In most types of erectile dysfunction, particularly in advanced stages, typical pathological features observed are reduced parenchymal cells coupled with increased tissue fibrosis. However, the current treatment methods have shown limited success in reversing these pathologic changes. Recent research has revealed that changes in autophagy levels, along with alterations in apoptosis and fibrosis-related proteins, are linked to the progression of erectile dysfunction, suggesting a significant association. Autophagy, known to significantly affect cell fate and tissue fibrosis, is currently being explored as a potential treatment modality for erectile dysfunction. However, these present studies are still in their nascent stage, and there are limited experimental data available. This review analyzes erectile dysfunction from a pathological perspective. It provides an in-depth overview of how autophagy is involved in the apoptotic processes of smooth muscle and endothelial cells and its role in the fibrotic processes occurring in the cavernosum. This study aimed to develop a theoretical framework for the potential effectiveness of autophagy in preventing and treating erectile dysfunction, thus encouraging further investigation among researchers in this area.
Male ; Humans ; Autophagy/physiology* ; Apoptosis/physiology* ; Erectile Dysfunction/physiopathology* ; Fibrosis ; Penis/pathology* ; Animals ; Endothelial Cells/pathology* ; Myocytes, Smooth Muscle/pathology*

Nonobstructive azoospermia (NOA), one of the most severe types of male infertility, etiology often remains unclear in most cases. Therefore, this study aimed to detect four biallelic detrimental variants (0.5%) in the minichromosome maintenance domain containing 2 ( MCMDC2 ) genes in 768 NOA patients by whole-exome sequencing (WES). Hematoxylin and eosin (H&E) demonstrated that MCMDC2 deleterious variants caused meiotic arrest in three patients (c.1360G>T, c.1956G>T, and c.685C>T) and hypospermatogenesis in one patient (c.94G>T), as further confirmed through immunofluorescence (IF) staining. The single-cell RNA sequencing data indicated that MCMDC2 was substantially expressed during spermatogenesis. The variants were confirmed as deleterious and responsible for patient infertility through bioinformatics and in vitro experimental analyses. The results revealed four MCMDC2 variants related to NOA, which contributes to the current perception of the function of MCMDC2 in male fertility and presents new perspectives on the genetic etiology of NOA.
Humans ; Male ; Azoospermia/genetics* ; Meiosis/genetics* ; Spermatogenesis/genetics* ; Adult ; Exome Sequencing ; Microtubule-Associated Proteins/genetics* ; Alleles ; Infertility, Male/genetics*

Objective To develop a nomogram-based predictive model for assessing the risk of respiratory distress syndrome(RDS)in premature infants in the high-altitude region of Dali.The predictive performance and clinical applicability of the model will be systematically evaluated to provide evidence-based guidance for the early diagnosis and clinical management of respiratory distress in premature infants.Methods A total of 680 preterm infants admitted to the Dali Maternal and Child Health Hospital between January 2020 and December 2024 were enrolled in the study and randomly divided into a training set(n=476)and a validation set(n=204)at a ratio of 7∶3.Independent predictors were identified through univariate logistic regression and multivariate stepwise regression analyses,and a nomogram model was subsequently developed using R software.The performance of the model,including its discrimination,calibration,stability,and clinical applicability,was evaluated using the receiver operating characteristic curve(ROC),Hosmer-Lemeshow goodness-of-fit test,bootstrap resampling method,and decision curve analysis(DCA).Results The final model incorporated seven independent variables:gestational age,birth weight,Apgar score,blood oxygen saturation,gestational hyperglycemia,prenatal glucocor-ticoid therapy,and maternal history of infection.The areas under the curve(AUCs)for the training and validation sets were 0.88(95%CI:0.84～0.92)and 0.83(95%CI:0.76～0.89),respectively,with all Hosmer-Lemeshow test p-values exceeding 0.05.The bootstrap-corrected AUC was 0.85(95%CI:0.81～0.89).DCA indicated that the model achieved the highest net benefit at a risk threshold range of 10%to 35%.Conclusions This model integrates multiple risk factors associated with the occurrence of RDS in plateau environments,demonstrating robust predictive performance for RDS in preterm infants residing in high-altitude areas such as Dali.It can serve as a valuable tool for risk stratification and clinical decision-making,and may also provide a reference for future multicenter prospective studies.

