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.

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

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/.

Objective To evaluate the association between self-reported neighborhood walkability environments and mortality in China.Methods The Prospective Urban Rural Epidemiology study in China(PURE-China)recruited 47 931 participants aged 35-70 from 12 provinces in China between 2005 and 2009.Neighborhood environmental indicators were collected using the Neighborhood Environment Walkability Scale(NEWS)questionnaire,with higher scores indicating better walkable environments.The primary outcomes were all-cause mortality and cardiovascular mortality,using Cox fragile model to evaluate the association between community walkability and outcomes,as well as exploring mediating pathways.Results Of 35 490 participants included in this study,60%were female,with a mean(SD)age of 51.5(9.6)years.The median follow-up was 11.7 years.This study found an association between higher community walkability score and reduced risk of all-cause mortality,with the total score(HR=0.85;95%CI,0.80-0.89),land-use mix(HR=0.84;95%CI,0.79-0.88),and crime safety(HR=0.84;95%CI,0.80-0.89)showing the most significant associations.NEWS can affect long-term adverse outcomes through lifestyle.Conclusions In the Chinese population,favorable community walkability is associated with lower all-cause mortality risk,which may support policymakers to take actions to mitigate the adverse effects of poor community en-vironments on health.

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/.

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/.

Objective:To preliminarily examine the feasibility and outcome of single-port laparoscopic radical prostatectomy with full-length urethral preservation (FLUP-SPRP).Method:This study was a prospective case series study. A total of 25 patients with prostate cancer who met the enrollment criteria and agreed to this surgical procedure from March 2022 to December 2022 were collected at the Department of Urology, the Second Affiliated Hospital of Nanjing Medical University. The age of the patients was (67.2±7.6) years (range: 61 to 76 years). This novel procedure was performed by an experienced surgeon who performed single hole radical prostatectomy skillfully. Patient urinary control, tumor control, and related surgical complications after surgery were regularly monitored. Postoperative urinary control was evaluated using the daily amount of urine pad, 0 to 1 piece of urine pad was to restore urinary control, and 0 to 1 piece of pad within 24 hours after catheter removal was immediate urinary control.Result:All prodecures were successfully completed without transit to open surgery. The surgical time was (128.4±22.4) minutes (range: 100 to 145 minutes), the intraoperative blood loss was (68.2±13.7) ml (range: 50 to 120 ml). The urethral injury occurred in 4 cases during surgery and was repaired by sutures. The urinary control recovery rates within 24 hours, 1 week, 4 weeks, and 7 weeks after surgery were 80.0%, 84.0%, 92.0% and 100%, respectively. Postoperative large section pathology revealed 1 case with a positive basal margin of the prostate and negative margins of all prostate glands around the urethra. Postoperative complications included urinary tract infection in 3 cases, urodynia in 2 cases, and acute urinary retention in 1 case. MRI follow-up 3 months after surgery showed normal anatomy of the bladder and urethra. The follow-up values of prostate specific antigen at 3 and 6 months after surgery were less than 0.1 μg/L.Conclusions:The preliminary results of this study indicate that the FLUP-SPRP procedure is safe and feasible. The early results of postoperative urinary control and oncology are as expected.

Objective:To preliminarily examine the feasibility and outcome of single-port laparoscopic radical prostatectomy with full-length urethral preservation (FLUP-SPRP).Method:This study was a prospective case series study. A total of 25 patients with prostate cancer who met the enrollment criteria and agreed to this surgical procedure from March 2022 to December 2022 were collected at the Department of Urology, the Second Affiliated Hospital of Nanjing Medical University. The age of the patients was (67.2±7.6) years (range: 61 to 76 years). This novel procedure was performed by an experienced surgeon who performed single hole radical prostatectomy skillfully. Patient urinary control, tumor control, and related surgical complications after surgery were regularly monitored. Postoperative urinary control was evaluated using the daily amount of urine pad, 0 to 1 piece of urine pad was to restore urinary control, and 0 to 1 piece of pad within 24 hours after catheter removal was immediate urinary control.Result:All prodecures were successfully completed without transit to open surgery. The surgical time was (128.4±22.4) minutes (range: 100 to 145 minutes), the intraoperative blood loss was (68.2±13.7) ml (range: 50 to 120 ml). The urethral injury occurred in 4 cases during surgery and was repaired by sutures. The urinary control recovery rates within 24 hours, 1 week, 4 weeks, and 7 weeks after surgery were 80.0%, 84.0%, 92.0% and 100%, respectively. Postoperative large section pathology revealed 1 case with a positive basal margin of the prostate and negative margins of all prostate glands around the urethra. Postoperative complications included urinary tract infection in 3 cases, urodynia in 2 cases, and acute urinary retention in 1 case. MRI follow-up 3 months after surgery showed normal anatomy of the bladder and urethra. The follow-up values of prostate specific antigen at 3 and 6 months after surgery were less than 0.1 μg/L.Conclusions:The preliminary results of this study indicate that the FLUP-SPRP procedure is safe and feasible. The early results of postoperative urinary control and oncology are as expected.