Abstract The prevention and control of myopia in Chinese children and adolescents has become a major public health issue. While maintaining increased outdoor activity as a cornerstone intervention, there is an urgent need to explore new complementary approaches that can be effectively implemented in both indoor and outdoor settings. In recent years, environmental spatial frequency has gained increasing attention as one of the key environmental factors influencing the development and progression of myopia. Both animal studies and human research have confirmed that indoor environments lacking mid to high spatial frequency components, often characterized as "visually impoverished", can promote axial elongation and myopia through mechanisms such as disruption of retinal neural signaling, impaired accommodative function, and altered expression of related molecules. Based on the scientific consensus, it is recommended that "enriching of environmental spatial frequency" should be integrated into the myopia prevention and control framework. Following the principles of schoolled organization, family cooperation, community involvement, and student participation, specific measures are put forward in three areas：optimizing school visual settings, improving home spatial environments, and promoting healthy visual behavior. The aim is to create "visually friendly" indoor environments as an important supplement to outdoor activity, thereby providing a novel perspective and strategy for comprehensively advancing myopia prevention and control among children and adolescents.

Background and aims The mechanism of cholestatic liver injury (CLI) is unclear, and effective therapies are lacking. While peroxisome proliferator-activated receptor alpha (PPARα) agonists show potential hepatoprotective effect and pyroptosis is implicated in hepatocellular damage, how PPARα activation mitigates lithocholic acid (LCA)-induced pyroptosis remains unknown. Methods The hepatoprotective effect of PPARα agonists was evaluated in a mouse model of intrahepatic cholestasis induced by LCA. Liver injury was assessed via serum biochemistry, hematoxylin and eosin and TUNEL staining, and electron microscopy. Pyroptosis pathways were analyzed using real-time quantitative polymerase chain reaction, Western blot, and co-immunoprecipitation. Results Combined morphological, histopathological, and biochemical analyses confirmed that PPARα activation protects against CLI. Compared with LCA treatment alone, PPARα activation significantly attenuated the elevation of serum lactate dehydrogenase (LDH), the increased TUNEL-positive cells, and the formation of hepatocyte membrane pores. Mechanistically, PPARα activation suppressed both NOD-like receptor protein 3 (NLRP3) inflammasome-mediated pyroptosis and apoptosis protease-activating factor-1 (APAF-1)/CASPASE-3/GSDME-mediated pyroptosis. Furthermore, PPARα agonist pretreatment inhibited activation of the nuclear factor-kappa B (NF-κB) and forkhead box O1 (FOXO1) signaling pathways. Conclusions PPARα protects against LCA-induced CLI by inhibiting both NLRP3 inflammasome-mediated pyroptosis associated with NF-κB and APAF-1/CASPASE-3/GSDME-mediated pyroptosis associated with the FOXO1 signaling pathway.

Objective To investigate the causal relationship between gut microbiota and viral pneumonia,as well as the underlying mechanisms,using two-sample and two-step Mendelian randomization(MR)approaches,thereby providing novel insights for the prevention and treatment of viral pneumonia.Methods All data were obtained from publicly available genome-wide association studies(GWAS)pooled datasets,including gut microbiota data from the MiBioGen Consortium and the Netherlands Microbiome Project,viral pneumonia data from the FinnGen R10 database,and plasma metabolome data from the publicly available GWAS Catalog.Instrumental variables(IVs)were extracted according to the predefined threshold values.MR analyses were conducted using inverse variance weighting(IVW),MR-Egger,weighted median(WME),weighted mode(WM),and Bayesian-weighted Mendelian randomization(BWMR)methods.Reverse MR analysis was performed to determine whether there was a reverse association.Two-step MR analysis was used to explore the potential mediating role of plasma metabolites,and a series of sensitivity analyses were performed to test the stability of the results.Results Among 196 gut microbiota taxa from the MiBioGen consortium GWAS,11 taxa were associated with viral pneumonia.An increase in the abundance of 4 taxa increased the risk of viral pneumonia,while an increase in the abundance of 7 taxa had a protective effect against viral pneumonia.Among the 207 gut microbiota taxa from the Dutch Microbiome Project GWAS data,10 taxa were associated with viral pneumonia,with 6 risk-increasing and 4 protective taxa identified.Mediation analysis results showed that the causal effect of Defluviitaleaceae on viral pneumonia(OR=0.708,95%CI 0.540-0.929,P=0.013)was mediated to some extent by the N6-acetyllysine levels,with a mediation ratio of 18.4%.Sensitivity analyses did not reveal significant heterogeneity or horizontal pleiotropy.Conclusions Specific gut microbiota are causally associated with viral pneumonia and show potential differences across different populations;the protective effect of Defluviitaleaceae against viral pneumonia may be mediated by the N6-acetyllysine levels.Targeting metabolites may become a potential therapeutic approach for viral pneumonia.

