Objective To investigate the relationship between the mean platelet volume(MPV)to platelet count(PLT)ratio(MPV/PLT),blood urea nitrogen(BUN)to lipoprotein a[Lp(a)]ratio[BUN/Lp(a)]and the prognosis of patients with acute exacerbation of chronic obstructive pulmonary disease(COPD).Methods A total of 106 patients with acute exacerbation of COPD admitted to the hospital from January 2021 to January 2024 were selected as the research objects.According to the prognosis,they were divided into sur-vival group(72 cases)and death group(34 cases).The results of routine laboratory tests,blood lipid and lipo-protein levels were compared between the two groups.Multivariate Logistic regression was used to analyze the influencing factors of death in patients with acute exacerbation of COPD.The receiver operating characteristic(ROC)curve was used to evaluate the predictive value of MPV/PLT and BUN/Lp(a)for the prognosis of pa-tients with acute exacerbation of COPD.Results Compared with the survival group,the invasive ventilation rate,acute physiology and chronic health evaluation Ⅱ(APACHE Ⅱ)score,C reactive protein(CRP),white blood cell count(WBC),MPV,BUN,MPV/PLT and BUN/Lp(a)were significantly increased in the death group(P＜0.05).The non-invasive ventilation rate,lymphocyte count,PLT and Lp(a)levels were signifi-cantly decreased(P＜0.05).Multivariate Logistic regression analysis showed that APACHE Ⅱ score,CRP,WBC,lymphocyte count,MPV,PLT,MPV/PLT,BUN,Lp(a)and BUN/Lp(a)were the influencing factors of death in patients with acute exacerbation of COPD(P＜0.05).ROC curve results showed that the sensitivity and specificity of MPV/PLT combined with BUN/Lp(a)for predicting the prognosis of patients with acute exacerbation of COPD were 88.2％and 84.7％,respectively,and the area under curve was 0.887.Conclusion MPV/PLT and BUN/Lp(a)are closely related to the prognosis of patients with acute exacerbation of COPD.The combination of MPV/PLT and BUN/Lp(a)has a high predictive value for the prognosis of patients.

Objective To explore the relationship between PANoptosis and hepatic ischemia-reperfusion injury (HIRI), and to screen the key genes of PANoptosis in HIRI. Methods PANoptosis-related differentially expressed genes (PDG) were obtained through the Gene Expression Omnibus database and GeneCards database. Gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Gene Set Enrichment Analysis (GSEA) were used to explore the biological pathways related to PDG. A protein-protein interaction network was constructed. Key genes were selected, and their diagnostic value was assessed and validated in the HIRI mice. Immune cell infiltration analysis was performed based on the cell-type identification by estimating relative subsets of RNA transcripts. Results A total of 16 PDG were identified. GO analysis showed that PDG were closely related to cellular metabolism. KEGG analysis indicated that PDG were mainly enriched in cellular death pathways such as apoptosis and immune-related signaling pathways such as the tumor necrosis factor signaling pathway. GSEA results showed that key genes were mainly enriched in immune-related signaling pathways such as the mitogen-activated protein kinase (MAPK) signaling pathway. Two key genes, DFFB and TNFSF10, were identified with high accuracy in diagnosing HIRI, with areas under the curve of 0.964 and 1.000, respectively. Immune infiltration analysis showed that the control group had more infiltration of resting natural killer cells, M2 macrophages, etc., while the HIRI group had more infiltration of M0 macrophages, neutrophils, and naive B cells. Real-time quantitative polymerase chain reaction results showed that compared with the Sham group, the relative expression of DFFB messenger RNA in liver tissue of HIRI group mice increased, and the relative expression of TNFSF10 messenger RNA decreased. Cibersort analysis showed that the infiltration abundance of naive B cells was positively correlated with DFFB expression (r=0.70, P=0.035), and the infiltration abundance of M2 macrophages was positively correlated with TNFSF10 expression (r=0.68, P=0.045). Conclusions PANoptosis-related genes DFFB and TNFSF10 may be potential biomarkers and therapeutic targets for HIRI.

