Stroke is one of the leading causes of disability and mortality worldwide. Research into its mechanisms and the development of therapeutic strategies heavily rely on animal models that accurately replicate the pathological features of human disease. An ideal animal model for stroke should not only reproduce the neurological deficits and pathological changes observed in clinical patients but also demonstrate good reproducibility and translational value. This review focuses on the preparation and evaluation methods of ischemic stroke animal models. Firstly, it elaborates on the selection criteria, advantages, and disadvantages of experimental animals, including rodents (rats, mice) and non-rodents (non-human primates, miniature pigs, rabbits, zebrafish). Secondly, it provides a detailed overview of the modeling principles, key procedures, and application scopes for ischemic stroke models and hemorrhagic stroke models. Furthermore, the review summarizes advances in the applications of emerging technologies—including gene editing [e.g., clustered regularly interspaced short palindromic repeats (CRISPR)/CRISPR-associated protein 9 (Cas9) gene editing], multimodal imaging (e.g., two-photon microscopy, photoacoustic imaging), artificial intelligence, optogenetics, 3D bioprinting, organoid models, and multi-omics–in model optimization, precise assessment, and mechanistic investigation. Finally, based on a systematic analysis of relevant domestic and international literature from 2019 to 2024, this review discusses model selection strategies based on research objectives, a multidimensional evaluation system encompassing behavioral, imaging, and molecular pathological assessments, and envisions future directions involving technological integration to achieve model precision and individualization. This article aims to provide a comprehensive methodological reference to help researchers select appropriate animal models of stroke according to specific scientific questions.

Objective: To evaluate the effect of community comprehensive management model intervention among patients with dyslipidemia, so as to provide the reference for optimizing community management strategies and improving the target achievement rate for blood lipids among this population. Methods: From May to June 2023, a multi-stage stratified random sampling method was employed to select patients with dyslipidemia from primary healthcare institutions in Jiaxing City, Zhejiang Province. Eligible participants were randomly assigned to either a control group or an intervention group. The control group received routine management, while the intervention group was subjected to a community comprehensive management model in addition to the routine care. Both groups were followed up for 24 months. Data on demographic characteristics, lifestyle behaviors, physical examination indices, and blood biochemical indicators were collected at baseline and after the intervention through questionnaires, physical examinations, and laboratory tests. Changes in obesity rate, central obesity rate, target achievement rates for blood lipids, blood pressure, and blood glucose, as well as lifestyle modifications, were analyzed. Differences between the two groups before and after the intervention were assessed using generalized estimating equations (GEE). Results: The control group consisted of 560 patients, including 303 females (54.11%) and 430 individuals aged ≥65 years (76.79%). The intervention group also included 560 patients, with 300 females (53.57%) and 431 individuals aged ≥65 years (76.96%). Before the intervention, no statistically significant differences were observed between the two groups in terms of gender, age, educational level, history of chronic diseases, and atherosclerotic cardiovascular disease risk stratification (all P>0.05). After 24 months of intervention, interaction effects between group and time were observed for obesity rate, central obesity rate, target achievement rate for blood lipids, target achievement rate for blood glucose, composite target achievement rate, physical activity rate, and medication adherence (all P<0.05). Specifically, the intervention group demonstrated lower rates of obesity and central obesity, and higher target achievement rate of blood lipids, target achievement rate of blood glucose, composite target achievement rate, physical activity rate, and medication adherence compared to the control group. Conclusion The community comprehensive management model contributed to improvements in multiple metabolic parameters (including body weight, waist circumference, blood lipids, and blood glucose) among patients with dyslipidemia, and was associated with increased physical activity rate and medication adherence.

ObjectiveThis paper aims to clarify the material basis of Gualou Niubangtang and establish a quantitative analysis method for its main constituents， providing a reference for the overall quality control of this preparation. MethodsThe constituents in the formula were systematically characterized based on ultra-performance liquid chromatography-quadrupole time-of-flight tandem mass spectrometry （UPLC-Q-TOF-MS/MS）. Identification was performed by matching with the UNIFI 9.6 software and utilizing database platforms such as PubChem， ChemicalBook， and ChemSpider， combined with relevant literature reports. A quantitative analysis method for the seven main constituents in Gualou Niubangtang was established by using high performance liquid chromatography （HPLC）. ResultsUPLC-Q-TOF-MS/MS analysis identified 155 constituents， including 69 flavonoids， 36 terpenoids， 23 phenylpropanoids， 8 phenylethanoid glycosides， and 19 other types of constituents. In the established quantitative analysis method， the seven main constituents showed good linearity within their respective linear ranges. The precision， repeatability， stability， and spike recovery all met the required standards. The results showed that the content ranges of geniposide， liquiritin， hesperidin， arctiin， baicalin， oroxylin A-7-O-β-D-glucuronide， and wogonoside in 15 batches of Gualou Niubangtang were 13.67-21.25， 1.20-7.64， 5.45-7.45， 22.97-33.51， 29.95-39.07， 2.58-4.80， and 6.56-9.31 mg·g^-1， respectively. ConclusionThis study successfully characterizes and attributes multi-category constituents in Gualou Niubangtang， clarifying that its material basis is primarily composed of flavonoids， terpenoids， phenylethanoid glycosides， and phenylpropanoids. Furthermore， it enables the quantification of seven constituents within the formula. This work lays a foundation for research on the quality control， action mechanism， and clinical application of this formula.

