ObjectiveThis study aims to systematically analyze the blood-absorbed components and metabolic profiles of Xiebaisan（XBS） in normal and chronic bronchitis （CB） mice using ultra performance liquid chromatography-quadrupole-electrostatic field orbitrap high resolution mass spectrometry（UPLC-Q-Exactive Orbitrap MS）， while comparing differences between the two states. MethodsThirty female BABL/c mice were randomly divided into the normal group， the normal drug administration group， the CB group， the CB drug administration group and the dexamethasone group， with 6 mice in each group. The CB mouse model was established by inducing with ovalbumin （OVA）. The mice in the normal drug administration group and the CB drug administration group started to be gavaged with XBS（13.2 g·kg^-1） from the 21^st day， and the dexamethasone group mice were simultaneously gavaged with dexamethasone （0.5 mg·kg^-1） until the end of the 35^th day of the experiment. Subsequently， serum samples were collected and evaluated for their efficacy， based on the pharmacological evaluation indicators， to determine the efficacy of XBS in treating CB. Then the UPLC-Q-Exactive Orbitrap MS was employed to identify and analyze the chemical constituents， blood-absorbed components， and metabolites of XBS. Chemometric analysis was conducted to reveal metabolic profile differences under "dual states". Concurrently， Real-time PCR technology was utilized to detect the expression levels of key liver metabolic enzymes CYP2E1， CYP3A1， UGT1A1， and UGT1A6. ResultsA total of 28 prototype components and 158 metabolites （including 48 phase Ⅰ metabolites and 110 phase Ⅱ metabolites） of XBS were unambiguously identified in the serum of normal mice. Additionally， a comprehensive characterization was performed on a total of 32 prototype components and 178 metabolites （including 50 phase Ⅰ metabolites and 128 phase Ⅱ metabolites） of XBS in the serum of CB mice. Among them， 27 prototype components were detected in both states， including 12 flavonoids， 2 alkaloids， 3 triterpenes， 4 organic acids， 3 amides， 1 stilbene and 2 other compounds. The chemometrics analysis revealed no significant difference in the prototype components and metabolites of XBS between normal and CB mice； however， there was a significant increase in the in-vivo exposure of XBS in CB mice. Compared to normal mice， the levels of phase Ⅰ metabolites such as oxidation， reduction and methylation of blood components of XBS as well as phase Ⅱ metabolites of glucuronidation showed significant changes in CB mice. Real-time PCR further confirmed that these alterations were attributed to the upregulation of CYP2E1 （P<0.05）， CYP3A1 （P>0.05）， UGT1A1 （P<0.01） and UGT1A6 （P<0.01） enzymes expression in the liver of CB mice. ConclusionThis study elucidated the disparities in the levels of the blood-absorbed components and metabolic profiles of XBS in normal and CB mice， especially in oxidation， reduction， methylation in phase Ⅰ metabolism and glucoaldehyde acidification in phase Ⅱ metabolism. And there are related to the differences in the expression levels of phase Ⅰ and phase Ⅱ metabolic enzymes CYP2E1， CYP3A1， UGT1A1 and UGT1A6 in the liver.

Objective To explore the latent profile characteristics, influencing factors of personal social capital among community-dwelling older adults and their differences in the degree of geriatric depression. Methods A cross-sectional survey was conducted among 1,026 community-dwelling older adults. Latent profile analysis was used to explore the latent profiles of personal social capital, and the predictors of different latent profiles were examined. Results Four latent profiles were identified and labeled as ^“Impoverished social capital type^”, ^“Moderate social capital type", ^“Abundant social capital type^” and ^“Extremely low bonding - extremely high bridging social capital exclusive type^”. The influencing factors included per capita monthly household income (χ²=3.500, P=0.015), whether taking long-term medication (χ²=9.587, P=0.022), whether drinking alcohol (χ²=23.942, P＜0.001), whether doing physical exercise (χ²=20.876, P＜0.001), and whether participating in social activities (χ²=40.135, P＜0.001), with statistically significant differences. Community-dwelling older adults with low frequency of physical exercise, low frequency of social participation and not taking long-term medication were more likely to belong to the ^“extremely low bonding - extremely high bridging social capital exclusive type^”. The degree of geriatric depression (F=12.385, P＜0.001), aging attitude (F=33.008, P＜0.001) and negative aging perception (F=81.481, P＜0.001) in this subgroup were relatively serious, with statistically significant differences. Conclusion The government and communities can formulate personalized intervention measures based on the influencing factors of latent profiles to effectively suppress the development of depression among community-dwelling older adults, improve aging attitudes and the positivity of aging perception, and promote healthy aging.

