ObjectiveTo explore the effect of serum containing Zhenwutang on myocardial mast cell apoptosis induced by angiotensin Ⅱ （AngⅡ） and the mechanism of the correlation between apoptosis and mitochondrial autophagy. MethodsIn this experiment， AngⅡ and serum containing Zhenwutang with different concentrations were used to interfere with H9C2 cardiomyocytes for 24 h， and the survival rate of H9C2 cardiomyocytes was detected by cell counting kit-8 （CCK-8） to screen the optimal concentration for the experiment. Enzyme-linked immunosorbent assay （ELISA） was used to detect the content of B-type natriuretic peptide （BNP） in cell culture supernatant， and immunofluorescence was used to detect the cell surface area to verify the construction of the myocardial mast cell model. Subsequently， the experiment was divided into a blank group （20% blank serum）， a model group （20% blank serum + 5×10^-5 mol·L^-1 AngⅡ）， low-， medium-， and high-dose （5%， 10% and 20%） serum containing Zhenwutang groups， an autophagy inhibitor group （1×10^-4 mol·L^-1 3-MA）， and autophagy inducer group （1×10^-7 mol·L^-1 rapamycin）. The apoptosis level of H9C2 cells and the changes of mitochondrial membrane potential were detected by flow cytometry. The lysosomal probe （Lyso Tracker） and mitochondrial probe （Mito Tracker） co-localization was employed to detect autophagy. Real-time fluorescence quantitative polymerase chain reaction （Real-time PCR） was used to detect Caspase-3， Caspase-9， B-cell lymphoma 2 （Bcl-2）， Bcl-2-related X protein （Bax）， and cytochrome C （Cyt C） in apoptosis-related pathways and the relative mRNA expression of ubiquitin ligase （Parkin）， phosphatase and tensin homolog （PTEN）-induced kinase 1 （PINK1）， and p62 protein in mitochondrial autophagy-related pathways. Western blot was used to detect cleaved Caspase-3， cleaved Caspase-9， Bax， Bcl-2， and Cyt C in apoptosis-related pathways， phosphorylated ubiquitin ligase （p-Parkin）， phosphorylated PTEN-induced kinase 1 （p-PINK1）， p62， and Bcl-2 homology domain protein Beclin1 in mitochondrial autophagy-related pathways， and the change of microtubule-associated protein 1 light chain 3 （LC3） Ⅱ/Ⅰ ratio. ResultsCCK-8 showed that when the concentration of AngⅡ was 5×10^-5 mol·L^-1， the cell activity was the lowest， and there was no cytotoxicity. At this concentration， the surface area of cardiomyocytes was significantly increased （P<0.01）， and the content of BNP in the supernatant of culture medium was significantly increased （P<0.05）. Therefore， AngⅡ with a concentration of 5×10^-5 mol·L^-1 was selected for the subsequent modeling of myocardial mast cells. Compared with the blank group， the model group and the autophagy inhibitor 3-MA group had a significantly increased apoptosis rate （P<0.01） and significantly decreased mitochondrial membrane potential （P<0.01）. The results of immunofluorescence co-localization showed that compared with the blank group， the model group had a significantly decreased number of red and green fluorescence spots. The results of Real-time PCR showed that compared with that in the blank group， the relative mRNA expression of Bax， Caspase-3， Caspase-9， Cyt C， and p62 in the model group was significantly up-regulated （P<0.01）， while the relative mRNA expression of Bcl-2， Parkin， and PINK1 was significantly down-regulated （P<0.01）. In addition， the relative protein expression of Bax， cleaved Caspase-3， cleaved Caspase-9， Cyt C， and p62 was significantly up-regulated （P<0.01）. The LC3Ⅱ/Ⅰ was significantly decreased， and the relative protein expression of Bcl-2， p-Parkin， p-PINK1， and Beclin1 was significantly down-regulated （P<0.01）. Compared with the model group， the serum containing Zhenwutang groups and the autophagy inducer group had significantly decreased apoptosis rate （P<0.01）， and the decrease ratio of mitochondrial membrane potential is significantly lowered （P<0.01） in a dose-dependent manner. Additionally， both red and green fluorescence spots became more in these groups. In the 3-MA group， the number of red and green fluorescence spots decreased significantly. The relative mRNA expression of Bax， Caspase-3， Caspase-9， Cyt C， and p62 was significantly down-regulated （P<0.05， P<0.01）， while that of Bcl-2， Parkin， and PINK1 was significantly up-regulated （P<0.01）. In the serum containing Zhenwutang groups， the relative protein expression levels of Bax， cleaved Caspase-3， cleaved Caspase-9， Cyt C， and p62 were significantly down-regulated （P<0.05，P<0.01）. The LC3Ⅱ/Ⅰ was significantly increased， and the relative protein expression levels of Bcl-2， p-Parkin， p-PINK1， and Beclin1 were significantly up-regulated （P<0.01）. ConclusionThe serum containing Zhenwutang can reduce the apoptosis of myocardial mast cells and increase mitochondrial autophagy. This is related to the inhibition of intracellular Bax/Bcl-2/Caspase-3 apoptosis pathway and regulation of Parkin/PINK1 mitochondrial autophagy pathway.