ObjectiveTo analyze the influencing factors for recurrence in successfully treated pulmonary tuberculosis patients with isoniazid-resistant and rifampicin-sensitive in Shaoxing City, Zhejiang Province. MethodsData on general demographic information, treatment information and drug susceptibility test results for pulmonary tuberculosis patients admitted to the designated tuberculosis medical institutions and registered in the tuberculosis information management system was collected in Shaoxing City from January 2011 to August 2024. A total of 428 patients with isoniazid resistance (including isoniazid single resistance and multiple resistance) but who were successfully treated were included in the study. Information for the recurrence after successful treatment of the patients was analyzed. The Cox proportional hazards models were used to analyze the influencing factors of recurrence in patients. ResultsAmong the 428 successfully treated patients included in the study, 31 cases (accounting for 7.24%) had recurrence by the end of the observation period, with a recurrence rate density of 1.31 per 100 person-years and a median recurrence time of 0.99 (0.08, 8.27) years. Among the relapsed population, 51.61% of the patients relapsed within one year after successful treatment. 77.42% of the patients relapsed within two years after successful treatment. Multivariate Cox regression analysis showed that when isoniazid resistance was discovered, the diagnosis classification of relapse (HR=4.115, 95%CI: 1.734‒9.767) and positive 0-month sequence smear (HR=4.457, 95%CI: 1.053‒18.866) were risk factors for recurrence after successful treatment in patients. ConclusionRegular follow-up should be strengthened for at least two years after the successful treatment of isoniazid-resistant pulmonary tuberculosis patients. Special attention should be paid to the treatment effect and regular re-examination and monitoring after the end of the treatment course of isoniazid-resistant pulmonary tuberculosis patients who have been re-treated and were sputum smear positive at baseline, so as to prevent recurrence and disease progression in high-risk populations.

ObjectiveTo explore the mechanism of the anti-cancer compound formula Feiyanning in inhibiting epithelial-mesenchymal transition （EMT） and invasion and metastasis of non-small cell lung cancer （NSCLC）. MethodsCell proliferation and activity were assessed using the cell counting kit-8（CCK-8） assay to evaluate the effect of Feiyanning on the proliferation of A549 and H1299 cells. Wound healing and Transwell assays were conducted to examine Feiyanning's impact on the metastasis of A549 and H1299 cells. The effects of Feiyanning on EMT and the transforming growth factor-β₁ （TGF-β₁）/Smad signaling pathway proteins in A549 and H1299 cells were detected by Western blot. Exogenous TGF-β₁ was used to induce EMT in A549 and H1299 cells. The effects of Feiyanning on TGF-β₁-induced NSCLC cell metastasis， EMT， and the TGF-β₁/Smad pathway proteins were assessed by wound healing assay， Transwell assay， and Western blot. In vivo， an A549 lung metastasis model was established via tail vein injection in nude mice. A total of 28 SPF male nude mice were randomly divided into four groups： Model （NC） group， Feiyanning low-dose （FYN1） group， Feiyanning high-dose （FYN2） group， and the positive control group （TGF-β receptor kinase inhibitor SB431542 group）. The corresponding interventions were performed. After 40 days， the mice were euthanized， and lung metastases were analyzed. The expression of E-cadherin， N-cadherin， p-Smad2， and p-Smad3 in each group was detected by immunohistochemistry （IHC）. ResultsAfter Feiyanning intervention， compared to the blank group， Feiyanning inhibited the proliferation of A549 and H1299 cells in a concentration-dependent manner （P<0.01）. The metastasis ability of Feiyanning-treated cells was significantly decreased compared to the blank group （P<0.01）. The expression of EMT marker proteins N-cadherin and zinc finger transcription factors （Zeb1， Snail， Slug） was significantly reduced in the Feiyanning groups compared to the blank group （P<0.05， P<0.01）. The expression of p-Smad2/3， Smad2/3， TβRI， and TβRⅡ， key proteins in the TGF-β₁/Smad signaling pathway， was also significantly decreased （P<0.01）. In the TGF-β₁-induced EMT model， compared to the TGF-β₁ group， the cell metastasis ability in the Feiyanning groups was reduced （P<0.01）， and the expression levels of N-cadherin， Zeb1， Snail， and Slug were significantly lower （P<0.01）. The expression levels of p-Smad2/3， Smad2/3， TβRI， and TβRⅡ were also significantly reduced （P<0.01）. In vivo results showed that compared to the model group， the number of lung metastases in the FYN1， FYN2， and SB431542 groups was reduced （P<0.01）， and the range of cell infiltration was narrowed. Immunohistochemical results showed that compared to the model group， the expression of E-cadherin in the FYN1， FYN2， and SB431542 groups was increased （P<0.01）， the expression of N-cadherin decreased （P<0.05， P<0.01）， and the expression of p-Smad2 and p-Smad3， key proteins of the TGF-β₁/Smad pathway， was reduced （P<0.01）. ConclusionFeiyanning inhibits the invasion and metastasis of NSCLC cells and EMT. The mechanism is related to the inhibition of TGF-β₁/Smad signaling pathway.

