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.

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.

Objective:To investigate the short-term efficacy, safety and factors affecting recurrence of intermediate-risk and high-risk non-muscle invasive bladder cancer (NMIBC) treated with intravesical instillation of domestic Bacillus Calmette-Guérin (BCG) infusion.Methods:This study was a retrospective cohort study. We collected the data of 163 patients with NMIBC treated with domestic BCG after transurethral resection of bladder tumor (TURBT) from October 2016 to October 2020 in the Department of Urology, the Affiliated Hospital of Qingdao University. There were 140 males and 23 females, the age was(67.3±10.3) years old. 23 cases had only been received BCG and 140 cases received other chemotherapy drugs before. The induction scheme of instillation was started after the TURBT at once a week for 6 consecutive weeks, continue instillation at every two weeks for 3 doses, then maintenance instillation once a month for 10 consecutive times, for a total of 19 instillations. Kaplan-Meier analysis was used to calculate recurrence-free survival. Binary Logistic regression was used to analyze factors and stepwise regression (backward method) was employed to identify independent risk factors for recurrence after BCG instillation. The incidence of adverse reactions was recorded. Measurement data with normal distribution were expressed as mean±standard deviation( ± s), measurement data with skewed distribution were expressed as M( Q1, Q3), and count data were expressed as frequency and percentage(%). Results:A total of 23 cases experienced recurrence within the 13-month of instillation, of which 7 cases were found to have progressed by pathological biopsy, and the cumulative recurrence-free rate was 85.9%. The results of binary logistic regression analysis showed that the history of re-TURBT ( P=0.010) was an independent predictor for recurrence after BCG intravesical instillation. Adverse events occurred in 110 cases. The main symptoms of the 65 cases were urgency of urination, pollakiuria and dysuria with urinary irritation, urinary tract infection in 15 cases, hematuria in 10 cases, and other symptoms in 20 cases. Instillation was terminated in 7 cases due to side effects, no serious adverse events such as spread of tuberculous bacteria were observed in the cases. Conclusions:Patients with NMIBC treated with intravesical instillation of domestic BCG have significant short-term efficacy, while patients who have previously received re-TURBT is an independent predictor for recurrence. The domestic BCG have the characteristic of slight side effect, good efficacy and safety.

Objective To explore the CT diagnosis and differential diagnosis of perivascular epithelioid cell neoplasms (PEComa), improve the accuracy of PEComa diagnosis, and reduce misdiagnosis. Methods CT findings of 8 cases of PEComa confirmed by pathology in Jining First People’s Hospital from January 2020 to April 2024 were retrospectively analyzed for the location, shape, size, boundary, plain scan density, and enhancement characteristics of the lesions. Results All 8 tumors were solitary, with 5 located in the kidney, 1 in the liver, 1 in the extraperitoneal space, and 1 in the retroperitoneal space. The tumors were round in 3 cases, oval in 1 case, and irregular in 4 cases. Seven cases were benign with clear boundaries and 1 case was malignant with unclear boundaries. On plain CT, 2 cases showed slightly low density, 3 cases showed slightly high density, and 3 cases showed low density. One tumor had uniform density, and 7 tumors had nonuniform density with internal necrosis and cystic changes. Contrast-enhanced CT revealed diverse enhancement patterns. Four cases showed a “fast in and fast out” enhancement pattern, with significant arterial-phase enhancement and reduced portal venous-phase enhancement. Three cases showed a “fast-in and slow-out” enhancement pattern, with significant enhancement in the arterial phase, persistent enhancement in the portal venous phase, and slightly reduced density in the delayed phase. One case showed mild enhancement in the arterial phase and significant enhancement in the portal venous phase. In 3 cases, multiple tortuous and thickened blood vessels were observed around the tumors, while 3 cases showed tortuous vascular shadows within the tumors. Conclusion PEComa demonstrates characteristic CT features, predominantly with “fast in and fast out” or “fast in and slow out” enhancement patterns. When thickened and tortuous blood vessels are observed within or around the tumor, PEComa should be considered in combination with clinical findings.

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.

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 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.