This article systematically reviews the current research status as well as diagnosis and treatment strategies of traditional Chinese medicine （TCM） for gastroesophageal reflux disease （GERD）. Studies demonstrate that TCM， based on the "disease-syndrome combination" approach， exhibits multi-target advantages in alleviating symptoms of various GERD subtypes， promoting mucosal repair， regulating emotions， and facilitating the reduction of western medication. To address clinical challenges such as symptom overlap and limited therapeutic efficacy， strategies have been proposed including "treating different diseases with the same method" and integrated regulation based on viscera correlation. Future efforts should focus on elucidating the mechanisms of compound prescriptions， promoting TCM drug development under the "three-combination" evaluation framework that integrates TCM theory， human experience and clinical trial evidence， and optimizing integrated traditional and western medicine models to enhance GERD management.

Objective:To explore the therapeutic effect of compound Kuijie Ankang Decoction on ulcerative colitis (UC) model mice by non targeted metabonomics; To explore its mechanism.Compound Kuijie Ankang.Methods:The mice were randomly divided into blank control group, model group, Kuijie Ankang Decoction group and sulfasalazine group, with 12 mice in each group. Except the blank control group, the other groups were given 1.5% DSS solution for free drinking to prepare UC model. After successful modeling, Kuijie Ankang Decoction group was intragastrically administered with compound Kuijie Ankang Decoction of 9.68 g/kg, sulfasalazine group was intragastrically administered with sulfasalazine capsule suspension of 320 mg/kg, model group and blank control group were intragastrically administered with equal volume of purified water, once a day, for 7 consecutive days. The body mass and disease activity index (DAI) score of mice were measured. ELISA was used to measure the levels of tumor necrosis factor-α (TNF-α), interleukin-1β (IL-1β), interleukin-6 (IL-6), and interleukin-10 (IL-10) in the colon tissue of mice; the protein expressions of Claudin-1 and Zo-1 in colon tissue were detected by immunofluorescence method. HE staining was used to observe the pathological changes in the colon, and UHPLC-OE-MS technology was used to analyze the endogenous metabolite structure of mouse colon tissue, differential metabolites and related metabolic pathways were screened.Results:Compared with the model group, the colon length in Kuijie Ankang Decoction group and sulfasalazine group increased ( P<0.01), the DAI score decreased ( P<0.01), the levels of TNF-α, IL-1β and IL-6 in colon tissue decreased ( P<0.01), the level of IL-10 increased ( P<0.01), and the average optical density of Claudin-1 and Zo-1 protein increased ( P<0.01 or P<0.05). Metabolomics analysis identified 26 potential differential metabolites, including nicotinamide adenine dinucleotide, guanine, gamma aminobutyric acid, and thiamine, affecting 26 key metabolic pathways, including lysine biosynthesis, thiamine metabolism, cysteine and methionine metabolism. Conclusion:Kuaijie Ankang Decoction may improve metabolites such as Gamma aminobutyric acid and thiamine through metabolic pathways such as lysine biosynthesis to alleviate inflammatory reactions, thereby exerting therapeutic effects on ulcerative colitis in mice.

Objective:To explore the association between circadian syndrome (CircS), metabolic syndrome (MetS) and mild cognitive impairment (MCI) in elderly rural adults in China.Methods:From March to September 2018, totally 5 765 participants aged 60 years or older from 52 villages in Yanlou Town, Yanggu County, Shandong Province were selected. The data included demographic, underlying disease and neuropsychological data were collected by questionaire survey. Having ≥3 of the following components was defined as MetS: elevated waist circumference, high triglycerides, low high-density lipoprotein cholesterol, elevated blood pressure and elevated fasting glucose. Having ≥4 of the following components was defined as CircS: short sleep (<6 h/d), depression and five other components which were used to define MetS, with elevated waist circumference as a mandatory item. MCI was diagnosed according to Petersen's criteria and further classified into amnestic MCI (aMCI) and non-amnestic MCI (naMCI) based on whether the memory domains impaired.Data were analyzed using multivariable Logistic regression and general linear regression models by R statistical software.Results:In the total sample ( n=4 898), 1 280 participants were diagnosed with MCI, of which 1 075 were aMCI and 205 were naMCI.Compared to the normal group, CircS alone was significantly associated with increased risks of MCI ( B=0.695, P=0.039) and aMCI ( B=0.782, P=0.024), as well as lower verbal fluency scores ( B=-0.244, P=0.045). No significant associations were found between MetS alone or both MetS and CircS and cognitive impairment( P>0.05). At the component level, short sleep and depression were associated with increased risks of MCI ( B=0.167, P=0.025; B=0.605, P<0.001) and aMCI ( B=0.185, P=0.020; B=0.600, P<0.001). Conclusion:Individuals with CircS are at a higher risk of cognitive impairment, CircS is more strongly associated with cognitive impairment than MetS, with short sleep duration and depressive symptoms potentially playing key roles.

