Objective To evaluate the efficacy and safety of 450 nm blue laser vaporization enucleation of the prostate in the treatment of benign prostatic hyperplasia(BPH)complicated with urinary retention, so as to promote its clinical application. Methods A retrospective analysis was conducted on the clinical data of 60 patients with BPH and urinary retention who underwent 450 nm blue laser vaporization enucleation of the prostate in our hospital during Feb. 2023 and Apr. 2024. All patients had urinary catheters indwelt preoperatively due to urinary retention. The perioperative indicators and complications were recorded. The maximum urinary flow rate(Qmax), postvoid residual(PVR), international prostate symptom score(IPSS), and quality of life score(QoL)were compared before surgery and 3 months after surgery. Results All procedures were completed successfully without conversion to transurethral resection of the prostate(TURP). No cases of severe intraoperative bleeding or transfusion were reported. The average operation time was(60.5±21.5)min, postoperative hemoglobin reduction(3.2±0.8)g/L, catheterization time(3.1±0.4)days, and hospital stay(3.2±0.4)days. No bladder perforation or urethral injury occurred. At 3 months postoperatively, Qmax [(19.2±4.9)mL/s vs.(3.5±2.3)mL/s], PVR [(50.8±20.1)mL vs.(369.2±163.2)mL], IPSS [(9.7±0.9)vs.(21.8±3.2)], and QoL [(1.4±0.5)vs.(4.9±0.1)]were significantly improved compared with preoperative values(P＜0.001). Urinary retention occurred in 2 patients after catheter removal on postoperative day 3, which was resolved one week after re-catheterization. Conclusion The 450 nm blue laser vaporization enucleation of the prostate is a safe and effective treatment for BPH complicated with urinary retention. It can significantly improve the short-term voiding function and quality of life with a low incidence of postoperative complications, demonstrating promising potential for clinical adoption.

With the rapid advancement of medical technology and the increasing complexity of clinical environments, competence in clinical ethical decision-making has become an indispensable core competency for exemplary physicians. However, traditional medical ethics education has long been confronted with the persistent challenges of theory-practice dissociation and the disconnect between pedagogical settings and clinical realities. Thus, how to construct a "Clinical Ethics" curriculum that aligns with the objectives of training high-caliber medical professionals has emerged as a critical and challenging issue in current medical education reform. To effectively address this challenge, the Office of Education at Peking University Third Hospital, since 2021, has assembled a multidisciplinary "Clinical Ethics" teaching and management team comprising faculty from Peking University School of Humanities and key clinical specialists from the hospital. In 2022, the "Clinical Ethics" elective course was formally launched for fourth-year students in the eight-year MD program who had completed coursework in "Medical Ethics" and organ-system-based curricula, with the aim of exploring and operationalizing a pre-clinical ethics education model tailored for senior medical students. The course employs a modular instructional design encompassing "theoretical review-case-based deliberation-clinical workshop", which effectively facilitates the deep integration of medical humanities with clinical practice.Following four years of iterative refinement through the "plan-act-observe-reflect" cycle, the course has demonstrated positive outcomes in enhancing students' ethical awareness and decision-making capabilities, and has preliminarily established a sustainable collaborative mechanism among the multidisciplinary faculty team. This article systematically delineates the course development background, pedagogical model and methodological innovations, and the multidisciplinary collaborative framework, alongside reflections and summaries based on implementation outcomes, with the intent of providing references for the future development of "Clinical Ethics" curricula.

Diabetic kidney disease(DKD)with increasing global prevalence lacks effective therapeutic targets to halt or reverse its progression.Therapeutic targets supported by causal genetic evidence are more likely to succeed in randomized clinical trials.In this study,we integrated large-scale plasma proteomics,genetic-driven causal inference,and experimental validation to identify prioritized targets for DKD using the UK Biobank(UKB)and FinnGen cohorts.Among 2844 diabetic patients(528 with DKD),we identified 37 targets significantly associated with incident DKD,supported by both observational and causal evi-dence.Of these,22％(8/37)of the potential targets are currently under investigation for DKD or other diseases.Our prospective study confirmed that higher levels of three prioritized targets-insulin-like growth factor binding protein 4(IGFBP4),family with sequence similarity 3 member C(FAM3C),and prostaglandin D2 synthase(PTGDS)—were associated with a 4.35,3.51,and 3.57-fold increased likeli-hood of developing DKD,respectively.In addition,population-level protein-altering variants(PAVs)analysis and in vitro experiments cross-validated FAM3C and IGFBP4 as potential new target candidates for DKD,through the classic NLR family pyrin domain containing 3(NLRP3)-caspase-1-gasdermin D(GSDMD)apoptotic axis.Our results demonstrate that integrating omics data mining with causal inference may be a promising strategy for prioritizing therapeutic targets.

