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.

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 investigate the mechanism of Kuijie Ankang Decoction in regulating immune,autophagy and intestinal flora in the treatment of ulcerative colitis(UC).Methods UC mouse model was established by free drinking with sodium dextran sulfate.The mice were randomly divided into blank control group,model group,Kuijie Ankang Decoction group,salazine sulfopyridine(SASP)group and 3-methyladenine(3-MA)group,with 12 mice in each group.Each drug group was given corresponding drugs for gavage,the blank control group and model group were given the same volume of distilled water for gavage for 7 days.The general condition of mice was observed and the disease activity index(DAI)was scored,the morphology of colon tissue was observed by HE staining,the contents of TNF-α,IL-1β,IL-6,IL-8 and IL-10 in colon tissue were detected by ELISA,the mRNA and protein expressions of LC3,Beclin-1 and p62 in colon tissue were detected by RT-qPCR and Western blot,respectively.16S rDNA sequencing was used to analyze the structure of intestinal flora.Results Compared with the blank control group,the mice in the model group showed a decrease in body mass,an increase in DAI score,a decrease in colon length,serious mucosal injury and inflammatory cell infiltration,the contents of TNF-α,IL-1β,IL-6 and IL-8 in colon tissue significantly increased(P<0.05,P<0.01),the content of IL-10 decreased(P<0.01),the mRNA expressions of LC3 and Beclin-1 in colon tissue decreased(P<0.05,P<0.01),the mRNA and protein expression of p62 increased(P<0.01),while the expressions of LC3Ⅱ/LC3Ⅰ and Beclin-1 proteins decreased(P<0.05,P<0.01).Compared with the model group,the body mass of mice in Kuijie Ankang Decoction group and SASP group increased(P<0.05),DAI score decreased(P<0.05),the colon length increased(P<0.05),the pathological damage of colon mucosa was alleviated,the contents of TNF-α,IL-1β,IL-6 and IL-8 in colon tissue decreased(P<0.05,P<0.01),the content of IL-10 increased(P<0.01),the expressions of LC3 and Beclin-1 mRNA in colon tissue increased(P<0.05,P<0.01),the expression of p62 mRNA and protein decreased(P<0.05,P<0.01),the expressions of LC3Ⅱ/LC3Ⅰand Beclin-1 protein increased(P<0.05,P<0.01).16S rDNA sequencing results showed that the diversity and evenness of the intestinal flora in the model group mice decreased,with a decrease in the relative abundance of Firmicutes,Actinobacteria and Patescibacteria(P<0.05),and an increase in the relative abundance of Bacteroidota,Verrucomicrobiota and Proteobacteria(P<0.05);the relative abundance of Bacilli and Coriobacteriia decreased(P<0.05),the relative abundance of Bacteroidia,Clostridia and Verrucomicrobiae increased(P<0.05);the relative abundance of Ligilactobacillus and Dubosiella decreased(P<0.05),the relative abundance of unclassified Muribaculaceae,Lachnospiraceae NK4A136_group,Akkermansia and unclassified Lachnospiraceae increased(P<0.05).Compared with the model group,the diversity and evenness of intestinal flora increased in Kuijie Ankang Decoction group and SASP group,with an increase in the relative abundance of Firmicutes(P<0.05),a decrease in the relative abundance of Bacteroidota and Verrucomicrobiota(P<0.05),the relative abundance of Bacteroidia and Bacilli increased(P<0.05),the relative abundance of Verrucomicrobiae decreased(P<0.05);the relative abundance of unclassified Muribaculaceae and Ligilactobacillus increased(P<0.05),while the relative abundance of Lachnospiraceae NK4A136_group and Akkermansia decreased(P<0.05).Conclusion Kuijie Ankang Decoction can significantly improve the intestinal mucosal injury of UC mice,and the mechanism may be related to the regulation of colon autophagy level and intestinal flora disorder.

Objective:To examine frailty in residents aged over 50 in the Chengdu community, investigate the risk factors linked to frailty, and analyze the impact of exercise habits on frailty.Methods:This retrospective study utilized a stratified whole-sample method to select Chengdu residents aged over 50 from 6 communities.Relevant information was collected through face-to-face interactions using a self-developed frailty questionnaire between May 2022 and May 2023.Exercise habits were defined as engaging in physical activity more than three times a week for at least 30 minutes per session, consistently maintained for a minimum of five years prior to the survey.The frailty index(FI)was established based on the multiple-deficit cumulative model.Respondents were classified into categories of no frailty(FI<0.1), mild frailty(0.1 ≤ FI<0.2), and moderate-severe frailty(FI≥0.2).Univariate and multivariate logistic regression analyses were conducted to investigate the factors influencing frailty and to compare the prevalence and progression of frailty among individuals with and without exercise habits across different age groups.Results:A total of 999 participants, comprising 483(48.3%)males and 516(51.7%)females, were enrolled in the study.The average age of the participants was 65.70±9.61 years.Among them, 346(34.6%)were classified as having mild frailty, while 107(10.7%)had moderate-severe frailty.Univariate analysis revealed that age, gender, marital status, education, smoking, alcohol consumption, and exercise habits were potential factors influencing frailty( P<0.05).Multifactorial logistic regression models indicated that individuals aged 70-79 and 80-85 were predictors of mild and moderate-severe frailty, respectively.Being female was identified as an independent risk factor for both mild frailty( OR: 1.196; 95% CI: 1.262-3.073)and moderate-severe frailty( OR: 2.721; 95% CI: 1.282-5.774).Additionally, not engaging in regular exercise was associated with an increased risk of mild frailty( OR: 1.588; 95% CI: 1.149-2.193)and moderate-severe frailty( OR: 3.186; 95% CI: 1.819-5.583).Statistical analysis revealed significant differences in frailty distribution and frailty index changes between individuals with and without exercise habits over the age of 60( P<0.05). Conclusions:Age and gender were found to be closely associated with frailty, with the impact of exercise habits on frailty becoming more pronounced after the age of 60.Encouraging middle-aged and older individuals to adopt regular exercise routines can play a significant role in promoting healthy aging in China.