With the widespread use of antibiotics, drug-resistant bacterial infections have become a significant threat to human health. Finding new antibacterial strategies that can effectively control drug-resistant bacterial infections has become an urgent task. Unlike small molecule drugs that target bacterial proteins, antisense oligonucleotide (ASO) can target genes related to bacterial resistance, pathogenesis, growth, reproduction and biofilm formation. By regulating the expression of these genes, ASO can inhibit or kill bacteria, providing a novel approach for the development of antibacterial drugs. To overcome the challenge of delivering antisense oligonucleotide into bacterial cells, various drug delivery systems have been applied in this field, including cell-penetrating peptides, lipid nanoparticles and inorganic nanoparticles, which have injected new momentum into the development of antisense oligonucleotide in the antibacterial realm. This review summarizes the current development of small nucleic acid drugs, the antibacterial mechanisms, targets, sequences and delivery vectors of antisense oligonucleotide, providing a reference for the research and development of antisense oligonucleotide in the treatment of bacterial infections.

Methods: Seventy-five patients with atlas fracture were included from January 2015 to December 2020. Based on the anatomy of the fracture line, atlas fractures were divided into three types. Each type was divided into two subtypes according to the fracture displacement. Unweighted Cohen kappa coefficients were applied to evaluate the reliability and reproducibility. Results: According to the new classification, 17 cases of type A1, 12 of type A2, seven of type B1, 13 of type B2, 12 of type C1, and 14 of type C2 were identified. The K-values of the interobserver and intraobserver reliability were 0.846 and 0.912, respectively, for the new classification. The K-values of interobserver reliability for types A, B, and C were 0.843, 0.799, and 0.898, respectively. The K-values of intraobserver reliability for types A, B, and C were 0.888, 0.910, and 0.935, respectively. The mean K-values of the interobserver and intraobserver reliability for subtypes were 0.687 and 0.829, respectively. Conclusions The new classification of atlas fractures can cover nearly all atlas fractures. This system is the first to evaluate the severity of fractures based on the C1 articular facet and fracture displacement and strengthen the anatomy ring of the atlas. It is concise, easy to remember, reliable, and reproducible.

Methods: Seventy-five patients with atlas fracture were included from January 2015 to December 2020. Based on the anatomy of the fracture line, atlas fractures were divided into three types. Each type was divided into two subtypes according to the fracture displacement. Unweighted Cohen kappa coefficients were applied to evaluate the reliability and reproducibility. Results: According to the new classification, 17 cases of type A1, 12 of type A2, seven of type B1, 13 of type B2, 12 of type C1, and 14 of type C2 were identified. The K-values of the interobserver and intraobserver reliability were 0.846 and 0.912, respectively, for the new classification. The K-values of interobserver reliability for types A, B, and C were 0.843, 0.799, and 0.898, respectively. The K-values of intraobserver reliability for types A, B, and C were 0.888, 0.910, and 0.935, respectively. The mean K-values of the interobserver and intraobserver reliability for subtypes were 0.687 and 0.829, respectively. Conclusions The new classification of atlas fractures can cover nearly all atlas fractures. This system is the first to evaluate the severity of fractures based on the C1 articular facet and fracture displacement and strengthen the anatomy ring of the atlas. It is concise, easy to remember, reliable, and reproducible.