Drug repurposing offers a promising alternative to traditional drug development and significantly re-duces costs and timelines by identifying new therapeutic uses for existing drugs.However,the current approaches often rely on limited data sources and simplistic hypotheses,which restrict their ability to capture the multi-faceted nature of biological systems.This study introduces adaptive multi-view learning(AMVL),a novel methodology that integrates chemical-induced transcriptional profiles(CTPs),knowledge graph(KG)embeddings,and large language model(LLM)representations,to enhance drug repurposing predictions.AMVL incorporates an innovative similarity matrix expansion strategy and leverages multi-view learning(MVL),matrix factorization,and ensemble optimization techniques to integrate heterogeneous multi-source data.Comprehensive evaluations on benchmark datasets(Fdata-set,Cdataset,and Ydataset)and the large-scale iDrug dataset demonstrate that AMVL outperforms state-of-the-art(SOTA)methods,achieving superior accuracy in predicting drug-disease associations across multiple metrics.Literature-based validation further confirmed the model's predictive capabilities,with seven out of the top ten predictions corroborated by post-2011 evidence.To promote transparency and reproducibility,all data and codes used in this study were open-sourced,providing resources for pro-cessing CTPs,KG,and LLM-based similarity calculations,along with the complete AMVL algorithm and benchmarking procedures.By unifying diverse data modalities,AMVL offers a robust and scalable so-lution for accelerating drug discovery,fostering advancements in translational medicine and integrating multi-omics data.We aim to inspire further innovations in multi-source data integration and support the development of more precise and efficient strategies for advancing drug discovery and translational medicine.

Objective To construct a nursing registry platform for percutaneous coronary intervention(PCI)to provide data support for subsequent real-world research on PCI nursing.Methods From April to December 2023,we established a variable list and data dictionary based on literature review and expert discussion,and constructed a web-based PCI nursing registry platform based on registry-related standards.Results A total of 191 variables were screened in this study,and a corresponding data dictionary was developed for each variable according to the variable name,variable code,variable definition,variable type,variable value range,data source and data collection node.Three levels of account privileges has been set up in the platform,which can realize different data management privileges,and the data can be saved only after filling in and reviewing at each level.The platform is also equipped with automatic data checking function,which reduces data filling errors and improves data quality.Conclusion The constructed PCI nursing registration platform has strong scientific and professional characteristics,and can provide data support for subsequent research,and the content and functions of the platform can be further optimized in the future.

A pyrene-phenol-based fluorescent probe PyP which showed typical intramolecular charge transfer(ICT)and monomer-excimer activities was synthesized by using pyrene carboxaldehyde hydrazone and 4-tert-butyl-2,6-diformylphenol as the raw materials.The effects of solvents on PyP were studied,and the results showed that the color of protic polar solvents(Ethanol,N,N-dimethylformamide,methanol and H2O)were successfully identified.Based on the solvent polarity-regulated PyP monomer-excimer switching,the rapid and highly sensitive ratiometric probe,"Turn-off"and"Turn-on"multimodal probes were established for detection of trace water content in organic solvents(Dimethyl sulfoxide,N,N-dimethylformamide,ethanol and methanol),with detection limits(3σ/k)of 0.0021％,0.046％,0.062％and 0.024％.The method was successfully used to detect water content in dimethyl sulfoxide,N,N-dimethy lformamide,ethanol and methanol commercial organic solvents,with recoveries ranging from 97.2％to 108.0％.The developed method showed good accuracy and stability,and had good application prospect.

Objective To investigate the technical capacity and service quality of occupational health technical service institutions (hereinafter referred to as "occupational health institutions") in Guangdong Province. Methods All occupational health institutions in Guangdong Province that had valid occupational health service qualifications and within the validity period were included for analysis. Data on basic information, employed personnel, and results of professional technical capacity assessments across occupational health institutions were obtained through the Guangdong Provincial Occupational Health Technical Quality Control Center. Results A total of 99 institutions with 2 732 technical staff were included in this study. Occupational health institutions in Guangdong Province were mainly distributed in the Pearl River Delta region, accounting for 87.9% (87/99) of the total. The number of public and private health institutions was 23 and 76, accounted for 23.2% and 76.8% respectively. In terms of technical personnel, the percentage of individuals worked in public or private health institutions was 24.1% and 75.9%, respectively. Personnel titles were predominantly intermediate level and no title, accounting for 38.7% and 26.4%, respectively. Individuals with a bachelor′s degree or above accounted for 67.4%. Engineering and other professionals accounted for 35.4% and 30.5%, respectively. Private institutions undertook 97.3% of testing and evaluation workload related to occupational hazard in the province. The number of occupational health institutes acquiring category Ⅰ and Ⅱ service license were 97 and 13. Among institutions participating in inter-laboratory comparisons, the overall pass rates for quantitative items were 95.5% in public and 70.3% in private institutions, while the pass rates for qualitative items were 100.0% and 94.5%, respectively. Conclusion Occupational health institutions in Guangdong Province face issues such as imbalanced regional distribution, uneven development, and insufficient technical competence and testing capacity of professional personnel. Health authorities at all levels should continue to strengthen supervision and quality control to solidify the technical foundation and comprehensively enhance service capacity and quality.

