OBJECTIVE To investigate a dynamic monitoring of drug consumption （DMDC） model based on the seasonal Mann-Kendall trend test， aiming to provide scientific evidence for the efficient and macroscopic monitoring of drug use. METHODS A monitoring list of key outpatient drugs was established based on the top 20% of drugs ranked by sales volume in the outpatient pharmacy in October 2024. A DMDC model based on the Mann-Kendall trend test was constructed using the monthly usage data of key outpatient drugs from November 2021 to October 2024， aiming to eliminate the impact of seasonal fluctuations and analyze the temporal trends in drug consumption. Taking mucolytic expectorants， triazole derivatives for dermatophytosis， and single-agent hydroxymethylglutaryl coenzyme A （HMG-CoA） reductase inhibitors as examples， the monitoring effectiveness of the DMDC model was demonstrated， and its performance was compared with that achieved by the traditional sequential growth rate ranking method. RESULTS A total of 215 drug varieties were included in the monitoring list， and DMDC models were successfully established for all of them. Among these， 119 showed a significant increasing trend （P＜0.05， S′＞0）. The model successfully monitored the monthly consumption of mucolytic expectorants， triazole derivatives for dermatophytosis， and single- agent HMG-CoA reductase inhibitors. The precision and recall rates of the DMDC model for identifying abnormal drug use were 60.7% and 85.0%， respectively， both significantly higher than those of the sequential growth rate ranking method （8.3% and 15.0%， respectively） （χ2＝20.114， P＜0.001； χ2＝19.600， P＜0.001）. CONCLUSIONS DMDC model based on the seasonal Mann-Kendall trend test can effectively identify long-term trends in drug consumption， eliminate seasonal interference， enhance monitoring accuracy and management efficiency， and is suitable for the dynamic monitoring of drug consumption.

OBJECTIVE To provide scientific evidence and practical references for refined antimicrobial stewardship in multi- branch medical institutions. METHODS Data of antibiotic use density （AUD） of inpatients as well as physician-level case mix index （CMI） were collected from the Liji Road main campus and Panlongcheng branch campus of our hospital from August 2023 to March 2026. The two-factor decomposition method was adopted to decompose total AUD variation into a level effect and structural effect. Grey relational analysis （GRA） was performed to quantify the correlation degree between ward CMI， physician CMI and physician AUD， so as to identify wards and physicians requiring targeted key intervention in different campuses. A refined antimicrobial management framework for multi-branch hospitals was constructed and implemented based on the above analytical results. An interrupted time series （ITS） model incorporating seasonal dummy variables was applied. The research period was divided into pre-intervention stage （August 2023 to March 2024） and post-intervention stage （April 2024 to March 2026）. The temporal changing trends of AUD in two campuses were compared to evaluate management efficacy. RESULTS Driving factors for AUD variation presented significant heterogeneity between the two campuses. AUD variation in the main campus was dominated by level effect， while AUD variation in the branch campus was jointly affected by structural effect and level effect. The grey relational degrees of ward CMI and physician CMI with physician AUD were 0.901 and 0.882， respectively. After refined management implementation， AUD decreased by 7.48 DDDs/（100 bed·days） and 20.54 DDDs/（100 bed·days） in the main campus and branch campus， respectively； the rising trends of AUD in both campuses reversed to declining trends after intervention. CONCLUSIONS Driving factors of AUD variation under the multi-branch hospital model show obvious inter-campus heterogeneity. CMI is highly correlated with AUD. The refined management system developed via AUD attribution decomposition and CMI correlation analysis matches the differentiated clinical characteristics of multi- branch hospitals and effectively improves the precision of antimicrobial stewardship.

ObjectiveTo construct a risk prediction model for carotid artery plaques in high-risk populations of stroke based on five machine learning methods. MethodsThe clinical information of the high-risk population of stroke was collected. Factor analysis and statistical analysis of syndrome elements were conducted on their traditional Chinese medicine （TCM） symptoms and tongue and pulse manifestations. On the basis of the results of factor analysis and variable screening， five machine learning methods-classification and regression tree （CART） decision tree， support vector machine （SVM）， back propagation（BP） neural network， logistic regression， and random forest-were used to construct the risk prediction model for carotid artery plaques. ResultsThe most common TCM syndrome elements in the high-risk population of stroke was Qi deficiency. The scores of Qi deficiency， Yin deficiency， and Yang deficiency in the population with carotid artery plaques were higher than those without carotid artery plaques （P<0.05）. The CART decision tree， SVM， logistic regression， BP neural network， and random forest models showed the areas under the receiver operating characteristic （ROC） curves of 0.71， 0.75， 0.76， 0.76， and 0.75， the accuracy rates of 68.94%， 69.27%， 69.44%， 70.10%， and 69.60%， the precision rates of 68.56%， 68.53%， 68.75%， 69.74%， and 69.42%， the recall rates of 68.91%， 67.93%， 67.92%， 68.10%， and 67.31%， and the F₁ values of 0.69， 0.68， 0.68， 0.68， and 0.68， respectively. ConclusionAmong the high-risk population of stroke， the most frequently distributed TCM syndrome element is Qi deficiency， with the rest mainly being fire heat， Yin deficiency， Yang deficiency， phlegm dampness， blood stasis， and Qi stagnation. Deficiency syndrome may be a major factor leading to carotid artery plaques in the high-risk population of stroke. The BP neural network model demonstrates better performance in predicting the risk of carotid artery plaques in the high-risk population of stroke. People with carotid artery plaques are more likely to present with symptoms such as a heavy head， dizziness， headache， and thready pulse. The primary community benefits more widely when the BP neural network model is adopted to predict the risk of carotid artery plaques in the high-risk population of stroke over 40 years old.

