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.

Cardiotoxicity is a critical issue in drug development that poses serious health risks,including potentially fatal arrhythmias.The human ether-à-go-go related gene(hERG)potassium channel,as one of the pri-mary targets of cardiotoxicity,has garnered widespread attention.Traditional cardiotoxicity testing methods are expensive and time-consuming,making computational virtual screening a suitable alter-native.In this study,we employed machine learning techniques utilizing molecular fingerprints and descriptors to predict the cardiotoxicity of compounds,with the aim of improving prediction accuracy and efficiency.We used four types of molecular fingerprints and descriptors combined with machine learning and deep learning algorithms,including Gaussian naive Bayes(NB),random forest(RF),support vector machine(SVM),K-nearest neighbors(KNN),eXtreme gradient boosting(XGBoost),and Trans-former models,to build predictive models.Our models demonstrated advanced predictive performance.The best machine learning model,XGBoost Morgan,achieved an accuracy(ACC)value of 0.84,and the deep learning model,Transformer_Morgan,achieved the best ACC value of 0.85,showing a high ability to distinguish between toxic and non-toxic compounds.On an external independent validation set,it achieved the best area under the curve(AUC)value of 0.93,surpassing ADMETlab3.0,Cardpred,and CardioDPi.In addition,we explored the integration of molecular descriptors and fingerprints to enhance model performance and found that ensemble methods,such as voting and stacking,provided slight improvements in model stability.Furthermore,the SHapley Additive exPlanations(SHAP)explanations revealed the relationship between benzene rings,fluorine-containing groups,NH groups,oxygen in ether groups,and cardiotoxicity,highlighting the importance of these features.This study not only improved the predictive accuracy of cardiotoxicity models but also promoted a more reliable and scientifically interpretable method for drug safety assessment.Using computational methods,this study facilitates a more efficient drug development process,reduces costs,and improves the safety of new drug candidates,ultimately benefiting medical and public health.

Objective:To develop a blockchain-based clinical configuration management model for medical equipment and evaluate its application value in optimizing clinical configuration management of hospital emergency and life-support devices.Methods:A data traceability management model was implemented.The Spatial Durbin Model(SDM)was used to identify issues in equipment configuration,and a blockchain-enabled review framework was established for procurement management of emergency and life-support devices.From January 2019 to December 2022,57 emergency and life-support devices deployed in Shaanxi Provincial People's Hospital were retrospectively analyzed.Among them,26 devices(January 2019-December 2020)were managed via conventional expert evaluation,while 31 devices(January 2021-December 2022)were managed using blockchain-based review.In the conventional mode,four traceability parameters-procurement declaration,supplier qualification,transaction records,and after-sales support-were randomly sampled 128,85,119,and 100 times,respectively;in the blockchain mode,these parameters were sampled 145,94,124,and 105 times.Procurement process compliance was evaluated across device categories:emergency,monitoring,therapeutic,and others required 25,40,30,and 35 review steps(conventional mode)versus 30,45,45,and 35 steps(blockchain mode).Comparative metrics included data traceability rates,process compliance rates,and procurement performance target achievement rates.Results:The blockchain mode demonstrated superior traceability rates:92.41%(134/145)for declarations,100.00%(94/94)for suppliers,97.58%(121/124)for transactions,and 97.14%(102/105)for after-sales support-all significantly higher than the conventional mode(x2=5.898,4.525,9.185,8.362,P＜0.05).Process compliance rates reached 100.00%(30/30)for emergency devices,95.56%(43/45)for monitoring devices,97.78%(44/45)for therapeutic devices,and 97.14%(34/35)for others,with statistically significant improvements(x2=5.176,4.936,5.103,3.968,P＜0.05).Procurement performance targets for progress,benefit,quality,and satisfaction were achieved at 96.77%(30/31),100.00%(31/31),100.00%(31/31),and 93.55%(29/31),respectively,surpassing the conventional mode(x2=6.581,6.535,5.129,5.780,P＜0.05).Conclusion:The blockchain-based clinical configuration management model enhances data traceability,standardizes procurement workflows,and improves performance goal attainment in hospital emergency and life-support device deployment.