OBJECTIVE: To investigate the therapeutic potential of octaarginine (R8)-modified essential oil from Fructus Alpiniae zerumbet (EOFAZ) lipid microspheres (EOFAZ@^R8LM) for cardiovascular therapy. METHODS: EOFAZ@^R8LM was developed by leveraging the volatilization of EOFAZ and integrating it with the oil phase of LM, followed by surface modification with cell-penetrating peptide R8 to target the site of vascular endothelial injury. The therapeutic effects of this formulation in alleviating lipopolysaccharide-induced vascular endothelial inflammation were evaluated by assessing mitochondrial membrane potential (MMP), intracellular reactive oxygen species (ROS) levels, as well as inflammatory factors interleukin-6 (IL-6) and interleukin-1β (IL-1β) levels. RESULTS: EOFAZ@^R8LM effectively delivered EOFAZ to the site of injury and specifically targeted the mitochondria in vascular endothelial cells, thereby ameliorating mitochondrial dysfunction through regulation of MMP and reduction of intracellular ROS levels. Moreover, it attenuated the expression levels of IL-6 and IL-1β, exerting protective effects on the vascular endothelium. CONCLUSION Our findings highlight the significant therapeutic potential of EOFAZ@^R8LM in cardiovascular therapy, providing valuable insights for developing novel dosage forms utilizing EOFAZ for effective treatment against cardiovascular diseases.

Natural antimicrobial peptides (AMPs) are promising candidates for the development of a new generation of antimicrobials to combat antibiotic-resistant pathogens. They have found extensive applications in the fields of medicine, food, and agriculture. However, efficiently screening AMPs from natural sources poses several challenges, including low efficiency and high antibiotic resistance. This review focuses on the action mechanisms of AMPs, both through membrane and non-membrane routes. We thoroughly examine various highly efficient AMP screening methods, including whole-bacterial adsorption binding, cell membrane chromatography (CMC), phospholipid membrane chromatography binding, membrane-mediated capillary electrophoresis (CE), colorimetric assays, thin layer chromatography (TLC), fluorescence-based screening, genetic sequencing-based analysis, computational mining of AMP databases, and virtual screening methods. Additionally, we discuss potential developmental applications for enhancing the efficiency of AMP discovery. This review provides a comprehensive framework for identifying AMPs within complex natural product systems.

In recent years, the application of artificial intelligence (AI) in the field of biology has witnessed remarkable advancements. Among these, the most notable achievements have emerged in the domain of protein structure prediction and design, with AlphaFold and related innovations earning the 2024 Nobel Prize in Chemistry. These breakthroughs have transformed our ability to understand protein folding and molecular interactions, marking a pivotal milestone in computational biology. Looking ahead, it is foreseeable that the accurate prediction of various physicochemical properties of proteins—beyond static structure—will become the next critical frontier in this rapidly evolving field. One of the most important protein properties is thermodynamic stability, which refers to a protein’s ability to maintain its native conformation under physiological or stress conditions. Accurate prediction of protein stability, especially upon single-point mutations, plays a vital role in numerous scientific and industrial domains. These include understanding the molecular basis of disease, rational drug design, development of therapeutic proteins, design of more robust industrial enzymes, and engineering of biosensors. Consequently, the ability to reliably forecast the stability changes caused by mutations has broad and transformative implications across biomedical and biotechnological applications. Historically, protein stability was assessed via experimental methods such as differential scanning calorimetry (DSC) and circular dichroism (CD), which, while precise, are time-consuming and resource-intensive. This prompted the development of computational approaches, including empirical energy functions and physics-based simulations. However, these traditional models often fall short in capturing the complex, high-dimensional nature of protein conformational landscapes and mutational effects. Recent advances in machine learning (ML) have significantly improved predictive performance in this area. Early ML models used handcrafted features derived from sequence and structure, whereas modern deep learning models leverage massive datasets and learn representations directly from data. Deep neural networks (DNNs), graph neural networks (GNNs), and attention-based architectures such as transformers have shown particular promise. GNNs, in particular, excel at modeling spatial and topological relationships in molecular structures, making them well-suited for protein modeling tasks. Furthermore, attention mechanisms enable models to dynamically weigh the contribution of specific residues or regions, capturing long-range interactions and allosteric effects. Nevertheless, several key challenges remain. These include the imbalance and scarcity of high-quality experimental datasets, particularly for rare or functionally significant mutations, which can lead to biased or overfitted models. Additionally, the inherently dynamic nature of proteins—their conformational flexibility and context-dependent behavior—is difficult to encode in static structural representations. Current models often rely on a single structure or average conformation, which may overlook important aspects of stability modulation. Efforts are ongoing to incorporate multi-conformational ensembles, molecular dynamics simulations, and physics-informed learning frameworks into predictive models. This paper presents a comprehensive review of the evolution of protein thermodynamic stability prediction techniques, with emphasis on the recent progress enabled by machine learning. It highlights representative datasets, modeling strategies, evaluation benchmarks, and the integration of structural and biochemical features. The aim is to provide researchers with a structured and up-to-date reference, guiding the development of more robust, generalizable, and interpretable models for predicting protein stability changes upon mutation. As the field moves forward, the synergy between data-driven AI methods and domain-specific biological knowledge will be key to unlocking deeper understanding and broader applications of protein engineering.

