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.

Purpose To investigate the clinicopathological features and diagnosis of breast epithelial-myoepithelial tumors.Methods Collect 10 cases of breast epithelial-myoepithelial tumors.Clinical features,histopathologic fea-tures,immunophenotype,and molecular characteristics were analyzed.Results All patients were female,aged 27-49 years,and the maximum diameter of the mass was 1.5 to 6.0 cm.Among them,8 cases were diagnosed as adeno-myoepithelioma(AME)of the breast,1 case as atypical AME,and 1 case as malignant adenomyoepithelioma(AME-M).All ten patients were alive with no evidence of disease or metastasis,with follow-up periods ranging from 4 to 64 months.All epithelial-myoepithelial tumors consisted of proliferative epithelial and myoepithelial cells,with myoepithe-lial hyperplasia being the predominant component.In the atypical AME case,myoepithelial cells exhibited mild to moderate atypia,with mitotic figures observed occasionally(3/10 HPF).AME-M was composed of solid and cystic ar-eas.In the solid area,myoepithelial cells exhibited moderate to severe atypia with a high mitotic rate(6-8/10 HPF).Additionally,necrosis and areas of squamous cell carcinoma were present,leading to a diagnosis of epithelial-myoepithelial carcinoma.Papillary squamous cell carcinoma was found in cystic area and it involved the adjacent lob-ules.The peripheral breast tissue contained focal ductal carcinoma in situ.The luminal epithelial cells showed expres-sion of CK8/18 and CK7.The myoepithelial cells showed expression of p63,CD10,and SMA.The Ki67 index was less than 10％in AME,40％in atypical AME.In the AME-M,it was 80％in the epithelial-myoepithelial carcinoma component,and 70％in the papillary squamous cell carcinoma component.Sanger sequencing of 6 cases AME and 1 case atypical AME revealed no hotspot mutations in HRAS or PIK3CA.In one case of AME-M,pyrosequencing indica-ted no hotspot mutations of AKT1,KRAS,HRAS,and PIK3 CA in the components of epithelial-myoepithelial carcino-ma and papillary squamous cell carcinoma while one KRAS(c.183A＞C/T,p.Q61H)mutation was present in the peripheral ductal carcinoma in situ.Molecular genetic analysis on 22 short tandem repeat loci showed an identical pat-tern including LOH at D19S433 in both components of AME-M and squamous cell carcinoma.Conclusion AME is an rare tumor with a heterogeneous morphology.The biphasic proliferation of epithelial and myoepithelial components is the key to correct diagnosis.Molecular genetic analysis suggested AME-M and squamous cell carcinoma were clonally independent from the peripheral ductal carcinoma in situ.

Recently, photodynamic therapy (PDT) has gained considerable attention as a promising therapeutic approach for the treatment of psoriasis. Unfortunately, the activation of high mobility group box 1 protein (HMGB1) by PDT triggers innate and adaptive immune responses, which exacerbate skin inflammation. Herein, we combined glycyrrhizic acid (GA), a natural anti-inflammatory compound and immunomodulator derived from the herb Glycyrrhiza uralensis Fisch., with PDT actuated by the photosensitizer chlorin e6 (Ce6) by co-loading them in GA-based lipid nanoparticles coated with a catechol-modified quaternary chitosan salt (GC NPs/QCS-C). GC NPs/QCS-C exhibited high drug loading efficacy, uniform size distribution, an ideal topical adhesive property, enhanced skin retention and penetration in psoriasis-like lesions, and high intracellular uptake in epidermal cells compared with the counterparts. Subsequently, the transdermal administration of GC NPs/QCS-C followed by near-infrared laser radiation in an imiquimod-induced psoriasis-like mouse model significantly ameliorated psoriasis symptoms, promoted the apoptosis of hyperproliferative epidermal cells, and alleviated the inflammatory cascade. The significant therapeutic outcomes of GC NPs/QCS-C were attributed to the synergistic effects of GA and PDT on modulating immune cell recruitment and inhibiting dendritic cell maturation. Our results demonstrated that the topical bio-adhesive nanosystem that combines GA and Ce6 offers a synergistic chemo-phototherapeutic strategy for psoriasis treatment.

Glioma represents the most prevalent malignant tumor of the central nervous system, with chemotherapy serving as an essential adjunctive treatment. However, most chemotherapeutic agents exhibit limited ability to penetrate the blood-brain barrier (BBB). This study introduced a novel dual-targeting strategy for glioma therapy by modulating the formation of nanobody-driven protein coronas to enhance the brain and tumor-targeting efficiency of hydrophobic cisplatin prodrug-loaded lipid nanoparticles (C8Pt-Ls). Specifically, nanobodies (Nbs) with fibrinogen-binding capabilities were conjugated to the surface of C8Pt-Ls, resulting in the generation of Nb-C8Pt-Ls. Within the bloodstream, Nb-C8Pt-Ls could bound more fibrinogen, forming the protein corona that specifically interacted with LRP-1, a receptor highly expressed on the BBB. This interaction enabled a "Hitchhiking Effect" mechanism, facilitating efficient trans-BBB transport and promoting effective brain targeting. Additionally, the protein corona interacted with LRP-1, which is also overexpressed in glioma cells, achieving precise tumor targeting. Computational simulations and SPR detection clarified the molecular interaction mechanism of the Nb-fibrinogen-(LRP-1) complex, confirming its binding specificity and stability. Our results demonstrated that this strategy significantly enhanced C8Pt accumulation in brain tissues and tumors, induced apoptosis in glioma cells, and improved therapeutic efficacy. This study provides a novel framework for glioma therapy and underscores the potential of protein corona modulation-based dual-targeting strategies in advancing treatments for brain tumors.

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.