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.

To investigate the association between sleep-related phenotypes and the risk of malignancy in pulmonary nodules, and to provide complementary evidence from a general population cohort and genetic analyses.

Background/Aims: Regorafenib is recommended by guidelines and trials as a sequential second-line therapy following progression on first-line sorafenib or lenvatinib in hepatocellular carcinoma (HCC). However, efficacy is limited, highlighting the urgent need to screen suitable patients and develop sensitization strategies. Methods: Acquired sorafenib- or lenvatinib-resistant (SR or LR) HCC cell lines and organoids were established. Genome-wide CRISPR library screen was performed in SR or LR cell strains to identify synthetic lethal targets of regorafenib. RNA-seq and FITC-regorafenib efflux assay were used to elucidate ERBB3-driven downstream signaling. Preclinical mouse models of cell line- and patient-derived xenografts and clinical cohorts of HCC patients were employed to validate the efficacy of ERBB3-guided patient stratification. Results: Screening with CRISPR library, we showed that inhibition of ERBB3 was synthetic lethal with regorafenib in SR or LR cell strains and organoids. Mechanistically, SR or LR triggered feedback activation of ERBB3 signaling and mediated regorafenib efflux via ERBB3-HIF1A-ABCB1 cascade pathway, limiting sensitivity to regorafenib. Moreover, ERBB3-low tumors following SR or LR exhibited significant sensitivity to regorafenib, suggesting its potential as a predictive biomarker to screen optimal candidates for sequential therapy. Seribantumab, an ERBB3-targeting monoclonal antibody, inhibited ERBB3-HIF1A-ABCB1 cascade, and its combination with regorafenib exerted marked synergistic anti-tumor effects on ERBB3-high tumors resistant to sorafenib or lenvatinib both in vitro and in vivo. Conclusions This study revealed that ERBB3 was a key resistance factor driving limited efficacy to sequential regorafenib, but also an effective therapeutic target whose inhibition enhanced regorafenib sensitivity after SR or LR.

The global rise in myopia prevalence and its increasingly earlier onset have become a serious public health concern. Drug intervention represents an important strategy for slowing myopia progression and reducing the risk of complications associated with high myopia. In studies of drug efficacy and underlying mechanisms, guinea pigs are widely regarded as an ideal animal model for experimental myopia because their ocular structure and refractive development are similar to those of humans. Current evidence indicates that chemical drugs, including muscarinic receptor antagonists, dopamine-related agents, and certain intraocular pressure-lowering drugs, may exert effects by modulating retina-choroid signaling pathways, inhibiting scleral remodeling, and suppressing excessive axial elongation. In contrast, traditional Chinese medicine and its bioactive components exhibit multitarget intervention advantages via improving retinal microcirculation, exerting antioxidant and anti-inflammatory effects, and regulating extracellular matrix metabolism.In addition, the exploration of novel therapeutic targets and advanced drug delivery systems, such as scleral collagen cross-linking, sustained-release formulations, and nanocarrier-based delivery systems, has opened new avenues for myopia control. However, this field still faces several challenges, including insufficient evidence for long-term drug safety, an incomplete understanding of the underlying molecular mechanisms, and limited progress in clinical translation. This review provides a reference for further research and clinical application of pharmacological strategies for myopia control.

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.

BACKGROUND:Buqi Huoxue Compounds have significant clinical efficacy in treating ischemic stroke with Qi deficiency and phlegm stasis;however,the exact mechanism of action is not clear. OBJECTIVE:To observe the effect of Buqi Huoxue Compounds on the expression of vascular endothelial growth factor,basic fibroblast growth factor,brain-derived neurotrophic factor and autophagy related protein Beclin1 and p62 in a rat model of cerebral ischemia/reperfusion. METHODS:Forty male Sprague-Dawley rats were randomly divided into sham operation group,model group,Buqi Huoxue Compounds group and autophagy inhibitor group,with 10 rats in each group.In the latter three groups,a rat model of cerebral ischemia/reperfusion injury was established.The Buqi Huoxue Compounds group was intragastrically given Buqi Huoxue Compounds(6.49 g/kg,administered three times a day)2 hours after reperfusion;the autophagy inhibitor group was intragastrically given Buqi Huoxue Compounds(6.49 g/kg,administered three times a day)2 hours after reperfusion and intraperitoneally given 3-methyladenine 2 hours before gavage and at days 1-3 of gavage.The sham operation group and model group were given equal amounts of saline by gavage for 7 consecutive days.Neurological function,cerebral infarct volume,brain tissue morphology and expression of vascular endothelial growth factor,basic fibroblast growth factor,brain-derived neurotrophic factor and autophagy-related proteins Beclin1 and p62 in the ischemic cortical region of rats were detected at 24 hours after the final administration. RESULTS AND CONCLUSION:Zea-Longa scoring results showed that the neurological function of rats was severely damaged after modeling and neurological deficit of rats in the Buqi Huoxue Compounds group was less than that in the model group and the autophagy inhibitor group(P＜0.05).TTC staining showed that cerebral infarct foci were observed in the model group,Buqi Huoxue Compounds group,and autophagy inhibitor group,and the cerebral infarct volume in the Buqi Huoxue Compounds group was lower than that in the model group and the autophagy inhibitor group(P＜0.05).The results of hematoxylin-eosin staining in ischemic brain tissues showed that there were large gaps between nerve cells in the model group and cell arrangement was not neat,and cytoplasmic agglutination and pyknosis were observed.Immunohistochemical staining results showed that vascular endothelial growth factor was mostly expressed in neuronal cells,glial cells and capillary endothelium;basic fibroblast growth factor and brain-derived neurotrophic factor were mostly expressed in neuronal cells and glial cells;and there was no significant difference in the expression of vascular endothelial growth factor,basic fibroblast growth factor,and brain-derived neurotrophic factor among the four groups(P＞0.05).The results of western blot assay showed that compared with the sham operation group,Beclin1 protein expression was decreased(P＜0.05)and p62 protein expression was elevated(P＜0.05)in the model group;compared with the model group,Beclin1 protein expression was increased(P＜0.05)and p62 protein expression was reduced(P＜0.05)in the Buqi Huoxue Compounds group;compared with the Buqi Huoxue Compounds group,Beclin1 protein expression was decreased(P＜0.05)and p62 protein expression was elevated(P＜0.05)in the autophagy inhibitor group.To conclude,Buqi Huoxue Compounds attenuate cerebral ischemia-reperfusion injury in rats by promoting autophagy.