Traditional open thyroid surgery often leaves a scar on the neck,which can affect cosmetic outcomes.Therefore,various endoscopic thyroidectomy approaches via extra-cervical approaches have been developed.However,due to the unique anatomical characteristics of the neck and limitations of endoscopic instruments,conventional endoscopic techniques have certain drawbacks.Robot-assisted endoscopic thyroid surgery can help overcome these limitations.At present,robotic surgical systems remain expensive and the associated surgical costs are high,limiting their widespread adoption.Most surgeons are still relatively unfamiliar with the technique.Nevertheless,with ongoing technological advancements and cost reductions,robot-assisted surgery holds great promise for broader application.Based on years of large-scale experience in endoscopic thyroid surgery at our center,and drawing upon both domestic and international experiences with robotic thyroidectomy,this paper summarizes and proposes a seven-step protocol for robot-assisted endoscopic thyroidectomy via the bilateral axillo-breast approach,aiming to provide a practical reference for the clinical adoption of this technique.

Objective To evaluate the effects of fasting and high-fat diet on the pharmacokinetics of rabeprazole sodium enteric-coated tablets in healthy Chinese subjects.Methods A single-center,randomized,open,two-agent,two-sequence,four-cycle,fully repeated crossover,single-dose trial design was used in this study,healthy subjects were assigned to receive single dose of rabeprazole sodium enteric-coated tablets 0.1 g in either fasting or high-fat diet state,and blood samples were taken at different time points,respectively.The concentrations of rabeprazole sodium enteric-coated in plasma were determined by liquid chromatography-tandem mass spectrometry(LC-MS/MS),the model method of the non-compartmental was used to calculate the pharmacokinetic parameters by Phoenix WinNonlin 8.2.Results The main pharmacokinetic parameters of rabeprazole sodium enteric-coated tablets in fasting state and high-fat diet state were as follows:Cmax were(339.63±156.47)and(318.86±132.13)ng·mL-1;t1/2 were(2.34±0.68)and(3.60±2.40)h;AUC0_t were(556.62±251.65)and(528.50±201.78)ng·mL-1·h;AUC0-∞ were(563.39±255.69)and(535.15±203.24)ng·mL-1·h;tmax were 3.65 and 6.99 h.After high-fat diet,the Cmax and AUC of rapeprazole sodium after high-fat and high-calorie diet decreased,Cmax decreased by 6.12％,AUC0-t decreased by 5.05％,AUC0-∞ decreased by 5.01％,andtmaxwas delayed by about 3.34 h.Cmax,AUC0-t and AUC0-∞ 90％confidence interval were 73.13％-115.10％,83.22％-112.28％and 83.40％-112.13％,respectively.Neither was between 85.00％-125.00％.Conclusion High-fat diet affects the absorption rate and degree of rabeprazole sodium enteric-coated,so it is suitable to be administered on an empty stomach.

