OBJECTIVE To develope a predictive model for medication deviation risks during the hospital-to-home transition period in coronary heart disease （CHD） patients with initial treatment， aiming to assist medical staff in rapidly identifying high-risk groups for medication deviation. METHODS A total of 462 CHD patients with initial treatment from the Affiliated Hospital of North China University of Science and Technology （hereinafter referred to as “our hospital”） between January and July 2024 were enrolled. The patients were randomly divided into a modeling group and an internal validation group. The modeling group was further categorized into a medication deviation group and a non-medication deviation group based on whether medication deviations occurred. Similarly， 57 CHD patients with initial treatment from the cardiology department of our hospital between June and September 2025 were collected as an external validation group. Univariate analysis was used to screen predictive factors， followed by multivariate Logistic regression to construct the predictive model. Internal validation methods were employed to evaluate model performance， while external validation methods were used to test the model’s generalizability. RESULTS The 462 patients were divided into a modeling group （319 cases） and an internal validation group （143 cases）. In the modeling group， the medication deviation group （192 cases， 60.19%） and the non-medication deviation group （127 cases， 39.81%） were identified. Multivariate Logistic regression analysis revealed that age， medication type， medication adherence， and self-efficacy in rational medication use were predictive factors for medication deviations in CHD patients with initial treatment （ P ＜0.05）. The predictive model equation was logit P ＝ln[ P /（1－ P ） ] ＝1.321+1.732×age+4.091×medication type －4.360×medication adherence －3.081×self-efficacy in rational medication use. The model demonstrated good discrimination， with a Hosmer-Lemeshow goodness-of-fit test P -value of 0.439， an area under the receiver operating characteristic curve （AUC） of 0.870， sensitivity of 0.970， and specificity of 0.607. A risk nomogram with a total score of 350 points and a cutoff value of 110 points was plotted. The internal validation group showed an AUC o f 0.787 and a prediction accuracy of 77.6%， while the external validation group exhibited an AUC of 0.802 and a prediction accuracy of 73.7%. CONCLUSIONS This study successfully developed a predictive model for medication deviation risks during the hospital-to-home transition period in CHD patients with initial treatment. The model demonstrates excellent discrimination and predictive accuracy， effectively identifying high-risk populations for medication deviations. Age （＞70 years）， number of drug types≥5， poor medication adherence， and poor self-efficacy in rational medication use are independent risk factors for medication deviations.

BackgroundThe violent aggressive behaviors of patients with schizophrenia usually have the characteristics of suddenness， unpredictability， high severity， and great difficulty in prevention. Early identification and accurate assessment of their risk of violent aggression have significant clinical significance. ObjectiveTo construct a predictive model for the violence risk in hospitalized patients with schizophrenia， to identify the key factors influencing the occurrence of violent behavior in these patients， so as to provide references for clinical precise quantitative assessment and early intervention. MethodsA total of 200 patients with schizophrenia who were hospitalized at Taiyuan Psychiatric Hospital from March 2022 to September 2024 and met the diagnostic criteria of the International Classification of Diseases， eleventh edition （ICD-11） were collected to form the modeling cohort. They were randomly divided into a training set （n=140） and a test set （n=60） at a ratio of 7∶3. Based on the least absolute shrinkage and selection operator （LASSO） regression algorithm， the feature variables were screened and dimension-reduced. The support vector machine （SVM） from machine learning was selected for model training and prediction. The discrimination efficacy of the model was evaluated by the area under the receiver operating characteristic （ROC） curve （AUC）， accuracy， precision， sensitivity， specificity， F1 value， and Brier value. ResultsLASSO regression screening identified 16 feature variables. Pearson correlation analysis revealed a positive correlation between prior violent behavior frequency and clinical psychiatric symptom scores （r=0.580， P<0.01）， a positive correlation between hospitalization compliance and current disease status （r=0.550， P=0.003）， and a positive correlation between educational level and family per capita monthly income （r=0.367， P<0.01）. The SVM model achieved an AUC of 0.853， accuracy of 0.800， precision of 0.810， sensitivity of 0.895， specificity of 0.636， F1 value of 0.850， and Brier value of 0.168. ConclusionThe SVM model has a relatively high level of applicability and overall predictive performance in the assessment of violent risk in schizophrenia patients， which is helpful for the early identification of violent risks in such patients. ［Funded by Specialized Research Project for Enhancing the Competence of Health Professionals in Taiyuan City （number， Y2023006）］

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.

