Objective To explore the feasibility and reference value of allogeneic lung transplantation and postoperative monitoring in miniature pigs for lung transplantation research. Methods Two miniature pigs (R1 and R2) underwent left lung allogeneic transplantation. Complement-dependent cytotoxicity tests and blood cross-matching were performed before surgery. The main operative times and partial pressure of arterial oxygen (PaO₂) after opening the pulmonary artery were recorded during surgery. Postoperatively, routine blood tests, biochemical blood indicators and inflammatory factors were detected, and pathological examinations of multiple organs were conducted. Results The complement-dependent cytotoxicity test showed that the survival rate of lymphocytes between donors and recipients was 42.5%-47.3%, and no agglutination reaction occurred in the cross-matching. The first warm ischemia times of D1 and D2 were 17 min and 10 min, respectively, and the cold ischemia times were 246 min and 216 min, respectively. Ultimately, R1 and R2 survived for 1.5 h and 104 h, respectively. Postoperatively, in R1, albumin (ALB) and globulin (GLB) decreased, and alanine aminotransferase increased; in R2, ALB, GLB and aspartate aminotransferase all increased. Urea nitrogen and serum creatinine increased in both recipients. Pathological results showed that in R1, the transplanted lung had partial consolidation with inflammatory cell infiltration, and multiple organs were congested and damaged. In R2, the transplanted lung had severe necrosis with fibrosis, and multiple organs had mild to moderate damage. The expression levels of interleukin-1β and interleukin-6 increased in the transplanted lungs. Conclusions The allogeneic lung transplantation model in miniature pigs may systematically evaluate immunological compatibility, intraoperative function and postoperative organ damage. The data obtained may provide technical references for subsequent lung transplantation research.

Objective: To investigate the prevalence of comorbidity of myopia, and overweight and obesity among primary and middle school students in Yanqing District, Beijing Municipality, so as to provide the evidence for \prevention and control of comorbidity. Methods: In September from 2020 to 2024, a stratified cluster sampling method was adopted annually to select primary, junior high school, senior high school, and vocational high school students in Yanqing District as survey subjects. Myopia was screened based on Screening Protocol for Myopia in Children and Adolescents. Height an weight were measured, and overweight and obesity were determined using the Screening for overweight and Obesity in School-Age Children and Adolescents. Results: A total of 9 883 individuals were surveyed, including 5 219 primary school students (52.81%), 2 486 junior high school students (25.15%), and 2 178 senior high school / vocational high school students (22.04%). There were 5 154 boys (52.15%) and 4 729 girls (47.85%). From 2020 to 2024, the numbers of primary and middle school students in Yanqing District with myopia, overweight, and obesity were 4 426, 1 897, and 3 164, respectively, with detection rates of 44.78%, 19.19%, and 32.01%. The detection rates of myopia, overweight, and obesity showed an increasing trend with the elevation of school stage (all P<0.05). The detection rate of myopia was higher in girls than in boys, while the detection rate of obesity was lower in girls than in boys (all P<0.05). There were 2 588 individuals with both myopia and overweight/obesity, representing a detection rate of 26.19%, which showed an increasing trend (P<0.05), rising from 25.04% in 2020 to 34.41% in 2024. The detection rates of comorbidity of myopia, and overweight and obesity among primary school, junior high school, and senior high school / vocational high school students were 13.22%, 36.93%, and 45.00%, respectively, showing an increasing trend with the elevation of school stage (P<0.05). The difference between genders was not statistically significant (P>0.05). Conclusions The detection rates of comorbidity of myopia, and overweight and obesity among primary and middle school students in Yanqing District is relatively high and shows an upward trend, with students in higher school stage being the key target group for prevention and control. It is recommended to strengthen health education on the co-prevention of multiple health issues and implement differentiated interventions based on school stages.