Objective:To explore the efficacy and safety of one-hole split endoscope (OSE) minimally invasive surgery for the treatment of single-segment thoracic ossification of the ligamentum flavum (TOLF).Methods:This retrospective non-randomized controlled study included 41 patients with single-segment TOLF who underwent surgery at Qilu Hospital of Shandong University between July 2019 and July 2023. Patients were divided into two groups: the OSE group (19 cases) treated with one-hole split endoscope minimally invasive surgery and the open group (22 cases) treated with traditional laminectomy and pedicle screw fixation. There were no significant differences between the two groups on gender, age, disease duration, affected segment, presence or absence of dural ossification, and residual cross-sectional vertebral canal area on CT ( P>0.05). Additionally, perioperative surgical time, estimated blood loss (EBL), incision length, hospital stay duration, hospitalization costs and follow-up duration were compared. The Japanese Orthopaedic Association (JOA) score and Oswestry Disability Index (ODI) were compared preoperatively and at the last follow-up. Complications were also recorded. Results:All patients successfully completed the surgery with no significant differences at the last follow-up ( P>0.05). Compared with the open group, the OSE group had a significantly shorter operative time (133.1±16.8 vs. 160.5±22.6 min), lower EBL (91.2±15.0 vs. 192.5±43.8 ml), shorter incision length (2.6±0.5 vs. 7.9±1.9 cm), reduced hospital stay (3.9±0.8 vs. 5.6±0.8 days), and lower hospitalization costs (34,874.9±4,568.6 vs. 53,162.3±9,815.6 yuan) (all P<0.05). AAt the final follow-up, JOA scores (8.5±0.8 vs. 8.6±1.2) and ODI values (16.7%±2.1% vs. 17.7%±4.4%) showed no significant differences between the OSE and open groups ( P>0.05). During the perioperative period and follow-up, complications occurred in 2 patients in the OSE group (1 cerebrospinal fluid leak, 1 poor wound healing) and in 8 patients in the open group (5 cerebrospinal fluid leaks, 1 neurological deterioration, 2 poor wound healing). Conclusion:OSE minimally invasive surgery is an effective treatment for single-segment thoracic ossification of the ligamentum flavum. Compared with open surgery, it provides advantages such as minimal invasiveness and fewer complications.

Objective:To develop a convolutional neural network model of whole slide imaging of cerebrospinal fluid cells for rapid and accurate identification and classification of tumor cells in cerebrospinal fluid.Methods:A total of 8 692 cerebrospinal fluid cytology smears from Huashan Hospital Affiliated to Fudan University from January 2nd, 2019, to December 27th, 2024. As randomly assigned, the training set included 4 941 benign and 1 745 malignant samples, while the validation set comprised of 1 368 benign and 638 malignant samples. Whole-slide digital images were acquired using a cytopathology scanner, cells (clusters) were annotated for classification, and a deep learning model was constructed via tiled image patches for cell detection and classification. Model performance was evaluated using accuracy, sensitivity, specificity, and other indicators. The classification efficiency of manual microscopy was compared.Results:The model achieved a mean precision of 96.75% for cerebrospinal fluid cell classification. For malignant tumor cells, the classification accuracy was 96.61% (mAP=98.36%, AUC=0.97). Subtype classification accuracies for epithelial/epithelioid tumors and small round cell tumors were 97.13% (AUC=0.98) and 95.58% (AUC=0.93), respectively. Compared with manual microscopy, which took (9.70±0.82) minutes for classifying 200 cells, (18.27±1.21) minutes for 500 cells, and often exceeded 60 minutes or infeasible for full slides, the AI model took (3.46±0.49) seconds for 200 cells, (6.76±0.82) seconds for 500 cells, and a median of 48.57 seconds for full slides ( P<0.001), representing an efficiency improvement of approximately 161-170 times, significantly enhancing diagnostic efficiency. Conclusion:This fully automated hierarchical deep learning model enables efficient and accurate tumor cell identification and classification in CSF, providing an effective auxiliary tool for the rapid diagnosis of meningeal carcinomatosis.

Objective To observe the value of spectral CT material separation technology for diagnosing traumatic bone marrow edema in limbs.Methods Totally 51 patients with limb traumatic bone marrow edema were retrospectively enrolled and divided into young group(n=26,18-43 years)and middle-aged group(n=25,46-74 years).Taken MRI as reference standard,the efficacy of spectral CT Water-hydroxyapatite(HAP)image for diagnosing bone marrow edema in trauma area was analyzed,and the Water-HAP density values were compared between groups.Results No significant difference of diagnosing bone marrow edema was found between spectral CT and MRI(x2=0.201,P=0.654),and the consistency was high(Kappa=0.774).Water-HAP density value in bone marrow edema area was higher than that in non bone marrow edema area(t=24.634,P＜0.05),and no significant difference of Water-HAP density values in bone marrow edema area nor non bone marrow edema area was found between young group and middle-aged group(both P＞0.05).Conclusion Spectral CT material separation technology was helpful for diagnosing traumatic bone marrow edema in limbs.