Objective To explore the diagnosis and treatment strategies for elderly patients with ground-glass opacity (GGO). Methods The imaging features and postoperative pathological findings of the elderly patients with pulmonary GGO receiving surgery in our hospital from 2017 to 2019 were retrospectively analyzed. The patients were divided into an elderly patient group and a non-elderly patient group based on their age. Results Finally 575 patients were included in the study. There were 281 elderly patients, including 83 males and 198 females, with an average age of (67.0±5.3) years. There were 294 non-elderly patients, including 88 males and 206 females, with an average age of (49.1±7.3) years. Compared with the non-elderly patients, elderly GGO patients showed the following distinct clinical features: long observation time for lesions (P=0.001), high proportion of rough edges of GGO (P<0.001), significant pleural signs (P＜0.001) and bronchial signs (P＜0.001), and high proportion of type Ⅱ-Ⅳ GGO (P<0.001), lobectomy type (P=0.013), and invasive lesions reported in postoperative pathology (P<0.001). There was no statistical difference in the average hospital stay between the two groups (P=0.106). Multivariate logistic regression analysis showed that GGO diameter and GGO type were the main factors affecting the operation. Observation time, GGO diameter, GGO type and pleural signs were the main influencing factors for postoperative pathological infiltrative lesions. The cut-off value of GGO diameter in predicting infiltrating lesions was 10.5 mm in the elderly patients group. Conclusion The size and type of GGO are important factors in predicting invasive lesions and selecting surgical methods. Elderly patients with radiographic manifestations of type Ⅱ-Ⅳ GGO lesions with a diameter greater than 10.5 mm should be closely followed up.

It is believed that there is a pathogenesis of yang deficiency in polycystic ovary syndrome （PCOS） infertility， and it is concluded that warm-yang method has a better effect in improving endometrial abnormality， enhancing the quality of follicles， correcting endocrine disorders， and resolving or alleviating clinical symptoms in PCOS infertility. Based on Yanghe Decoction （阳和汤）， a representative traditional Chinese medicine decoction for warming yang， Yanghe Xiaonang Decoction （阳和消囊汤） was formulated， combining with warm medicinals according to symptoms， and aerobic exercise was also advocated to help generate and develop yang qi， in order to provide ideas for clinical treatments.

AIM: To explore the factors influencing heart rate(HR)changes during small incision lenticule extraction(SMILE)surgery by monitoring HR trends at different time points of the procedure.METHODS: Prospective cohort study. A total of 69 patients who underwent SMILE surgery at the Laser Vision Correction Center of Xi'an No.1 Hospital from April to May 2024 were enrolled. Before the surgery, patients completed the State Anxiety Inventory(S-AI, questions 1-20)to assess their preoperative anxiety scores related to the next day's surgery. Baseline HR was recorded using medical pulse oximeter, and real-time HR was recorded during patient positioning, lenticule scanning, lenticule separation and extraction, and the application of postoperative eye drops.RESULTS: The HR during patient positioning was 83.61±13.87 bpm, which was significantly different from the baseline HR(77.52±10.88 bpm), HR during lenticule separation and extraction(75.54±12.52 bpm), and HR during postoperative eye drop application(76.65±10.54 bpm; all P<0.001). When stratified by median age, older patients(>26 years)had the HR during lenticule separation and extraction 76.27±9.93 bpm, which differed from the HR at positioning(84.82±14.10 bpm)and at lens scanning(82.76±13.72 bpm; all P<0.005). Stratified by gender, the HR of male patients at positioning was the highest(85.31±16.61 bpm), which differed significantly from the baseline HR(78.26±12.63 bpm), HR during lenticule separation and extraction(77.14±14.59 bpm), and HR during postoperative eye drop application(77.11±12.49 bpm; all P<0.005). There was no correlation between HR during positioning and preoperative anxiety scores(r=0.124, P=0.418).CONCLUSION: HR changes during SMILE surgery vary with different procedural stages, peaking during patient positioning and reaching the lowest point during lenticule separation and extraction. Older patients showed higher HR during positioning, and male patients exhibited higher HR during positioning.

