ObjectiveTo systematically evaluate the application of narrative education in undergraduate nursing teaching， to understand the current application status of narrative education， and to provide a theoretical basis for the subsequent establishment of a sound narrative education system. MethodsA systematic search was conducted for studies published in Chinese and English databases on applying narrative education to undergraduate nursing teaching， with the search period ranging from database inception to February 23， 2025. Literature was screened， and relevant information was extracted. A rigorous quality evaluation was conducted on the included studies， and a descriptive analysis was performed on their content. ResultsA total of 20 papers were included， involving 3，180 research subjects， all of whom were undergraduate nursing students. The results of descriptive analysis showed that the teaching model of narrative education primarily encompassed reading narrative works， watching films and videos， performing narrative scenarios， and writing reflective journals. The course setting and content covered pre-teaching preparation and in-teaching implementation. The evaluation of teaching effectiveness included the evaluation of teachers’ teaching methods （student evaluation/self-evaluation） and the evaluation of students’ learning effectiveness （course grade evaluation/humanistic care scale/empathy scale assessment， and others）. ConclusionNarrative education combines abstract concepts with concrete clinical situations， which not only enriches students’ learning experiences but also enhances their humanistic literacy. Meanwhile， it provides teachers with opportunities to develop their narrative teaching skills， which requires them to possess profound professional knowledge and employ narrative techniques to guide students in reflection and critical thinking， thereby improving teaching quality and learning outcomes. Future efforts should consistently deepen the connotation research of narrative education and build a systematic nursing education system.

Chinese hamster ovary (CHO) cells are the most established and versatile mammalian expression system for the large-scale production of recombinant therapeutic proteins, owing to their genetic stability, adaptability to serum-free suspension culture, and ability to perform human-like post-translational modifications. More than 70% of biologics approved by the U.S. Food and Drug Administration rely on CHO-based production platforms, underscoring their central role in modern biopharmaceutical manufacturing. Despite these advantages, CHO systems continue to face three persistent bottlenecks that limit their potential for high-yield, reproducible, and cost-efficient production: excessive metabolic burden during high-density culture, heterogeneity of glycosylation patterns, and progressive loss of long-term expression stability. This review provides an integrated analysis of recent advances addressing these challenges and proposes a forward-looking framework for constructing intelligent and sustainable CHO cell factories. In terms of metabolic regulation, excessive lactate and ammonia accumulation disrupts energy balance and reduces recombinant protein synthesis efficiency. Optimization of culture parameters such as temperature, pH, dissolved oxygen, osmolarity, and glucose feeding can effectively alleviate metabolic stress, while supplementation with modulators including sodium butyrate, baicalein, and S-adenosylmethionine promotes specific productivity (qP) by modulating apoptosis and chromatin structure. Furthermore, genetic engineering strategies—such as overexpression of MPC1/2, HSP27, and SIRT6 or knockout of Bax, Apaf1, and IGF-1R—have demonstrated significant improvements in cell viability and product yield. The combination of multi-omics metabolic modeling with artificial intelligence (AI)-based prediction offers new opportunities for building self-regulating CHO systems capable of dynamic adaptation to environmental stress. Regarding glycosylation uniformity, which determines therapeutic efficacy and immunogenicity, gene editing-based glycoengineering (e.g., FUT8 knockdown or ST6Gal1 overexpression) has enabled the humanization of CHO glycan profiles, minimizing non-human sugar residues and enhancing drug stability. Process-level strategies such as galactose or manganese co-feeding and fine control of temperature or osmolarity further allow rational regulation of glycosyltransferase activity. Additionally, in vitro chemoenzymatic remodeling provides a complementary route to construct human-type glycans with defined structures, though industrial applications remain constrained by cost and scalability. The integration of model-driven process design and AI feedback control is expected to enable real-time prediction and correction of glycosylation deviations, ensuring batch-to-batch consistency in continuous biomanufacturing. Long-term expression stability, another critical challenge, is often impaired by promoter silencing, chromatin condensation, and random genomic integration. Molecular optimization—such as the use of improved promoters (CMV, EF-1α, or CHO endogenous promoters), Kozak and signal peptide refinement, and incorporation of chromatin-opening elements (UCOE, MAR, STAR)—helps maintain durable transcriptional activity, while site-specific integration systems including Cre/loxP, Flp/FRT, φC31, and CRISPR/Cas9 can enable single-copy, position-independent gene insertion at genomic safe-harbor loci, ensuring stable, predictable expression. Collectively, this review highlights a paradigm shift in CHO system optimization driven by the convergence of genome editing, synthetic biology, and artificial intelligence. The transition from empirical optimization to rational, data-driven design will facilitate the development of programmable CHO platforms capable of autonomous regulation of metabolic flux, glycosylation fidelity, and transcriptional activity. Such intelligent cell factories are expected to accelerate the transformation from laboratory-scale research to industrial-scale, high-consistency, and economically sustainable biopharmaceutical manufacturing, thereby supporting the next generation of efficient and customizable biologics manufacturing.