ObjectiveTo construct a risk prediction model for carotid artery plaques in high-risk populations of stroke based on five machine learning methods. MethodsThe clinical information of the high-risk population of stroke was collected. Factor analysis and statistical analysis of syndrome elements were conducted on their traditional Chinese medicine （TCM） symptoms and tongue and pulse manifestations. On the basis of the results of factor analysis and variable screening， five machine learning methods-classification and regression tree （CART） decision tree， support vector machine （SVM）， back propagation（BP） neural network， logistic regression， and random forest-were used to construct the risk prediction model for carotid artery plaques. ResultsThe most common TCM syndrome elements in the high-risk population of stroke was Qi deficiency. The scores of Qi deficiency， Yin deficiency， and Yang deficiency in the population with carotid artery plaques were higher than those without carotid artery plaques （P<0.05）. The CART decision tree， SVM， logistic regression， BP neural network， and random forest models showed the areas under the receiver operating characteristic （ROC） curves of 0.71， 0.75， 0.76， 0.76， and 0.75， the accuracy rates of 68.94%， 69.27%， 69.44%， 70.10%， and 69.60%， the precision rates of 68.56%， 68.53%， 68.75%， 69.74%， and 69.42%， the recall rates of 68.91%， 67.93%， 67.92%， 68.10%， and 67.31%， and the F₁ values of 0.69， 0.68， 0.68， 0.68， and 0.68， respectively. ConclusionAmong the high-risk population of stroke， the most frequently distributed TCM syndrome element is Qi deficiency， with the rest mainly being fire heat， Yin deficiency， Yang deficiency， phlegm dampness， blood stasis， and Qi stagnation. Deficiency syndrome may be a major factor leading to carotid artery plaques in the high-risk population of stroke. The BP neural network model demonstrates better performance in predicting the risk of carotid artery plaques in the high-risk population of stroke. People with carotid artery plaques are more likely to present with symptoms such as a heavy head， dizziness， headache， and thready pulse. The primary community benefits more widely when the BP neural network model is adopted to predict the risk of carotid artery plaques in the high-risk population of stroke over 40 years old.

To address the challenges of fragmented data, inconsistent standards, and delayed feedback in the full-cycle competency development process at teaching hospitals, this paper proposes the LEADS data governance system for Precision Medical Education (LEADS-PME), drawing on the practical experience of building the integrated medical education management platform at Zhongshan Hospital, Fudan University. The LEADS-PME framework comprises five interconnected core components: Longitudinal foundation, Evidence-chain generation, Analytics and profiling, Decision-support and prediction, and Simulation and digital-twin governance. Currently, Zhongshan Hospital has established a full-cycle educational data infrastructure and achieved multi-system data integration and competency-based profiling and evaluation, while the prediction and simulation modules are still in the prototype exploration phase. A mixed-methods evaluation will be subsequently conducted from the perspectives of data quality, management effectiveness, and predictive intervention. As a locally adapted framework, LEADS-PME can transform operational data derived from clinical practice, teaching, assessment, and management processes into traceable, interpretable, and reusable evidence of competency development. It is expected to facilitate the transformation of medical education management from a outcome-oriented approach to process monitoring, evidence-driven decision-making, and precision governance, thereby providing both a theoretical framework and practical reference for precision medical education in Chinese teaching hospitals.

