Polycystic ovary syndrome（PCOS） and its resulting infertility is one of the common diseases of gynecology and reproductive endocrinology. The phosphatidylinositol 3-kinase/protein kinase B（PI3K/Akt） signaling pathway is relatively well-studied in the development of intervention in PCOS， and the experiments on PCOS in rats conducted by traditional Chinese medicine through this signaling pathway is also the main direction of mechanistic research. In this paper， 20 articles published in academic journals in the past 5 years were selected through the corresponding criteria， and the objective situation and existing problems of the selected research projects were analyzed from five aspects， namely， baseline data， modeling and treatment， grouping， evaluative indexes， and pharmacodynamic indexes. It is found that there were different degrees of problems in each research project， such as the observation indicators of modeling， criteria for judging the success of the model， the treatment period， the calculation of dosage of prescription/active ingredients and specific dosage were not clearly defined， which could easily lead the bias of the results or reduce the validity of experimental data. Based on this， the list of PCOS rat experimental research operations was formed， involving five categories of experimental rats， model construction， study implementation， outcome measures and analysis and report with a total of 21 operation lists， with a view to provide a reference for the subsequent PCOS experiments related to scientific research and helping to form high-quality results.

ObjectiveTo construct a large language model （LLM）-based intelligent pre-consultation system for traditional Chinese medicine （TCM） to improve efficacy of clinical practice. MethodsA TCM large language model was fine-tuned using DeepSpeed ZeRO-3 distributed training strategy based on YAYI 2-30B. A weighted undirected graph network was designed and an agent-based syndrome differentiation model was established based on relationship data extracted from TCM literature and clinical records. An agent collaboration framework was developed to integrate the TCM LLM with the syndrome differentiation model. Model performance was comprehensively evaluated by Loss function， BLEU-4， and ROUGE-L metrics， through which training convergence， text generation quality， and language understanding capability were assessed. Professional knowledge test sets were developed to evaluate system proficiency in TCM physician licensure content， TCM pharmacist licensure content， TCM symptom terminology recognition， and meridian identification. Clinical tests were conducted to compare the system with attending physicians in terms of diagnostic accuracy， consultation rounds， and consultation duration. ResultsAfter 100 000 iterations， the training loss value was gradually stabilized at about 0.7±0.08， indicating that the TCM-LLM has been trained and has good generalization ability. The TCM-LLM scored 0.38 in BLEU-4 and 0.62 in ROUGE-L， suggesting that its natural language processing ability meets the standard. We obtained 2715 symptom terms， 505 relationships between diseases and syndromes， 1011 relationships between diseases and main symptoms， and 1 303 600 relationships among different symptoms， and constructed the Agent of syndrome differentiation model. The accuracy rates in the simulated tests for TCM practitioners， licensed pharmacists of Chinese materia medica， recognition of TCM symptom terminology， and meridian recognition were 94.09%， 78.00%， 87.50%， and 68.80%， respectively. In clinical tests， the syndrome differentiation accuracy of the system reached 88.33%， with fewer consultation rounds and shorter consultation time compared to the attending physicians （P＜0.01）， suggesting that the system has a certain pre- consultation ability. ConclusionThe LLM-based intelligent TCM pre-diagnosis system could simulate diagnostic thinking of TCM physicians to a certain extent. After understanding the patients' natural language， it collects all the patient's symptom through guided questioning， thereby enhancing the diagnostic and treatment efficiency of physicians as well as the consultation experience of the patients.