ObjectiveTo construct an actor-network for integrating physical activity into rehabilitation services based on the World Health Organization Rehabilitation in Health Service System framework and actor-network theory (ANT). MethodsContent analysis was employed using the six building blocks of health service systems as the theoretical framework. Actors related to rehabilitation services were extracted and categorized into a rehabilitation actor pool, while a physical activity actor pool was formed based on four major physical activity scenarios. Actors from both pools were integrated, deduplicated and classified to form a final list of integrated actors. Using ANT, the construction process of the actor network integrating physical activity into rehabilitation was analyzed through the four stages of translation: problematization, interessment, enrollment and mobilization. ResultsA dynamic integration network was constructed, comprising human actors (patients, rehabilitation professionals, researchers, sports coaches, government departments, medical institutions, community organizations and industry media, etc.) and non-human actors (assistive devices, sports infrastructure, smart equipment, information systems, online exercise guidance systems, laws and regulations, strategic documents, and exercise prescriptions, etc.). The study identified maximizing rehabilitation outcomes as the mandatory passage point and elaborated on the critical role of government departments as focal actors in coordinating various stakeholders. ConclusionThe integration of physical activity into rehabilitation services is a dynamic network constructed by diverse actors through a process of translation. ANT provides an operational theoretical framework for cross-departmental governance of rehabilitation policies in China, promotes the spatial expansion of the rehabilitation field, and drives its transformation toward a networked and ecological system. The government needs to play a leading role in facilitating role reconstruction and synergy among heterogeneous actors in both the sports and rehabilitation sectors through mechanism design, to create a bidirectional empowerment mechanism that fosters mutual progress and ensures the sustainable development of integrated services.

Diabetic kidney disease （DKD） is one of the common microvascular complications of diabetes mellitus （DM） and a primary cause of chronic kidney disease （CKD） and end-stage renal disease （ESRD）. Inflammation is currently a hot topic in exploring the pathogenesis of DKD. Macrophages， T cells， interleukins， tumor necrosis factor， NOD-like receptor protein 3 （NLRP3） inflammasome， Janus kinase/signal transducer and activator of transcription （JAK/STAT） signaling pathway， and nuclear factor-kappa B （NF-κB）-related signaling pathway all play a role in regulating the inflammation of DKD and accelerating its progression. Astragali Radix， a Chinese herbal medicine， is widely used in the treatment of DKD and possesses strong anti-inflammatory effects. Studies have revealed that active ingredients of Astragali Radix， including polysaccharides， astragaloside Ⅳ， total flavonoids， calycosin， and quercetin， can regulate multiple signaling pathways to ameliorate the microinflammatory state and alleviate kidney damage， thereby slowing down the progression of DKD. This article systematically reviews the factors influencing the inflammation in DKD and analyzes recent research findings and mechanisms concerning active ingredients of Astragali Radix in the management of DKD inflammation， aiming to offer novel insights and directions for the prevention， treatment， and research of DKD.

Objective To investigate the clinical and radiological features of patients with different phenotypes of multiple system atrophy （MSA）. Methods A total of 400 patients with MSA who attended the outpatient service or were hospitalized in Department of Neurology，Chinese PLA General Hospital，were enrolled，among whom there were 294 patients with MSA-cerebellar type （MSA-C） and 106 patients with MSA-Parkinsonian type （MSA-P）. The t-test and the chi-square test were used to analyze the clinical manifestations，radiological features，and blood biochemical indicators of the two groups. Results As for clinical manifestations，there were significant differences in cerebellar symptoms，constipation，and Babinski sign between MSA-C patients and MSA-P patients （P<0.05）. As for radiological features，positron emission tomography/computed tomography （PET/CT） showed that MSA-P patients had a typical reduction in DAT uptake in the putamen and the caudate nucleus，while magnetic resonance imaging showed that compared with the MSA-C group，the MSA-P group had a significantly higher proportion of patients with putamen fissure sign or white matter demyelination； for MSA-C patients，PET/CT showed cerebellar hypometabolism，and magnetic resonance imaging showed the cross sign and high signal intensity in the pontine arm，as well as a significantly higher proportion of patients cerebellar and pontine atrophy than MSA-P patients （P<0.05）. As for laboratory markers，MSA-P patients had a significantly lower level of uric acid than MSA-C patients （P=0.029）. Conclusion Patients with different subtypes of MSA have specific clinical features，radiological features，and uric acid level，which has a certain significance in the accurate diagnosis of MSA.

