Pulmonary fibrosis（PF） is a progressive and life-threatening disease with limited therapeutic options， highlighting the urgent need for innovative drug discovery strategies. To address this challenge， the authors propose the formula-originated rational intelligent screening&translation（FIRST）， a systematic framework for developing anti-fibrotic monomers derived from classical traditional Chinese medicine（TCM）. The strategy integrates three key dimensions， including tissue-oriented intelligent screening of active compounds， structural optimization based on drug-target spatial interactions and plant biosynthetic pathways， and cross-scale validation of drug. We further highlight its applications in discovering tissue-oriented novel drugs from clinically validated TCM， the development and mechanistic elucidation of anti-fibrotic therapeutics， as well as the clinical translation and secondary development of candidate drugs. This strategy paves the way for first-in-class， formula-derived monomeric drugs with defined structures， clarified mechanisms， and proven safety， offering a transformative avenue to meet the urgent therapeutic needs of PF and setting a new paradigm for TCM-based drug innovation.

Pulmonary fibrosis（PF） is a progressive and life-threatening disease with limited therapeutic options， highlighting the urgent need for innovative drug discovery strategies. To address this challenge， the authors propose the formula-originated rational intelligent screening&translation（FIRST）， a systematic framework for developing anti-fibrotic monomers derived from classical traditional Chinese medicine（TCM）. The strategy integrates three key dimensions， including tissue-oriented intelligent screening of active compounds， structural optimization based on drug-target spatial interactions and plant biosynthetic pathways， and cross-scale validation of drug. We further highlight its applications in discovering tissue-oriented novel drugs from clinically validated TCM， the development and mechanistic elucidation of anti-fibrotic therapeutics， as well as the clinical translation and secondary development of candidate drugs. This strategy paves the way for first-in-class， formula-derived monomeric drugs with defined structures， clarified mechanisms， and proven safety， offering a transformative avenue to meet the urgent therapeutic needs of PF and setting a new paradigm for TCM-based drug innovation.

Objective: To investigate the regulatory role of miR-6824-3p in macrophage function and its molecular mechanism in inhibiting hepatitis B virus (HBV) replication, thereby providing experimental evidence to elucidate the immune regulatory mechanisms underlying persistent HBV infection. Methods: miR-6824-3p mimic and inhibitor were transfected into human THP-1-induced macrophages. Real-time quantitative PCR (qRT-PCR), enzyme-linked immunosorbent assay (ELISA), neutral red uptake, reactive oxygen species (ROS) production, and fluorescent latex particle phagocytosis assays were employed to evaluate the effects of miR-6824-3p on macrophage phenotype and function. Through a combination of bioinformatics analysis, dual luciferase reporter assays, western blot, and siRNA interference techniques, we identified the target gene of miR-6824-3p and examined their effects on downstream signaling pathways. qRT-PCR and western blot analyses were performed to assess the impact of miR-6824-3p-regulated macrophages on HBV DNA, pgRNA, cccDNA, and HBV-associated antigen levels in HepAD38 cells. Key effector molecules were identified through neutralization assays. Results: miR-6824-3p mimic significantly promoted the expression and secretion of proinflammatory factors, such as TNF-α and IL-1β, in macrophages (P<0.001), while concurrently reducing ROS production and phagocytosis (P<0.05). Furthermore, miR-6824-3p downregulated NRAS expression in macrophages, which was accompanied by a reduction in MAPK signalling path-way activity (p-MEK, p-ERK). Compared to the control group, the medium of macrophages with overexpressed miR-6824-3p inhibited the expression of HBV DNA, pgRNA, cccDNA, and HBV-associated antigens HBsAg, HBeAg, and HBcAg in HepAD38 cells (P<0.01). Similar results were also observed in the co-culture system of macrophages with HepAD38 cells. The addition of TNF-α neutralizing antibodies markedly attenuated the aforementioned antiviral effects (P<0.001). Conclusion: miR-6824-3p targets NRAS to affect the downstream MAPK signaling pathway, regulating the immune function of macrophages. The TNF-α induced by miR-6824-3p is one of the key molecules that suppress HBV replication. This study provides evidence for further elucidating the molecular mechanisms by which miRNAs influence HBV replication via modulating the host immune microenvironment.