ObjectiveTo analyze the infection status of main respiratory pathogens in influenza-like illness (ILI) cases in Anji County, Huzhou City, Zhejiang Province, and to provide a reference for the prevention, diagnosis, and treatment of respiratory infections. MethodsThroat swab samples were collected from 520 ILI cases in an influenza sentinel surveillance hospital in Anji County of Zhejiang Province from December 2023 to November 2024. Multiplex real-time fluorescence quantitative polymerase chain reaction (mRT-PCR) was used to detect 18 pathogens and their subtypes, including severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), influenza A virus (Flu A), influenza A (H1N1) virus, influenza A (H3N2) virus, influenza B virus (Flu B), influenza B virus Victoria lineage (BV), influenza B virus Yamagata lineage (BY), coronavirus (CoV), human parainfluenza virus (HPIV), respiratory syncytial virus (RSV), human metapneumovirus (HMPV), adenovirus (ADV), human bocavirus (HBoV), enterovirus (EV), rhinovirus (RV), Mycoplasma pneumoniae (MP), Chlamydia pneumoniae (CP), and Streptococcus pneumoniae (SP). ResultsThe overall positivity rate of pathogens in 520 samples was 33.65%, among which the detection rates of Flu (9.14%), ADV (7.50%), SARS-CoV-2 (6.15%), and EV (3.65%) were relatively high. There were statistically significant differences in the overall positivity rate of pathogens by age and season (all P<0.05). The highest overall positivity rate was observed in the 5‒14 years old group (42.77%), and the overall positivity rate in winter (53.08%) was significantly higher than that in other seasons. ConclusionFrom 2023 to 2024, the main respiratory pathogens detected in ILI cases in Anji County were Flu, ADV, SARS-CoV-2, and EV. The epidemic characteristics showed age and seasonal specificity, so it is necessary to strengthen prevention and control for high-risk populations and epidemic seasons in a targeted manner.

OBJECTIVE To investigate the risk factors for sodium valproate （VPA）-induced hyperammonemia in neurocritical patients， and to construct a risk prediction model. METHODS Clinical data were retrospectively collected from 172 neurocritical patients who received VPA treatment in the Department of Critical Care Medicine， the Fourth Affiliated Hospital of Soochow University from January 2022 to June 2025. Patients were divided into the hyperammonemia group （73 cases） and the normal group （99 cases） based on their blood ammonia levels. Univariate analysis and LASSO regression analysis were used to screen for predictive variables. Independent factors were identified through multivariate Logistic regression analysis， and a nomogram was constructed accordingly. The performance of the model was evaluated using receiver operating characteristic （ROC） curve， calibration curve， and decision curve analysis （DCA）. RESULTS Combination of univariate analysis and LASSO regression analysis screened out seven predictive variables： body mass index （BMI）≥24.0 kg/m 2 ， concomitant use of benzodiazepines， VPA blood concentration， hemoglobin， serum urea， average daily VPA dose， and albumin. Multivariate Logistic regression analysis showed that concomitant use of benzodiazepines， BMI≥24.0 kg/m 2 ， VPA blood concentration， albumin and serum urea level （with odds ratios of 1.615， 1.538， 1.623， 1.942 and 0.637， respectively； 95% confidence intervals of 1.128-2.359， 1.059-2.251， 1.112-2.431， 1.106-3.598 and 0.402-0.980， respectively） were all significantly associated with VPA-induced hyperammonemia in neurocritical patients （ P ＜0.05）. The nomogram prediction model constructed based on these variables was evaluated， showing that the area under the ROC curve was 0.810 for the test set and 0.844 for the validation set. The calibration curves closely approximated t he actual curves， and the application of this model could improve the clinical net benefit. CONCLUSIONS Concomitant use of benzodiazepines， BMI≥24.0 kg/m 2 ， high VPA blood concentration and high albumin level are independent risk factors for VPA-induced hyperammonemia in neurocritical patients， while high serum urea level is an independent protective factor. The risk prediction model constructed based on these factors exhibits good discrimination， consistency， and clinical applicability， making it applicable for predicting the risk of VPA-induced hyperammonemia in neurocritical patients.

