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.

OBJECTIVE: The investigation of the correlation between ecological factors and the genetic characteristics or metabolites of plants offers valuable insights into the regional causes of genetic and metabolic diversity. Here, Gastrodia elata, a medicinal plant, is employed as a model to explore the environmental factors that influence its genetic characteristics and metabolic accumulations. METHODS: A total of 23 G. elata populations from six cultispecies and 11 cultivated regions were selected based on the predictions of the global geographic information system. The genetic characteristics of these populations were evaluated using highly polymorphic simple sequence repeat markers. Additionally, the metabolic accumulations and antioxidant capacity of mature tubers were measured employing colorimetry and high performance liquid chromatography (HPLC). Ecological data of each region were obtained from the WorldClim-global climate database and harmonized world soil database. To assess the influence of ecological factors on the genetic characteristics and metabolic profiles of G. elata, Pearson's correlation analysis was conducted. RESULTS: Genetic variation among G. elata populations exceeded that within populations. Genetic diverisity, distance and structure manifested regional and species-specific patterns. Metabolic profiling and antioxidant capacity exhibited regional variations. Notably, the Lueyang region demonstrated that a content range of total polysaccharide, total protein, and phenolic glycosides was 9.34%-189.67% higher than the average. Similarly, in the Hubei region, total phenolic content, p-hydroxybenzyl alcohol content, and antioxidant indicators were observed to be higher than the average levels, by 106.57%, 136.47% and 12.50%-91.14%, respectively. Furthermore, ecological factors had a significant comprehensive impact on G. elata genetic characteristics (r > 0.256 and P < 0.05). Multivariate metabolite accumulations in G. elata were influenced by dominant ecological factors. Temperature notably impacted the accumulation of total protein (|r| > 0.528 and P < 0.05). Moisture, encompassing precipitation and soil content, significantly affected the production of phenolic glycosides (|r| > 0.503 and P < 0.05). CONCLUSION The genetic characteristics of G. elata manifested regional and species-specific patterns, with the metabolic accumulations and antioxidant capacity of mature tubers exhibited regional variations. Specifically, multivariate ecological factors comprehensively influenced genetic characteristics. Temperature and moisture played pivotal roles in regulating the accumulations of proteins and phenolic glycosides, respectively. These findings underscore the significant impact of ecological factors on the shaping of G. elata, highlighting their crucial role in enhancing the quality of Chinese medicinal materials.

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.

Traditional Chinese medicine (TCM) serves as a treasure trove of ancient knowledge, holding a crucial position in the medical field. However, the exploration of TCM's extensive information has been hindered by challenges related to data standardization, completeness, and accuracy, primarily due to the decentralized distribution of TCM resources. To address these issues, we developed a platform for TCM knowledge discovery (TCMKD, https://cbcb.cdutcm.edu.cn/TCMKD/). Seven types of data, including syndromes, formulas, Chinese patent drugs (CPDs), Chinese medicinal materials (CMMs), ingredients, targets, and diseases, were manually proofread and consolidated within TCMKD. To strengthen the integration of TCM with modern medicine, TCMKD employs analytical methods such as TCM data mining, enrichment analysis, and network localization and separation. These tools help elucidate the molecular-level commonalities between TCM and contemporary scientific insights. In addition to its analytical capabilities, a quick question and answer (Q&A) system is also embedded within TCMKD to query the database efficiently, thereby improving the interactivity of the platform. The platform also provides a TCM text annotation tool, offering a simple and efficient method for TCM text mining. Overall, TCMKD not only has the potential to become a pivotal repository for TCM, delving into the pharmacological foundations of TCM treatments, but its flexible embedded tools and algorithms can also be applied to the study of other traditional medical systems, extending beyond just TCM.