Objective:To investigate the current situation and influencing factors of work ability in gynecological cancer patients, so as to provide a reference for occupational rehabilitation management of cancer patients.Methods:A total of 183 gynecological cancer patients who visited the gynecological oncology outpatient department of Women′s Hospital of Nanjing Medical University from November 2022 to March 2023 were selected by the convenience sampling. The General Information Questionnaire, Return-to-Work Self-efficacy Questionnaire, Social Support Rating Scale, the European Organization for Research and Treatment of Cancer Quality of Life Quesionnaire Core 30, Work Ability Index were selected for cross-sectional investigaton. The multiple linear regression analysis was used to explore the influencing factors of work ability in gynecological cancer patients.Results:A total of 189 questionnaires were sent out in this study, and 183 were effectively collected, with an effective recovery rate of 96.83% (183/189). The patients were (44.64 ± 7.06) years old. The scores of the patients were (27.77 ± 7.58) points on Work Ability Index, (4.72 ± 1.14) points on Return-to-Work Self-efficacy Questionnaire, (86.93 ± 23.44) points on Social Support Rating Scale, (79.46 ± 19.53) points on overall health status area and (41.23 ± 27.80) points on the field of fatigue symptoms area of the European Organization for Research and Treatment of Cancer Quality of Life Quesionnaire Core 30. Multiple linear regression analysis showed that fatigue, comorbidities, clinical stage of disease, primary treatment, per capita monthly income of families, return-to-work self efficacy and job nature were independent influencing factors of work ability in gynecological cancer patients ( t values were -10.47-2.86, all P<0.05), which explained 67.9% of the total variance. Conclusions:Clinical medical staff should pay attention to the influencing factors that affect work ability in gynecological cancer patients, in order to take targeted occupational rehabilitation measures to improve their work ability.

Objective:To explore the association between circadian syndrome (CircS), metabolic syndrome (MetS) and mild cognitive impairment (MCI) in elderly rural adults in China.Methods:From March to September 2018, totally 5 765 participants aged 60 years or older from 52 villages in Yanlou Town, Yanggu County, Shandong Province were selected. The data included demographic, underlying disease and neuropsychological data were collected by questionaire survey. Having ≥3 of the following components was defined as MetS: elevated waist circumference, high triglycerides, low high-density lipoprotein cholesterol, elevated blood pressure and elevated fasting glucose. Having ≥4 of the following components was defined as CircS: short sleep (<6 h/d), depression and five other components which were used to define MetS, with elevated waist circumference as a mandatory item. MCI was diagnosed according to Petersen's criteria and further classified into amnestic MCI (aMCI) and non-amnestic MCI (naMCI) based on whether the memory domains impaired.Data were analyzed using multivariable Logistic regression and general linear regression models by R statistical software.Results:In the total sample ( n=4 898), 1 280 participants were diagnosed with MCI, of which 1 075 were aMCI and 205 were naMCI.Compared to the normal group, CircS alone was significantly associated with increased risks of MCI ( B=0.695, P=0.039) and aMCI ( B=0.782, P=0.024), as well as lower verbal fluency scores ( B=-0.244, P=0.045). No significant associations were found between MetS alone or both MetS and CircS and cognitive impairment( P>0.05). At the component level, short sleep and depression were associated with increased risks of MCI ( B=0.167, P=0.025; B=0.605, P<0.001) and aMCI ( B=0.185, P=0.020; B=0.600, P<0.001). Conclusion:Individuals with CircS are at a higher risk of cognitive impairment, CircS is more strongly associated with cognitive impairment than MetS, with short sleep duration and depressive symptoms potentially playing key roles.