OBJECTIVE To systematically evaluate the efficacy and safety of 6 kinds of GLP-1RAs in the treatment of type 2 diabetes mellitus （T2DM） patients with overweight or obesity， and to provide evidence-based reference for clinical practice. METHODS A comprehensive search was conducted in PubMed， Embase， Web of Science， the Cochrane Library， CNKI， VIP， Wanfang Data， and CBM from the inception to December 1， 2025. Randomized controlled trials （RCTs） were screened according to inclusion and exclusion criteria. Data extraction and risk of bias assessment were performed on the included studies. Network meta-analysis was conducted using Stata 17.0 software. RESULTS A total of 29 eligible RCTs were included， involving 7 404 patients. Six GLP-1RAs were evaluated： semaglutide， liraglutide， exenatide， dulaglutide， polyethylene glycol loxenatide， and beinaglutide. In terms of glycemic control， semaglutide had the highest probability of ranking first in reducing glycated hemoglobin （HbA1c） and fasting plasma glucose levels， followed by polyethylene glycol loxenatide. In terms of weight management， semaglutide showed the highest probability of ranking first， followed by liraglutide and exenatide. Regarding safety， dulaglutide had the highest probability of ranking first in reducing the incidence of gastrointestinal adverse events； none of the GLP-1RAs significantly increased the risk of severe hypoglycemia. Subgroup analysis revealed that liraglutide 1.8 mg， qd and exenatide extend-release 2.0 mg， qw demonstrated superior efficacy in reducing HbA1c and body weight compared with other doses/dosage forms of the same agents. CONCLUSIONS For T2DM patients with overweight or obesity， semaglutide offers the greatest benefits in glycemic control and weight reduction， while dulaglutide demonstrates superior gastrointestinal tolerability. Liraglutide 1.8 mg， qd and exenatide extend-release 2.0 mg， qw show relatively better overall efficacy in glycemic control and weight reduction among the same agents.

Objective: To evaluate the incremental vaccine effectiveness (VE) of two dose of the mumps containing vaccine (MuCV) in chidren, so as to provide a basis for optimizing mumps immunization strategies. Methods: A 1∶2 frequency matched case-control study was conducted by using reported mumps cases in childcare centers or schools from Lu an, Hefei, Ma anshan and Huainan cities of Anhui Province from September 1, 2023 to June 30, 2024, as a case group(383 cases). And healthy children in the same classroom were selected as a control group(766 cases). The MuCV immunization histories of participants were collected to estimate the incremental VE of the second dose of MuCV against mumps. Group comparisons were performed using the Chi square test or t-test. For matched case-control pairs, the Cox regression model was employed to calculate the odds ratio (OR) with 95% confidence interval (CI) for two dose MuCV vaccination and to estimate the incremental vaccine effectiveness (VE). Results: There were no statistically significant differences between the case and control groups regarding gender, age, dosage of MuCV vaccination and the time interval since the last dose vaccination( χ 2/t=0.05, 0.20, 0.94, -0.02, P >0.05). The proportions of the case and control groups vaccinated with two doses of MuCV were 26.63% and 29.37%, respectively, and the overall incremental VE of the second dose of MuCV was 40.73% (95% CI=3.03%-63.77%, P <0.05). Subgroup analyses revealed that the incremental VE for children with a period of ≥1 year between the two doses of MuCV was 54.13% (95% CI=1.90%-78.56%, P <0.05), while for children with a period of <1 year, it was 30.63% (95% CI=-28.59%-62.58%, P >0.05). The incremental VE of the second dose of MuCV was 30.36% (95% CI=-25.95%-61.50%, P >0.05) in kindergarten children and 66.73% (95% CI=14.92%-86.99%, P <0.05) in elementary and secondary school students. The incremental VE was 28.78% (95% CI=-27.46%-60.21%, P >0.05) within five years of the last dose of MuCV vaccination and 66.07% (95% CI=-41.56%-91.87%, P >0.05) for vaccinations administered beyond five years. Conclusions The second dose of MuCV may offer additional protection for children; however, extending the interval between two dose of MuCV (<1 year) has shown limited incremental protective effects. Therefore, it is crucial to consider optimizing current immunization strategies for mumps.

