The management of localized prostate cancer requires a balance between oncological control and functional preservation.Focal therapy, as an emerging strategy between radical treatment and active surveillance, enables precise tumor ablation based on multiparametric magnetic resonance imaging, prostate-specific membrane antigen (PSMA) -guided imaging and targeted biopsy.Techniques including cryotherapy, high-intensity focused ultrasound, irreversible electroporation, and vascular-targeted photodynamic therapy have demonstrated favorable functional outcomes in short-to mid-term follow-up. However, their long-term efficacy and optimal indications remain to be verified.This article summarizes recent international advances in focal therapy for prostate cancer and introduces the “Penta-Managing System” developed by the multidisciplinary urology team at Fudan University Shanghai Cancer Center based on the full-process application of irreversible electroporation, providing insights for the standardized and individualized use of focal therapy.

Objective To investigate the effect of glycine N-methyl transferase (GNMT)on intrauterine adhesion (IUA)fibrosis and its related mechanism.Methods In vivo experiment:A total of 36 healthy female SD rats (SPF grade,6～8 weeks old and weighing from 180～220 g)were subjected in this study.IUA model of SD rats and IUA model of GNMT overexpressed rats were established.RT-qPCR and immunofluorescence assay were applied to detect GNMT expression level in normal uterus and model group.RT-qPCR and Western blotting were used to detect the mRNA and protein levels of fibrosis-related molecules and the activation of TGF-β1/Smad3 signaling pathway in each group.The number of endometrial glands in each group was observed by HE staining.Masson staining was used to analyze the severity of endometrial fibrosis in each group.In vitro experiment:transformed human endometrial stromal cells (THESCs)fibrotic phenotype model was constructed using TGF-β1,and THESCs stably transfected with GNMT overexpression lentvirus were treated with TGF-β1.RT-qPCR and Western blotting were used to detect the mRNA and protein expression of fibrosis-related molecules.The expression of TGF-β1/Smad3 signaling pathway was detected by Western blotting.TGF-β1/Smad3 signaling pathway was activated by TGF-β1/Smad signaling pathway activator (SRI-011381),and the expression of TGF-β1/Smad3 signaling pathway and key molecular proteins of fibrosis phenotype was measured with Western blotting.Results In vivo experiment,the mRNA and protein expression levels of GNMT were significantly decreased in the IUA rats than the control rats (P<0.05).Overexpression of GNMT decreased the mRNA and protein levels of fibrosis related molecules,Collagen Ⅰ,Collagen Ⅲ and FN in the IUA rats (P<0.05),and decreased the phosphorylation levels of TGF-β1 and its downstream Smad3 protein (P<0.05).HE and Masson staining showed that overexpression of GNMT could increase the number of endometrial glands and reduce the severity of fibrosis in the IUA rats (P<0.05).In vitro experiments:overexpression of GNMT decreased the mRNA and protein levels of Collagen Ⅰ,Collagen Ⅲ and FN associated with fibrotic phenotype of THESCs (P<0.05),and reduced the phosphorylation level of Smad3 protein,downstream of TGF-β1 (P<0.05).After activation of TGF-β1/Smad3 signaling pathway,the protein levels of TGF-β1/Smad3 signaling pathway and downstream fibrosis phenotype molecules,Collagen Ⅲ and FN,were significantly decreased in the LV-GNMT+SRI-011381 group.Conclusion Overexpression of GNMT can inhibit endometrial fibrosis by regulating TGF-β1/Smad3 signaling pathway,thus achieving therapeutic effect on IUA.

