Objective To explore the safety of Yttrium-90 resin microsphere selective internal radiation therapy (⁹⁰Y-SIRT) on malignant liver tumors. Methods A retrospective analysis was conducted on 64 patients with malignant liver tumors who underwent ⁹⁰Y-SIRT from February 2023 to November 2024 at Weifang People’s Hospital. The clinical characteristics of the patients and the occurrence of adverse reactions after treatment were analyzed to assess the safety of ⁹⁰Y-SIRT. Results Among the 64 patients, there were 52 males (81.25%) and 12 females (18.75%); the average age was (56.29±11.08) years. Seven patients (10.94%) had tumors with maximum diameter of less than 5 cm, 38 patients (59.38%) had tumors with maximum diameter of 5-10 cm, and 19 patients (29.68%) had tumors with maximum diameter of greater than 10 cm. There were 47 cases (73.44%) of solitary lesions and 17 cases (26.56%) of multiple lesions; 53 cases (82.81%) were primary liver cancers and 11 cases (17.19%) were metastatic liver cancers. Of the 64 patients, 63 successfully completed the Technetium-99m macroaggregated albumin (^99mTc-MAA) perfusion test and received the ⁹⁰Y-SIRT; one patient received ⁹⁰Y-SIRT after the second ^99mTc-MAA perfusion test due to a work error. The most common adverse reactions included grade 1 alanine aminotransferase (ALT) elevation in 26 cases (40.62%) and grade 2 in 2 cases (9.37%), grade 1 aspartate aminotransferase (AST) elevation in 27 cases (42.18%) and grade 2 in 7 cases (10.93%); grade 1 nausea in 17 cases (26.56%) and grade 2 in 6 cases (9.37%); grade 1 abdominal pain in 12 cases (18.75%), grade 2 in 5 cases (7.81%), and grade 3 in 1 case (1.56%); grade 1 vomiting in 11 cases (17.18%), grade 2 in 5 cases (7.81%), and grade 3 in 1 case (1.56%). Conclusion The adverse reactions of ⁹⁰Y-SIRT for treating malignant liver tumors are mild, indicating good safety.

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.

OBJECTIVE To optimize the processing technology of Epimedium koreanum roasted with suet oil and analyze its characteristic chromatogram before and after processing. METHODS The optimal processing technology was optimized by orthogonal experiments with frying power， frying time and medicinal temperature as factors using the comprehensive score of appearance traits （color+odor）， alcohol-soluble extract， the contents of icariin and baohuoside Ⅰ as evaluation index， then proceed with verification. The E. koreanum roasted with suet oil was prepared with the optimal technology. The characteristic chromatograms of 15 batches of E. koreanum were established with the Similarity Evaluation System for Chromatographic Fingerprint of Traditional Chinese Medicine （2012 edition）， and then similarity analysis was also conducted. Clustering analysis， principal component analysis， and orthogonal partial least squares discriminant analysis were used to evaluate the differences in E. koreanum before and after processing. RESULTS The optimal processing technique for E. koreanum roasted with suet oil was as follows： first， heat 4 g of suet oil at a power of 600 W until it melts； next， when the temperature at the bottom of the pan reaches 140 ℃， add 20 g of E. koreanum silk and stir-fry for 6 minutes； finally， remove it and let it cool. Comprehensive score of 3 validation tests was 98.94 points　（RSD＜3%）. The established characteristic chromatogram of E. koreanum and E. koreanum roasted with suet oil were calibrated with 16 and 15 common peaks， respectively. Chromatographic peak 2 was determined to be chlorogenic acid， peak 5 to be chaohuoding A， peak 6 to be chaohuoding B， peak 7 to be chaohuoding C， peak 8 to be icariin， and peak 14 to be baohuoside Ⅰ. The similarities were all greater than 0.9. Results of cluster analysis and principal component analysis showed that E. koreanum and E. koreanum roasted with suet oil were clustered into distinct groups. The results of orthogonal partial least squares discriminant analysis showed that the variable importance projection values for peak 13， peak 15， peak 9， peak 1， peak 8， peak 12， peak 7， and peak 10 were all greater than 1. CONCLUSIONS The study successfully optimized the processing technology of suet oil-roasted E. koreanum. There are significant differences in the characteristic chromatograms of E. koreanum before and after processing. Among them， the chemical components corresponding to peak 13， peak 15， peak 9， peak 1， peak 8， peak 12， peak 7， and peak 10 may be the differential components of E. koreanum before and after processing.