Qingfeitang， specialized in resolving phlegm to stop cough and producing fluid to moisten dryness， is a classic prescription inherited and developed by physicians of successive generations and has been included in the Catalogue of Ancient Classic Prescriptions （First Batch） published by the National Administration of Traditional Chinese Medicine （TCM） in 2018. Relevant ancient books data and modern literature were collected by bibliometrics to analyze the historic origin， formula composition， herb origin， preparation methods， processing methods， clinical effect， and indications of Qingfeitang. The key information of Qingfeitang was summarized to provide reference for the clinical application of the decoction. In this study， a total of 43 pieces of effective data on relevant ancient literature， including 35 ancient TCM books， were collected based on a systematic collation of relevant historic and modern literature. Results showed that "Qingfeitang" was originated from the "Renshen Qingfeitang" recorded in the Taiping Holy Prescriptions for Universal Relief from the Qing dynasty. The name of "Qinfeitang" was first recorded in the Yeshi Luyanfang written by YE Dalian in the Song dynasty. We suggested the modern dosage and usage of Qingfeitang as follows： "Scutellariae Radix of 5.60 g， Platycodon grandiflora， Poria， Tangerine， Fritillaria， and Cortex Mori of 3.73 g respectively， Angelicae Sinensis Radix， Asparagi Radix， Gardeniae Fructus， Armeniacae Semen Amarum， and Ophiopogonis Radix of 2.61 g respectively， Schisandra of 1 g， and Glycyrrhizae Radix et Rhizoma of 1.12 g， and they were taken 3 times daily. The above formula is recommended to be decocted with 400 mL of water， with 3.37 g ginger and 6 g jujubae fructus， to 320 mL， and taken after a meal， three times per day". Qingfeitang has the effect of resolving phlegm to stop cough and producing fluid to moisten dryness， specialized in treating cough， asthma， rash， and other symptoms in ancient times. Modern applications are mainly focused on the respiratory system， used for treating diseases such as bronchopneumonia and cough. The above research results provide a reference basis for the later development and research of Qingfeitang.

OBJECTIVES: To develop a risk prediction model for left ventricular adverse remodeling (LVAR) based on cardiac magnetic resonance (CMR) parameters in patients undergoing percutaneous coronary intervention (PCI) for acute ST-segment elevation myocardial infarction (STEMI). METHODS: A total of 329 acute STEMI patients undergoing primary PCI at 8 medical centers from January, 2018 to December, 2021 were prospectively enrolled. The parameters of CMR, performed at 7±2 days and 6 months post-PCI, were analyzed using CVI42 software. LVAR was defined as an increase >20% in left ventricular end-diastolic volume or >15% in left ventricular end-systolic volume at 6 months compared to baseline. The patients were randomized into training (n=230) and validation (n=99) sets in a 7∶3 ratio. In the training set, potential predictors were selected using LASSO regression, followed by univariate and multivariate logistic regression to construct a nomogram. Model performance was evaluated using receiver-operating characteristic (ROC) curves, area under the curve (AUC), calibration curves, and decision curve analysis. RESULTS: LVAR occurred in 100 patients (30.40%), who had a higher incidence of major adverse cardiovascular events than those without LVAR (58.00% vs 16.16%, P<0.001). Left ventricular global longitudinal strain (LVGLS; OR=0.76, 95% CI: 0.61-0.95, P=0.015) and left atrial active strain (LAAS; OR=0.78, 95% CI: 0.67-0.92, P=0.003) were protective factors for LVAR, while infarct size (IS; OR=1.05, 95% CI: 1.01-1.10, P=0.017) and microvascular obstruction (MVO; OR=1.26, 95% CI: 1.01-1.59, P=0.048) were risk factors for LVAR. The nomogram had an AUC of 0.90 (95% CI: 0.86-0.94) in the training set and an AUC of 0.88 (95% CI: 0.81-0.94) in the validation set. CONCLUSIONS LVGLS, LAAS, IS, and MVO are independent predictors of LVAR in STEMI patients following PCI. The constructed nomogram has a strong predictive ability to provide assistance for management and early intervention of LVAR.
Humans ; Percutaneous Coronary Intervention ; Prospective Studies ; ST Elevation Myocardial Infarction/diagnostic imaging* ; Ventricular Remodeling ; Magnetic Resonance Imaging ; Male ; Female ; Middle Aged ; Risk Factors ; Aged ; Risk Assessment

