The prescription of Qidong Yixin oral liquid is derived from the experience of national medical master Ren Jixue in treating viral myocarditis （VMC）. It has the functions of tonifying Qi， nourishing the heart，calming the mind， and relieving palpitations. It is used to treat VMC and angina pectoris of coronary heart disease caused by deficiency of both Qi and Yin. However，the understanding of its efficacy evidence， advantageous aspects， dosage and administration， and medication safety remains insufficient in clinical practice. Therefore，the development of the Expert Consensus on the Clinical Application of Qidong Yixin Oral Liquid （hereinafter referred to as consensus） was initiated. Consensus strictly followed the process and methods of the expert consensus on the clinical application of Chinese patent medicines of the China Association of Chinese Medicine，successively completing multiple tasks such as the consensus project initiation，determination of clinical problems，evidence search and evaluation，formation of recommendation opinions and consensus suggestions，solicitation of opinions，peer review， submission for review and release， and so on. Consensus formed a total of 10 recommendation opinions and 12 consensus suggestions，clarifying the clinical positioning，efficacy advantages，syndrome differentiation，dosage and administration，combination therapy，timing of medication，adverse reactions，contraindications， and precautions of Qidong Yixin oral liquid，indicating that it has good clinical advantages and safety in the treatment of VMC and angina pectoris of coronary heart disease，providing norms and references for physicians to safely and rationally apply Qidong Yixin oral liquid. Consensus was reviewed and approved for release by the Standardization Office of the China Association of Chinese Medicine on December 23， 2024. Standard number：GSCACM-376-2024.

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/.

The aim of this study is to explore a new path of empowering ideological and political educa-tion in courses with digital intelligence technology,therefore to improve the teaching quality of biology courses in universities,promote the comprehensive implementation of the fundamental task of cultivating morality and talents,and continuously improve the level of"three pronged education".By using the biol-ogy course"Immunology"as a practical carrier,we have constructed a dual-driven framework of"ideo-logical and political guidance+digital intelligence empowerment"utilizing digital tools to further highlight pedagogical feature enhancement,curricular optimization,innovative teaching models,multidimensional assessment,and enhanced teaching refinement.The empowerment of digital intelligence in the ideological and political construction of the course"Immunology"has achieved notable progress.Students' profes-sional abilities and ideological and political literacy have significantly improved,and the proportion of students with excellent grades has been increasing year by year(81.93％of students with good grades or above in 2024),demonstrating outstanding performance in various social practice activities;The course exhibits excellent demonstration and radiation effects on campus,and the teaching results have been sup-ported by the Ministry of Education and provincial-level education reform projects.This study serves as a practical example for the digital transformation of ideological and political education in higher education courses,providing reference and innovative insights for implementing ideological and political elements in other courses of biology majors.

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 summarize the changing prevalence of carbapenem resistance in Enterobacterales based on the data of CHINET Antimicrobial Resistance Surveillance Program from 2015 to 2021 for improving antimicrobial treatment in clinical practice.Methods Antimicrobial susceptibility testing was performed using a commercial automated susceptibility testing system according to the unified CHINET protocol.The results were interpreted according to the breakpoints of the Clinical & Laboratory Standards Institute(CLSI)M100 31st ed in 2021.Results Over the seven-year period(2015-2021),the overall prevalence of carbapenem-resistant Enterobacterales(CRE)was 9.43％(62 342/661 235).The prevalence of CRE strains in Klebsiella pneumoniae,Citrobacter freundii,and Enterobacter cloacae was 22.38％,9.73％,and 8.47％,respectively.The prevalence of CRE strains in Escherichia coli was 1.99％.A few CRE strains were also identified in Salmonella and Shigella.The CRE strains were mainly isolated from respiratory specimens(44.23±2.80)％,followed by blood(20.88±3.40)％and urine(18.40±3.45)％.Intensive care units(ICUs)were the major source of the CRE strains(27.43±5.20)％.CRE strains were resistant to all the β-lactam antibiotics tested and most non-β-lactam antimicrobial agents.The CRE strains were relatively susceptible to tigecycline and polymyxins with low resistance rates.Conclusions The prevalence of CRE strains was increasing from 2015 to 2021.CRE strains were highly resistant to most of the antibacterial drugs used in clinical practice.Clinicians should prescribe antimicrobial agents rationally.Hospitals should strengthen antibiotic stewardship in key clinical settings such as ICUs,and take effective infection control measures to curb CRE outbreak and epidemic in hospitals.

