OBJECTIVE To mine and analyze adverse event signals associated with inebilizumab， and to provide reference for safe and rational clinical use. METHODS Reports of adverse event related to inebilizumab were collected from the FDA adverse event reporting system （FAERS） database， from Q2 2020 to Q4 2024. Adverse events were standardized and categorized according to the preferred term （PT） and system organ class （SOC） of the Medical Dictionary for Regulatory Activities （MedDRA） version 26.0. Signals were mined using the reporting odds ratio （ROR） method and the Bayesian confidence propagation neural network （BCPNN） method. RESULTS A total of 783 adverse event reports with inebilizumab as the primary suspected drug were identified， involving 297 patients. Most reports originated from the United States and Japan， with physicians being the primary reporters. Female patients outnumbered males， and the most common age group was 45-64 years. Using the ROR method and BCPNN method， a total of 29 valid adverse event signals were detected， involving 12 SOCs and comprising 225 adverse event reports. The five most frequently reported PTs were headache， nausea， fatigue， infectious pneumonia and arthralgia. The five PTs with the strongest signal intensity were： B-cell recovery， decreased blood immunoglobulin G， spinal compression fracture， COVID-19 and acute respiratory distress syndrome. Among the 29 valid signals for adverse event， 19 were not documented in the drug package inserts， involving 10 SOCs and comprising 107 adverse event reports. These encompassed nervous system disorders， general disorders and administration site conditions， eye disorders， among others. CONCLUSIONS Inebilizumab treatment not only causes adverse events documented in the product information， such as infections， immunoglobulin reduction and infusion-related reactions but also leads to potential signals， including B-cell recovery， spinal compression fracture. When using this drug in clinical practice， the patient’s risk of infection and baseline immune status should be assessed， relevant indicators should be closely monitored， and targeted preventive measures should be considered when necessary.

ObjectiveTo investigate the mechanism by which Gegen Qinliantang（GQT） improves skeletal muscle insulin resistance. MethodsThe db/m mice were used as the normal group， while db/db mice were assigned to a model group， low-dose （3.12 g·kg^-1）， medium-dose （6.24 g·kg^-1）， and high-dose （12.48 g·kg^-1） GQT groups， and a Western medicine group （semaglutide， 0.045 mg·kg^-1），n=6 in each group. All groups received corresponding interventions. Intraperitoneal glucose tolerance test （IPGTT）， intraperitoneal insulin tolerance test （IPITT）， and hematoxylin-eosin （HE） staining were used to evaluate insulin resistance and therapeutic efficacy. Serum lipid levels were measured using an automatic biochemical analyzer， and apoptosis in skeletal muscle was assessed via TUNEL assay. Transcriptome sequencing combined with gene ontology （GO） and Kyoto Encyclopedia of Genes and Genomes （KEGG） analyses was performed to identify differentially expressed genes （DEGs）. Real-time quantitative polymerase chain reaction （Real-time PCR） was used to validate gene expression. Molecular docking was applied to evaluate the binding patterns between active components of GQT and key regulatory genes to elucidate pharmacological mechanisms. ResultsCompared with the model group， the medium-dose and high-dose GQT groups showed significantly reduced fasting blood glucose （FBG） levels （P<0.01）. Triglycerides （TG）， total cholesterol （TC）， and low-density lipoprotein cholesterol （LDL-C） were markedly decreased （P<0.01）， while high-density lipoprotein cholesterol （HDL-C） was significantly increased （P<0.01）. IPGTT， IPITT， and HE staining demonstrated that GQT enhanced insulin sensitivity and restored skeletal muscle morphology. GQT also alleviated apoptosis in skeletal muscle tissue. Transcriptome analysis revealed that GQT primarily affected biological processes such as oxidative phosphorylation， metabolic pathways， cellular processes， and protein binding. Real-time PCR results showed that CBR2， CDK6， F830016B08Rik， IL-1β， Rab27b， and COLEC12 were key regulatory genes. Molecular docking demonstrated that CBR2， IL-1β， Rab27b， and COLEC12 formed stable binding with the main active components of GQT. The therapeutic effects of high- and medium-dose GQT were comparable to those of the semaglutide group. ConclusionGQT improves skeletal muscle insulin resistance， potentially by regulating apoptosis as part of its underlying biological mechanism.