Interferon stimulating factor STING, a transmembrane protein residing in the endoplasmic reticulum, is extensively involved in the sensing and transduction of intracellular signals and serves as a crucial component of the innate immune system. STING is capable of directly or indirectly responding to abnormal DNA originating from diverse sources within the cytoplasm, thereby fulfilling its classical antiviral and antitumor functions. Structurally, STING is composed of 4 transmembrane helices, a cytoplasmic ligand binding domain (LBD), and a C terminal tail structure (CTT). The transmembrane domain (TM), which is formed by the transmembrane helical structures, anchors STING to the endoplasmic reticulum, while the LBD is in charge of binding to cyclic dinucleotides (CDNs). The classical second messenger, cyclic guanosine monophosphate-adenosine monophosphate (cGAMP), represents a key upstream molecule for STING activation. Once cGAMP binds to LBD, STING experiences conformational alterations, which subsequently lead to the recruitment of Tank-binding kinase 1 (TBK1) via the CTT domain. This, in turn, mediates interferon secretion and promotes the activation and migration of dendritic cells, T cells, and natural killer cells. Additionally, STING is able to activate nuclear factor-κB (NF-κB), thereby initiating the synthesis and release of inflammatory factors and augmenting the body’s immune response. In recent years, an increasing number of studies have disclosed the non-classical functions of STING. It has been found that STING plays a significant role in organelle regulation. STING is not only implicated in the quality control systems of organelles such as mitochondria and endoplasmic reticulum but also modulates the functions of these organelles. For instance, STING can influence key aspects of organelle quality control, including mitochondrial fission and fusion, mitophagy, and endoplasmic reticulum stress. This regulatory effect is not unidirectional; rather, it is subject to organelle feedback regulation, thereby forming a complex interaction network. STING also exerts a monitoring function on the nucleus and ribosomes, which further enhances the role of the cGAS-STING pathway in infection-related immunity. The interaction mechanism between STING and organelles is highly intricate, which, within a certain range, enhances the cells’ capacity to respond to external stimuli and survival pressure. However, once the balance of this interaction is disrupted, it may result in the occurrence and development of inflammatory diseases, such as aseptic inflammation and autoimmune diseases. Excessive activation or malfunction of STING may trigger an over-exuberant inflammatory response, which subsequently leads to tissue damage and pathological states. This review recapitulates the recent interactions between STING and diverse organelles, encompassing its multifarious functions in antiviral, antitumor, organelle regulation, and immune regulation. These investigations not only deepen the comprehension of molecular mechanisms underlying STING but also offer novel concepts for the exploration of human disease pathogenesis and the development of potential treatment strategies. In the future, with further probing into STING function and its regulatory mechanisms, it is anticipated to pioneer new approaches for the treatment of complex diseases such as inflammatory diseases and tumors.

Objective To construct large medical model named by "Huaxi HongYi"and explore its application effectiveness in assisting medical record generation. Methods By the way of a full-chain medical large model construction paradigm of "data annotation - model training - scenario incubation", through strategies such as multimodal data fusion, domain adaptation training, and localization of hardware adaptation, "Huaxi HongYi" with 72 billion parameters was constructed. Combined with technologies such as speech recognition, knowledge graphs, and reinforcement learning, an application system for assisting in the generation of medical records was developed. Results Taking the assisted generation of discharge records as an example, in the pilot department, after using the application system, the average completion times of writing a medical records shortened (21 min vs. 5 min) with efficiency increased by 3.2 time, the accuracy rate of the model output reached 92.4%. Conclusion It is feasible for medical institutions to build independently controllable medical large models and incubate various applications based on these models, providing a reference pathway for artificial intelligence development in similar institutions.

The professional development of medical social workers currently faces several dilemmas， such as insufficient professionalism， unbroken professional barriers， and an incomplete policy system. To address these dilemmas， medical social workers should clarify the dimensions of their professional capabilities and strive to improve their professional capabilities， building a professional capability framework focusing on problem-solving. Meanwhile， with the popularization of AI technology， the cross-field integration and development of “AI + medical social workers” have brought new opportunities for the professional capability construction of medical social workers. For example， big data can enhance the optimization of electronic health record services； virtual reality （VR） and augmented reality can facilitate the electronic standardization of social worker training for clients； and machine learning can promote the transformation of the social work service model towards “risk prediction and advance intervention，” assisting in improving direct service capabilities. ChatGPT and VR remote consultation systems can be used for supervision services， big data cloud classrooms and robot teaching assistants can broaden learning channels， and natural language processing technology can assist in professional document writing， aiding in enhancing indirect service capabilities. However， applying AI technology to the professional capacity construction of medical social workers may also lead to the alienation of the professional service relationship of medical social workers from needs assessment and relationship building to service intervention， further causing ethical risks brought by demand bias， relationship alienation， and weakened subjectivity. Therefore， it is necessary to rationally view AI’s “double-edged sword” effect. While enjoying the field transformation brought about by high and new technology， it is also imperative to address the subsequent challenges from the perspectives of social work professional assessment and review， educational supervision， and ethical awareness enhancement.