Objective To explore the current situation and influencing factors that affect stroke specialist nurses in Henan Province and provide a reference for targeted interventions.Methods A total of 140 stroke specialist nurses from over 103 general hospitals in Henan Province were selected to participate the study with the convenience sampling method,between March and November 2022.General data questionnaire and the core competence evaluation questionnaire of stroke specialist nurses in Henan Province were employed for the investigation.Influencing factors were analysed by binary Logistic regression.Results A total of 134 specialist nurses responded to the survey.The findings revealed that the total score of core competence in the stroke specialist nurses was 279.5(257.0,306.0).Among the dimensions of core competence,the clinical thinking ability scored the highest at 4.4(4.0,4.9),while professional development ability scored the lowest at 3.7(3.4,4.0).Cluster analysis categorised that the scores＜250 were of a low score group(n=26)and those≥250 were of a high score group(n=108).Binary Logistic regression analysis indicated that education level,years working as a stroke specialist nurse,and the working experience in the stoke fast track team of A&E were the factors that influenced the core competence of the stroke specialist nurses in Henan Province(all P＜0.05).Conclusions This study has found that the core competence of stroke specialist nurses in Henan Province is at an acceptable level.Education,years working as a stroke specialist nurse and the working experience in the fast track team of A&E are the primary factors which influence core competence.Nursing managers should devise supportive programs to target the influencing factors and to improve the core competence of the stroke specialist nurses.

Objective To study the distribution and drug resistance characteristics of pathogenic bacteria in the elderly population of China by collecting and analyzing the standardized case data on the pathogens of infections in elderly patients,and to facilitate the establishment of a standardized layered surveillance system for pathogenic bacteria in China.Methods We collected the case data of elderly patients(≥65 years old)from 62 sentinel hospitals across the country in 2021.Then,we statistically analyzed the data by patient age,their geographical region,the distribution of pathogenic bacteria,and the drug resistance characteristics of main pathogens.Results A total of 3468 cases from across the country were included in the study.The top three sources of patients were the intensive care unit(13.2%),the department of respiratory medicine(11.2%),and the department of general surgery(8.4%).The top three types of specimens were urine(25.5%),sputum(20.6%),and blood(18.7%).A total of 3468 strains of pathogens were isolated,among which,78.9%were gram-negative bacteria and 21.1%were gram-positive bacteria.The top five types of bacteria were Escherichia coli(20.9%),Klebsiella pneumoniae(18.3%),Pseudomonas aeruginosa(11.2%),Staphylococcus aureus(9.0%),and Acinetobacter baumannii(7.0%).The isolation rates of common important drug-resistant bacteria were 38.0%for methicillin-resistant Staphylococcus aureus(MRSA),68.7%for carbapenem-resistant Acinetobacter baumannii(CRAB),and 38.2%for carbapenem-resistant Pseudomonas aeruginosa(CRPA),20.1%for carbapenem-resistant Klebsiella pneumoniae(CRKP),5.2%for carbapenem-resistant Escherichia coli(CRECO),and 2.1%for vancomycin-resistant Enterococcus(VRE).There were differences in the isolation rates of CRAB and CRKP in clinical care in the elderly population in seven geographical regions of China(P＜0.05).Klebsiella pneumoniae is the most important pathogen in the elderly population≥85 years old,and the isolation rates of CRKP showed significant differences in different age groups(P＜0.05).Conclusion There are significant differences in the drug resistance of pathogenic bacteria in the elderly populations of different regions and age groups in China.Therefore,monitoring the distribution and drug resistance of pathogenic bacteria in the elderly population and formulating targeted treatment plans according to the characteristics of the specific regions and age groups are of great significance to the improvement in the treatment outcomes and prognosis of the elderly population.

The high-content image-based assay is commonly leveraged for identifying the pheno-typic impact of genetic perturbations in biology field.However,a persistent issue remains unsolved during experiments:the interferential technical noises caused by systematic errors(e.g.,tempera-ture,reagent concentration,and well location)are always mixed up with the real biological signals,leading to misinterpretation of any conclusion drawn.Here,we reported a mean teacher-based deep learning model(DeepNoise)that can disentangle biological signals from the experimental noises.Specifically,we aimed to classify the phenotypic impact of 1108 different genetic perturbations screened from 125,510 fluorescent microscopy images,which were totally unrecognizable by the human eye.We validated our model by participating in the Recursion Cellular Image Classification Challenge,and DeepNoise achieved an extremely high classification score(accuracy:99.596％),ranking the 2nd place among 866 participating groups.This promising result indicates the success-ful separation of biological and technical factors,which might help decrease the cost of treatment development and expedite the drug discovery process.The source code of DeepNoise is available at https://github.com/Scu-sen/Recursion-Cellular-Image-Classification-Challenge.