Chinese hamster ovary (CHO) cells are the most established and versatile mammalian expression system for the large-scale production of recombinant therapeutic proteins, owing to their genetic stability, adaptability to serum-free suspension culture, and ability to perform human-like post-translational modifications. More than 70% of biologics approved by the U.S. Food and Drug Administration rely on CHO-based production platforms, underscoring their central role in modern biopharmaceutical manufacturing. Despite these advantages, CHO systems continue to face three persistent bottlenecks that limit their potential for high-yield, reproducible, and cost-efficient production: excessive metabolic burden during high-density culture, heterogeneity of glycosylation patterns, and progressive loss of long-term expression stability. This review provides an integrated analysis of recent advances addressing these challenges and proposes a forward-looking framework for constructing intelligent and sustainable CHO cell factories. In terms of metabolic regulation, excessive lactate and ammonia accumulation disrupts energy balance and reduces recombinant protein synthesis efficiency. Optimization of culture parameters such as temperature, pH, dissolved oxygen, osmolarity, and glucose feeding can effectively alleviate metabolic stress, while supplementation with modulators including sodium butyrate, baicalein, and S-adenosylmethionine promotes specific productivity (qP) by modulating apoptosis and chromatin structure. Furthermore, genetic engineering strategies—such as overexpression of MPC1/2, HSP27, and SIRT6 or knockout of Bax, Apaf1, and IGF-1R—have demonstrated significant improvements in cell viability and product yield. The combination of multi-omics metabolic modeling with artificial intelligence (AI)-based prediction offers new opportunities for building self-regulating CHO systems capable of dynamic adaptation to environmental stress. Regarding glycosylation uniformity, which determines therapeutic efficacy and immunogenicity, gene editing-based glycoengineering (e.g., FUT8 knockdown or ST6Gal1 overexpression) has enabled the humanization of CHO glycan profiles, minimizing non-human sugar residues and enhancing drug stability. Process-level strategies such as galactose or manganese co-feeding and fine control of temperature or osmolarity further allow rational regulation of glycosyltransferase activity. Additionally, in vitro chemoenzymatic remodeling provides a complementary route to construct human-type glycans with defined structures, though industrial applications remain constrained by cost and scalability. The integration of model-driven process design and AI feedback control is expected to enable real-time prediction and correction of glycosylation deviations, ensuring batch-to-batch consistency in continuous biomanufacturing. Long-term expression stability, another critical challenge, is often impaired by promoter silencing, chromatin condensation, and random genomic integration. Molecular optimization—such as the use of improved promoters (CMV, EF-1α, or CHO endogenous promoters), Kozak and signal peptide refinement, and incorporation of chromatin-opening elements (UCOE, MAR, STAR)—helps maintain durable transcriptional activity, while site-specific integration systems including Cre/loxP, Flp/FRT, φC31, and CRISPR/Cas9 can enable single-copy, position-independent gene insertion at genomic safe-harbor loci, ensuring stable, predictable expression. Collectively, this review highlights a paradigm shift in CHO system optimization driven by the convergence of genome editing, synthetic biology, and artificial intelligence. The transition from empirical optimization to rational, data-driven design will facilitate the development of programmable CHO platforms capable of autonomous regulation of metabolic flux, glycosylation fidelity, and transcriptional activity. Such intelligent cell factories are expected to accelerate the transformation from laboratory-scale research to industrial-scale, high-consistency, and economically sustainable biopharmaceutical manufacturing, thereby supporting the next generation of efficient and customizable biologics manufacturing.