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.

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

Alzheimer's disease (AD) is a common neurodegenerative disorder among the elderly, and BuChE has emerged as a potential therapeutic target. In this study, we reported the development of compound 8e, a selective reversible BuChE inhibitor (eqBuChE IC₅₀ = 0.049 μmol/L, huBuChE IC₅₀ = 0.066 μmol/L), identified through extensive virtual screening and lead optimization. Compound 8e demonstrated favorable blood-brain barrier permeability, good drug-likeness property and pronounced neuroprotective efficacy. Additionally, 8e exhibited significant therapeutic effects in zebrafish AD models and scopolamine-induced cognitive impairments in mice. Further, 8e significantly improved cognitive function in APP/PS1 transgenic mice. Proteomics analysis demonstrated that 8e markedly elevated the expression levels of very low-density lipoprotein receptor (VLDLR), offering valuable insights into its potential modulation of the Reelin-mediated signaling pathway. Thus, compound 8e emerges as a novel and potent BuChE inhibitor for the treatment of AD, with significant implications for further exploration into its mechanisms of action and therapeutic applications.

OBJECTIVES: To explore the correlation of ELFN1 expression level with prognosis of colorectal cancer and its regulatory role in colorectal cancer cell proliferation and metastasis. METHODS: We analyzed the expression levels of ELFN1 across 33 cancer types using publicly available databases and identified differential genes related to ELFN1 in colorectal cancer. Gene function annotation and enrichment analysis were used to identify the involved signaling pathways. Logistic analysis, Kaplan-Meier analysis and Cox regression analysis were performed to evaluate the correlation between ELFN1 expression and clinicopathological parameters and survival of colorectal cancer patients. qPCR and Western blotting were used to validate the expression levels of ELFN1 in different colorectal cancer cell lines and tissues, and Transwell and EDU experiments were carried out to assess the effect of ELFN1 knockdown on biological behaviors of SW480 cells. RESULTS: ELFN1 was highly expressed in 14 cancers, and its expression was significantly higher in colon cancer tissues than in adjacent tissues. A high expression of ELFN1 mRNA was associated with a poorer overall survival of colorectal cancer patients. Cox regression analysis indicated that ELFN1 expression was an independent prognostic factor for overall survival of the patients. ELFN1 was significantly enriched in tumor metastasis and proliferation and participated in several tumor signaling pathways. The colon cancer cell lines showed significantly higher expression levels of ELFN1 than normal cells, ELFN1 knockdown obviously inhibited proliferation and migration of SW480 cells in vitro. CONCLUSIONS ELFN1 is overexpressed in colorectal cancer and is associated with poor clinical prognosis of the patients. A high ELFN1 expression is associated with malignant phenotypes of colorectal cancer and promotes cancer cell proliferation and metastasis, suggesting its potential as a prognostic biomarker for colorectal cancer.
Humans ; Colorectal Neoplasms/diagnosis* ; Cell Proliferation ; Prognosis ; Cell Line, Tumor ; Biomarkers, Tumor/metabolism* ; Neoplasm Metastasis ; Gene Expression Regulation, Neoplastic ; Nerve Tissue Proteins/metabolism* ; Female ; Male

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/.

Drug development encompasses multiple processes, wherein protein subcellular localization is essential. It promotes target identification, treatment development, and the design of drug delivery systems. In this research, a deep learning framework called LocPro is presented for predicting protein subcellular localization. Specifically, LocPro is unique in (a) combining protein representations from the pre-trained large language model (LLM) ESM2 and the expert-driven tool PROFEAT, (b) implementing a hybrid deep neural network architecture that integrates convolutional neural network (CNN), fully connected (FC) layer, and bidirectional long short-term memory (BiLSTM) blocks, and (c) developing a multi-label framework for predicting protein subcellular localization at multiple granularity levels. Additionally, a dataset was curated and divided using a homology-based strategy for training and validation. Comparative analyses show that LocPro outperforms existing methods in sequence-based multi-label protein subcellular localization prediction. The practical utility of this framework is further demonstrated through case studies on drug target subcellular localization. All in all, LocPro serves as a valuable complement to existing protein localization prediction tools. The web server is freely accessible at https://idrblab.org/LocPro/.