This study aims to isolate and culture primary rabbit spinal cord microvascular endothelial cells in vitro,providing a practical source of test cells for spinal cord injury research.Spinal cord tissue was aseptically extracted from one-month-old rabbits and processed sequentially through mincing,bovine serum albumin density gradient centrifugation,mesh filtration,and type Ⅱ collagenase digestion to ob-tain purified spinal cord microvascular segments.The microvascular segments were homogeneously mixed with an apprapriate volume of M199 complete culture medium and seeded into a culture dish for primary culture.Throughout the culture period,cell growth performance were continuously observed and recor-ded.Additionally,immunocytochemical staining was performed to evaluate the expression of factor Ⅷ-re-lated antigen.The results showed that after 24 hours of inoculation,a small amount of endothelial-like cells were observed to emerge from the spinal cord microvascular segments.Within 36～60 hours,the cell colonies gradually expanded and fused.After 72 hours,the cells spread across the base of the dish,forming a"cobblestone-like"monolayer.Immunocytochemical staining showed that more than 99％of the cells showed brown-red cytoplasm and were positive for factor Ⅷ-related antigen.It is these results that suggest this study has successfully established a convenient and stable primary rabbit spinal cord micro-vascular endothelial cells culture method.

OBJECTIVE: To evaluate the dynamic changes of glucocorticoid (GC) dose and the feasibility of GC discontinuation in rheumatoid arthritis (RA) patients under the background of Chinese medicine (CM). METHODS: This multicenter retrospective cohort study included 1,196 RA patients enrolled in the China Rheumatoid Arthritis Registry of Patients with Chinese Medicine (CERTAIN) from September 1, 2019 to December 4, 2023, who initiated GC therapy. Participants were divided into the Western medicine (WM) and integrative medicine (IM, combination of CM and WM) groups based on medication regimen. Follow-up was performed at least every 3 months to assess dynamic changes in GC dose. Changes in GC dose were analyzed by generalized estimator equation, the probability of GC discontinuation was assessed using Kaplan-Meier curve, and predictors of GC discontinuation were analyzed by Cox regression. Patients with <12 months of follow-up were excluded for the sensitivity analysis. RESULTS: Among 1,196 patients (85.4% female; median age 56.4 years), 880 (73.6%) received IM. Over a median 12-month follow-up, 34.3% (410 cases) discontinued GC, with significantly higher rates in the IM group (40.8% vs. 16.1% in WM; P<0.05). GC dose declined progressively, with IM patients demonstrating faster reductions (median 3.75 mg vs. 5.00 mg in WM at 12 months; P<0.05). Multivariate Cox analysis identified age <60 years [P<0.001, hazard ratios (HR)=2.142, 95% confidence interval (CI): 1.523-3.012], IM therapy (P=0.001, HR=2.175, 95% CI: 1.369-3.456), baseline GC dose ⩽7.5 mg (P=0.003, HR=1.637, 95% CI: 1.177-2.275), and absence of non-steroidal anti-inflammatory drugs use (P=0.001, HR=2.546, 95% CI: 1.432-4.527) as significant predictors of GC discontinuation. Sensitivity analysis (545 cases) confirmed these findings. CONCLUSIONS RA patients receiving CM face difficulties in following guideline-recommended GC discontinuation protocols. IM can promote GC discontinuation and is a promising strategy to reduce GC dependency in RA management. (Trial registration: ClinicalTrials.gov, No. NCT05219214).
Adult ; Aged ; Female ; Humans ; Male ; Middle Aged ; Arthritis, Rheumatoid/drug therapy* ; Glucocorticoids/therapeutic use* ; Medicine, Chinese Traditional ; Retrospective Studies