The analysis presented here is based on the blood components of Guanxin Qiwei tablets, the key anti-atherosclerosis pathway of Guanxin Qiwei tablets was screened by network pharmacology, and the anti-atherosclerosis mechanism of Guanxin Qiwei tablets was clarified and verified by cell experiments. HPLC-Q-Exactive-MS/MS technique was used to analyze the components of Guanxin Qiwei tablets into blood, to determine the precise mass charge ratio of the compounds, and to conduct a comprehensive analysis of the components by using secondary mass spectrometry fragments and literature comparison. Finally, a total of 42 components of Guanxin Qiwei tablets into blood were identified. To better understand the interactions, we employed the Swiss Target Prediction database to predict the associated targets. Atherosclerosis (AS) disease targets were searched in disease databases Genecard, OMIM and Disgent, and 181 intersection targets of disease targets and component targets were obtained by Venny 2.1.0 software. Protein interactions were analyzed by String database. The 32 core targets were selected by Cytscape software. Gene Ontology (GO) enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were performed in DAVID database. It was found that the anti-atherosclerosis pathways of Guanxin Qiwei tablets mainly include lipid metabolism and atherosclerosis and AGE-RAGE signaling pathway in diabetic complications and other signal pathways. The core targets and the core compounds were interlinked, and it was found that cryptotanshinone and tanshinone ⅡA in Guanxin Qiwei tablets were well bound to TNF, PPARγ, AKT1, PTG2 and other targets. The lipid metabolism and atherosclerotic pathway was verified using human hl-7702 hepatocytes. This study preliminarily identified the potential pharmacodynamic components of Guanxin Qiwei tablets in the treatment of AS diseases and predicted their pathways of action, and verified the relationship between regulating lipid metabolism and atherosclerosis of Guanxin Qiwei tablets in vitro, providing a reference for the further study of the pharmacodynamic material basis and mechanism of action of this prescription. This experiment was approved by the Medical Ethics Committee of Inner Mongolia Medical University (No. YKD202401262).

Nine compounds were isolated and purified from 90% ethanol extract of Alstonia mairei Lévl by using various chromatographic methods, including silica gel, SephadexLH-20, MCI Gel and ODS column chromatography, combined with semi-preparative liquid phase separation methods. Modern spectroscopic methods (1D and 2D NMR, UV, IR, MS, etc.) were used to identify the structures of the isolated compounds. They were identified as mairoside A (1), 3′,6-di-O-sinaloylsucrose (2), myristic acid (3), methyl myristate (4), ethyl myristate (5), 3,4,5-trimethoxycinnamic acid (6), 3,4,5-trimethoxybenzoic acid (7), vinoline (8), kaempferol-3-O-rutinoside (9), among which compound 1 is a new glycoside, compounds 4 and 5 are new natural products, and the nuclear magnetic data of compound 4 were reported for the first time.

