Objective To study the relation between function of sperm membrane and seminal parameters.Methods Five hundred and three semen samples were collected from 149 normal men and 354 infertile men.Sperm concentration,motility,viability,semen WBC concentration,morphology and sperm tail hypo-osmotic swelling(HOS) were analyzed according to WHO criteria. Results Sperm HOS score in infertile group was lower than that in control group(P

Epimedii folium is a commonly used Traditional Chinese Medicine (TCM) for warming the kidney and strengthening the yang qi. It has androgen-estrogen-like effect. It can not only directly act on sexual organs to regulate hormone levels, but also exert sex-hormone-like effect through hypothalamus-pituitary-gonadal axis. Its regulation of hormone levels is similar to that of plant hormones. At present, Epimedii folium is commonly used with other TCMs to treat diseases caused by sex-hormone deficiency, such as male spermatopenia, asthenospermia, benign prostatic hyperplasia, functional erectile dysfunction, female premature ovarian failure, perimenopausal syndrome, dysfunctional infertility during ovulation, hyperandrogenemia of PCOS patients, etc.

OBJECTIVE To provide a reference for medical institutions in selecting and establishing a traditional Chinese medicine （TCM） clinical pharmaceutical care mode that suits their clinical characteristics. METHODS Combining clinical cases， this paper introduces the workflow and service content of the joint consultation mode between TCM physicians and TCM clinical pharmacists in our hospital and analyzes its effectiveness. RESULTS The workflow of the pharmaceutical care mode in our hospital involves six stages：assessing the patient’s medication status， consultation rounds and formulating medication regimen， bedside education， pharmaceutical monitoring， pharmacist rounds， and extended pharmaceutical care. The care content primarily includes thoroughly collecting clinical information and evaluating the patient’s medication regimen before the consultation. During the consultation， TCM clinical pharmacists and TCM physicians jointly developed a dosage regimen tailored to the patient’s individual conditions. After the consultation， bedside medication education was conducted to ensure the correct use of medications and a pharmaceutical monitoring plan was developed and implemented， during which the patient’s post-consultation condition changes were monitored and timely feedback to the TCM physicians for medication plan adjustments was provided， offering extended pharmaceutical care post-consultation， including medication consultation， pharmaceutical outpatient services， and joint clinics. Since its implementation， nearly 1 000 inpatients have been served annually， with a 100% medical order review rate for patients taking TCM decoction pieces. Individualized medication education reached 78.80%， and patient’s degree of satisfaction was 100%. The prescription compliance rate of TCM decoction pieces in the wards increased from 89.33% before the implementation of the joint consultation to 97.08% afterward. Additionally， the mode provided TCM-related consultations to over 1 000 patients annually and compiled and provided nearly a hundred pharmaceutical documents to healthcare personnel. CONCLUSIONS By establishing the joint consultation mode between TCM physicians and TCM clinical pharmacists and conducting TCM clinicalpharmaceutical care， the level of rational use of TCMdecoction pieces has been promoted， demonstrating the value of TCM clinical pharmacists.

Objective Previous studies have shown that the triglyceride-glucose (TyG) index reflects insulin resistance and predicts the risk of ischemic stroke. Massive cerebral infarction, as a type of ischemic stroke with poor prognosis, is often more prone to complications and sequelae. However, no studies have yet explored the relationship between the TyG index and massive cerebral infarction. This study aims to investigate the role of the TyG index in assessing the infarct area in ischemic stroke. Methods This retrospective study included 212 adult patients with ischemic stroke diagnosed by cranial magnetic resonance imaging and admitted to our hospital from January 2020 to June 2024. The patients were divided into massive cerebral infarction group and non-massive cerebral infarction group according to the study purpose. Univariable and multivariable logistic regression analyses were performed on each group using SPSS 26 software. The receiver operating characteristic curves were analyzed for the prediction models. Results Variables with P＜0.2 in the univariable logistic regression analysis were included in the multivariable logistic regression analysis. The results showed that age (OR 1.043, 95% CI 1.004-1.084, P＜0.05), National Institutes of Health Stroke Scale score (OR 22.986, 95% CI 8.679-60.882, P＜0.001), and TyG index (OR 1.729, 95% CI 1.017-2.941, P＜0.05) were significant predictors. The area under the receiver operating characteristic curve of the predictive model was 0.868 (P＜0.001), indicating high predictive performance. Conclusion The TyG index is an independent risk factor for massive cerebral infarction. The combination of the TyG index with clinical laboratory indicators can effectively predict massive cerebral infarction.

Objective Previous studies have shown that the triglyceride-glucose (TyG) index reflects insulin resistance and predicts the risk of ischemic stroke. Massive cerebral infarction, as a type of ischemic stroke with poor prognosis, is often more prone to complications and sequelae. However, no studies have yet explored the relationship between the TyG index and massive cerebral infarction. This study aims to investigate the role of the TyG index in assessing the infarct area in ischemic stroke. Methods This retrospective study included 212 adult patients with ischemic stroke diagnosed by cranial magnetic resonance imaging and admitted to our hospital from January 2020 to June 2024. The patients were divided into massive cerebral infarction group and non-massive cerebral infarction group according to the study purpose. Univariable and multivariable logistic regression analyses were performed on each group using SPSS 26 software. The receiver operating characteristic curves were analyzed for the prediction models. Results Variables with P＜0.2 in the univariable logistic regression analysis were included in the multivariable logistic regression analysis. The results showed that age (OR 1.043, 95% CI 1.004-1.084, P＜0.05), National Institutes of Health Stroke Scale score (OR 22.986, 95% CI 8.679-60.882, P＜0.001), and TyG index (OR 1.729, 95% CI 1.017-2.941, P＜0.05) were significant predictors. The area under the receiver operating characteristic curve of the predictive model was 0.868 (P＜0.001), indicating high predictive performance. Conclusion The TyG index is an independent risk factor for massive cerebral infarction. The combination of the TyG index with clinical laboratory indicators can effectively predict massive cerebral infarction.

Objective To explore the accuracy of artificial intelligence-based diagnosis of ovarian malignant tumors and the identification of benign and malignant tumors under transabdominal scanning and transvaginal scanning methods. Methods A dataset of transabdominal and transvaginal two-dimensional ultrasound images was used and the images were preprocessed to enhance quality. The region of interest was segmented and divided into a training set and a test set. A convolutional neural network (CNN) was trained on the images in the training set, and the accuracy of the model on the test set was calculated. Results Transvaginal scanning was 14% more accurate in diagnosing malignant ovarian tumors than transabdo-minal scanning on the test set. For identifying the benign and malignant ovarian tumors containing cystic components, a mixture of transvaginal and transabdominal scanning increased the accuracy by 9.7% over transabdominal scanning alone. Conclusion CNN can identify ovarian malignant tumors under both scanning methods, but the accuracy of transvaginal scanning is higher than that of transabdominal scanning, and the CNN model has a higher accuracy in identifying benign and malignant ovarian tumors under transvaginal scanning.