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）］

Objective To develop a predictive model for postoperative pulmonary complications (PPC) following video-assisted thoracic surgery (VATS) in lung cancer patients by integrating cardiopulmonary exercise testing (CPET) parameters and machine learning techniques. Methods A retrospective analysis was conducted on patients with early-stage non-small cell lung cancer who underwent CPET and VATS at Guangdong Provincial People’s Hospital between October 2021 and July 2023. Patients were divided into a PPC group and a non-PPC group. The least absolute shrinkage and selection operator (LASSO) regression was used to select important features associated with PPC. Six machine learning algorithms were utilized to construct prediction models, including logistic regression, support vector machine, k-nearest neighbors, random forest, gradient boosting machine, and extreme gradient boosting. The optimal model was interpreted using SHapley Additive exPlanations (SHAP). Results A total of 325 patients were included, with an average age of 60.36 years, and 55.1% were male. Significant differences were observed between the PPC and non-PPC groups in age, diabetes, coronary heart disease, surgical approach, forced expiratory volume in 1 second (FEV1), forced vital capacity (FVC), FVC% predicted, peak oxygen uptake (peak VO2), anaerobic threshold (AT), and ventilatory equivalent for carbon dioxide slope (VE/VCO2 slope) (P<0.05). In the predictive model constructed by selecting 7 key features using LASSO regression, the random forest model demonstrated the best overall performance across various metrics, with an area under the receiver operating curve of 0.930, an F1 score of 0.836, and a Brier score of 0.133 in the training set. It also exhibited good predictive ability and calibration in the test set. SHAP analysis ranked feature importance as follows: peak VO2, VE/VCO2 slope, age, FEV1, smoking history, diabetes, and surgical approach. Conclusion Integrating CPET parameters, the random forest model can effectively identify high-risk patients for PPC and has the potential for clinical application.

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 mechanism by which zinc-regulated transporters, iron-regulated transporter-likeprotein 4 (ZIP4) regulates glycolysis and its impact on tumor progression in cholangiocarcinoma (CCA), providing a theoretical basis for targeted therapy of CCA. Methods ZIP4 expression in CCA was analyzed using the GEPIA database. Immuno-histochemistry (IHC) was used to detect ZIP4 expression in 20 paired CCA and adjacent non-tumor tissues. Stable ZIP4-overexpressing CCA cell lines (ZIP4-OE) were established. Gene set enrichment analysis was used to screen differentially expressed genes and pathways in ZIP-OE CCA cells. ZIP4, N-myc proto-oncogene protein (MYCN), and histone-lysine N-methyltransferase 2E (KMT2E) were knocked down using small interfering RNAs (siRNAs). The expression of glycolysis-related gene (glucose transporter 1 [Glut1], hexokinase 2 [HK2], and lactate dehydrogenase A [LDHA]) was measured by real-time quantitative reverse transcription polymerase chain reaction (qRT-PCR). Glycolytic activity was assessed by measuring the extracellular acidification rate (ECAR). Cell proliferation was evaluated using colony formation assays, and cell migration was assessed using Transwell assays. A xenograft mouse model was constructed to examine CCA tumor growth. Protein levels of ZIP4, KMT2E, H3K4me3 (tri-methylation of lysine 4 on histone H3), and MYCN were detected by Western blotting. Results GEPIA database analysis and IHC results confirmed significantly higher ZIP4 expression levels in CCA tissues compared to adjacent non-tumor tissues (P＜0.05). Compared to the control group, the ZIP4-OE group exhibited a significantly increased ECAR, along with significantly enhanced proliferation and migration abilities (P＜0.01). Conversely, knockdown of ZIP4 suppressed CCA cells proliferation and migration. GEPIA analysis indicated that ZIP4 upregulates the transcription of oncogene MYCN, as well as glycolysis-related genes. Knockdown of MYCN abolished the ZIP4 overexpression-induced upregulation of Glut1, HK2, and LDHA gene transcription, reduced glycolysis, and significantly inhibited CCA cell proliferation and migration (P＜0.05). Mechanistic studies demonstrated that ZIP4 increases H3K4me3 level via KMT2E, leading to MYCN transcription. Knockdown of KMT2E in CCA cells suppressed the ZIP4 overexpression-induced enhancement in H3K4me3 modification, resulting in MYCN downregulation and significantly reduced CCA cells proliferation and migration (P＜0.05). Conclusions ZIP4 upregulates H3K4me3 modification through KMT2E, which recruits transcription factors to activate the transcription of MYCN. This subsequently enhances cellular glycolysis and promotes the proliferation and migration of CCA cells.