Intravitreal anti-vascular endothelial growth factor(anti-VEGF)therapy has been widely used, but the variability in its therapeutic efficacy limits individualized treatment. In recent years, the application of artificial intelligence(AI)has opened up new avenues for personalized treatment response prediction, and its core branches include machine learning(ML)and deep learning(DL). This review systematically retrieved and analyzed 41 relevant studies published up to April 2025. Comprehensive analysis reveals that AI predictive models are evolving from forecasting single endpoints(such as visual acuity or central retinal thickness)to integrating multi-dimensional endpoints(encompassing anatomical, functional, and treatment demand parameters)and generating predictive imaging outputs. In terms of technical approaches, DL models(28 studies, accounting for 68.3%)dominate this field due to their robust image interpretation capabilities, while ML models(10 studies, 24.4%)retain significant value in the analysis of structured clinical data. Cross-disease comparisons indicate that research efforts are most concentrated on age-related macular degeneration(ARMD)and diabetic macular edema(DME), with shared conceptual frameworks for model construction, yet distinct anatomical and functional indicators are prioritized for each disease. Currently, the field confronts several key challenges, including insufficient prospective clinical validation, limited model interpretability(the “black box problem”), and a scarcity of high-quality multi-center datasets. Moving forward, it is imperative to advance real-world validation and develop explainable AI techniques to expedite the clinical translation of these predictive models.

Objective Orosomucoid1 （ORM1） is a novel target in the quest for anti-fatigue pharmacotherapy. Preliminary investigations have illuminated oleuropein （OLE） as a promising candidate molecule, poised to enhance ORM1 expression. To elucidate the influence of OLE on ORM1 protein expression and assess its ramifications on muscle endurance. Methods The impact of OLE on ORM1 protein expressions within hepatocytes and liver tissue was meticulously quantified through Western blotting; the effects of OLE on muscle endurance were evaluated via the rotarod and forced swimming tests; glycogen content within liver and muscle tissues was determined utilizing a specialized kit; and PAS staining was employed to visualize glycogen deposition in the gastrocnemius muscle. Results OLE demonstrated a capacity to elevate ORM1 protein expression in hepatocytes in a time- and dose-dependent manner, concurrently prolonging the duration of swimming and rotarod performance in mice, also in a time- and dose-dependent manner. Furthermore, OLE augmented ORM1 expression in liver tissue, elevated serum ORM1 levels, and enhanced glycogen reserves within the liver and muscle. Conclusion OLE may serve to amplify muscle endurance by elevating ORM1 levels in vivo and augmenting glycogen stores within skeletal muscle.

Tuberculosis (TB), a chronic infectious disease caused by Mycobacterium tuberculosis, is primarily airborne and remains a global health problem, especially in resource-limited countries and regions. The emergence of drug resistance in M. tuberculosis has rendered the existing means ineffective in the treatment of TB. Therefore, research in new therapeutic directions has become imperative. In this review, we outline functional peptides in terms of the mechanisms of action, anti-TB attempts, advantages and disadvantages, and latest advances, aiming to analyze the research progress in anti-TB peptides. Furthermore, we investigate the potential applications of bioactive compounds found in traditional Chinese Medicine within the context of peptides.

Objective: To investigate the association between uric acid level and elevated blood pressure among children and adolescents in Guangzhou, so as to provide a scientific basis for prevention and control of hypertension in the pediatric population. Methods: From March to December 2019, using stratified cluster random sampling, 3 626 children and adolescents aged 6-17 years without elevated blood pressure at baseline were recruited from 6 primary and secondary schools in Guangzhou. Serum uric acid levels were measured at baseline, and divided into four groups ( Q1-Q 4) based on age and sex specific quartiles for analyses. Two waves of follow up were carried out from October 2020 to May 2021 and from October 2021 to May 2022, respectively. Multilevel robust Poisson regression was used to evaluate the association between uric acid level and elevated blood pressure among children and adolescents. The additive interaction between high uric acid level and sugar sweetened beverage (SSB) intake on elevated blood pressure was assessed by stratification analysis and calculation of the relative excess risk due to interaction (RERI). Results: A total of 1 131 new cases of elevated blood pressure were identified during the two follow ups, with an incidence density of 130.5 cases 1 000 person years. Multilevel robust Poisson regression analysis showed that after adjusting for confounders such as gender, age and annual family income, compared with those in the uric acid Q 1 level, the risk of elevated blood pressure in individuals in the Q 4 level increased by 25% ( RR=1.25, 95%CI =1.08-1.44), and the risk of elevated systolic blood pressure increased by 20% ( RR=1.20, 95%CI =1.05-1.36) (both P <0.05); no statistical association was observed between uric acid and elevated diastolic blood pressure (all P >0.05). In the stratified analysis, the statistical associations of uric acid Q 4 level with elevated blood pressure ( RR=1.34, 95%CI =1.13-1.58) and elevated systolic blood pressure ( RR=1.33, 95%CI =1.14-1.55) were only observed in the group with SSB intake ≥ 250 mL/week (both P <0.05). The RERI values for the effect of uric acid Q 4 and Q 3 levels with SSB intake ≥ 250 mL/week on elevated systolic and diastolic blood pressure were 0.29 and 0.44 (both P <0.05). Conclusions High uric acid level is a risk factor for elevated blood pressure among children and adolescents, with a predominant effect on systolic blood pressure. High uric acid level and SSB intake synergistically promote the increase of blood pressure of children and adolescents.