Objective:To determine whether spinal cord injury (SCI) triggers secondary neuroinflammation and neuronal injury in remote brain regions by impairing the drainage function of the meningeal lymphatic vessels (MLVs).Methods:Fifty-two female C57BL/6 mice were assigned with the random number table into four groups ( n=13 per group): sham group, SCI group, adeno-associated virus negative control group (negative control group), and adeno-associated virus overexpressing VEGF-C group (VEGF-C group). The sham group underwent laminectomy without spinal cord injury. In the SCI group, negative control group and VEGF-C group, T 9 contusion was made to establish the SCI models using a modified Allen′s impactor. At 4 weeks before SCI modeling, the negative control group and VEGF-C group were injected via the cisterna magna with 3 μl adeno-associated virus for negative control or adeno-associated virus for VEGF-C overexpression. At 56 days after injury, Alexa Fluor? 647 ovalbumin conjugate (OVA-647) was injected via the cisterna magna as a tracer. Two hours later, the proportion of OVA-647 in the deep cervical lymph nodes (dCLN) was detected. Immunofluorescence was performed to assess the proportion of MLVs marker lymphatic vessel endothelial hyaluronan receptor-1 (LYVE-1) and expression levels of microglial marker ionized calcium-binding adaptor molecule 1 (Iba1) in the cerebral cortex, hippocampus, midbrain, and thalamus across the experimental groups. ELISA was employed to quantify the levels of tumor necrosis factor-α (TNF-α) and interleukin-1β (IL-1β) and Nissl staining was used to assess neuronal counts in these regions. Results:At 56 days after injury, the OVA-647 proportion in the dCLN was higher in the sham group than that in the SCI group and negative control group ( P<0.01), whereas the SCI group and negative control group showed a lower OVA-647 proportion in the dCLN than the VEGF-C group ( P<0.05). At 56 days after injury, the dural LYVE-1 proportion was higher in the sham group than that in the SCI group and negative control group ( P<0.01), whereas it was lower in the SCI group and negative control group than that in the VEGF-C group ( P<0.05). At 56 days after injury, the count of Iba1-positive microglia across all the above-mentioned regions was increased in the SCI group and negative control group ( P<0.01), compared with that in the sham group, whereas it was reduced in these regions in the VEGF-C group, compared with that in the SCI group and negative control group ( P<0.01). At 56 days after injury, TNF-α and IL-1β levels in these regions were both elevated in the SCI group and negative control group when compared with those in the sham group ( P<0.05), whereas they were reduced in the VEGF-C group, compared with those in the SCI group and negative control group ( P<0.05). At 56 days after injury, neuronal survival in the regions was decreased in the SCI group and negative control group, compared with that in the sham group ( P<0.05), whereas it was increased in the VEGF-C group, compared with that in the SCI group and negative control group ( P<0.05). Conclusion:SCI can induce secondary neuroinflammation and neuronal damage in remote brain regions by impairing the drainage function of MLVs.

The history of cystic fibrosis (CF) can be traced back to the Middle Ages when a salty taste was recognized as an early symptom. In the 1930s, Dorothy Anderson gradually revealed the characteristics and mechanisms of CF through clinical and pathological studies. In 1953, the sweat test became an important tool for diagnosing CF. In 1989, scientists identified the CFTR genes and their association with CF, leading to significant advancement in genetic research and treatment of CF. By tracing the history and research progress of CF, we witness the transition from ancient legends to modern scientific exploration. This journey not only demonstrates the advancement of medical technology but also reflects the continuous deepening of human understanding of diseases, highlighting the immense potential of scientific research in improving human health.

This article reviews the evolution of neonatal cystic fibrosis (CF) screening in Europe and North America, with a focus on the development and clinical application of sweat testing and immunoreactive trypsinogen assays. It highlights the growing recognition of early screening benefits and associated challenges. In China, neonatal CF screening has not been widely implemented due to low disease incidence and atypical clinical presentations, which complicate early diagnosis. Advances in genetic testing technologies and research on CFTR gene variants specific to the Chinese population may drive future progress in screening and therapeutic strategies.

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.