With the rapid development of generative artificial intelligence(GAI)technologies,their widespread application in academic research and writing is continuously expanding the boundaries of sci-entific inquiry.However,this trend has also raised a series of ethical and regulatory challenges,inclu-ding issues related to authorship,content authenticity,citation accuracy,and accountability.In light of the growing involvement of AI in generating academic content,establishing an open,controllable,and trustworthy ethical governance framework has become a key task for safeguarding research integrity and maintaining trust within the academic community.This expert consensus outlines ethical requirements across key stages of AI-assisted academic writing-including topic selection,data management,citation practices,and authorship attribution.It aims to clarify the boundaries and ethical obligations surrounding AI use in academic writing,ensuring that technological tools enhance efficiency without compromising in-tegrity.The goal is to provide guidance and institutional support for building a responsible and sustainable research ecosystem.

Objective To develop a nomogram-based predictive model for assessing the risk of respiratory distress syndrome(RDS)in premature infants in the high-altitude region of Dali.The predictive performance and clinical applicability of the model will be systematically evaluated to provide evidence-based guidance for the early diagnosis and clinical management of respiratory distress in premature infants.Methods A total of 680 preterm infants admitted to the Dali Maternal and Child Health Hospital between January 2020 and December 2024 were enrolled in the study and randomly divided into a training set(n=476)and a validation set(n=204)at a ratio of 7∶3.Independent predictors were identified through univariate logistic regression and multivariate stepwise regression analyses,and a nomogram model was subsequently developed using R software.The performance of the model,including its discrimination,calibration,stability,and clinical applicability,was evaluated using the receiver operating characteristic curve(ROC),Hosmer-Lemeshow goodness-of-fit test,bootstrap resampling method,and decision curve analysis(DCA).Results The final model incorporated seven independent variables:gestational age,birth weight,Apgar score,blood oxygen saturation,gestational hyperglycemia,prenatal glucocor-ticoid therapy,and maternal history of infection.The areas under the curve(AUCs)for the training and validation sets were 0.88(95%CI:0.84～0.92)and 0.83(95%CI:0.76～0.89),respectively,with all Hosmer-Lemeshow test p-values exceeding 0.05.The bootstrap-corrected AUC was 0.85(95%CI:0.81～0.89).DCA indicated that the model achieved the highest net benefit at a risk threshold range of 10%to 35%.Conclusions This model integrates multiple risk factors associated with the occurrence of RDS in plateau environments,demonstrating robust predictive performance for RDS in preterm infants residing in high-altitude areas such as Dali.It can serve as a valuable tool for risk stratification and clinical decision-making,and may also provide a reference for future multicenter prospective studies.

With the rapid development of generative artificial intelligence(GAI)technologies,their widespread application in academic research and writing is continuously expanding the boundaries of sci-entific inquiry.However,this trend has also raised a series of ethical and regulatory challenges,inclu-ding issues related to authorship,content authenticity,citation accuracy,and accountability.In light of the growing involvement of AI in generating academic content,establishing an open,controllable,and trustworthy ethical governance framework has become a key task for safeguarding research integrity and maintaining trust within the academic community.This expert consensus outlines ethical requirements across key stages of AI-assisted academic writing-including topic selection,data management,citation practices,and authorship attribution.It aims to clarify the boundaries and ethical obligations surrounding AI use in academic writing,ensuring that technological tools enhance efficiency without compromising in-tegrity.The goal is to provide guidance and institutional support for building a responsible and sustainable research ecosystem.

In recent years, with the increasing societal focus on drug quality and safety, quality issues have become a major challenge faced by the pharmaceutical industry, directly impacting consumer health and market trust. By combining multispectral imaging technology with machine learning, it is possible to achieve rapid, non-destructive, and precise detection of traditional Chinese medicine(TCM) preparations, thereby revolutionizing traditional detection methods and developing more convenient and automated solutions. This paper provides a comprehensive review of the current applications of rapid, non-destructive detection techniques based on machine learning algorithms in the field of TCM preparations. It analyzed the principles and advantages of commonly used rapid, non-destructive detection techniques, offering a reference for the application and promotion of these technologies in TCM preparation detection. Additionally, this paper explored various data preprocessing techniques, operational processes, and machine learning algorithms to enhance data utilization efficiency. Finally, it focused on the challenges of applying machine learning in TCM preparation detection and offered corresponding recommendations, providing guidance for the future integration of machine learning with rapid, non-destructive detection techniques in practical production.
Machine Learning ; Drugs, Chinese Herbal/analysis* ; Medicine, Chinese Traditional/methods* ; Humans ; Quality Control