ObjectiveTo investigate the optimal ratio of goat horn replacing antelope horn in Fufang Lingjiao Jiangya pills and the blood pressure-lowering mechanism of this medicine. MethodsThe blood pressure-lowering efficacy of Fufang Lingjiao Jiangya pills with varying proportions of goat horn replacing antelope horn was evaluated on spontaneous hypertensive rats （SHR）. In this experiment， 50 SHR rats were randomly grouped as follows： model （n=8）， captopril （0.01 g·kg^-1） （n=6）， low-dose blank Fufang Lingjiao Jiangya pills （0.342 g·kg^-1） （n=6）， high-dose blank Fufang Lingjiao Jiangya pills （0.684 g·kg^-1） （n=6）， low-dose antelope horn-containing Fufang Lingjiao Jiangya pills （0.378 g·kg^-1） （n=6）， high-dose antelope horn-containing Fufang Lingjiao Jiangya pills （0.756 g·kg^-1） （n=6）， low-dose goat horn-containing Fufang Lingjiao Jiangya pills （0.378 g·kg^-1） （n=6）， and high-dose goat horn-containing Fufang Lingjiao Jiangya pills （0.756 g·kg^-1） （n=6）. Additionally， 8 WKY rats were used as the normal group. Drugs were administered by gavage for 4 weeks while an equal volume of distilled water was administered for the normal and model groups. Blood pressure was measured before administration， 3 h post administration， and biweekly thereafter. In the experiment for Fufang Lingjiao Jiangya pills with goat horn replacing antelope horn in different proportions， 48 SHR rats were randomly grouped as follows： model， blank Fufang Lingjiao Jiangya pills （0.684 g·kg^-1）， antelope horn-containing Fufang Lingjiao Jiangya pills （0.756 g·kg^-1）， 2× goat horn-containing Fufang Lingjiao Jiangya pills （0.824 g·kg^-1）， 4× goat horn Fufang Lingjiao Jiangya pills （0.969 g·kg^-1）， and 6× goat horn Fufang Lingjiao Jiangya pills （1.112 g·kg^-1）. The normal group included 8 WKY rats， and the normal group and model group received an equal volume of distilled water. The treatment lasted for 2 weeks， and blood pressure was recorded at various time points （pre-administration， 3 h post administration， and on days 4， 7， 10， and 14 of administration）. Serum levels of angiotensin-converting enzyme （ACE）， angiotensin Ⅱ（Ang Ⅱ）， renin， and interleukin-6 （IL-6） were measured by enzyme-linked immunosorbent assay. Histopathological changes in the heart， kidney， and thoracic aorta were observed by hematoxylin-eosin staining. The protein levels of ACE2， angiotensin Ⅱ type 1 receptor （AT1R）， and angiotensinogen （AGT） in the kidney tissue were determined by Western blot， while the expression of nuclear factor （NF）-κB p65 and Toll-like receptor 4 （TLR4） in the thoracic aorta tissue was assessed by immunohistochemistry. ResultsCompared with the model group， all treatment groups showed lowered blood pressure （P<0.05， P<0.01）， and the 6× goat horn-containing Fufang Lingjiao Jiangya pills group showed consistent blood pressure-lowering effect with the antelope horn-containing Fufang Lingjiao Jiangya pills group. Compared with the normal group， the model group showed elevated serum levels of ACE， Ang Ⅱ， renin， and IL-6， while the elevations were declined in the Fufang Lingjiao Jiangya pills groups （P<0.05， P<0.01）. Pathological changes in the heart， kidney， and thoracic aorta were alleviated in all the treatment groups， with the 6× goat horn- and antelope horn-containing Fufang Lingjiao Jiangya pills groups exhibited the best effect. Western blot and immunohistochemistry results showed that all the treatment groups exhibited down-regulated protein levels of AT1R， AGT， NF-κB p65， and TLR4 and up-regulated protein levels of ACE2 （P<0.05， P<0.01） compared with model group， with the 6×goat horn- and antelope horn-containing Fufang Lingjiao Jiangya pills groups showcasing the best effect. ConclusionReplacing antelope horn with 6×goat horn in Fufang Lingjiao Jiangya pills can achieve consistent blood pressure-lowering effect with the original prescription. The prescription may exert the effect by inhibiting the renin-angiotensin-aldosterone system （RAAS） and TLR4/NF-κB signaling pathways.