Chronic heart failure （CHF） is the terminal stage of various heart diseases， with high morbidity， mortality， and hospitalization rate. According to the theory of collateral diseases， the core pathogenesis of CHF is blood stasis of collaterals， water retention and stagnation， and pathogen accumulation in collaterals. Accordingly， the treatment should focus on reinforcing healthy Qi to warm Yang， activating blood to dredge collaterals， and excreting water to alleviate edema. On this basis， Qili Qiangxin capsules （QLQX） are created. This prescription can effectively treat chronic heart failure. Modern studies have shown that QLQX contains a variety of pharmacological components such as flavonoids， alkaloids， terpenoids， phenylpropanoids， phenolic acids， and cardiac glycosides. QLQX can improve the cardiac function， inhibit myocardial fibrosis， improve hemodynamics， mitigate inflammation， reduce cardiomyocyte apoptosis， and regulate the nervous system， with mild adverse reactions. This study analyzed the etiology and pathogenesis of CHF based on the theory of collateral diseases， explored the relationship between the prescription and syndrome， and delved into the material basis and mechanism of QLQX in the treatment of CHF， aiming to provide reference for the clinical application and scientific research of QLQX.

Syringa pinnatifolia, belonging to the family Oleaceae, is a species endemic to China. It is predominantly distributed in the Helan Mountains region of Inner Mongolia and Ningxia of China. The peeled roots, stems, and thick branches have been used as a distinctive Mongolian medicinal material known as "Shan-chen-xiang", which has effects such as suppressing "khii", clearing heat, and relieving pain and is employed for the treatment of cardiovascular and pulmonary diseases and joint pain. Over the past five years, significant increase was achieved in research on chemical constituents and pharmacological effects. There were a total of 130 new constituents reported, covering sesquiterpenoids, lignans, and alkaloids. Its effects of anti-myocardial ischemia, anti-cerebral ischemia/reperfusion, sedation, and analgesia were revealed, and the mechanisms of agarwood formation were also investigated. To better understand its medical value and potential of clinical application, this review updates the research progress in recent five years focusing on the chemical constituents and pharmacological effects of S. pinnatifolia, providing reference for subsequent research on active ingredient and support for its innovative application in modern medicine system.
Medicine, Mongolian Traditional ; Humans ; Drugs, Chinese Herbal/pharmacology* ; Animals ; Syringa/chemistry*

As a new type of production factor that can empower the development of new quality productivity, the data element is an important engine to promote the high quality development of the industry. Traditional Chinese medicine(TCM) dispensing is the most basic work of TCM clinical pharmacy, and its quality directly affects the clinical efficacy of TCM. The integration of data elements and TCM dispensing can stimulate the innovation and vitality of the TCM dispensing industry and promote the high-quality and sustainable development of the industry. A large-scale, detailed, and systematic study on TCM dispensing was conducted. The innovative practice path of data fusion construction in the whole process of TCM dispensing was investigated by integrating the digital resources "nine full activities" of TCM dispensing, creating the digital dictionary of "TCM clinical information data elements", and exploring innovative applications of TCM dispensing driven by data and technology, so as to promote the standardized, digital, and intelligent development of TCM dispensing in medical health services. The research content of this project was successfully selected as the second batch of "Data element×" typical cases of National Data Administration in 2024, which is the only selected case in the field of TCM.
Medicine, Chinese Traditional/methods* ; Drugs, Chinese Herbal ; Humans