OBJECTIVE To explore the potential mechanism of Yishenqinglihuoxue Formula(YSQLF)in treating chronic kidney disease fibrosis in rats based on UHPLC-Q-TOF-MS technology and network pharmacology.METHODS UHPLC-Q-TOF-MS was used for qualitative analysis of Yishenqinglihuoxue Formula-containing serum.Targets of the plasma constituents and the disease were retrieved from SwissTargetPrediction,OMIM,GeneCards,and other databases.Then the protein-protein interaction(PPI)network was constructed and core targets were screened for GO term enrichment and KEGG pathway enrichment.Cytoscape software was em-ployed to construct the"drug-compound-core target-pathway"network and the targets and signaling pathways of Yishenqinglihuoxue Formula against fibrosis were predicted.A model of renal fibrosis was established to verify the core targets and pathway proteins.RE-SULTS A total of 56 constituents migrating to blood of Yishenqinglihuoxue Formula were identified.97 common targets of the constit-uents and the disease and 33 core targets were screened out.KEGG enrichment and PPI network analysis showed that Yishenqingli-huoxue Formula may play a role in the treatment of fibrosis through PI3K/Akt and other pathways.Furthermore,the results of animal experiments showed that Yishenqinglihuoxue Formula could reduce the levels of Scr and BUN,improve fibrosis areas,inhibit the acti-vation of the PI3K/Akt pathway,and reduce the protein expression of TNF-α.CONCLUSION Yishenqinglihuoxue Formula may play a role in the treatment of fibrosis by inhibiting PI3K/Akt signaling pathway and inhibiting TNF-α expression.

Objective: To evaluate the effect of Tai Chi and Qigong on patients with lumbar disc herniation (LDH). Methods: Relevant data were retrieved from nine English and Chinese databases, including Cochrane Library, PubMed, and Wanfang Data, etc. from inception to June 2024. All published randomized controlled trials assessing the effect of Tai Chi and Qigong on visual analog scale (VAS), Japanese Orthopedic Association (JOA) score, and other health indicators in participants with LDH compared to usual medical care or other treatments were included. Grey literature, trials involving the pushing of hands (Tui Shou) or Tai Chi with weapons, and trials with co-interventions (Tai Chi/Qigong plus another treatment) were excluded. Methodological quality was analyzed using the Cochrane risk of bias tool, and evidence quality was assessed using the Grading of Recommendations, Assessment, Development, and Evaluations (GRADE) tool. Results: Fourteen trials (954 patients) were included in this study. Tai Chi and Qigong were associated with lower VAS pain scores (standardized mean difference −0.55, 95% confidence interval [CI] −0.95 to −0.15, P = .01), higher JOA scores (mean difference [MD] 4.40, 95% CI 2.62 to 6.18, P < .001) and straight leg raise test results (MD 9.40°, 95% CI 7.64 to 11.15, P < .001) in patients with LDH. Furthermore, compared with usual care, Tai Chi and Qigong showed enhanced effects on pain and JOA scores. When compared to other exercises or massage, the effect on pain scores was similar but that on JOA scores was significant. Conclusions Tai Chi and Qigong may have favorable effects on VAS pain and JOA scores compared with usual care, and on JOA scores compared with other exercises or massage in patients with LDH. Given the overall poor quality of the evidence, the results of current study should be interpreted cautiously.

Objective To explore the potential categories and influencing factors of dietary behavioral adherence trajectories in young and middle-aged patients with type 2 diabetes mellitus.Methods A convenience sampling method was used to investigate 277 young and middle-aged patients with type 2 diabetes mellitus admitted to the Department of Endocrinology of a tertiary A hospital in Huai'an City,China,from September 2022 to March 2023.The baseline data were collected using a general information questionnaire,the Dietary Behavioral Compliance Scale for Patients with Type 2 Diabetes Mellitus,the Diabetes Self-Efficacy Scale,the Diabetes Knowledge Text,the Health Beliefs Questionnaire,and the Family APGAR Index Questionnaire.The baseline information was collected on the patients'behavioral adherence trajectories a day prior to hospital discharge(T0),a week post-discharge(T1),a month post-discharge(T2),and 3 months post-discharge(T3)to assess the level of patients'dietary behavioral adherence,using latent variable growth mixed models to identify trajectory categories,and univariate and multivariate logistic regression to analyze the influences on dietary adherence trajectories.Results A total of 3 trajectories of dietary behavior adherence in young and middle-aged patients with type 2 diabetes mellitus were identified,namely,low adherence-fluctuating group(49.8%),high adherence-slowly regressing group(31.4%),and medium adherence-continuously rising group(18.8%),and the results showed that age,literacy level,self-efficacy,health beliefs,and family caring were the factors influencing potential categories of dietary behavioral adherence for young and middle-aged patients with type 2 diabetes mellitus.Conclusion There is heterogeneity in the adherence trajectories of young and middle-aged patients with type 2 diabetes mellitus,and healthcare professionals can develop targeted interventions according to the influencing factors of the trajectory categories in order to improve their adherence.