Methods: Seventy-five patients with atlas fracture were included from January 2015 to December 2020. Based on the anatomy of the fracture line, atlas fractures were divided into three types. Each type was divided into two subtypes according to the fracture displacement. Unweighted Cohen kappa coefficients were applied to evaluate the reliability and reproducibility. Results: According to the new classification, 17 cases of type A1, 12 of type A2, seven of type B1, 13 of type B2, 12 of type C1, and 14 of type C2 were identified. The K-values of the interobserver and intraobserver reliability were 0.846 and 0.912, respectively, for the new classification. The K-values of interobserver reliability for types A, B, and C were 0.843, 0.799, and 0.898, respectively. The K-values of intraobserver reliability for types A, B, and C were 0.888, 0.910, and 0.935, respectively. The mean K-values of the interobserver and intraobserver reliability for subtypes were 0.687 and 0.829, respectively. Conclusions The new classification of atlas fractures can cover nearly all atlas fractures. This system is the first to evaluate the severity of fractures based on the C1 articular facet and fracture displacement and strengthen the anatomy ring of the atlas. It is concise, easy to remember, reliable, and reproducible.

ObjectiveTo improve the quality and service performance of community visual health services in Shanghai, and to establish a set of reasonable and effective evaluation index system for community visual health services. MethodsCentered on the national and Shanghai-based visual health policies and based on the current status and development trends of community visual health service program in Shanghai, the candidate indicators were formed through literature review and expert interviews, firstly. The framework of an evaluation index system was formulated through qualitative research successively, which was further revised and perfected using the Delphi method. Coefficient weights were calculated using the analytic hierarchy process (AHP), culminating in the establishment of the community visual health evaluation index system, lastly. ResultsA total of 22 visual health experts from district-level center for disease control, hospital ophthalmology and leaders in charging of visual health service in community health centers participated in the Delphi questionnaire survey, with a questionnaire recovery rate of 100% and an expert authority coefficient of 0.86, indicating high credibility. After a round of correspondence to experts’ importance ratings and discussions, a comprehensive evaluation index system comprising 3 primary indicators, 12 secondary indicators, and 47 tertiary indicators, along with 5 additional indicators, was finalized. ConclusionAn index system tailored to effective evaluation for community visual health initiatives was drawn up in this study, which can promote the capacity building in community eye health services, facilitating the high-quality development of visual health courses, and enhancing residents’ eye health.

To promote convergence education among national and international students, Nanjing Medical University designed an optional curriculum Two Sides of the Same Coin in Parasitic Diseases among international students for the Bachelor of Medicine and Bachelor of Surgery (MBBS) program, and a research-based teaching method was employed to teach 31 international students. This curriculum contained three parts, including parasitology-related knowledge, basic knowledge and skills of scientific research, and frontier topics in scientific research advances, and the assessment contents included course sign-in, classroom activity participation and scientific research presentation. Curriculum evaluation showed 100.0% (8/8) of students in batch 2018—2021, 81.8% (9/11) in batch 2022, and 83.3% (10/12) in batch 2023 with a total score of 80 points and higher, with median scores (interquartile range) of 91.1 (7.0), 90.8 (5.7) points and 90.8 (5.7) points, respectively (H = 0.04, P > 0.05). In addition, a questionnaire survey was performed with a Likert scale to assess the interests in curriculum learning, and the curriculum practicality and importance among 31 international students, and a total of 27 valid questionnaires were recovered, with a recovery rate of 87.1%. Questionnaire survey showed that the median total scores (interquartile range) were 7.0 (0.8) points among batch 2018—2021 students, 6.4 (1.4) points among batch 2022 students and 6.0 (1.1) points among batch 2023 students (H = 2.64, P > 0.05). Collectively, these data demonstrate that this optional curriculum improves the interests in parasitology learning of the international students for MBBS program, as well as their capability of self-directed learning, teamwork and innovative.