Objective To measure radiation filed distribution in the treatment room of the Varian Halcyon medical linear accelerator, and to provide a basis for shielding design and potential exposure analysis of treatment rooms for this type of accelerator. Methods Under the 6 MV X-ray (FFF) mode at a maximum dose rate of 800 MU/min and a maximum irradiation field of 28.00 cm × 28.00 cm, a total of 540 MU was delivered during gantry rotation. Radiation field distribution was measured using thermoluminescence dosimeters located at multiple points in the room. The measured data were then applied to shielding calculations, and the results were compared with those obtained using empirical formulas. Results The overall radiation levels in the treatment room were in the range of 12.2 µGy/540 MU to 5.520 Gy/540 MU, with the highest dose (5.520 Gy/540 MU) observed at the isocenter, and the lowest dose (12.2 µGy/540 MU) recorded at approximately 6.5 m from the gantry head. The radiation levels at most points were within the range of 100-1000 µGy/540 MU. At heights of 0.5, 1.1, and 1.7 m, the radiation field exhibited a rapid attenuation from the beam center outward, with a faster decay along the treatment couch direction (Y-axis) than along the perpendicular direction (X-axis). Shielding calculations based on the measured radiation field yielded required wall thicknesses of 1219 mm (east wall), 949 mm (south wall), 1235 mm (west wall), and 1252 mm (north wall), all of which were smaller than those calculated using empirical formulas. Conclusion Thermoluminescence dosimeter is suitable for multi-point in situ measurement of radiation field distribution in Halcyon accelerator treatment rooms. The measurement results can be used to optimize the shielding design of Halcyon accelerator treatment rooms.

This case report introduces Professor ZOU Wei 's experience of treating a patient with Wernicke encephalopathy using the "regulating spirit and promoting yang acupuncture method". The patient was diagnosed as spleen and stomach deficiency with internal liver wind. The treatment principle focused on regulating spirit and awakening the brain, strengthening the spleen, calming wind, and relaxing the tendons. Three groups of acupoints were selected: ①acupoints for awakening the brain by regulating spirit and unblocking meridians (Baihui [GV20], Qianshencong [EX-HN1] and bilateral Taiyang [EX-HN5], Fengchi [GB20]), etc.; ②acupoints for harmonizing the spleen, stomach, qi, and blood (bilateral Tianshu [ST25], Daheng [SP15], Taixi [KI3], etc.); ③acupoints for relaxing and softening the tendons (bilateral Waiguan [TE5], Hegu [LI4], Yanglingquan [GB34], Xuanzhong [GB39]).The needles were retained for 50 min per session, once daily, 7 days a week. After 16-week treatment, the patient was able to walk a few steps slowly with assistance, and other symptoms returned to normal. A two-month follow-up showed the patient's condition remained stable, walking distance further increased, and overall health significantly improved.
Humans ; Acupuncture Points ; Acupuncture Therapy ; Wernicke Encephalopathy/physiopathology*

BACKGROUND: Temozolomide (TMZ) resistance is a significant challenge in treating glioblastoma (GBM). Collagen remodeling has been shown to be a critical factor for therapy resistance in other cancers. This study aimed to investigate the mechanism of TMZ chemoresistance by GBM cells reprogramming collagens. METHODS: Key extracellular matrix components, including collagens, were examined in paired primary and recurrent GBM samples as well as in TMZ-treated spontaneous and grafted GBM murine models. Human GBM cell lines (U251, TS667) and mouse primary GBM cells were used for in vitro studies. RNA-sequencing analysis, chromatin immunoprecipitation, immunoprecipitation-mass spectrometry, and co-immunoprecipitation assays were conducted to explore the mechanisms involved in collagen accumulation. A series of in vitro and in vivo experiments were designed to assess the role of the collagen regulators prolyl 4-hydroxylase subunit alpha 1 (P4HA1) and yes-associated protein (YAP) in sensitizing GBM cells to TMZ. RESULTS: This study revealed that TMZ exposure significantly elevated collagen type I (COL I) expression in both GBM patients and murine models. Collagen accumulation sustained GBM cell survival under TMZ-induced stress, contributing to enhanced TMZ resistance. Mechanistically, P4HA1 directly binded to and hydroxylated YAP, preventing ubiquitination-mediated YAP degradation. Stabilized YAP robustly drove collagen type I alpha 1 ( COL1A1) transcription, leading to increased collagen deposition. Disruption of the P4HA1-YAP axis effectively reduced COL I deposition, sensitized GBM cells to TMZ, and significantly improved mouse survival. CONCLUSION P4HA1 maintained YAP-mediated COL1A1 transcription, leading to collagen accumulation and promoting chemoresistance in GBM.
Temozolomide ; Humans ; Glioblastoma/drug therapy* ; Animals ; Mice ; Cell Line, Tumor ; Drug Resistance, Neoplasm/genetics* ; YAP-Signaling Proteins ; Hydroxylation ; Dacarbazine/pharmacology* ; Adaptor Proteins, Signal Transducing/metabolism* ; Transcription Factors/metabolism* ; Collagen/biosynthesis* ; Collagen Type I/metabolism* ; Prolyl Hydroxylases/metabolism* ; Antineoplastic Agents, Alkylating/therapeutic use*