Acute lung injury (ALI) is a severe disease caused by viral infection that triggers an uncontrolled inflammatory response. This study investigated the capacity of jasurolignoside (JO), a natural compound, to bind to Toll-like receptor 4 (TLR4) and treat ALI. The anti-inflammatory properties of JO were evaluated in vitro through Western blotting, enzyme-linked immunosorbent assay (ELISA), immunofluorescence staining, and co-immunoprecipitation. The investigation utilized a lipopolysaccharide (LPS)-induced ALI animal model to examine the therapeutic efficacy and mechanism of JO in vivo. JO attenuated inflammatory symptoms in infected cells and tissues by modulating the NOD-like receptor family pyrin domain containing protein 3 (NLRP3) inflammasome and the nuclear factor κB (NF-κB)/mitogen-activated protein kinase (MAPK) pathway. Molecular docking simulations revealed JO binding to TLR4 active sites, confirmed by cellular thermal shift assay. Surface plasmon resonance (SPR) demonstrated direct interaction between JO and TLR4 with a Kd value of 35.1 μmol·L^-1. Moreover, JO inhibited tumor necrosis factor α (TNF-α), interleukin-1β (IL-1β), and IL-6 secretion and reduced leukocyte, neutrophil, lymphocyte, and macrophage infiltration in ALI-affected mice. JO also enhanced lung function and reduced ALI-related mortality. Immunohistochemical staining demonstrated JO's ability to suppress TLR4 expression in ALI-affected mouse lung tissue. This study establishes that JO can bind to TLR4 and effectively treat ALI, indicating its potential as a therapeutic agent for clinical applications.
Toll-Like Receptor 4/chemistry* ; Animals ; Acute Lung Injury/chemically induced* ; Mice ; Humans ; Ilex/chemistry* ; Molecular Docking Simulation ; Male ; NF-kappa B/immunology* ; Mice, Inbred C57BL ; NLR Family, Pyrin Domain-Containing 3 Protein/immunology* ; Tumor Necrosis Factor-alpha/genetics* ; Interleukin-1beta/genetics* ; RAW 264.7 Cells ; Disease Models, Animal

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 evaluate the clinical outcomes of 514 cases of anterior cruciate ligament(ACL)reconstruction using hamstring tendon autograft and to observe postoperative changes and recovery of the grafts through second-look arthroscopy.Methods This retrospective study collected data from 514 patients who underwent ACL reconstruction with hamstring tendon autograft between May 2015 and June 2018,with a follow-up of at least one year.Knee function recovery and stability were assessed using the Lysholm score,International Knee Documentation Committee(IKDC)score,and Tegner score,along with the pivot shift test and Lachman test.During the second-look arthroscopy,key observations included the synovial coverage,continuity of the reconstructed ligament,and any intra-articular abnormalities.Results The time interval between ACL reconstruction and second-look arthroscopy ranged from 12 to 28 months,with an average of 20 months.Postoperative infection occurred in 2 cases,both of which were successfully treated with arthroscopic debridement and drainage.No other patients experienced infections,graft resorption,or other complications.At the second-look arthroscopy,the Lysholm score significantly improved from(43.56±9.89)preoperative to(92.21±6.12)postoperatively,the difference was statistically significant(P<0.05);The IKDC score increased from(20.32±7.87)to(85.67±10.43),the difference was statistically significant(P<0.05);The Tegner score improved from(4.31±0.82)to(6.61±1.21),the difference was statistically significant(P<0.05).Second-look arthroscopy revealed that the ligament remained intact in 375 patients,with partial tears in 139 patients,ligament tension was maintained in 447 patients,while 67 patients had laxity,the reconstructed ACL graft was deemed to be in good condition in 435 patients and suboptimal in 79 patients,there were significant differences observed in pre-and post-pivot shift test and Lachman test(P<0.05);Among the 514 patients,188(36.58%)successfully returned to sport.Conclusion ACL reconstruction using hamstring tendon autograft effectively restores knee function and stability.In patients followed for more than one years,the grafts show good vascularization and synovial coverage.Emphasis should be placed on systematic postoperative rehabilitation to optimize recovery.