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*

Traditional Chinese Medicine (TCM), as a treasure of the Chinese nation, plays a significant role in maintaining public health. In 2019, the Central Committee of the Communist Party of China and the State Council proposed for the first time the establishment of a TCM registration and evaluation evidence system that integrates TCM theory, "personal experience" and clinical trials (referred to as the "Three-in-One" System) to promote the inheritance and innovation of TCM. Subsequently, the National Medical Products Administration issued several guiding principles to advance the improvement and implementation of this system. Owing to the complexity of its implementation, there are still differing understandings within the TCM industry regarding the positioning of the "Three-in-One" Registration and Evaluation Evidence System, as well as the connotation and value orientation of the "personal experience." To address this, Academician WANG Qi, President of the TCM Association, China International Exchange and Promotion Association for Medical and Healthcare and TCM master, led a group of academicians, TCM masters, TCM pharmacology experts and clinical TCM experts to convene a "Seminar on Promoting the Implementation of the ′Three-in-One′ Registration and Evaluation Evidence System for Chinese Medicinals." Through extensive discussions, an expert consensus was formed, clarifying the different roles of the TCM theory, "personal experience" and clinical trials within the system. It was further emphasized that the "personal experience" is the core of this system, and its data should be derived from clinical practice scenarios. In the future, the improvement of this system will require collaborative efforts across multiple fields to promote the high-quality development of the Chinese medicinal industry.

ObjectiveTo establish the multi-technique characteristic profiles of Alumen by X-ray diffraction（XRD）， Fourier-transform infrared spectroscopy（FTIR） and thermogravimetric-differential thermal analysis（TG-DTA）， and to explore the spectral characteristics for rapid identification of Alumen and its potential adulterant， Ammonium alum. MethodsA total of 27 batches of Alumen samples from 8 production regions were collected for preliminary identification based on visual characteristics. The PDF standard cards of XRD were used to differentiate Alumen from A. alum， and the XRD characteristic profiles of Alumen were established， and then the common peaks were screened. Based on hierarchical clustering analysis（HCA） and orthogonal partial least squares-discriminant analysis（OPLS-DA）， the characteristic information that could be used for identification of Alumen was selected with variable importance in the projection（VIP） value>1. FTIR characteristic profiles of Alumen were established， and key wavenumbers for identification were screened by HCA and OPLS-DA with VIP value>1. Meanwhile， the thermogravimetric differences between Alumen and A. alum were analyzed by TG-DTA， and the thermogravimetric traits that could be used for identification were screened. ResultsAlumen and A. alum could not be effectively distinguished by traits alone. However， by comparing the PDF standard cards of XRD， 15 batches of Alumen and 12 batches of A. alum could be distinguished. In the XRD profiles， 10 characteristic peaks were confirmed， corresponding to diffraction angles of 14.560°， 24.316°， 12.620°， 32.122°， 17.898°， 34.642°， 27.496°， 46.048°， 40.697° and 21.973°. In the FTIR profiles， 4 wavenumber ranges（399.193-403.050， 1 186.010-1 471.420， 1 801.190-2 620.790， 3 612.020-3 997.710 cm^-1） and 12 characteristic wavenumbers（1 428.994， 1 430.922， 1 432.851， 1 434.779， 1 436.708， 1 438.636， 1 440.565， 1 442.493， 1 444.422， 1 446.350， 1 448.279， 1 450.207 cm^-1） were identified. In the TG-DTA profiles， there were characteristic decomposition peaks of ammonium ion and mass reduction features near 555.34 ℃ for A. alum. These characteristics could serve as important criteria for distinguishing the authenticity of Alumen. ConclusionXRD， FTIR and TG-DTA can be used to rapidly detect Alumen and A. alum， and combined with the discriminant features selected through chemometrics， the rapid and accurate identification of Alumen and A. alum can be achieved. The research findings provide new approaches for the rapid identification of Alumen.