Patients with severe trauma require an extremely timely treatment and transfusion plays an irreplaceable role in the emergency treatment of such patients. An increasing number of evidence-based medicinal evidences and clinical practices suggest that patients with severe traumatic bleeding benefit from early transfusion of low-titer group O whole blood or hemostatic resuscitation with red blood cells, plasma and platelet of a balanced ratio. However, the current domestic mode of blood supply cannot fully meet the requirements of timely and effective blood transfusion for emergency treatment of patients with severe trauma in clinical practice. In order to solve the key problems in blood supply and blood transfusion strategies for emergency treatment of severe trauma, Branch of Clinical Transfusion Medicine of Chinese Medical Association, Group for Trauma Emergency Care and Multiple Injuries of Trauma Branch of Chinese Medical Association, Young Scholar Group of Disaster Medicine Branch of Chinese Medical Association organized domestic experts of blood transfusion medicine and trauma treatment to jointly formulate Chinese expert consensus on blood support mode and blood transfusion strategies for emergency treatment of severe trauma patients ( version 2024). Based on the evidence-based medical evidence and Delphi method of expert consultation and voting, 10 recommendations were put forward from two aspects of blood support mode and transfusion strategies, aiming to provide a reference for transfusion resuscitation in the emergency treatment of severe trauma and further improve the success rate of treatment of patients with severe trauma.

Objective To construct and preliminarily apply a graded exercise rehabilitation program for patients with acute exacerbation of chronic obstructive pulmonary disease(AECOPD),and to provide a theoretical basis for the scientific implementation of exercise rehabilitation by medical staff.Methods Based on Triangle model and evidence-based method,the first draft of the graded exercise rehabilitation program was constructed,and the items were revised through 2 rounds of expert consultations from March to October,2023.The graded exercise rehabilitation program was preliminarily applied in 10 patients with AECOPD.Results The effective recovery rates of the 2 rounds of expert consultation questionnaires were 100％;the expert authority coefficients were 0.857 and 0.863;the coefficients of variation were 0-0.285 and 0.052-0.244;the Kendall's harmony coefficients were 0.167 and 0.145,respectively(all P＜0.001).The final plan includes 4 first-level indicators,13 second-level indicators,and 25 third-level indicators.The scores of 6 min walking test,mMRC and CAT after exercise were improved compared with those before exercise,and the differences were statistically significant(all P＜0.05).Conclusion The graded exercise rehabilitation program for patients with AECOPD constructed in this study has good scientificity and practicability,which can provide references for clinical implementation of exercise rehabilitation.

Objective To investigate the current situation and influencing factors of latent tuberculosis infection(LTBI)among close contacts of positive etiology pulmonary tuberculosis(PTB)patients,provide basis for formula-ting intervention measures for LTBI.Methods A multi-stage stratified cluster random sampling method was used to select close contacts of positive etiology PTB patients from 39 districts and counties in Chongqing City as the study objects.Demographic information was collected by questionnaire survey and the infection of Mycobacterium tuberculosis was detected by interferon gamma release assay(IGRA).The influencing factors of LTBI were analyzed by x2 test and binary logistic regression model.Results A total of 2 591 close contacts were included,the male to female ratio was 0.69∶1,with the mean age of(35.72±16.64)years.1 058 cases of LTBI were detected,Myco-bacterium tuberculosis latent infection rate was 40.83％.Univariate analysis showed that the infection rate was dif-ferent among peoples of different age,body mass index(BMI),occupation,education level,marital status,wheth-er they had chronic disease or major surgery history,whether they lived together with the indicator case,and whether the cumulative contact time with the indicator case ≥250 hours,difference were all statistically significant(all P＜0.05);infection rate presented increased trend with the increase of age and BMI(both P＜0.001),and decreased trend with the increase of education(P＜0.05).Logistic regression analysis showed that age 45-54 years old(OR=1.951,95％CI:1.031-3.693),age 55-64 years old(OR=2.473,95％CI:1.279-4.781),other occupations(OR=0.530,95％CI:0.292-0.964),teachers(OR=0.439,95％CI:0.242-0.794),students(OR=0.445,95％CI:0.233-0.851),junior high school education or below(OR=1.412,95％CI:1.025-1.944),BMI＜18.5 kg/m2(OR=0.762,95％CI:0.586-0.991),co-living with indicator cases(OR=1.621,95％CI1.316-1.997)and cumu-lative contact time with indicator cases ≥250 hours(OR=1.292,95％CI:1.083-1.540)were the influential fac-tors for LTBI(all P＜0.05).Conclusion The close contacts with positive etiology PTB have a high latent infection rate of Mycobacterium tuberculosis,and it is necessary to pay attention to close contacts of high age,farmers,and frequent contact with patients,and take timely targeted interventions to reduce the risk of occurrence of disease.