The glucagon receptor (GCGR) is a critical target for the treatment of metabolic disorders such as Type 2 Diabetes Mellitus (T2DM) and obesity. Activation of GCGR enhances systemic insulin sensitivity through paracrine stimulation of insulin secretion, presenting a promising avenue for treatment. However, the discovery of effective GCGR agonists remains a challenging and resource-intensive process, often requiring time-consuming wet-lab experiments to synthesize and screen potential compounds. Recent advances in artificial intelligence technologies have demonstrated great potential in accelerating drug discovery by streamlining screening and efficiently predicting bioactivity. In the present work, we propose DeepGCGR, a two-layer deep learning model that leverages graph convolutional networks (GCN) integrated with a multiple attention mechanism to expedite the identification of GCGR agonists. In the first layer, the model predicts the bioactivity of various compounds against GCGR, efficiently filtering large chemical libraries to identify promising candidates. In the second layer, DeepGCGR classifies high bioactive compounds based on their functional effects on GCGR signaling, identifying those with potential agonistic or antagonistic effects. Moreover, DeepGCGR was specifically applied to identify novel GCGR-regulating compounds for the treatment of T2DM from natural products derived from traditional Chinese medicine (TCM). The proposed method will not only offer an effective strategy for discovering GCGR-targeting compounds with functional activation properties but also provide new insights into the development of T2DM therapeutics.
Deep Learning ; Drug Discovery/methods* ; Humans ; Diabetes Mellitus, Type 2/metabolism* ; Medicine, Chinese Traditional ; Drugs, Chinese Herbal/pharmacology*

ObjectiveThis study proposes a novel single-cell protein localization method based on a class perception graph convolutional network (CP-GCN) to overcome several critical challenges in protein microscopic image analysis, including the scarcity of cell-level annotations, inadequate feature extraction, and the difficulty in achieving precise protein localization within individual cells. The methodology involves multiple innovative components designed to enhance both feature extraction and localization accuracy. MethodsFirst, a class perception module (CPM) is developed to effectively capture and distinguish semantic features across different subcellular categories, enabling more discriminative feature representation. Building upon this, the CP-GCN network is designed to explore global features of subcellular proteins in multicellular environments. This network incorporates a category feature-aware module to extract protein semantic features aligned with label dimensions and establishes a subcellular relationship mining module to model correlations between different subcellular structures. By doing so, it generates co-occurrence embedding features that encode spatial and contextual relationships among subcellular locations, thereby improving feature representation. To further refine localization, a multi-scale feature analysis approach is employed using the K-means clustering algorithm, which classifies multi-scale features within each subcellular category and generates multi-cell class activation maps (CAMs). These CAMs highlight discriminative regions associated with specific subcellular locations, facilitating more accurate protein localization. Additionally, a pseudo-label generation strategy is introduced to address the lack of annotated single-cell data. This strategy segments multicellular images into single-cell images and assigns reliable pseudo-labels based on the CAM-predicted regions, ensuring high-quality training data for single-cell analysis. Under a transfer learning framework, the model is trained to achieve precise single-cell-level protein localization, leveraging both the extracted features and pseudo-labels for robust performance. ResultsExperimental validation on multiple single-cell test datasets demonstrates that the proposed method significantly outperforms existing approaches in terms of robustness and localization accuracy. Specifically, on the Kaggle 2021 dataset, the method achieves superior mean average precision (mAP) metrics across 18 subcellular categories, highlighting its effectiveness in diverse protein localization tasks. Visualization of the generated CAM results further confirms the model’s capability to accurately localize subcellular proteins within individual cells, even in complex multicellular environments. ConclusionThe integration of the CP-GCN network with a pseudo-labeling strategy enables the proposed method to effectively capture heterogeneous cellular features in protein images and achieve precise single-cell protein localization. This advancement not only addresses key limitations in current protein image analysis but also provides a scalable and accurate solution for subcellular protein studies, with potential applications in biomedical research and diagnostic imaging. The success of this method underscores the importance of combining advanced deep learning architectures with innovative training strategies to overcome data scarcity and improve localization performance in biological image analysis. Future work could explore the extension of this framework to other types of microscopic imaging and its application in large-scale protein interaction studies.