Triptolide （TP）， the core active component of the traditional Chinese medicine Tripterygium wilfordii ， exhibits remarkable pharmacological activities including anti-inflammatory， immunosuppressive and anti-tumor effects， and holds broad application prospects in the treatment of major diseases such as autoimmune diseases and malignant tumors. However， TP has a narrow therapeutic window and causes multi-organ toxicities including liver， kidney and reproductive toxicities， which severely restrict its safe clinical application and new drug development. Therefore， toxicity reduction and efficacy enhancement has become a core scientific problem urgently to be solved in this field. This paper systematically reviews the four core strategies for TP toxicity reduction and efficacy enhancement， including structural modification， dosage form improvement， herbal compatibility， and external therapies of traditional Chinese medicine. Among them， structural modification optimizes the toxic and efficacy characteristics of TP from the molecular structure level， with typica l derivatives including （5 R ）-5-hydroxy triptolide， ZT01， PG490-88， etc. Dosage form modification achieves toxicity reduction and efficacy enhancement via targeted and sustained-controlled drug release of diverse delivery systems. It includes triptolide preparations such as nanoparticles， liposomes， microemulsion gels and liquid crystals， possessing favorable clinical transformation potential. The herbal compatibility and external therapies of traditional Chinese medicine conform to the holistic view of traditional Chinese medicine and have a profound clinical application foundation， but their mechanisms of action are insufficiently elucidated， and they lack unified standardized specifications and high-quality evidence-based proof. In the future， we should rely on multi-omics technology to elucidate the toxic and efficacy mechanisms， integrate technologies to optimize preparations， improve the evaluation system and promote clinical transformation.

Objective To understand the current situation of healthcare-associated infection(HAI)in China,pro-vide data support and decision-making basis for formulating scientific and effective strategies for HAI prevention and control.Methods A nationwide cross-sectional survey on HAI was conducted among various types and levels of medical institutions in China according to a unified protocol of bedside surveys and case investigations.Results In 2024,a total of 5 736 medical institutions and 2 751 765 patients were surveyed.Among them,34 889 HAI cases were identified,with a prevalence rate of 1.27％.The number of HAI episodes was 38 032,and case prevalence rate was 1.38％.The prevalence rate of HAI in medical institutions in different regions of China ranged from 0.66％to 2.35％.Among medical institutions of different scales,those with a bed capacity of ≥900 had the high-est incidence of HAI,reaching 1.65％.The most common infection site was the lower respiratory tract(44.66％),followed by the urinary tract(12.94％),surgical site(9.32％),upper respiratory tract(7.02％),and bloodstream infection(5.78％).The top 3 departments with the highest HAI rates were the general intensive care unit(10.02％),department of neurosurgery(5.51％),and department(group)of hematology(5.34％).A total of 23 238 strains of HAI pathogens were detected,with 10 714 strains(46.10％)from lower respiratory tract speci-mens.The top 5 detected strains were Klebsiella pneumoniae(14.76％),Pseudomonas aeruginosa(13.33％),Escherichia coli(12.79％),Acinetobacter baumannii(9.23％),and Staphylococcus aureus(7.88％).231 944 pa-tients underwent class Ⅰ incision surgery were monitored,with 1 647 cases experienced surgical site infection,and the prevalence rate of surgical site infection was 0.71％.The number of patients who should undergo pathogen de-tection(patients receiving therapeutic and therapeutic combined prophylactic antimicrobial agents)was 715 179,while the actual number was 480 492,with a pathogen detection rate of 67.18％.425 225 patients received patho-genic detection before treatment,with a detection rate of 59.46％.Conclusion The overall HAI prevalence in Chi-na is lower,showing disparities among medical institutions of different regions and scales.Therefore,precise imple-mentation of measures is necessary for HAI prevention and control,with a focus on high-risk institutions and high-risk departments,key areas,and critical procedures.All levels of medical institutions should continuously reduce the incidence of HAI by strengthening monitoring,standardizing the use of antimicrobial agents,and reinforcing basic HAI prevention and control measures.

Objective To analyze the association between lifestyle and the risk of type 2 diabetes(T2D)among adult residents.Methods The data was sourced from the Shanghai Suburban Adult Cohort and Biobank.A total of 42 096 adult residents who had not developed T2D were recruited from four districts of Shanghai(Songjiang,Jiading,Minhang,and Xuhui)between 2016 and 2019.The follow-up ended on Feb 28,2023.A structured questionnaire was used to collect information on six lifestyle-related items,including smoking,alcohol consumption,BMI,waist circumference(WC),physical activity,and diet.The unhealthy lifestyle scores(UHLS)were calculated by counting the number of all the unhealthy lifestyle items,with a range of 0-6.New-onset T2D events diagnosed by physicians were obtained through the medical information system.Cox proportional hazards regression model and restricted cubic spline model were utilized to evaluate the association between unhealthy lifestyles and the risk of T2D incidence.Results About 28.1%of the participants led 4-6 unhealthy lifestyles.A total of 1 752 new T2D cases were identified during 218 513.4 person-years of follow-up.Analysis of single unhealthy lifestyle showed that abnormal WC(HR=1.5,95%CI:1.4-1.7)and abnormal BMI(HR=1.3,95%CI:1.2-1.5)were associated with an increased risk of T2D.Compared with individuals with a UHLS of 0-1,those with a UHLS of 3 and 4-6 had 30%(95%CI:1.1-1.6)and 50%(95%CI:1.2-1.8)higher risks of T2D,respectively.Each additional unhealthy lifestyle was associated with a 10%increase in T2D incidence risk(HR=1.1,95%CI:1.1-1.2).Conclusion The risk of T2D in adult residents increases with the cumulative number of unhealthy lifestyles.Adult residents with abnormal WC or BMI,or have three or more unhealthy lifestyles accumulated,will increase the risk of new-onset T2D.