ObjectiveTo explore the mechanism of the anti-cancer compound formula Feiyanning in inhibiting epithelial-mesenchymal transition （EMT） and invasion and metastasis of non-small cell lung cancer （NSCLC）. MethodsCell proliferation and activity were assessed using the cell counting kit-8（CCK-8） assay to evaluate the effect of Feiyanning on the proliferation of A549 and H1299 cells. Wound healing and Transwell assays were conducted to examine Feiyanning's impact on the metastasis of A549 and H1299 cells. The effects of Feiyanning on EMT and the transforming growth factor-β₁ （TGF-β₁）/Smad signaling pathway proteins in A549 and H1299 cells were detected by Western blot. Exogenous TGF-β₁ was used to induce EMT in A549 and H1299 cells. The effects of Feiyanning on TGF-β₁-induced NSCLC cell metastasis， EMT， and the TGF-β₁/Smad pathway proteins were assessed by wound healing assay， Transwell assay， and Western blot. In vivo， an A549 lung metastasis model was established via tail vein injection in nude mice. A total of 28 SPF male nude mice were randomly divided into four groups： Model （NC） group， Feiyanning low-dose （FYN1） group， Feiyanning high-dose （FYN2） group， and the positive control group （TGF-β receptor kinase inhibitor SB431542 group）. The corresponding interventions were performed. After 40 days， the mice were euthanized， and lung metastases were analyzed. The expression of E-cadherin， N-cadherin， p-Smad2， and p-Smad3 in each group was detected by immunohistochemistry （IHC）. ResultsAfter Feiyanning intervention， compared to the blank group， Feiyanning inhibited the proliferation of A549 and H1299 cells in a concentration-dependent manner （P<0.01）. The metastasis ability of Feiyanning-treated cells was significantly decreased compared to the blank group （P<0.01）. The expression of EMT marker proteins N-cadherin and zinc finger transcription factors （Zeb1， Snail， Slug） was significantly reduced in the Feiyanning groups compared to the blank group （P<0.05， P<0.01）. The expression of p-Smad2/3， Smad2/3， TβRI， and TβRⅡ， key proteins in the TGF-β₁/Smad signaling pathway， was also significantly decreased （P<0.01）. In the TGF-β₁-induced EMT model， compared to the TGF-β₁ group， the cell metastasis ability in the Feiyanning groups was reduced （P<0.01）， and the expression levels of N-cadherin， Zeb1， Snail， and Slug were significantly lower （P<0.01）. The expression levels of p-Smad2/3， Smad2/3， TβRI， and TβRⅡ were also significantly reduced （P<0.01）. In vivo results showed that compared to the model group， the number of lung metastases in the FYN1， FYN2， and SB431542 groups was reduced （P<0.01）， and the range of cell infiltration was narrowed. Immunohistochemical results showed that compared to the model group， the expression of E-cadherin in the FYN1， FYN2， and SB431542 groups was increased （P<0.01）， the expression of N-cadherin decreased （P<0.05， P<0.01）， and the expression of p-Smad2 and p-Smad3， key proteins of the TGF-β₁/Smad pathway， was reduced （P<0.01）. ConclusionFeiyanning inhibits the invasion and metastasis of NSCLC cells and EMT. The mechanism is related to the inhibition of TGF-β₁/Smad signaling pathway.

