Objective:To explore the equity and driving path of primary healthcare human resource allocation across 31 provinces in China,providing references for optimizing such allocations.Methods:Using data from the 2022 primary healthcare human resources in 31 provinces in China,the Health Resource Density Index(HRDI)was used to measure the equity of primary healthcare human resource allocation.The fuzzy-set Qualitative Comparative Analysis(fsQCA)was applied to construct configurational pathways influencing on allocation patterns.Results:In 2022,the HRDI for primary healthcare human resources in China was 2.349 0 in the East,1.198 6 in the Central region,and 0.775 2 in the West.Configurational analysis revealed three paths that promote high equity:the internal-external balance-driven model(H1),the government-led model(H2),and the economic-demand combined model(H3),with overall consistency and coverage of 0.955 and 0.794,respectively.Seven paths lead to low equity:internal-external constraint models(L1,L2),economic constraint models(L3),and demand constraint models(L4,L5,L6,L7),with overall consistency and coverage of 0.967 and 0.795,respectively.Conclusions:Significant regional disparities exist in the equity of primary healthcare human resource allocation in China.Population density is a critical factor influencing allocation equity.The collaboration of various factors contributes to enhancing the equity of primary healthcare human resources distribution.Therefore,future efforts to enhance equity should focus on promoting inter-regional mobility and resource sharing,while precisely addressing regional shortcomings to achieve high equity in primary healthcare human resource allocation.

Aim To reveal the mechanism of CIP4 homologs protein 1(RICH1)are involved in the regu-lation of myocardial fibrosis.Methods Mouse cardiac fibroblasts(MCFs)cells were treated with transforming growth factor-β(TGF-β1)to induce the formation of a myocardial fibrosis cell model;the level of the target protein was detected by Western blotting;and the RICH1 gene was detected by transfection of the cells with plasmid.The RICH1 gene was overexpressed(RICH 1 OE)using plasmid transfection;the RICH1 gene was silenced using siRNA fragment(siRICH1);and the expression levels of myocardial fibrosis marker genes,such as Col1 a1,Col3 a1,and Acta2,were de-tected using RT-qPCR.Results RICH1 was signifi-cantly down-regulated in TGF-β1-treated MCFs;the expression levels of myocardial fibrosis marker genes,such as Col1 a1,Col3a1,and Acta2,were down-regu-lated in the RICH1 OE+TGF-β1 group;and in the siRICH1+TGF-β1 group,myocardial fibrosis marker genes,such as Col1 a1,Col3a1 and Acta2 were up-regulated at the expression level;phosphorylated SMAD2(p-SMAD2)and phosphorylated SMAD3(p-SMAD3)levels were down-regulated in the siRICH1 OE+TGF-β1 group.p-SMAD2 and P-SMAD3 levels were upregulated in the siRICH1+TGF-β1 group.Conclusion RICH1 inhibits TGF-β1-induced myo-cardial fibrosis;RICH1 inhibits TGF-β1-induced myo-cardial fibrosis by negatively regulating the SMAD2/3 signaling pathway.

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.

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.

The lab module of exploratory experiment is newly designed in the practical course of bio-chemistry.Here we describe one of the experimental projects,and it originates from new scientific re-search results on the dynamic structure of ATP synthase.This exploratory experiment is organized in the form of real scientific research,which would fully mobilize the initiative and creativity of students in learning theoretical knowledge and experimental technology.Students work in groups and start with refer-ence reading.Through cooperation,they must develop certain experimental plan,handle samples with photocrosslinking technique and utilize the high-throughput electrophoresis method to analyze the dynamic structural change of ε subunit in ATP synthase under different physiological conditions.High quality re-sults from high-throughput electrophoresis can only be obtained through optimized operation and treat-ment,from which students would experience the process of technological innovation.The teaching process of this lab module embodies the student-centered teaching concept and is widely approved and supported by students.The project of ATP synthase closely combines the content of lab course with cut-ting-edge technology.Students can deeply experience the importance of experimental technology innova-tion in solving scientific problems.The practical ability of students would be comprehensively improved through this lab module.