Objective To investigate the correlation of dietary lipid intake and apolipoprotein with Helicobacter pylori (Hp) infection combined with gastric mucosal lesions. Methods A retrospective analysis was conducted on 320 patients diagnosed with Hp infection during physical examinations from March 2024 to March 2025. The daily dietary lipid intake of all patients was statistically analyzed. The levels of apolipoprotein A1‌ (Apo-A1) and apolipoprotein B‌ (Apo B) were detected. Logistic regression was used to analyze the risk factors influencing Hp infection combined with gastric mucosal lesions. Results Compared with the non-gastric mucosal lesion group, patients in the gastric mucosal lesion group had higher daily dietary lipid intake, lower Apo-A1, higher Apo B, and higher proportion of family history of gastric cancer (P<0.05). Logistic regression analysis revealed that daily dietary lipid intake, low level of Apo-A1, and high level of Apo B were independent risk factors of Hp infection with gastric mucosal lesion (P<0.05). Conclusion Daily dietary lipid intake, Apo-A1, and Apo B are closely related to Hp infection with gastric mucosal lesion. For patients with high daily dietary lipid intake, decreased Apo-A1 level, increased Apo B level, and complicated Hp infection, it is necessary to further screen gastroscopy to determine whether gastric mucosal lesion occurs.

ObjectiveTo explore the function and related molecular mechanisms of magnoflorine against colon cancer via network pharmacology， molecular docking， and in vitro cell experiments. MethodsIn this study， the canonical SMILES of magnoflorine was obtained from the PubChem database， and the potential targets of magnoflorine were predicted by the Swiss Target Prediction database， while the disease targets of colon cancer were obtained from the DisGeNET， GeneCards and OMIM databases. The intersecting targets between magnoflorine's predicted targets and colon cancer disease targets were taken， and a protein-protein interaction （PPI） network was constructed and analyzed. The DAVID online database was employed to conduct Gene Ontology （GO） and KEGG pathway enrichment analyses on core targets. The top five key targets screened were docked with magnoflorine using AutoDock software. Finally， cellular experiments including CCK-8 assays， EdU experiments， cell scratch assays， and Transwell assays were conducted to validate the results from network pharmacology and molecular docking. Results44 key targets of magnoflorine in resisting colon cancer were acquired. The molecular docking results showed that magnoflorine had a strong binding activity with the core target signal transducer and activator of transcription 3 （STAT3） in the top five of the PPI network. Cellular experiments confirmed that magnoflorine could inhibit the proliferation and migration of colon cancer cells by suppressing the JAK/STAT3 signaling pathway. ConclusionMagnoflorine may inhibit the proliferation and migration of colon cancer cells by regulating the JAK/STAT3 signaling pathway.