ObjectiveTo establish the multi-technique characteristic profiles of Alumen by X-ray diffraction（XRD）， Fourier-transform infrared spectroscopy（FTIR） and thermogravimetric-differential thermal analysis（TG-DTA）， and to explore the spectral characteristics for rapid identification of Alumen and its potential adulterant， Ammonium alum. MethodsA total of 27 batches of Alumen samples from 8 production regions were collected for preliminary identification based on visual characteristics. The PDF standard cards of XRD were used to differentiate Alumen from A. alum， and the XRD characteristic profiles of Alumen were established， and then the common peaks were screened. Based on hierarchical clustering analysis（HCA） and orthogonal partial least squares-discriminant analysis（OPLS-DA）， the characteristic information that could be used for identification of Alumen was selected with variable importance in the projection（VIP） value>1. FTIR characteristic profiles of Alumen were established， and key wavenumbers for identification were screened by HCA and OPLS-DA with VIP value>1. Meanwhile， the thermogravimetric differences between Alumen and A. alum were analyzed by TG-DTA， and the thermogravimetric traits that could be used for identification were screened. ResultsAlumen and A. alum could not be effectively distinguished by traits alone. However， by comparing the PDF standard cards of XRD， 15 batches of Alumen and 12 batches of A. alum could be distinguished. In the XRD profiles， 10 characteristic peaks were confirmed， corresponding to diffraction angles of 14.560°， 24.316°， 12.620°， 32.122°， 17.898°， 34.642°， 27.496°， 46.048°， 40.697° and 21.973°. In the FTIR profiles， 4 wavenumber ranges（399.193-403.050， 1 186.010-1 471.420， 1 801.190-2 620.790， 3 612.020-3 997.710 cm^-1） and 12 characteristic wavenumbers（1 428.994， 1 430.922， 1 432.851， 1 434.779， 1 436.708， 1 438.636， 1 440.565， 1 442.493， 1 444.422， 1 446.350， 1 448.279， 1 450.207 cm^-1） were identified. In the TG-DTA profiles， there were characteristic decomposition peaks of ammonium ion and mass reduction features near 555.34 ℃ for A. alum. These characteristics could serve as important criteria for distinguishing the authenticity of Alumen. ConclusionXRD， FTIR and TG-DTA can be used to rapidly detect Alumen and A. alum， and combined with the discriminant features selected through chemometrics， the rapid and accurate identification of Alumen and A. alum can be achieved. The research findings provide new approaches for the rapid identification of Alumen.

Objective To evaluate the clinical efficacy of hip-knee-ankle active and passive exercise therapy in patients with early-to mid-stage knee osteoarthritis(KOA).Methods A total of 180 patients with early to mid-stage knee osteoarthritis(KOA)were recruited from the First Affiliated Hospital of Hebei University of Tradi-tional Chinese Medicine between March 2023 and March 2024.Patients were randomly assigned to one of four groups:active movement group,passive movement group,combined movement group,and control group,with 45 patients in each group.The active movement group received hip-knee-ankle active movement therapy daily until the end of follow-up.The passive movement group underwent hip-knee-ankle passive movement therapy three times per week for two weeks.The combined movement group received both active and passive therapies.The control group was administered oral celecoxib capsules(200 mg once daily for two weeks).Joint function was assessed in all four groups before treatment,at two weeks post-treatment,and at 14 weeks post-treatment.The primary outcome measure was the WOMAC joint function score,while secondary outcomes included the WOMAC pain score,stiffness score,and quality of life score(SF-12).Results A total of 160 patients completed the trial,with 39 in the active group,42 in the passive group,40 in the combined group,and 39 in the control group.There were no significant differences in baseline characteristics among the groups(P>0.05).Compared to baseline,the WOMAC scores for function,pain,and stiffness in the passive,combined,and control groups decreased significantly at both 2 and 14 weeks post-treatment(P<0.05),while the SF-12 scores increased significantly(P<0.05).Between 2 and 14 weeks post-treat-ment,the active and combined groups showed further significant decreases in WOMAC function,pain,and stiffness scores(P<0.05)and increases in SF-12 scores(P<0.05).At 2 weeks post-treatment,compared to the control group,the passive and combined groups exhibited significantly lower WOMAC function scores(P<0.05),with no significant difference between the passive and combined groups(P>0.05).By 14 weeks post-treatment,the active and combined groups demonstrated significantly lower WOMAC function scores(P<0.05),with the combined group showing a significantly lower score than the active group(P<0.05).Conclusion The four therapeutic approaches demonstrate a certain degree of efficacy in improving joint function for patients with early and mid-stage KOA.The passive therapy group exhibits superior short-term outcomes,while the active therapy group shows better long-term benefits.The combined therapy group presents notable advantages in both short-term and long-term effi-cacy,although its short-term effectiveness does not surpass that of the passive therapy group.It is recommended for patients with early and mid-stage KOA who have underlying gastrointestinal and cardiovascular conditions.

