice induced by senna leaf.It re-duced the rate of loose stools,diarrhea index,cumula-tive number of loose stools in five hours,and the intes-tinal propulsion rate.Allicin also protected the intesti-nal mucosa,decreased serum TNF-α and IL-6 levels,and lowered MDA content in the intestines.It in-creased serum NO levels and enhanced SOD and CAT activities in the intestines.Additionally,allicin upreg-ulated the mRNA expression of AQP1,AQP4,and ZO-1 in intestinal tissues.Conclusions Allicin has a significant therapeutic effect on senna-induced diarrhea in mice.The underlying molecular mechanisms may involve anti-inflammatory and antioxidant effects,in-creased NO content,and upregulation of mRNA ex-pression of aquaporins and tight-junction proteins.

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.

Objective To investigate the effects of three types of twin-block(TB)appliances on the stress and displacement of anterior teeth,periodontal ligaments,and alveolar bone.Methods A three-dimensional(3D)finite element model was constructed,including maxillofacial bones,articular discs,teeth,and periodontal ligaments.Three types of twin-block appliances were designed:classic twin-block(classic-TB),twin-block with acrylic capping(capping-TB),and clear twin-block aligner(CTBA).All appliances had an inclination angle of 70°,and a masticatory force of 200 N was applied to their inclined planes.The finite element method was used to analyze the stress distribution and displacement differences of anterior teeth.Results All three types of TB appliances induced lingual tilting of maxillary anterior teeth and labial tilting of mandibular anterior teeth.The CTBA group showed the greatest lingual displacement and stress of maxillary anterior teeth,with a maximum stress of 30.6 MPa,while the mandibular anterior teeth in this group exhibited the smallest labial displacement(approximately 0.02 mm)and stress.Additionally,the CTBA group had the lowest compressive stress in mandibular anterior teeth,periodontal ligaments,and alveolar bone,whereas the classic-TB group had the highest.Conclusions In the treatment of Angle Class Ⅱ malocclusion,classic-TB(with or without acrylic capping)causes labial inclination of mandibular anterior teeth.Compared with classic-TB,CTBA effectively reduces the compressive stress and displacement of mandibular anterior teeth,potentially minimizing adverse periodontal risks.However,attention should be paid to the lingual displacement of maxillary anterior teeth.

Objective To investigate the clinical efficacy and safety of facilitated percutaneous coronary intervention(PCI)with half-dose recombinant staphylokinase(r-SAK)in patients with ST-segment elevation myocardial infarction(STEMI)who are expected to undergo PCI within 120 minutes.Methods From October 2021 to August 2022,a total of 200 STEMI patients in eight centers were included and randomly assigned in a 1﹕1 ratio to either r-SAK group or control group.Patients received loading doses of aspirin and ticagrelor and intravenous heparin and were randomized to receive an intravenous bolus of either 5 mg r-SAK or normal saline prior to PCI.The outcomes were set as ST-segment resolution(STR)at 60-90 minutes after PCI,the proportion and transition of pathological Q waves on the 5th day after PCI,and the proportion of high-sensitivity cardiac troponin T(hs-cTnT)peaking within 12 hours of onset.The safety outcome was major bleeding events defined as Bleeding Academic Research Consortium(BARC)≥type 3 bleeding during hospitalization.Results Compared with the control group,the r-SAK group had a higher proportion of STR≥70%within 60-90 minutes after PCI(58.3%vs.40.3%,P=0.009);a lower proportion of pathological Q waves(59.1%vs.74.1%,P=0.040);a lower rate of Q wave progression(14.8%vs.43.2%,P＜0.001);a higher rate of Q wave disappearance(12.5%vs.3.7%,P=0.027);and a higher proportion of hs-cTnT peaking within 12 hours of symptom onset[31/40(77.5%)vs.17/33(51.5%),P=0.027].Regarding the safety outcome,no significant difference in BARC≥type 3 bleeding was found between the two groups during hospitalization(P＞0.05).Conclusions For STEMI patients who were expected to undergo primary PCI within 120 minutes of symptom onset,the facilitated PCI with half-dose r-SAK significantly increased the proportion of STR≥70%at 60-90 minutes after PCI,reduced the formation of pathological Q waves,and shortened the time to peak hs-cTnT,without increasing the risk of bleeding,which should be an alternative reperfusion strategy worthy of further study.

Objective To investigate the effects of three types of twin-block(TB)appliances on the stress and displacement of anterior teeth,periodontal ligaments,and alveolar bone.Methods A three-dimensional(3D)finite element model was constructed,including maxillofacial bones,articular discs,teeth,and periodontal ligaments.Three types of twin-block appliances were designed:classic twin-block(classic-TB),twin-block with acrylic capping(capping-TB),and clear twin-block aligner(CTBA).All appliances had an inclination angle of 70°,and a masticatory force of 200 N was applied to their inclined planes.The finite element method was used to analyze the stress distribution and displacement differences of anterior teeth.Results All three types of TB appliances induced lingual tilting of maxillary anterior teeth and labial tilting of mandibular anterior teeth.The CTBA group showed the greatest lingual displacement and stress of maxillary anterior teeth,with a maximum stress of 30.6 MPa,while the mandibular anterior teeth in this group exhibited the smallest labial displacement(approximately 0.02 mm)and stress.Additionally,the CTBA group had the lowest compressive stress in mandibular anterior teeth,periodontal ligaments,and alveolar bone,whereas the classic-TB group had the highest.Conclusions In the treatment of Angle Class Ⅱ malocclusion,classic-TB(with or without acrylic capping)causes labial inclination of mandibular anterior teeth.Compared with classic-TB,CTBA effectively reduces the compressive stress and displacement of mandibular anterior teeth,potentially minimizing adverse periodontal risks.However,attention should be paid to the lingual displacement of maxillary anterior teeth.