Objective To evaluate the current status in the implementation of GBZ/T 201.5-2015 Radiation shielding requirements for radiotherapy rooms-Part 5: Radiotherapy room of proton accelerators, identify issues in the application of its technical indicators, and provide a basis for the in-depth implementation and further revision of the standard. Methods In accordance with the Standardization Law of the People’s Republic of China and the Guidelines for Health Standards Tracking Evaluation (WS/T 536-2017), a combination of cluster sampling and stratified sampling methods was employed to select professionals involved in proton accelerator radiotherapy devices and facilities in three provinces (or municipalities directly under the central government) as the subjects of the survey. A questionnaire was developed to collect basic information about the subjects and their understanding and application of the technical indicators in the standard. A standard evaluation indicator system with a total score of 100 points was established to score the implementation of the standard (40 points), the technical content (30 points), and the effectiveness of the implementation (30 points). Results A total of 169 professionals from 107 institutions participated in the survey, with 79.88% of the respondents having at least 5 years of experience in radiation therapy and 74.56% holding intermediate or higher professional titles. The score of standard implementation was 18.3 points. The awareness rate exceeded 80%, indicating a high level of awareness about the standard. However, the scores for the dissemination and application of the standard were relatively low, accounting for 28% and 32% of their respective full marks. The technical content of the standard and the effectiveness of its implementation scored 27.0 and 26.6 points, respectively. The overall score in the evaluation of standard implementation was 72 points, with scores of 68.6, 72.3, and 75.0 for Beijing City, Shanghai City, and Jiangsu Province, respectively. Conclusion GBZ/T 201.5-2015 Radiation shielding requirements for radiotherapy rooms-Part 5: Radiotherapy room of proton accelerators is scientific and operable, and it is well-coordinated with relevant laws and standards. However, considering the development in FLASH technology and multi-chamber radiotherapy room, it is necessary to revise and improve the standard.

Objective: To explore the feasibility of constructing a lung cancer early-warning risk model based on facial image features, providing novel insights into the early screening of lung cancer. Methods: This study included patients with pulmonary nodules diagnosed at the Physical Examination Center of Shuguang Hospital Affiliated to Shanghai University of Traditional Chinese Medicine from November 1, 2019 to December 31, 2024, as well as patients with lung cancer diagnosed in the Oncology Departments of Yueyang Hospital of Integrated Traditional Chinese and Western Medicine and Longhua Hospital during the same period. The facial image information of patients with pulmonary nodules and lung cancer was collected using the TFDA-1 tongue and facial diagnosis instrument, and the facial diagnosis features were extracted from it by deep learning technology. Statistical analysis was conducted on the objective facial diagnosis characteristics of the two groups of participants to explore the differences in their facial image characteristics, and the least absolute shrinkage and selection operator (LASSO) regression was used to screen the characteristic variables. Based on the screened feature variables, four machine learning methods: random forest, logistic regression, support vector machine (SVM), and gradient boosting decision tree (GBDT) were used to establish lung cancer classification models independently. Meanwhile, the model performance was evaluated by indicators such as sensitivity, specificity, F1 score, precision, accuracy, the area under the receiver operating characteristic (ROC) curve (AUC), and the area under the precision-recall curve (AP). Results: A total of 1 275 patients with pulmonary nodules and 1 623 patients with lung cancer were included in this study. After propensity score matching (PSM) to adjust for gender and age, 535 patients were finally included in the pulmonary nodule group and the lung cancer group, respectively. There were significant differences in multiple color space metrics (such as R, G, B, V, L, a, b, Cr, H, Y, and Cb) and texture metrics [such as gray-levcl co-occurrence matrix (GLCM)-contrast (CON) and GLCM-inverse different moment (IDM)] between the two groups of individuals with pulmonary nodules and lung cancer (P < 0.05). To construct a classification model, LASSO regression was used to select 63 key features from the initial 136 facial features. Based on this feature set, the SVM model demonstrated the best performance after 10-fold stratified cross-validation. The model achieved an average AUC of 0.8729 and average accuracy of 0.799 0 on the internal test set. Further validation on an independent test set confirmed the model’s robust performance (AUC = 0.823 3, accuracy = 0.729 0), indicating its good generalization ability. Feature importance analysis demonstrated that color space indicators and the whole/lip Cr components (including color-B-0, wholecolor-Cr, and lipcolor-Cr) were the core factors in the model’s classification decisions, while texture indicators [GLCM-angular second moment (ASM)_2, GLCM-IDM_1, GLCM-CON_1, GLCM-entropy (ENT)_2] played an important auxiliary role. Conclusion The facial image features of patients with lung cancer and pulmonary nodules show significant differences in color and texture characteristics in multiple areas. The various models constructed based on facial image features all demonstrate good performance, indicating that facial image features can serve as potential biomarkers for lung cancer risk prediction, providing a non-invasive and feasible new approach for early lung cancer screening.