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.

Background: s/Aims: Non-invasive models stratifying clinically significant portal hypertension (CSPH) are limited. Herein, we developed a new non-invasive model for predicting CSPH in patients with compensated cirrhosis and investigated whether carvedilol can prevent hepatic decompensation in patients with high-risk CSPH stratified using the new model. Methods: Non-invasive risk factors of CSPH were identified via systematic review and meta-analysis of studies involving patients with hepatic venous pressure gradient (HVPG). A new non-invasive model was validated for various performance aspects in three cohorts, i.e., a multicenter HVPG cohort, a follow-up cohort, and a carvediloltreating cohort. Results: In the meta-analysis with six studies (n=819), liver stiffness measurement and platelet count were identified as independent risk factors for CSPH and were used to develop the new “CSPH risk” model. In the HVPG cohort (n=151), the new model accurately predicted CSPH with cutoff values of 0 and –0.68 for ruling in and out CSPH, respectively. In the follow-up cohort (n=1,102), the cumulative incidences of decompensation events significantly differed using the cutoff values of <–0.68 (low-risk), –0.68 to 0 (medium-risk), and >0 (high-risk). In the carvediloltreated cohort, patients with high-risk CSPH treated with carvedilol (n=81) had lower rates of decompensation events than non-selective beta-blockers untreated patients with high-risk CSPH (n=613 before propensity score matching [PSM], n=162 after PSM). Conclusions Treatment with carvedilol significantly reduces the risk of hepatic decompensation in patients with high-risk CSPH stratified by the new model.

Although teniposide(VM26)is widely used in the treatment of lymphoma,its poor water sol-ubility,low bioavailability and systemic toxicities still limit its clinical application.Nano-delivery systems are effective in increasing the bioavailability and reducing the toxicity of VM26,but there is an urgent need to overcome the problem of its non-specific targeting.Therefore,in this paper,we designed and constructed a hyaluronic acid-modified teniposide-targeted nano-delivery system(VM26-TNDS),and characterised its drug encapsulation rate,particle size and zeta potential.We also investigated the effects of VM26-TNDS on B-cell lymphoma cells with different expression of CD44 receptor,in terms of cellular targeting,inhibitory effect of proliferation,and induction of apoptosis and necrosis.The results showed that the drug encapsulation efficiency of VM26-TNDS exceeded 85％,and its liquid formulation could be stably stored at 4 ℃ for more than 6 months without precipitation.Based on CD44 receptor expression,Granta-519(high expression),Raji(medium-low expression)and SU-DHL-4(almost no expression)were screened for cellular experiments.Compared with VM26-NDS,the targeted modification could effec-tively reduce the uptake of VM26-TNDS by RAW264.7 and increase the uptake of VM26-TNDS by CD44 receptor-expressing lymphoma cells.The inhibitory proliferative effect and apoptotic necrosis-inducing a-bility of VM26-TNDS were stronger than those of VM26-NDS for Granta-519 and Raji cells,whereas there was no significant difference in the inhibitory effect on proliferation and ability to induce apoptosis and necrosis between VM26-NDS and VM26-TNDS in SU-DHL-4 cells,reflecting the targeting advantage for VM26-TNDS,as expected.However,its toxic effect on B-cell lymphoma cells only reflected the targeting advantage at some concentrations(0.25 μmol/L and 0.5 μmol/L),which met the expectation.The a-bove results indicate that a teniposide-targeted nano-delivery system,VM26-TNDS,has been successfully prepared in this study.VM26-TNDS improves the delivery efficiency of VM26 by targeting human B-cell lymphoma cells expressing the CD44 receptor,thus killing human B-cell lymphoma cells more effectively and overcoming the problem of non-specific targeting in drug delivery to improve the therapeutic effect.Its biological therapeutic effects and mechanisms still need to be proved by more in vitro and in vivo ex-perimental evidence.

Although teniposide(VM26)is widely used in the treatment of lymphoma,its poor water sol-ubility,low bioavailability and systemic toxicities still limit its clinical application.Nano-delivery systems are effective in increasing the bioavailability and reducing the toxicity of VM26,but there is an urgent need to overcome the problem of its non-specific targeting.Therefore,in this paper,we designed and constructed a hyaluronic acid-modified teniposide-targeted nano-delivery system(VM26-TNDS),and characterised its drug encapsulation rate,particle size and zeta potential.We also investigated the effects of VM26-TNDS on B-cell lymphoma cells with different expression of CD44 receptor,in terms of cellular targeting,inhibitory effect of proliferation,and induction of apoptosis and necrosis.The results showed that the drug encapsulation efficiency of VM26-TNDS exceeded 85％,and its liquid formulation could be stably stored at 4 ℃ for more than 6 months without precipitation.Based on CD44 receptor expression,Granta-519(high expression),Raji(medium-low expression)and SU-DHL-4(almost no expression)were screened for cellular experiments.Compared with VM26-NDS,the targeted modification could effec-tively reduce the uptake of VM26-TNDS by RAW264.7 and increase the uptake of VM26-TNDS by CD44 receptor-expressing lymphoma cells.The inhibitory proliferative effect and apoptotic necrosis-inducing a-bility of VM26-TNDS were stronger than those of VM26-NDS for Granta-519 and Raji cells,whereas there was no significant difference in the inhibitory effect on proliferation and ability to induce apoptosis and necrosis between VM26-NDS and VM26-TNDS in SU-DHL-4 cells,reflecting the targeting advantage for VM26-TNDS,as expected.However,its toxic effect on B-cell lymphoma cells only reflected the targeting advantage at some concentrations(0.25 μmol/L and 0.5 μmol/L),which met the expectation.The a-bove results indicate that a teniposide-targeted nano-delivery system,VM26-TNDS,has been successfully prepared in this study.VM26-TNDS improves the delivery efficiency of VM26 by targeting human B-cell lymphoma cells expressing the CD44 receptor,thus killing human B-cell lymphoma cells more effectively and overcoming the problem of non-specific targeting in drug delivery to improve the therapeutic effect.Its biological therapeutic effects and mechanisms still need to be proved by more in vitro and in vivo ex-perimental evidence.

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.

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.

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.