Advancements in artificial intelligence (AI) and emerging technologies are rapidly expanding the exploration of chemical space, facilitating innovative drug discovery. However, the transformation of novel compounds into safe and effective drugs remains a lengthy, high-risk, and costly process. Comprehensive early-stage evaluation is essential for reducing costs and improving the success rate of drug development. Despite this need, no comprehensive tool currently supports systematic evaluation and efficient screening. Here, we present druglikeFilter, a deep learning-based framework designed to assess drug-likeness across four critical dimensions: 1) physicochemical rule evaluated by systematic determination, 2) toxicity alert investigated from multiple perspectives, 3) binding affinity measured by dual-path analysis, and 4) compound synthesizability assessed by retro-route prediction. By enabling automated, multidimensional filtering of compound libraries, druglikeFilter not only streamlines the drug development process but also plays a crucial role in advancing research efforts towards viable drug candidates, which can be freely accessed at https://idrblab.org/drugfilter/.

Traditional Chinese medicine (TCM) represents a paradigmatic approach to personalized medicine, developed through the systematic accumulation and refinement of clinical empirical data over more than 2000 years, and now encompasses large-scale electronic medical records (EMR) and experimental molecular data. Artificial intelligence (AI) has demonstrated its utility in medicine through the development of various expert systems (e.g., MYCIN) since the 1970s. With the emergence of deep learning and large language models (LLMs), AI's potential in medicine shows considerable promise. Consequently, the integration of AI and TCM from both clinical and scientific perspectives presents a fundamental and promising research direction. This survey provides an insightful overview of TCM AI research, summarizing related research tasks from three perspectives: systems-level biological mechanism elucidation, real-world clinical evidence inference, and personalized clinical decision support. The review highlights representative AI methodologies alongside their applications in both TCM scientific inquiry and clinical practice. To critically assess the current state of the field, this work identifies major challenges and opportunities that constrain the development of robust research capabilities-particularly in the mechanistic understanding of TCM syndromes and herbal formulations, novel drug discovery, and the delivery of high-quality, patient-centered clinical care. The findings underscore that future advancements in AI-driven TCM research will rely on the development of high-quality, large-scale data repositories; the construction of comprehensive and domain-specific knowledge graphs (KGs); deeper insights into the biological mechanisms underpinning clinical efficacy; rigorous causal inference frameworks; and intelligent, personalized decision support systems.
Medicine, Chinese Traditional/methods* ; Artificial Intelligence ; Humans ; Precision Medicine ; Decision Support Systems, Clinical

Objective To identify key patterns of programmed cell death(PCD)and core genes during ischemia-reperfusion injury(IRI)in kidney transplantation.Methods Kidney transplant datasets were obtained from gene expression database,and PCD-related differentially expressed genes were screened.The non-negative matrix factorization algorithm was used to classify patients and analyze subtype-specific biological functions and key PCD patterns.Machine learning models combined with univariate Cox regression and Kaplan-Meier survival analysis were employed to identify core PCD genes during IRI in kidney transplantation and explore their correlation with key PCD patterns.A rat kidney transplant model was used to assess IRI severity through hematoxylin-eosin staining,serum creatinine(Scr),blood urea nitrogen(BUN),and Western blotting for key gene protein expression.Results Fourteen PCD-related genes were identified.Patients were classified into metabolic(subtype 1)and inflammatory(subtype 2)subtypes.Subtype 2 activated four key PCD patterns:pyroptosis,necroptosis,apoptosis and immunogenic cell death.The optimal model(XGBoost-CV:10 fold+Lasso-CV:10 fold)and survival analysis identified MCL1,BAG3,and RHOB as core PCD genes during IRI in kidney transplantation,which were broadly correlated with key PCD patterns.Experimental results showed that compared to the sham group,rats in the model group had more severe tubular injury,higher Scr and BUN levels,and increased BAG3,RHOB and MCL1 protein expression(all P<0.001).Conclusions These four PCD patterns are crucial in the pathogenesis of IRI in kidney transplantation.MCL1,BAG3 and RHOB may serve as potential biomarkers and therapeutic targets for IRI in kidney transplantation.