Objective:To evaluate the impact of prophylactic cranial irradiation (PCI) on the prognosis of patients with limited-stage small cell lung cancer (SCLC) in the era of widespread application of MRI.Methods:Clinical data were collected from an open-lable prospective clinical trial on thoracic radiotherapy target volumes for limited-stage SCLC conducted in Sun Yat-sen University Cancer Center and Zhejiang Cancer Hospital between June 2002 and January 2017. In this study, patients who achieved complete remission (CR) or partial remission (PR) after definitive chemoradiotherapy (CRT) were retrospectively analyzed. Stratified analysis was performed according to different clinical efficacies. Patients were divided into different groups according to whether PCI was conducted or not. Survival analysis of patients was carried out. Survival data were calculated by Kaplan-Meier method, and Cox proportional hazards model was applied for multivariate prognostic analysis.Results:Among 309 patients with limited-stage SCLC who received CRT, 133 patients achieved CR and 140 cases obtained PR. These 273 patients were enrolled in this study. Among 133 patients with CR, 29 of them did not receive PCI, and 89 (85.6%) of the remaining 104 patients receiving PCI underwent brain MRI to exclude brain metastasis (BM) before PCI. With a median follow-up time of 22.1 months, the cumulative BM rates were 18.3% and 37.9% in patients who received or did not receive PCI ( P=0.020). The median overall survival (OS) was 30.2 and 30.5 months, and the 1-, 3- and 5-year OS rates were 93.3%, 41.9%, 27.7% and 82.8%, 44.8%, 40.8%, respectively ( P=0.910). Multivariate analysis indicated that baseline Karnofsky performance status (KPS) = 90 was a favorable independent prognostic factor for OS in CR patients ( HR=0.93, 95% CI: 0.89-0.98, P=0.006). Among 140 patients achieving PR, 52 cases did not receive PCI and 80 (90.9%) of the remaining 88 patients received brain MRI before PCI. With a median follow-up time of 18.9 months, the cumulative BM rates were 10.2% and 44.2% ( P<0.001). The median OS was 26.0 and 18.0 months, and the 1-, 3-, and 5-year OS rates were 86.4%, 37.9%, 32.2% and 75.0%, 17.3%, 10.8%, respectively ( P=0.001). Baseline KPS = 90 ( HR=0.93, 95% CI: 0.89-0.97, P=0.001) and PCI ( HR=0.54, 95% CI: 0.36-0.80, P=0.002) were favorable prognostic factors for OS in PR patients. Conclusions:PCI significantly reduces the incidence of BM and prolongs the OS in patients with limited-stage SCLC who achieve PR after CRT, but it fails to significantly prolong the OS of CR patients.

Objective To develop a deep learning model based on fundus retinal images to improve the detection rate of mild cognitive impairment(MCI)in patients with coronary heart disease,achieve early intervention and improve prognosis.Methods The study was a single-center cross-sectional study that retrospectively included patients diagnosed with coronary heart disease(CHD)by coronary angiography(≥50％ stenosis of at least one coronary vessel)from Beijing Anzhen Hospital between November 2021 and December 2022.The whole data set was randomly divided into the training set and the testing set according to the ratio of 8∶2 for model development.After that,the patient data of the same center from January 2023 to April 2023 were included in the time verification method to verify the model.The diagnostic criteria for MCI were MMSE＜27 or MoCA＜26.Four kinds of convolutional neural network(CNN)architectures were used to train fundus images,and a comprehensive vision model of MCI detection was established through model integration.The area under the curve(AUC),sensitivity and specificity of the receiver operating curve(ROC)were used to evaluate the performance of the AI model.Results We collected 5 880 eligible fundus images from 3 368 CHD patients.Based on the results of the MMSE scale,the algorithm was labeled,including 2 898 males and 527 MCI patients.The AUC of the deep learning model in the test group is 0.733(95％CI 0.688-0.778),and the sensitivity of the algorithm in the test group is 0.577(95％CI 0.528-0.625)by using the operating point with the maximum sum of sensitivity and specificity.With a specificity of 0.758(95％CI 0.714-0.802),corresponding to a validated AUC of 0.710(95％CI 0.601-0.818).Based on the results of the MoCA scale,the algorithm labels 2 437 males and 1 626 MCI patients.The AUC of the deep learning model in the test group was 0.702(95％CI 0.671-0.733).The operating point with the maximum sum of sensitivity and specificity was selected,and the sensitivity of the algorithm was 0.749(95％CI 0.719-0.778)and the specificity was 0.561(95％CI 0.527-0.595),corresponding to the AUC value of the verification group was 0.674(95％CI 0.622-0.726).Conclusions The deep learning algorithm model based on fundus images has good diagnostic performance,and may be used as a new non-invasive,convenient and rapid screening method for MCI in CHD population.