BackgroundSchizophrenia is a highly heterogeneous disease with different clinical subtypes. Artificial intelligence technology represented by deep learning models has provided considerable benefits for the electroencephalogram （EEG）-based schizophrenia diagnosis， treatment and research， however， to date little research has been conducted regarding any of these benefits among Chinese schizophrenic patients. ObjectiveTo investigate the application of deep learning techniques utilizing EEG parameters for the diagnosis of first-episode schizophrenia and grading of EEG abnormalities in patients， with the aim of contributing to improved clinical diagnosis and treatment strategies for the disorder. MethodsFrom January 2020 to January 2023， a total of 130 patients with first-episode schizophrenia who met the diagnostic criteria of International Classification of Diseases， tenth edition （ICD-10）， and attended at the Third People's Hospital of Fuyang， along with 150 health checkup examinees， were enrolled. All of them underwent EEG examination. An optimized long short-term memory （LSTM） deep learning model was developed utilizing EEG signals. Ten-fold cross-validation method was employed to evaluate the model's performance. The dataset was then split into two components： a training set （90%） for LSTM model development and a test set （10%） for validation. The accuracy， recall rate， precision， F1-score， schizophrenia diagnosis and EEG abnormality grading were used as evaluation indicators， and the results of the proposed model were compared to the assessments made by experienced psychiatrists. ResultsFor schizophrenia diagnosis， the modeling group achieved the following performance metrics： precision （94.40±3.03）%， recall rate （94.30±3.23）%， accuracy （94.60±2.22）%， and F1-score （94.20±2.20）%. In the validation group， the corresponding metrics were precision （90.90±2.85）%， recall rate （92.20±1.14）%， accuracy （92.20±1.69）%， and F1-score （91.50±1.78）%. Statistical analysis revealed no significant differences between the LSTM diagnostic model and the experienced psychiatrists in terms of precision， recall rate， accuracy， and F1-score for schizophrenia diagnosis （χ²=1.500， 0.750， 2.722， 1.056， P>0.05）. The modeling group demonstrated an accuracy rate of （91.71±1.73）% in grading EEG abnormalities. For Grade 1 abnormalities， the modeling group reported a precision of （96.40±2.39）%， a recall rate of （94.77±1.40）%， and an F1-score of （95.55±1.14）%. In the case of Grade 2 abnormalities， the precision was （85.89±2.04）%， the recall rate was （88.10±6.18）%， and the F1-score was （87.06±3.12）%. For the more severe Grade 3 abnormalities， the modeling group's precision was （79.61±7.33）%， the recall rate was （81.79±9.87）%， and the F1-score was （80.41±6.79）%. Additionally， the validation group exhibited an accuracy rate of （85.61±6.16）%. The precision， recall rate， and F1-score for Grade 1 abnormalities were （91.43±6.25）%， （92.64±9.65）% and （91.56±4.83）%， respectively. For Grade 2 abnormalities， these metrics were （71.17±19.02）%， （77.64±17.24）% and （71.88±11.33）%. In the case of Grade 3 abnormalities， the precision was （90.00±21.08）%， the recall rate was （80.00±25.82）%， and the F1-score was （81.67±19.95）%. There was no significant difference in the accuracy， recall， accuracy and F1 value between LSTM model and senior doctors in evaluating the abnormal degree of EEG in schizophrenia （χ²=0.098， 0.036， 0.020， 0.336， P>0.05）. The LSTM model takes less time to diagnose schizophrenia and EEG abnormalities than senior doctors， and the differences were statistically significant （t=57.147， 43.104， P<0.01）. ConclusionThe study utilizes an EEG-based LSTM deep learning model for diagnosing first-episode schizophrenia and grading EEG abnormalities， and the model not only matches the performance of experienced psychiatrists but also significantly reduces the time required for diagnosis.

Hypoxia-related diseases,such as altitude sickness,acute lung injury,and heart failure,have emerged as significant public health challenges worldwide and are strongly associated with increased mortality risks.These conditions tend to cause functional impairment and structural alterations in multiple organs,primarily due to inadequate oxygen supply or the inability of tissues to utilize oxygen effectively.To elucidate the pathophysiological mechanisms underlying hypoxia-associated diseases and to explore potential therapeutic strategies,researchers have employed animal models to conduct extensive mechanistic studies and drug development trials.In this review,we provide a comprehensive overview of the methods used to construct animal models for various hypoxia-related diseases.We made a thorough evaluation of the strengths,weaknesses,limitations,and applicability of each approach.In particular,we focus on modeling strategies for key pathologies such as altitude sickness,acute lung injury,pulmonary hypertension,and heart failure,discussing their respective advantages and restrains.Furthermore,we examined the potential application of gene editing technologies in optimizing animal models,especially its role in enhancing the accuracy and reproducibility of the models.The integration of these advanced technologies holds great promise for developing animal models with improved specificity and simulation of key features of diseases,which will serve as a solid foundation for basic research,drug screening,and preclinical trials concerning hypoxia-related diseases.Herein,we present a systematic summary and future outlook of animal models for hypoxia-related diseases,ultimately providing theoretical support for developing clinical treatment strategies and improving patient outcomes.