Objective:Using graph theory analysis, this study compares the topological and node attributes of the brain network to explore the differences in gray matter structural and functional network topological properties between bipolar depression (BD) patients with and without obsessive-compulsive symptoms (OCS).Methods:A total of 90 BD patients (27 males, 63 females; median age 19.0(22.0, 25.0) years) were recruited from the psychiatric outpatient and inpatient departments of the First Affiliated Hospital of Jinan University between March 2018 and December 2022. Fifty healthy controls (19 males, 31 females; median age: 23.0 (20.0, 27.0) years) were also enrolled. The BD patients were divided into two groups based on the presence of OCS: 53 with OCS (OCS group) and 37 without OCS (NOCS group). Resting-state structural and functional MRI data were collected for all participants to construct gray matter structural and functional networks. Graph therory analysis was applied to calculate network topological metrics such as small-world properties. The structural and functional network topological properties were compared among the BD-OCS, BD-nOCS, and control groups. Partial correlation analysis was conducted to examine the association between network topological metrics with significant group differences and Yale-Brown Obsessive-Compulsive Scale (Y-BOCS) scores. Support vector machines (SVM) were used with these metrics as classification feature values to improve diagnostic accuracy through pairwise group classification.Results:Structural network analysis of gray matter: compared to HC group, both OCS group and NOCS group showed increased shortest path length and standardized characteristic path length (shortest path length: 0.78 and 0.80 vs. 0.69; normalized characteristic path length: 0.48 and 0.49 vs. 0.43), and decreased global efficiency (0.21 and 0.21 vs. 0.24) compared to the HC group (permutation test, all P<0.05). Compared to NOCS and HC groups, the OCS group showed increased nodal centrality and betweenness centrality in the right rolandic operculum and left superior occipital gyrus (permutation test, all P<0.05). Functional network analysis of gray matter: compared to the NOCS group, the OCS group showed increased node efficiency and decreased betweenness centrality in the cerebellum ( t=2.15, -3.04; all P<0.05); compared to HC groups, the OCS group showed decreased betweenness centrality in the cerebellum and left inferior frontal gyrus, along with increased node centrality and nodal efficiency in the right transverse temporal gyrus ( t=-2.99, -3.61, 3.06, 3.10; all P<0.05). In the OCS group, betweenness centrality in the left inferior frontal gyrus positively correlated with Y-BOCS scale obsessive thinking score ( r=0.303, P=0.034). Nodal centrality and node efficiency of the right transverse temporal gyrus negatively correlated with Y-BOCS total score ( r=-0.301, -0.311) and Y-BOCS obsessional thinking scores ( r=-0.385, -0.380) separately(all P<0.05). SVM classification: the combined network features achieved an area under the curve of 0.80 in distinguising OCS from NOCS patients. Conclusion:BD-OCS and BD-nOCS patients both exhibit consistent changes in gray matter structural network topology, with the OCS group displaying more pronounced nodal topological abnormalities. Multi-network feature integration demostrates potential for diagnostic classfication.