Objective This study aimed to investigate the antimicrobial resistance profiles of bacterial strains isolated from pediatric intensive care units(PICU)in China for better antimicrobial therapy.Methods Clinical isolates were collected from 17 institutions,including tertiary care children's hospitals and pediatric department of tertiary general hospitals in China from January 1,2020 to December 31,2022.Antimicrobial susceptibility testing was carried out according to a unified protocol using Kirby-Bauer method or automated systems.Results were interpreted according to the breakpoints released by the Clinical and Laboratory Standards Institute(CLSI)in 2020.Results A total of 10 688 isolates were collected,including gram-positive organisms(39.2％)and gram-negative organisms(60.8％).The top three organisms were S.aureus(13.6％,1 453/10 688),A.baumannii(10.0％,1 067/10 688),and coagulase-negative Staphylococcus(9.9％,1 058/10 688).Multi-drug resistant organisms(MDROs)were very common in children.The prevalence of methicillin-resistant Staphylococcus aureus(MRSA),carbapenem-resistant Enterobacterales(CRE),carbapenem-resistant E.coli,carbapenem-resistant K.pneumoniae(CRKP),carbapenem-resistant A.baumannii(CRAB),and carbapenem-resistant P.aeruginosa(CRPA)was 41.1％,19.4％,8.8％,30.9％,67.4％,and 28.8％,respectively.Overall,more than 50％of Enterobacteriales isolates were resistant to cephalosporins,while nearly 25％of Enterobacteriales isolates were resistant to carbapenems.MDROs were highly resistant to commonly used antibiotics.More than 80％of CRE and CRAB strains were resistant to all beta-lactam antibiotics.CRE and CRAB showed low resistance rates to tigecycline and polymyxin.CRPA showed lower resistance rates to piperacillin,beta-lactamase inhibitor combinations than the resistance rates to third and fourth generation cephalosporins.All of the Staphylococcus and Enterococcus isolates were susceptible to vancomycin and tigecycline.None of PRSP strains isolated from meningitis and nonmeningitis samples were resistant to rifampicin,vancomycin,or linezolid.The prevalence of β-lactamase-negative ampicillin-resistant(BLNAR)strains was 43.3％in Haemophilus influenzae.Conclusions MDROs were prevalent in PICU.It is necessary to establish an effective multidisciplinary team(MDT)to control the antimicrobial resistance.

Purine nucleotides,fundamental constituents of nucleic acids,represent essential organic molecules within cells.These molecules serve critical roles not only in nucleic acid biosynthesis,the transmission of genetic information and signal transduction cascades,but also function as precursors for the synthesis of energy substances.Consequently,purine metabolism is subject to stringent regulatory mechanisms that preserve cellular homeostasis and function integrity.Emerging evidence demonstrates that dysregulated purine metabolism exhibits significant associations with tumorigenesis and existing anti-tumor drugs have developed varying degrees of drugs resistance during therapy.Elucidating the mechanistic links between purine metabolic aberrations and tumorigenesis holds dual significance:it enhances our understanding of cancer pathophysiology while potentially informing novel therapeutic paradigms.This review systematically examines the pathophysiological contributions of key purine metabolic enzymes and intermediate metabolites to malignant transformation,while critically evaluating emerging anti-tumor strategies that target purine metabolism pathways.

Objective To develop dynamic prediction models based on multiple postnatal time points to support early diagnosis and individualized intervention for bronchopulmonary dysplasia(BPD)in preterm infants with gestational age<32 weeks.Methods Clinical data of 472 preterm infants with gestational age<32 weeks admitted to the Second Xiangya Hospital of Central South University between January 2016 and November 2020 were retrospectively analyzed.Multivariable logistic regression was applied to develop five independent prediction models at postnatal days 1,7,14,21,and 28.The performance of the models was assessed using the area under the receiver operating characteristic curve(AUC)and the Hosmer-Lemeshow test.Results Baseline characteristics such as gestational age and birth weight differed significantly between the BPD group(n=147)and the non-BPD group(n=325)(P<0.05).Predictors of BPD evolved across time points:on day 1,key predictors included gestational age,birth weight,Score for Neonatal Acute Physiology II(SNAP-II),invasive mechanical ventilation,and fraction of inspired oxygen>30%;by day 7,additional variables emerged,including fasting duration>2 days,mean feeding advancement rate<8.5 mL/(kg·d),neonatal respiratory distress syndrome,apnea of prematurity,and positive sputum culture;from day 14 onward,nutrition-and treatment-related indicators were incorporated additionally.The models demonstrated good discrimination at postnatal days 1,7,14,21,and 28,with AUCs of 0.917,0.927,0.939,0.944,and 0.968,respectively,and good calibration(Hosmer-Lemeshow P>0.05).Internal validation showed AUCs ranging from 0.899 to 0.958,indicating robust performance.Conclusions Dynamic postnatal prediction models incorporating indicators spanning perinatal factors,respiratory support,nutritional management,and therapeutic interventions demonstrate high predictive performance and facilitate dynamic risk assessment for BPD in preterm infants with gestational age<32 weeks.