Poly(ADP-ribosyl)ation (PARylation) is a specific form of post-translational modification (PTM) predominantly triggered by the activation of poly-ADP-ribose polymerase 1 (PARP1). However, the role and mechanism of PARylation in the advancement of acute kidney injury (AKI) remain undetermined. Here, we demonstrated the significant upregulation of PARP1 and its associated PARylation in murine models of AKI, consistent with renal biopsy findings in patients with AKI. This elevation in PARP1 expression might be attributed to trimethylation of histone H3 lysine 4 (H3K4me3). Furthermore, a reduction in PARylation levels mitigated renal dysfunction in the AKI mouse models. Mechanistically, liquid chromatography-mass spectrometry indicated that PARylation mainly occurred in receptor for activated C kinase 1 (RACK1), thereby facilitating its subsequent phosphorylation. Moreover, the phosphorylation of RACK1 enhanced its dimerization and accelerated the ubiquitination-mediated hypoxia inducible factor-1α (HIF-1α) degradation, thereby exacerbating kidney injury. Additionally, we identified a PARP1 proteolysis-targeting chimera (PROTAC), A19, as a PARP1 degrader that demonstrated superior protective effects against renal injury compared with PJ34, a previously identified PARP1 inhibitor. Collectively, both genetic and drug-based inhibition of PARylation mitigated kidney injury, indicating that the PARylated RACK1/HIF-1α axis could be a promising therapeutic target for AKI treatment.

Cardiotoxicity is a critical issue in drug development that poses serious health risks, including potentially fatal arrhythmias. The human ether-à-go-go related gene (hERG) potassium channel, as one of the primary targets of cardiotoxicity, has garnered widespread attention. Traditional cardiotoxicity testing methods are expensive and time-consuming, making computational virtual screening a suitable alternative. In this study, we employed machine learning techniques utilizing molecular fingerprints and descriptors to predict the cardiotoxicity of compounds, with the aim of improving prediction accuracy and efficiency. We used four types of molecular fingerprints and descriptors combined with machine learning and deep learning algorithms, including Gaussian naive Bayes (NB), random forest (RF), support vector machine (SVM), K-nearest neighbors (KNN), eXtreme gradient boosting (XGBoost), and Transformer models, to build predictive models. Our models demonstrated advanced predictive performance. The best machine learning model, XGBoost Morgan, achieved an accuracy (ACC) value of 0.84, and the deep learning model, Transformer_Morgan, achieved the best ACC value of 0.85, showing a high ability to distinguish between toxic and non-toxic compounds. On an external independent validation set, it achieved the best area under the curve (AUC) value of 0.93, surpassing ADMETlab3.0, Cardpred, and CardioDPi. In addition, we explored the integration of molecular descriptors and fingerprints to enhance model performance and found that ensemble methods, such as voting and stacking, provided slight improvements in model stability. Furthermore, the SHapley Additive exPlanations (SHAP) explanations revealed the relationship between benzene rings, fluorine-containing groups, NH groups, oxygen in ether groups, and cardiotoxicity, highlighting the importance of these features. This study not only improved the predictive accuracy of cardiotoxicity models but also promoted a more reliable and scientifically interpretable method for drug safety assessment. Using computational methods, this study facilitates a more efficient drug development process, reduces costs, and improves the safety of new drug candidates, ultimately benefiting medical and public health.

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*

Objective To investigate the current situation and risk factors of"socialized hospitalization"in elderly stroke patients.Methods A total of 329 elderly stroke patients who were admitted to No.971 Hospital of PLA Navy from January 2022 to December 2022 were enrolled in this retrospective study.The patients without"socialized hospitalization"were assigned to the control group(n=258),and those with"socialized hospitalization"were assigned to observation group(n=71).The age,gender,admission route,department transfer during hospitalization,hospital-acquired infection,depressive symptoms,cognitive impairment,activity of daily living(ADL)level at discharge were compared between the two groups.Multivariate logistic regression analysis was used to analyze the risk factors of"socialized hospitalization"in elderly stroke patients.Results There were no significant differences in gender,age,route of admission,or department transfer during hospitalization between the two groups(P>0.05).The proportions of patients with hospital-acquired infections,cognitive impairment,depressive symptoms and ADL meeting discharge standards in the observation group were higher than those in the control group(P<0.05).Multivariate logistic regression analysis showed that hospital-acquired infections,depressive symptoms and severe ADL level at discharge were independent risk factors for"socialized hospitalization"in elderly stroke patients(P<0.05).Conclusion There is a high proportion of"socialized hospitalization"in elderly patients with stroke.The main risk factors are hospital-acquired infection,cognitive impairment,depressive symptoms and ADL level at discharge.Active and effective measures should be taken to deal with them.