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.

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.

As one of the chronic diseases with high incidence in contemporary society, cervical spondylosis has increasing patient groups who gradually present a low age, and it seriously affects social and public health. Although modern medicine has made great progress in the pathological research and clinical treatment of cervical spondylosis, patients still face gastrointestinal side effects of nonsteroidal anti-inflammatory drugs(NSAIDs), neck pain, limited mobility, upper limb numbness, and other symptoms after conservative or surgical treatment. In the theory of traditional Chinese medicine(TCM), cervical spondylosis belongs to the categories of "Bi syndrome" "stiff neck" "stiff Bi", etc. With the change of the times, the change of lifestyle, and the application of western medicine treatment, the etiology and pathogenesis of TCM in cervical spondylosis also show new characteristics. In terms of etiology and pathogenesis, it involves the invasion of wind, cold, and dampness, long-term strain, liver and kidney deficiency, Qi and blood stasis, which are associated with factors such as cervical degeneration, muscle tension and spasm, intervertebral disc herniation, and nerve root compression in modern medicine. In terms of the evolution of pathogenesis, in the early stage, wind, cold, and dampness, were more common in Xuanfu, resulting in unfavorable muscles and bones, poor flow of Qi and blood, and cervical spondylosis and radiculopathy. Medium-term phlegm stasis and internal knots, sluggish muscles and veins, and long-term weathering and fire are more likely to occur in the vertebral artery and sympathetic radiculopathy. In the later stage, the positive Qi is depleted; the true Yin is damaged, and the viscera Qi and blood are deficient, which is most common in cervical myelopathy. The strategy of treating cervical spondylosis with TCM classic formulas applies Gegen Decoction, Wutou Decoction, Qianghuo Shengshi Decoction, Mahuang Jiazhu Decoction to patients with wind, cold, and dampness. Patients with phlegm dampness and blood stasis are treated with Huoxue Xiaoling Dan, Jinlingzi Powder, Siwu Decoction, Banxia Baizhu Tianma Decoction, Shuanghe Decoction, etc. For those patients with liver, spleen, and kidney deficiency, Huangqi Guizhi Wuwu Decoction, Tianma Gouteng Decoction, Guishao Dihuang Pills, Shenling Baizhu Powder, and Lizhong Decoction are used to invigorate the spleen, nourish Qi and blood, and tonify liver and kidney. In clinical practice, the authors advocate a safe and effective treatment plan of classic formulas based on deficiency and excess, the integration of formulas and syndromes, and the combination of modern research results, so as to relieve symptoms, reduce recurrence, and reduce medical burden.
Humans ; Spondylosis/drug therapy* ; Medicine, Chinese Traditional/methods* ; Drugs, Chinese Herbal/therapeutic use* ; Cervical Vertebrae/pathology*

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*

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 analyze the burden and changing trend of testicular cancer in China from 1990 to 2019.Methods Based on the 2019 Global Burden of Disease Database(GBD 2019),analyze the incidence,mortality,disability-adjusted life years(DALYs),years of life lost(YLLs),years lived with disability(YLDs)and their variation trend of testicular cancer in Chinese population from 1990 to 2019.Evaluating changes in age standardized rate(ASR)by calculating annual estimated percentage change(EAPC).According to the age grouping,analyze the age distribution characteristics of testicular cancer disease burden by age group.Results In 2019,the incident cases,deaths,age-standardized incidence rate,and age-standardized mortality rate of testicular cancer in China were 17.17×103,1.21×103,2.39/105,and 0.16/105,respectively.Compared to 1990,incident cases,deaths,and age-standardized incidence rate increased obviously in China,which was consistent with the global change trend,while the increase was higher than the global level.However,both Chinese and global age-standardized mortality rate showed a downward trend.From 1990 to 2019,DALYs,YLLs and YLDs of testicular cancer increased by 29.66%,9.83%and 720.91%respectively in China.The two age groups,0-15 years group and 30-35 years group,were with highest incidence of testicular cancer,while the highest disease burden of testicular cancer was 30-35 years.Conclusion From 1990 to 2019,the disease burden of testicular cancer in China showed an upward trend.Adolescents and young adults should be the priority population for screening and prevention due to their higher incidence and disease burden.

Female ; Humans ; Breast Neoplasms ; Testosterone ; Prolactin ; Estradiol ; Testosterone Congeners ; China

Objective To study the diagnostic value of miR-571 for liver fibrosis by detecting miR-571 expression in the peripheral blood of patients with liver fibrosis.Methods From December 2022 to September 2023,40 patients with liver fibrosis,40 patients with chronic hepatitis,and 40 healthy controls were chosen as research subjects.The expression level of miR-571 in peripheral blood was detected using a real-time quantitative polymerase chain reaction,and the relative expression of miR-571 in each group was evaluated.The Spearman correlation method was utilized to examine the relationship between miR-571 and clinical detection indices.To assess the capacity of miR-571 and the multivariate diagnostic model to identify liver fibrosis,binary logistic regression was used to create a multivariate diagnostic model,and ROC curves were generated.Results The expression of miR-571 was significantly higher in the liver fibrosis group than in the healthy control and hepatitis groups,and the difference was statistically significant(P<0.001).The expression level of miR-571 was positively connected with ALT,APRI score,and FIB-4 index(r = 0.23,0.30,0.22,P<0.05)and negatively correlated with PLT(r =-0.19,P<0.05)according to Spearman correlation analysis.Logistic regression research revealed that miR-571 and the FIB-4 index were independent risk factors for liver fibrosis.The AUC for miR-571 to diagnose fibrosis was 0.91(95%CI:0.85～0.96),while the AUC for miR-571 paired with the FIB-4 index was 0.94(95%CI:0.90～0.98).Conclusion MiR-571 expression was shown to be considerably higher in the peripheral blood of hepatic fibrosis patients,and the combined FIB-4 index offers some clinical diagnostic value for hepatic fibrosis.