Objective To explore the mechanism of Kangliu Zengxiao Jiandu Prescription(KLJD)in enhancing the efficacy of cisplatin chemotherapy in non-small cell lung cancer(NSCLC)through network pharmacology and in vivo/in vitro experiments.Methods Components of KLJD were screened via the TCMSP database to identify active components and potential targets.Lung cancer-related genes were obtained from the GeneCards and OMIM databases.GO and KEGG pathway enrichment analysis was performed on drug-disease intersection targets using the Metascape database;molecular docking was performed between core target proteins and main active components.A Lewis lung cancer mouse model was established,and intervened with KLJD and cisplatin.Organ indexes and tumor inhibition rate were counted,and Western blot and RT-PCR were used to detect the expressions of key pathway target proteins and mRNA;A549 and H1299 cells were intervened with KLJD,and Western blot was used to detect key target protein expressions.Results Network pharmacology identified 74 active components and 20 key targets of KLJD,primarily involved in biological processes such as cell proliferation and inflammatory response,and pathways in cancer and PI3K/AKT signaling pathway;molecular docking revealed stable binding between EGFR and major compounds.Animal experiments demonstrated that,compared with the model group,the KLJD group showed significantly higher tumor inhibition rate(P<0.01)and downregulation of EGFR,AKT and PI3K protein and mRNA expression in tumor tissues(P<0.05).Compared with the cisplatin group,the combination group exhibited significantly enhanced tumor inhibition rate(P<0.01),elevated thymic and splenic indices(P<0.01),and decreased EGFR,PI3K and AKT protein and mRNA expressions(P<0.01).Cell experiments showed that KLJD concentration-dependently inhibited A549 and H1299 cell proliferation(IC50:14.72 mg/mL and 14.68 mg/mL,respectively).Combined with cisplatin,KLJD synergistically down-regulated EGFR PI3K and AKT protein expressions(P<0.01).Conclusion KLJD effectively enhances cisplatin's chemotherapeutic efficacy in NSCLC by inhibiting the EGFR/PI3K/AKT pathway while improving immune organ function.Its mechanism likely involves multi-target regulation,including suppression of tumor proliferation,promotion of apoptosis,and modulation of the immune microenvironment.

Advancements in artificial intelligence(AI)and emerging technologies are rapidly expanding the exploration of chemical space,facilitating innovative drug discovery.However,the transformation of novel compounds into safe and effective drugs remains a lengthy,high-risk,and costly process.Comprehensive early-stage evaluation is essential for reducing costs and improving the success rate of drug development.Despite this need,no comprehensive tool currently supports systematic evaluation and efficient screening.Here,we present druglikeFilter,a deep learning-based framework designed to assess drug-likeness across four critical dimensions:1)physicochemical rule evaluated by systematic determination,2)toxicity alert investigated from multiple perspectives,3)binding affinity measured by dual-path analysis,and 4)compound synthesizability assessed by retro-route prediction.By enabling automated,multidimensional filtering of compound libraries,druglikeFilter not only streamlines the drug development process but also plays a crucial role in advancing research efforts towards viable drug candidates,which can be freely accessed at https://idrblab.org/drugfilter/.