Objective:To design a multi-epitope antigen of norovirus(NoV)based on bioinformatics technology and to pre-pare and characterize it.Methods:Bioinformatics methods were used to construct and analyze the NoV multi-epitope antigen NoV-ZH.Recombinant proteins were prepared and characterized by prokaryotic expression system,and monoclonal antibodies were prepared by animal immunization and hybridoma technology,and initially applied in colloidal gold platform.Results:The designed multi-epitope antigen had a large proportion of random curls in the secondary structure,with theoretical molecular mass and isoelectric point(PI)of 13.1 ku and 7.16,which were stable and hydrophilic.It had good immunogenicity and could activate humoral and cellular immune re-sponses.The proteins prepared by ligating pET-28a(+)and pET-32a vectors with antigenic sequences were expressed as inclusion body proteins and soluble proteins,respectively.A pair of paired antibodies was obtained by animal immunization and hybridoma tech-nique,and applied to colloidal gold test strips with a sensitivity of 0.5 ng/ml,and the test strips could specifically bind two genotypes of NoV recombinant capsid proteins.Conclusion:The successful preparation and characterization of multi-epitope antigen of norovirus provides a reference for the subsequent exploration of NoV universal detection targets and the development of diagnostic raw materials.

Objective:To evaluate psychometric properties of infertility related stress assessment instruments and the methodological quality of research.Methods:Research on the psychometric property evaluation of infertility related stress assessment instruments was electronically searched on China National Knowledge Infrastructure, China Biology medicine disc, WanFang Data, VIP, PubMed, Web of Science, Embase, Scopus, and CINHAL. The search period was from database establishment to November 1, 2023. Two researchers independently screened literature and extracted data, evaluated the included assessment tools using the Consensus-based Standards for the Selection of Health Measurement Instruments (COSMIN) guideline, and formed recommendations.Results:A total of 20 studies were included, including 8 assessment instruments of the Fertility Problem Stress Inventory, Infertility Distress Scale, Fertility Problem Inventory, Fertility Problem Inventory-Short Form, Copenhagen Multi-Centre Psychosocial Infertility-Fertility Problem Stress Scales, Copenhagen Multi-Centre Psychosocial Infertility-Fertility Problem Stress Scales-Short Form, Infertility-Related Stress Scale, and the Symptom and Stress Inventory for Infertility. The content validity of assessment instrument was not reported or uncertain, and the quality of evidence for the assessment tools was moderate, all of which were B-level recommendations.Conclusions:The Fertility Problem Inventory, Copenhagen Multi-Centre Psychosocial Infertility-Fertility Problem Stress Scales, Copenhagen Multi-Centre Psychosocial Infertility-Fertility Problem Stress Scales-Short Form and the Infertility-Related Stress Scale can be temporarily recommended for use, but the psychometric property of each tool still needs to be fully verified. Clinical medical and nursing staff need to choose appropriate assessment instruments based on actual situation.

Objective To construct the machine learning models based on the radiomic features of non-contrast and enhanced CT and to evaluate the predictive value in the risk stratification of gastrointestinal stromal tumor(GIST).Methods A total of 182 patients with pathologically confirmed GIST were randomly divided into a training set and a validation set at a ratio of 7∶3.The volume of interest(VOI)was outlined in the non-contrast phase,arterial phase and venous phase,and its radiomic features were extracted.The most valuable radiomic features were selected using the least absolute shrinkage and selection operator(LASSO)algorithm.The logistic regression(LR)classifier was used to construct the prediction models based on single-phase or multi-phase images.The predictive efficacy of the different models was compared by using receiver operating characteristic(ROC)curves.Results Four,three,and four radiomic features were selected in the non-contrast phase,arterial phase and venous phase,and 4 models were constructed in total.Among the single-phase models,the venous phase had better predictive efficacy,with the area under the curve(AUC)of 0.932[95％confidence interval(CI)0.873-0.969]and 0.924(95％CI 0.819-0.979)in the training and validation sets.The predictive efficacy of the combined model was improved,with the AUC of 0.946(95％CI 0.891-0.978)and 0.938(95％CI 0.838-0.986).Conclusion The venous phase model can predict the risk stratification of GIST accurately,and the prediction efficacy can be improved by combining the non-contrast and arterial phases.