Heatstroke,especially in high-temperature and high-humidity environments,is a life-threatening acute heat-injury disease that seriously endangers human health.Timely and effective on-site treatment is crucial for patients'survival and prognosis.Early recognition,rapid assessment,and on-site cooling are the core of pre-hospital treatment of heatstroke.Currently,there is a lack of standardized application procedures for pre-hospital emergency care of heatstroke.Therefore,the"Expert Consensus on Pre-hospital Emergency Management of Heatstroke(2024 edition)"was initiated by the Expert Group on Heatstroke Prevention of the People's Liberation Army and developed in collaboration with experts from local pre-hospital emergency care,emergency departments,and intensive care units.This consensus focuses on heatstroke prevention,on-site and ambulance-based treatment,and early emergency room interventions,and puts forward 10 evidence-based recommendations,aiming to provide a reference for scientific and standardized pre-hospital emergency care of heatstroke.

Objective:To explore changes in brain activity in patients with post-traumatic stress disorder(PTSD).Methods:A total of 40 participants involved in car accidents were included,and functional magnetic reso-nance imaging(fMRI)scans were collected within one week.Anxiety,depression,and personality assessments were conducted with the Hamilton Anxiety Scale(HAMA),Hamilton Depression Scale(HAMD),and Eysenck Person-ality Scale for Adult(EPQ).After two months,a second fMRI scan was conducted,and a PTSD diagnosis was made.Participants were divided into a trauma-exposed group(n=23)and a PTSD group(n=17)based on wheth-er they developed PTSD.Changes in brain functional activity between the trauma-exposed group and the PTSD group were compared using the percentage of amplitude fluctuation(perAF)method.Results:Compared to the trauma-exposed group,the PTSD group showed a decreased perAF value in the left hippocampus at 1 week,and de-creased perAF values in the right mid-cingulate gyrus and left postcentral gyrus at 2 months(P＜0.05).When comparing the PTSD group at different times,the perAF values in the left middle temporal gyrus and left medial su-perior frontal gyrus decreased at 2 months(P＜0.05).Correlation analysis revealed that PCL-5 scores were posi-tively correlated with EPQ Psychoticism(r=0.32,P=0.041),HAMA(r=0.35,P＜0.05),and HAMD(r=0.34,P＜0.05).Regression analysis found that higher scores of EPQ psychoticism(OR=11.79)and HAMA(OR=1.62)were risk factors for post-accident PTSD,while higher scores of EPQ extraversion(OR=0.32)were pro-tective factors.Conclusion:It suggests that patients with post-traumatic stress disorder may show decreased activity in the right middle cingulate cortex,left postcentral gyrus,left middle temporal gyrus,and left medial superior fron-tal gyrus within two months after the traumatic event.

As a non-invasive physiological indicator,electroencephalography signal provides an objective assessment of psychological stress levels among nursing staff in major public health emergencies,offering a scientific basis for targeted psychological interventions while overcoming the limitations of subjective bias inherent in traditional questionnaire-based methods.A psychological stress classification model based on bidirectional long short-term memory and attention mechanism is proposed to classify the psychological stress of clinical nurses more effectively by analyzing their electroencephalography signals.Experimental results show that the proposed model exhibits better classification performance than the traditional long short-term memory model on the DREAMER dataset,the Feeling Emotions dataset and the self-built dataset.This study provides a novel approach for assessing psychological stress,which is helpful to improve the pertinence and effectiveness of clinical nursing work.

As a non-invasive physiological indicator,electroencephalography signal provides an objective assessment of psychological stress levels among nursing staff in major public health emergencies,offering a scientific basis for targeted psychological interventions while overcoming the limitations of subjective bias inherent in traditional questionnaire-based methods.A psychological stress classification model based on bidirectional long short-term memory and attention mechanism is proposed to classify the psychological stress of clinical nurses more effectively by analyzing their electroencephalography signals.Experimental results show that the proposed model exhibits better classification performance than the traditional long short-term memory model on the DREAMER dataset,the Feeling Emotions dataset and the self-built dataset.This study provides a novel approach for assessing psychological stress,which is helpful to improve the pertinence and effectiveness of clinical nursing work.