Objective To evaluate the performance of the artificial intelligence (AI)-enabled snail identification system for recognition of Oncomelania hupensis robertsoni and Tricula in schistosomiasis-endemic areas of Yunnan Province. Methods Fifty O. hupensis robertsoni and 50 Tricula samples were collected from Yongbei Township, Yongsheng County, Lijiang City, a schistosomiasis-endemic area in Yunnan Province in May 2024. A total of 100 snail sample images were captured with smartphones, including front-view images of 25 O. hupensis robertsoni and 25 Tricula samples (upward shell opening) and back-view images of 25 O. hupensis robertsoni and 25 Tricula samples (downward shell opening). Snail samples were identified as O. hupensis robertsoni or Tricula by schistosomiasis control experts with a deputy senior professional title and above according to image quality and morphological characteristics. A standard dataset for snail image classification was created, and served as a gold standard for recognition of snail samples. A total of 100 snail sample images were recognized with the AI-enabled intelligent snail identification system based on a WeChat mini program in smartphones. Schistosomiasis control professionals were randomly sampled from stations of schistosomisis prevention and control and centers for disease control and prevention in 18 schistosomiasis-endemic counties (districts, cities) of Yunnan Province, for artificial identification of 100 snail sample images. All professionals are assigned to two groups according the median years of snail survey experiences, and the effect of years of snail survey experiences on O. hupensis robertsoni sample image recognition was evaluated. A receiver operating characteristic (ROC) curve was plotted, and the sensitivity, specificity, accuracy, Youden’s index and the area under the curve (AUC) of the AI-enabled intelligent snail identification system and artificial identification were calculated for recognition of snail sample images. The snail sample image recognition results of AI-enabled intelligent snail identification system and artificial identification were compared with the gold standard, and the internal consistency of artificial identification results was evaluated with the Cronbach’s coefficient alpha. Results A total of 54 schistosomiasis control professionals were sampled for artificial identification of snail sample image recognition, with a response rate of 100% (54/54), and the accuracy, sensitivity, specificity, Youden’s index, and AUC of artificial identification were 90%, 86%, 94%, 0.80 and 0.90 for recognition of snail sample images, respectively. The overall Cronbach’s coefficient alpha of artificial identification was 0.768 for recognition of snail sample images, and the Cronbach’s coefficient alpha was 0.916 for recognition of O. hupensis robertsoni snail sample images and 0.925 for recognition of Tricula snail sample images. The overall accuracy of artificial identification was 90% for recognition of snail sample images, and there was no significant difference in the accuracy of artificial identification for recognition of O. hupensis robertsoni (86%) and Tricula snail sample images (94%) (χ² = 1.778, P > 0.05). There was no significant difference in the accuracy of artificial identification for recognition of snail sample images with upward (88%) and downward shell openings (92%) (χ² = 0.444, P > 0.05), and there was a significant difference in the accuracy of artificial identification for recognition of snail sample images between schistosomiasis control professionals with snail survey experiences of 6 years and less (75%) and more than 6 years (90%) (χ² = 7.792, P < 0.05). The accuracy, sensitivity, specificity and AUC of the AI-enabled intelligent snail identification system were 88%, 100%, 76% and 0.88 for recognition of O. hupensis robertsoni snail sample images, and there was no significant difference in the accuracy of recognition of O. hupensis robertsoni snail sample images between the AI-enabled intelligent snail identification system and artificial identification (χ² = 0.204, P > 0.05). In addition, there was no significant difference in the accuracy of artificial identification for recognition of snail sample images with upward (90%) and downward shell openings (86%) (χ² = 0.379, P > 0.05), and there was a significant difference in the accuracy of artificial identification for recognition of snail sample images between schistosomiasis control professionals with snail survey experiences of 6 years and less and more than 6 years (χ² = 5.604, P_adjusted < 0.025). Conclusions The accuracy of recognition of snail sample images is comparable between the AI-enabled intelligent snail identification system and artificial identification by schistosomiasis control professionals, and the AI-enabled intelligent snail identification system is feasible for recognition of O. hupensis robertsoni and Tricula in Yunnan Province.