OBJECTIVE: To evaluate the dynamic changes of glucocorticoid (GC) dose and the feasibility of GC discontinuation in rheumatoid arthritis (RA) patients under the background of Chinese medicine (CM). METHODS: This multicenter retrospective cohort study included 1,196 RA patients enrolled in the China Rheumatoid Arthritis Registry of Patients with Chinese Medicine (CERTAIN) from September 1, 2019 to December 4, 2023, who initiated GC therapy. Participants were divided into the Western medicine (WM) and integrative medicine (IM, combination of CM and WM) groups based on medication regimen. Follow-up was performed at least every 3 months to assess dynamic changes in GC dose. Changes in GC dose were analyzed by generalized estimator equation, the probability of GC discontinuation was assessed using Kaplan-Meier curve, and predictors of GC discontinuation were analyzed by Cox regression. Patients with <12 months of follow-up were excluded for the sensitivity analysis. RESULTS: Among 1,196 patients (85.4% female; median age 56.4 years), 880 (73.6%) received IM. Over a median 12-month follow-up, 34.3% (410 cases) discontinued GC, with significantly higher rates in the IM group (40.8% vs. 16.1% in WM; P<0.05). GC dose declined progressively, with IM patients demonstrating faster reductions (median 3.75 mg vs. 5.00 mg in WM at 12 months; P<0.05). Multivariate Cox analysis identified age <60 years [P<0.001, hazard ratios (HR)=2.142, 95% confidence interval (CI): 1.523-3.012], IM therapy (P=0.001, HR=2.175, 95% CI: 1.369-3.456), baseline GC dose ⩽7.5 mg (P=0.003, HR=1.637, 95% CI: 1.177-2.275), and absence of non-steroidal anti-inflammatory drugs use (P=0.001, HR=2.546, 95% CI: 1.432-4.527) as significant predictors of GC discontinuation. Sensitivity analysis (545 cases) confirmed these findings. CONCLUSIONS RA patients receiving CM face difficulties in following guideline-recommended GC discontinuation protocols. IM can promote GC discontinuation and is a promising strategy to reduce GC dependency in RA management. (Trial registration: ClinicalTrials.gov, No. NCT05219214).
Adult ; Aged ; Female ; Humans ; Male ; Middle Aged ; Arthritis, Rheumatoid/drug therapy* ; Glucocorticoids/therapeutic use* ; Medicine, Chinese Traditional ; Retrospective Studies

Drug repurposing offers a promising alternative to traditional drug development and significantly reduces costs and timelines by identifying new therapeutic uses for existing drugs. However, the current approaches often rely on limited data sources and simplistic hypotheses, which restrict their ability to capture the multi-faceted nature of biological systems. This study introduces adaptive multi-view learning (AMVL), a novel methodology that integrates chemical-induced transcriptional profiles (CTPs), knowledge graph (KG) embeddings, and large language model (LLM) representations, to enhance drug repurposing predictions. AMVL incorporates an innovative similarity matrix expansion strategy and leverages multi-view learning (MVL), matrix factorization, and ensemble optimization techniques to integrate heterogeneous multi-source data. Comprehensive evaluations on benchmark datasets (Fdataset, Cdataset, and Ydataset) and the large-scale iDrug dataset demonstrate that AMVL outperforms state-of-the-art (SOTA) methods, achieving superior accuracy in predicting drug-disease associations across multiple metrics. Literature-based validation further confirmed the model's predictive capabilities, with seven out of the top ten predictions corroborated by post-2011 evidence. To promote transparency and reproducibility, all data and codes used in this study were open-sourced, providing resources for processing CTPs, KG, and LLM-based similarity calculations, along with the complete AMVL algorithm and benchmarking procedures. By unifying diverse data modalities, AMVL offers a robust and scalable solution for accelerating drug discovery, fostering advancements in translational medicine and integrating multi-omics data. We aim to inspire further innovations in multi-source data integration and support the development of more precise and efficient strategies for advancing drug discovery and translational medicine.

Diabetic kidney disease (DKD) with increasing global prevalence lacks effective therapeutic targets to halt or reverse its progression. Therapeutic targets supported by causal genetic evidence are more likely to succeed in randomized clinical trials. In this study, we integrated large-scale plasma proteomics, genetic-driven causal inference, and experimental validation to identify prioritized targets for DKD using the UK Biobank (UKB) and FinnGen cohorts. Among 2844 diabetic patients (528 with DKD), we identified 37 targets significantly associated with incident DKD, supported by both observational and causal evidence. Of these, 22% (8/37) of the potential targets are currently under investigation for DKD or other diseases. Our prospective study confirmed that higher levels of three prioritized targets-insulin-like growth factor binding protein 4 (IGFBP4), family with sequence similarity 3 member C (FAM3C), and prostaglandin D2 synthase (PTGDS)-were associated with a 4.35, 3.51, and 3.57-fold increased likelihood of developing DKD, respectively. In addition, population-level protein-altering variants (PAVs) analysis and in vitro experiments cross-validated FAM3C and IGFBP4 as potential new target candidates for DKD, through the classic NLR family pyrin domain containing 3 (NLRP3)-caspase-1-gasdermin D (GSDMD) apoptotic axis. Our results demonstrate that integrating omics data mining with causal inference may be a promising strategy for prioritizing therapeutic targets.