Purpose: To evaluate the clinical outcomes of endovascular treatment for ruptured vertebral artery dissecting aneurysms (VADAs), with a focus on endovascular protocols tailored to different anatomical subtypes. Materials and Methods: We retrospectively analyzed 44 consecutive patients with ruptured VADAs treated from December 2014 to October 2024. Treatment strategies were guided by aneurysm location: parent artery occlusion (PAO) was used for lesions in non-dominant VAs, while stent-assisted coiling (SAC) reconstruction was used for aneurysms in dominant VAs. Patients were stratified by modified Rankin Scale (mRS) scores at 3-month follow-up (favorable: mRS 0–2; unfavorable: mRS 3–6). Results: All procedures were technically successful, including 12 single SAC, 13 overlapping SAC, and 19 with PAO (2 involving posterior inferior cerebellar artery preservation). Immediate complete occlusion was achieved in 50.0% of single SAC cases, 76.9% overlapping SAC, and 100% in the PAO group. Six (13.6%) procedure-related complications occurred, including 2 hemorrhagic and 4 ischemic events. Thirty patients (68.2%) had favorable outcomes, and the unfavorable outcomes group showed a higher incidence of ischemic events (P=0.012). An initial poor Hunt–Hess grade (≥IV) was an independent risk factor for unfavorable outcomes (odds ratio 6.35, 95% confidence interval 1.24–32.59; P=0.027). Angiographic follow-up was performed for 34 aneurysms, with complete occlusion achieved in 32 (94.1%); the remaining 2 patients were retreated. Conclusion Endovascular management is safe and effective for ruptured VADAs. Anatomical stratification—PAO for non-dominant VA lesions and SAC for dominant VA aneurysms—may help achieve favorable outcomes.

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

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.

This review summarizes recent advances in neoadjuvant immunotherapy for advanced gastric cancer.Through literature search in PubMed,Web of Science,and CNKI databases from 2020 to 2023,we systematically analyzed the mechanisms,clinical applications,and bio-marker research.Programmed death-1(PD-1)inhibitors combined with chemotherapy significantly improve patient outcomes,while mi-crosatellite instability(MSI),programmed death-ligand 1(PD-L1)expression,and tumor mutational burden(TMB)have been identified as important predictive biomarkers.Multi-omics analysis shows great potential in identifying optimal responders,with pyroptosis-related gene scoring system(PRS)positively correlating with anti-tumor immune infiltration.Metabolic reprogramming and epigenetic regulation in the tumor microenvironment play key roles in immune evasion,while emerging targets such as Claudin 18.2 and combination targeting strategies further enhance therapeutic efficacy.Despite significant progress,precise patient selection and overcoming resistance mechan-isms remain major challenges.Future research should focus on biomarker validation,personalized treatment strategy development,tumor microenvironment dynamic analysis,and novel combination therapy exploration to improve clinical outcomes.

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.

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/.

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.Compar-ative 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/.