Objective To characterize the changing species distribution and antibiotic resistance profiles of respiratory isolates in hospitals participating in the CHINET Antimicrobial Resistance Surveillance Program from 2015 to 2021.Methods Commercial automated antimicrobial susceptibility testing systems and disk diffusion method were used to test the susceptibility of respiratory bacterial isolates to antimicrobial agents following the standardized technical protocol established by the CHINET program.Results A total of 589 746 respiratory isolates were collected from 2015 to 2021.Overall,82.6％of the isolates were Gram-negative bacteria and 17.4％were Gram-positive bacteria.The bacterial isolates from outpatients and inpatients accounted for(6.0±0.9)％and(94.0±0.1)％,respectively.The top microorganisms were Klebsiella spp.,Acinetobacter spp.,Pseudomonas aeruginosa,Staphylococcus aureus,Haemophilus spp.,Stenotrophomonas maltophilia,Escherichia coli,and Streptococcus pneumoniae.Each microorganism was isolated from significantly more males than from females(P＜0.05).The overall prevalence of methicillin-resistant S.aureus(MRSA)was 39.9％.The prevalence of penicillin-resistant S.pneumoniae was 1.4％.The prevalence of extended-spectrum β-lactamase(ESBL)-producing E.coli and K.pneumoniae was 67.8％and 41.3％,respectively.The overall prevalence of carbapenem-resistant E.coli,K.pneumoniae,Enterobacter cloacae,Pseudomonas aeruginosa,and Acinetobacter baumannii was 3.7％,20.8％,9.4％,29.8％,and 73.3％,respectively.The prevalence of β-lactamase was 96.1％in Moraxella catarrhalis and 60.0％in Haemophilus influenzae.The H.influenzae isolates from children(＜18 years)showed significantly higher resistance rates to β-lactam antibiotics than the isolates from adults(P＜0.05).Conclusions Gram-negative bacteria are still predominant in respiratory isolates associated with serious antibiotic resistance.Antimicrobial resistance surveillance should be strengthened in clinical practice to support accurate etiological diagnosis and appropriate antimicrobial therapy based on antimicrobial susceptibility testing results.

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/.

Insulin-like growth factor 2 (IGF2) is a critical endocrine mediator implicated in male reproductive physiology. To investigate the correlation between IGF2 protein levels and various aspects of male infertility, specifically focusing on sperm quality, inflammation, and DNA damage, a cohort of 320 male participants was recruited from the Women's Hospital, Zhejiang University School of Medicine (Hangzhou, China) between 1 st January 2024 and 1 st March 2024. The relationship between IGF2 protein concentrations and sperm parameters was assessed, and Spearman correlation and linear regression analysis were employed to evaluate the independent associations between IGF2 protein levels and risk factors for infertility. Enzyme-linked immunosorbent assay (ELISA) was used to measure IGF2 protein levels in seminal plasma, alongside markers of inflammation (tumor necrosis factor-alpha [TNF-α] and interleukin-1β [IL-1β]). The relationship between seminal plasma IGF2 protein levels and DNA damage marker phosphorylated histone H2AX (γ-H2AX) was also explored. Our findings reveal that IGF2 protein expression decreased notably in patients with asthenospermia and teratospermia. Correlation analysis revealed nuanced associations between IGF2 protein levels and specific sperm parameters, and low IGF2 protein concentrations correlated with increased inflammation and DNA damage in sperm. The observed correlations between IGF2 protein levels and specific sperm parameters, along with its connection to inflammation and DNA damage, underscore the importance of IGF2 in the broader context of male reproductive health. These findings lay the groundwork for future research and potential therapeutic interventions targeting IGF2-related pathways to enhance male fertility.
Humans ; Male ; Insulin-Like Growth Factor II/metabolism* ; Infertility, Male/genetics* ; DNA Damage ; Adult ; Inflammation/metabolism* ; Spermatozoa/metabolism* ; Semen Analysis ; Semen/metabolism* ; Tumor Necrosis Factor-alpha/metabolism* ; Histones/metabolism* ; Interleukin-1beta/metabolism*