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.

Thousands of variant gene sequences of SARS-CoV-2 have been emerged since the COVID-19 epidemic broke out in Dec.2019.Alpha(B.1.1.7),Beta(B.1.351),Gamma(P.1),Delta(B.1.617.2)and Omicron(B.1.1.529)were the most representative variants.With the continuous emergence of new variants,the predomi-nant strain in the global pandemic as of Jan.2025 is the Omicron BA.2.86-derived mutant,JN.1.The effectiveness of drugs against Omicron variants remains a key research focus in the treatment of SARS-CoV-2 infections.The infectiousness and pathogenicity of the variants altered remarkably due to mutations in the genome on S protein of the mutant strains,and these emerging variants are more likely to evade immunity and were more infectious than the previous prevalent variants.During the process of combating with the constantly emerging novel variants,drugs showed various effects on treatment of diseases caused by different variants.New drugs and treatment coun-termeasures are constantly updated with the prevalence of various variants.The current status of research on pres-ent drugs for treatment of SARS-CoV-2 and the therapeutic effects on emerging variants are reviewed in the article so as to provide reference for prevention and treatment of the upcoming evolved variants.

OBJECTIVE To introduce the confirmation of research issues related to hospital-associated infections,data cleaning and selection of univariate analysis method.METHODS The data of hospital-associated infections were characterized by complexity and variability,the research subjects were determined through the framework of 'population-intervention-comparison-outcome-study design'(PICOS).The data cleaning serves as the foundational processing for statistical analysis,and its core modes included missing value processing,repeated value processing and abnormal value processing.An appropriate method for statistical analysis of medical data plays an crucial role in drawing a reliable,valuable and significant research conclusion.The previous common errors were sorted out by reviewing literatures,which may help to avoid the repeated occurrence of problems regarding the data han-dling and data analysis.RESULTS The principles for confirmation of research subjects for hospital-acquired infec-tion,collection and cleaning of related data,and selection of method for univariate analysis of different types of da-ta were introduced.The data cleaning methods and the selection of methods for univariate analysis based on the type of data and research objective,including descriptive analysis,univariate measurement data analysis,univari-ate enumeration data analysis and univariate ranked data analysis,were emphatically discussed.A flow chart of univariate statistical analysis was established.CONCLUSION A definite research subject,standardized data clean-ing and appropriate method for univariate statistical analysis may facilitate the improvement of scientificity and re-liability of hospital-acquired infection-related clinical researches.