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.

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.

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.

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.

The spatio-temporal heterogeneity of breast cell subsets forms the fundamental biological basis for physiological development and pathological progression, including tumorigenesis; however, its complex regulatory mechanisms are not yet fully elucidated. With its high-resolution capabilities, single-cell RNA sequencing (scRNA-seq) technology offers a powerful tool for dissecting this cellular heterogeneity. This technology enables the construction of high-precision breast cell atlases, the accurate identification of distinct cell subsets, and the reconstruction of differentiation trajectories from stem/progenitor cells to functional epithelial cells. By resolving the transcriptional regulatory networks that govern cell fate determination, intercellular communication patterns, and dynamic microenvironmental interactions, scRNA-seq has unveiled the molecular foundations of breast development and provided new perspectives on the pathogenesis of related diseases such as breast cancer and macromastia. Furthermore, scRNA-seq demonstrates significant potential for discovering early molecular markers of disease, deciphering tumor heterogeneity, and elucidating mechanisms of therapeutic resistance. The continued application of scRNA-seq for dissecting breast cell heterogeneity, combined with its integration with multi-modal data such as spatial omics, promises to provide critical evidence and new insights for revealing the molecular mechanisms of breast development-related diseases and for formulating precision therapeutic strategies.
Humans ; Single-Cell Analysis/methods* ; Female ; Breast Neoplasms/pathology* ; Sequence Analysis, RNA/methods* ; Breast/cytology*

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.

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.

Objective:To compare the protective effects of the static cold storage (SCS) and normothermic machine perfusion (NMP) on the skeletal muscle of the amputated limbs of pigs.Methods:Four Landrace pigs were selected, from which eight limbs were amputated and divided into SCS group ( n=5) and NMP group ( n=3) according to the random number table method. After blood collection from the carotid artery, an amputated limb model was established by amputating the limbs at the scapulohumeral joints. The limbs in the SCS group were wrapped in sterile cloth and stored at 4 ℃ for 24 hours. In the NMP group, the limbs were mechanically perfused with a red blood cell-containing perfusion fluid at 37 ℃ for 24 hours, with 70% of the perfusion fluid replaced every 6 hours. Before the experiment, cross-matching tests with the saline medium were conducted between donor and recipient pigs to evaluate blood coagulation and blood safety in the NMP group. An allogeneic red blood cell perfusion fluid was prepared and the levels of pH, Na +, K +, Cl -, Ca 2+, glucose (Glu), hematocrit (Hct), lactic acid (Lac) and osmotic pressure of the perfusion fluid were measured. At 0, 6, 12, 18, and 24 hours after perfusion, the skin temperature and oxyhemoglobin saturation (SaO 2) levels in the NMP group were monitored and the levels of pH, Glu, creatine kinase (Ck), K +, Ca 2+, and Na +levels of the perfusion fluid were analyzed to evaluate the metabolism of the skeletal muscle in the amputated limbs. The mean intercellular distance and apoptosis index of the myocytes were quantitatively analyzed and histopathological changes were observed by performing HE staining and TUNEL staining on the skeletal muscle of the amputated limbs in both groups at 0 and 24 hours after perfusion. After perfusion was ended, the weight gain rate and swelling degree of the amputated limbs were compared between the two groups and the overall state of the amputated limbs was evaluated. Results:The result of the cross-matching test between donor and recipient pig blood was negative. The parameters in the prepared red blood cell-containing perfusion fluid generally maintained within a normal range: pH 7.38±0.04, Na + concentration (138.30±4.48)mmol/L, K + concentration (3.50±0.26)mmol/L, Glu concentration (6.11±2.08)mmol/L, and osmotic pressure (305.67±3.79)mmol/L. However, slightly higher Cl - and Ca 2+ concentrations [(118.34±12.00)mmol/L and (2.00±0.15)mmol/L] and lower Hct and lactate concentrations [0.30±0.03 and (1.54±0.38)mmol/L] were detected when compared with the reference range. During the perfusion, the average skin temperature of the amputated limbs in the NMP group was (36.13±0.98)℃, with the skin temperatures at 6, 12, 18, and 24 hours after perfusion being significantly higher than that at 0 hour ( P<0.01), while no significant difference among the skin temperatures at 6, 12, 18, and 24 hours after perfusion was observed ( P>0.05). The SaO 2 levels in the skin of the amputated limbs in the NMP group averaged over 95%, which showed no significant difference at 0, 12, 18, and 24 hours after perfusion ( P>0.05), while a significant elevation was observed at 6 hours compared with that at 0 hour ( P<0.05). There were no significant differences in pH, Glu, Na +, and Ca 2+ levels in the NMP group at 0, 6, 12, 18, and 24 hours after perfusion ( P>0.05), while the Ck levels at 18 and 24 hours were both significantly higher than that at 6 hours after perfusion ( P<0.05), and the Ck levels at 6, 12, 18, and 24 hours were all significantly higher than that at 0 hour ( P<0.05). The K + level progressively increased with the perfusion time, with significant elevations at 18 and 24 hours after perfusion compared with that at 0 hour ( P<0.05). HE staining revealed well-preserved muscle fiber continuity and regular arrangement in the NMP group and the SCS group at 0 hour, with an intercellular distance of (8.95±0.60)μm. At 24 hours, the NMP group exhibited slight skeletal muscle fiber rupture and swelling, with a slightly increased intercellular distance of (14.75±0.90)μm, significantly greater than that at 0 hour ( P<0.01). At 24 hours, the SCS group showed marked skeletal muscle fiber rupture and swelling, with a significantly increased intercellular distance of (23.51±1.49)μm, significantly larger than those at 0 hour in the same group and at 24 hours in the NMP group ( P<0.01). TUNEL immunofluorescence staining indicated a tiny amount of apoptotic cells in the skeletal muscle in both groups at 0 hour, with an apoptotic index of (4.26±1.62)%. There was a small number of apoptotic cells in the skeletal muscle in the NMP group at 24 hours, with an apoptotic index of (25.94±2.69)%, significantly larger than that in the same group at 0 hour ( P<0.01). The SCS group exhibited a large number of apoptotic cells at 24 hours, with an apoptotic index of (62.97±3.22)%, significantly larger than those at 0 hour in the same group and at 24 hours in the NMP group ( P<0.01). In comparison with the SCS group at 24 hours, the amputated limbs in the NMP group showed red color in the appearance, no symptoms of ischemic muscle contracture and good joint movement despite slight edema in the subcutaneous layer. At 24 hours, the weight gain rate of the amputated limbs was (15.82±0.89)% in the NMP group, significantly higher than (0.97±0.28)% in the SCS group ( P<0.01). Conclusion:Compared with SCS, NMP with the red blood cell-containing perfusion fluid prepared with the allogeneic blood for the amputated limbs of pigs can alleviate the ischemic injury of the muscle fibers and inhibit the apoptosis of the muscle cells by sustaining stable energy and oxygen supply and balancing ion homeostasis and pH of the perfusion fluid.