Background With the progression of industrialization, an increasing number of emerging contaminants are entering aquatic environments, posing significant threats to the safety of drinking water. Therefore, establishing a system for identifying unknown hazardous factors and implementing safety warning mechanisms for drinking water is of paramount importance. Among these efforts, non-target screening plays a critical role, but its effectiveness is largely constrained by the scope of coverage of sample pre-treatment methods. Objective To integrate modern chromatography/mass spectrometry techniques with advanced data mining methods to develop a non-discriminatory sample pre-treatment method for comprehensive enrichment of unknown contaminants in drinking water, laying a technical foundation for the discovery and identification of unknown organic hazardous factors in drinking water. Methods A non-discriminatory pre-treatment method based on supramolecular and solid-phase extraction was developed. The final target compounds including 333 pesticides, 194 pharmaceuticals and personal care products (PPCPs), and 59 per- and polyfluoroalkyl substances (PFASs) were used for optimizing the pre-treatment method, confirming its coverage. The impacts of different eluents on the absolute recovery rates of target compounds were compared to select the conditions with the highest recovery for sample pre-treatment. The effects of different supramolecular solvents and salt concentrations on target compound recovery were also evaluated to determine the most suitable solvent and salt concentration. Results The solid-phase extraction elution solvents, supramolecular extraction solvents, and salt concentrations were optimized based on the target compound recovery rates. The optimal recovery conditions were achieved using 2 mL methanol, 2 mL methanol (containing 1% formic acid), 2 mL ethyl acetate, 2 mL dichloromethane, hexanediol supramolecular solvent, and 426 mg salt. The detection method developed based on these conditions showed a good linear relationship for all target compounds in the range of 0.1-100.0 ng·mL⁻¹, with R² > 0.99. The method’s limit of detection ranged from 0.01 ng⁻¹ to 0.95 ng⁻¹, and 95% of target compounds were recovered in the range of 20%-120%, with relative standard deviation (RSD) less than 30%, indicating good precision. Conclusion The combined pre-treatment method of solid-phase extraction and supramolecular solvent extraction can effectively enrich contaminants in drinking water across low, medium, and high polarities, enabling broad-spectrum enrichment of diverse trace contaminants in drinking water. It provides technical support for broad-spectrum, high-throughput screening and identification of organic pollutants in drinking water, and also serves as a reference for establishing urban drinking water public safety warning 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.

Objective: To explore the relationship between beverage dependence and sleep quality among college students, providing empirical evidence for improving their sleep quality. Methods: From December 2024 to January 2025, a convenience sampling method was used to conduct a questionnaire survey among 3 974 college students from four universities in Anhui Province. The Beverage Addiction Scale for College Students (BASCS) was used to assess beverage dependence, and the Self rating Scale of Sleep(SRSS) was used to evaluate sleep quality. A multivariate Logistic regression model was employed to analyze the relationship between beverage dependence and sleep quality, and a restricted cubic spline model was used to examine the dose response relationship between the two. Results: The positive rate of beverage dependence symptoms among college students was 7.6%, with positive rates of 9.6%, 13.8%, and 7.4% for the withdrawal symptoms, health effects, and dependence symptoms dimensions, respectively. The detection rate of sleep disorders was 23.6%. Multivariate Logistic regression analysis showed that after adjusting for covariates such as grade, gender, and body mass index, compared with the no beverage dependence group, students with positive beverage dependence symptoms had a higher risk of sleep disorders( OR =3.71, 95% CI =2.87-4.80, P <0.01). The OR (95% CI ) for sleep disorders among students with positive symptoms in the withdrawal symptoms, health effects, and dependence symptoms dimensions were 2.80(2.22-3.53), 2.38(1.95-2.91), and 2.45(1.89-3.18)(all P <0.01). Further analysis using a restricted cubic spline model revealed that the overall beverage dependence score and its three dimensional scores were approximately linearly related to the risk of sleep disorders among college students (all nonlinear P >0.05). Conclusions Beverage dependence is associated with sleep quality among college students. Schools should take multiple approaches, such as health education on beverage awareness, to improve students sleep quality.