Objective:To explore the application effects of improved case-based learning (CBL) combined with hierarchical and progressive teaching in the standardized training of nuclear medicine resident physicians.Methods:A total of 43 resident physicians who rotated in the nuclear medicine base of our hospital between 2018 and 2023 were selected as the research subjects and divided into an experimental group ( n=22) and a control group ( n=21) according to the order of enrollment. The control group received the traditional teacher-centered teaching model and was divided into junior and senior subgroups according to the training years. The experimental group received the improved CBL combined with hierarchical and progressive teaching. First, through multidimensional evaluation (theoretical testing + standardized case analysis), the students were scientifically divided into preliminary, intermediate, and advanced levels. Subsequently, progressive teaching objectives were set for different levels, and real clinical cases embedded with hierarchical learning tasks were published. During the teaching process, a dynamic discussion mode combining homogeneous and heterogeneous grouping was adopted, and personalized guidance was provided by the teachers. Finally, dynamic hierarchical adjustment was implemented through process evaluation. After the training, the two groups were compared in terms of exit assessment performance (including professional basic theory and practical skills) and teaching satisfaction. Results:The total score of the exit assessment of the experimental group was higher than that of the control group [(86.90±6.78) vs. (75.09±8.45)], and the difference was statistically significant. In terms of practical skill assessment, the experimental group scored higher than the control group in modules such as imaging symptom description [(22.34±2.56) vs. (19.85±3.12)], localization diagnosis [(23.01±2.11) vs. (20.12±2.98)], qualitative diagnosis [(22.89±2.67 vs. 18.67±3.45)], and differential diagnosis [(21.56±2.89) vs. (17.23±3.78)] ( P<0.01). The teaching satisfaction survey showed that the satisfaction scores of the experimental group were higher than those of the control group in nine domains, including theoretical knowledge mastery, clinical thinking, image interpretation, and learning initiative ( P<0.05). Conclusions:The improved CBL combined with hierarchical and progressive teaching can effectively improve the exit assessment performance, clinical practice skills, and teaching satisfaction of nuclear medicine resident trainees, and is worthy of promotion.

OBJECTIVE The emergence of evolving variants of Coronavirus disease 2019(COVID-19)has fostered the need for change of newer and adaptive treatments for these infections.During the COVID-19 pandemic and persists,traditional Chinese medicine(TCM)herbs exhibit significant bioactivity and therapeutic effect.This study is aimed to evaluate the efficacy of four TCM preparations on 28-day mortality risk of patients and changes of the laboratory indicators.METHODS The retrospective cohort study included patients with COVID-19 who were admitted to the Jiangsu Province Hospital of Chinese Medicine from December 15,2022 to January 15,2023,and those died within 48 hours of admission or cannot be tracked for outcomes were excluded.The pri-mary outcome was survival status in 28 days(death or survival)starting from the day of admission.The second outcomes were labora-tory indicators,including absolute lymphocyte count,lactate dehydrogenase,creatinine,and blood urea nitrogen.Binary logistic re-gressions were used to estimate the effect of TCM preparations on the primary and secondary outcomes in main analysis.Meanwhile,heterogeneity and robustness of results from main analysis were assessed by subgroup analyses and multiple sensitivity analyses.RESULTS 1 816 eligible patients were included in analysis dataset,including 573 patients received standard care(control group)and 1 243 patients received TCM preparations(hospital preparation group).The 28-day mortality rate of hospital preparation group was lower than that of control group(4.75%vs.14.83%),and the difference was statistically significant(χ2=54.666,P<0.001).The risk of 28-day mortality was 0.535 times lower in the hospital preparation group as compared with the control group(OR=0.46,95%CI:0.305-0.708,P<0.001)showed by multivariable binary logistic regressions.Subgroup analyses showed that taking TCM preparations reduced the 28-day mortality risk.Sensitivity analyses demonstrated that the results of the main analysis for primary outcomes were robust.For secondary outcomes,the risk of abnormal absolute lymphocyte counts at discharge in the hospital prepara-tion group decreased by 0.284 times(OR=0.703,95%CI:0.515-0.961,P=0.027).CONCLUSION Compared with standard of care,taking four hospital preparations including Kanggan Heji,Feining Heji,Qishen Gubiao Keli,and Qianghuo Qushi Qingwen Heji decreased risk of 28-day mortality among hospitalized COVID-19 patients.TCM therapy achieves adequate therapeutic effects in COVID-19.