Objective:To measure the length and curvature of the external auditory canal (EAC) centerline using a three-dimensional (3D) measurement method based on high-resolution computed tomography (HRCT).Methods:A retrospective analysis was conducted on HRCT images of healthy EAC from 49 patients (64 ears) examined at the Sixth Medical Center of the PLA General Hospital from June to October 2024. The cohort included 25 males (28 ears) and 24 females (36 ears), aged 22-78 years (mean age 47.3 years), categorized into age groups: 18-30 years (20 ears), 31-60 years (22 ears), and 61-80 years (22 ears). The EAC comprised 29 left and 35 right ears. 3D reconstruction of the EAC was performed using medical software (3D Slicer), and the central curve of the EAC was extracted using an improved measurement method to determine its length and curvature. Statistical analysis was performed using Origin Pro 2023 software to compare differences across genders, sides, and age groups.Results:The measurements of EAC length and curvature revealed that the EAC was significantly longer in males than in females (27.46 mm vs. 25.44 mm, P=0.014) and longer on the right side than on the left (27.99 mm vs. 25.87 mm, P=0.031). No statistically significant difference in EAC length was observed among the different age groups ( P>0.05). Furthermore, no statistically significant differences in EAC curvature were found regarding gender, side, or age group (all P>0.05). Conclusion:The HRCT-based 3D reconstruction technique enables the establishment of a rapid and effective automated workflow for extracting the central curve of the EAC and systematically measuring its length and curvature. The data obtained provide a reference and methodological support for further research on anatomical variations of the human EAC and its potential clinical applications, such as the design of otological instruments or surgical planning.

Objective To develop a Self-Report Scale of Symptoms for Post Intensive Care Syndrome-Family(PICS-F)and validate its reliability and validity,providing a scientific tool for healthcare professionals to comprehensively assess post intensive care syndrome-family.Methods Based on the symptom experience dimension of Symptom Management Theory,a preliminary version of the scale was developed through literature analysis,semi-structured interviews,expert consultations,and a pilot survey.A convenience sampling method was used to conduct the pre-trial version of the scale questionnaire survey on 442 family members of ICU patients at a tertiary A comprehensive hospital in Wuhan from June to September 2024.The test of the validity and reliability was conducted.Results The developed scale consists of 4 dimensions and 20 items.Exploratory factor analysis identified 4 factors,with a cumulative variance contribution rate of 70.921％.The content validity index at the scale level was 0.941,and the content validity indices for individual items ranged from 0.809 to 1.000.Confirmatory factor analysis indicated that the model fit indices were within acceptable ranges.The overall Cronbach's αcoefficient for the scale was 0.941,and the split-half reliability was 0.832.Conclusion The Self-Report Scale of Symptoms for PICS-F demonstrates good reliability and validity.It can comprehensively reflect the primary symptoms of PICS-F,and can serve as an effective measurement tool for clinicians to assess PICS-F in family members of ICU patients.

Objective To investigate the risk factors associated with onset of vertebrobasilar doli-choectasia(VBD)symptoms in elderly patients.Methods A retrospective study was conducted on 206 elderly VBD patients admitted in Chinese PLA General Hospital between July 2014 and July 2024.Based on the presence of VBD-related symptoms or not,they were divided into a symptom-atic group(112 cases)and an asymptomatic group(94 cases).All patients underwent cranial mag-netic resonance angiography(MRA)and MRI.And routine blood tests were performed and related indicators were detected,including following parameters:neutrophil-to-lymphocyte ratio(NLR),systemic immune-inflammation(SII)index,total cholesterol(TC),low-density lipoprotein choles-terol(LDL-C),and C-reactive protein(CRP).Multivariate logistic regression analysis was per-formed to identify risk factors associated with symptomatic progression in elderly VBD patients.Results The symptomatic group showed significantly higher SII index and NLR,longer bilateral vertebral artery length,and larger proportion of elevated vertebral artery confluence when compared with the asymptomatic group(P＜0.05,P＜0.01).Significant differences were also observed be-tween the two groups in basilar artery height grade and proportion of basilar artery hyperintensity of vessel wall signal(BA-HVS,P＜0.01).Multivariate logistic regression analysis revealed that elevated vertebral artery confluence and high-grade basilar artery hyperintensity of vessel wall signal were risk factors for symptomatic onset in elderly VBD patients.The risk of symptomatic onset in elderly VBD patients with elevated confluence of the vertebral artery was 2.143 times higher than in those without elevation(95％CI:1.144-4.013,P=0.017).Similarly,the risk in elderly VBD patients with high-grade basilar artery hyperintensity of vessel wall signal was 4.113 times higher than in those without such signal(95％CI:2.213-7.646,P=0.000).Conclusion Elevated vertebral artery confluence and high-grade basilar artery hyperintensity of vessel wall signal may serve as clinical indicators for monitoring disease progression in elderly patients with VBD,thereby facilitating the optimization of clinical management strategies in the patients.