Metabolic associated fatty liver disease （MAFLD） is a common chronic liver disease worldwide， and timely and precise intervention can delay disease progression and significantly reduce the risk of serious complications such as liver fibrosis， liver cirrhosis， and liver cancer. Although traditional liver biopsy combined with metabolic markers is the gold standard， it may cause complications such as pain and bleeding as an invasive examination， which has promoted scientific research to shift its focus to the construction of noninvasive assessment systems. In recent years， noninvasive diagnostic technologies based on multi-dimensional detection strategies have been continuously updated， including serological models， imaging techniques， and clinical algorithms. This article systematically reviews the screening methods for MAFLD during the fibrotic stages F1—F3， especially deep learning models based on artificial intelligence， in order to provide ideas for the early screening of MAFLD， as well as a scientific reference for optimizing disease management strategies.

AIM:To investigate the current status of astigmatism and its associated factors among children and adolescents from kindergarten through senior high school in Yangpu District, Shanghai, and to provide scientific evidence for the prevention and control of refractive errors.METHODS:A cluster sampling method was employed. From September to November 2024, students from 14 randomly selected schools in Yangpu District, Shanghai, were enrolled as study subjects. Autorefraction and ocular biometry were performed under non-cycloplegic conditions, and questionnaire surveys were administered. Comparisons of astigmatism degree, detection rate, and axis were made across age groups, educational stages, and sexes. Multiple linear and logistic regression analyses were conducted to identify factors associated with astigmatism.RESULTS:A total of 7 233 students aged 4-18 y were enrolled including 3 731 males, 3 502 females, with the mean age 10.13±4.18 y, astigmatism -0.76±0.82 D, and the detection rate(≤-1.00 D)was 29.23%. Both the degree and detection rate of astigmatism were correlated with age and educational stage, peaking in senior high school, with the most pronounced changes observed during primary school. Besides age, astigmatic refractive power was influenced by spherical equivalent(β=0.105, P<0.001), mean keratometry(β=-0.118, P<0.001), sex(with boys as the reference, girls: β=0.054, P<0.001), frequent sustained near work for 30-40 min(with “almost never” as the reference, β=-0.030, P=0.038), preterm birth(β=-0.023, P=0.044), maternal myopia(β=-0.028, P=0.020), and homework completion time on rest days(β=-0.035, P=0.041). The detection rate of astigmatism was associated with maternal myopia(OR=1.130, P=0.039), male sex(OR=0.773, P<0.001), shorter sleep duration on school days(OR=0.909, P=0.028), shorter sleep duration on rest days(OR=0.937, P=0.046), preterm birth(OR=1.382, P=0.012), frequent excessive head lowering(OR=1.216, P=0.045), homework completion time on rest days(OR=1.043, P=0.046), higher spherical equivalent(OR=0.891, P<0.001), and higher mean keratometry(OR=1.188, P<0.001).CONCLUSION:The degree and detection rate of astigmatism among children and adolescents from kindergarten to senior high school in Yangpu District, Shanghai are correlated with educational stage, and primary school may represent a critical period for astigmatism prevention and control. Male sex, maternal myopia, preterm birth, high-intensity near work, and insufficient sleep and other factors may be associated with the onset and progression of astigmatism. It is recommended that education and health departments strengthen visual health screening during primary school, while promoting behavioral interventions such as scientific eye-use habits and adequate sleep across different educational stages to delay the onset and progression of astigmatism.

Glycogen synthase 1 (GYS1) is the rate-limiting enzyme responsible for glycogen synthesis in skeletal muscle, heart, brain, and other extrahepatic tissues, playing a central role in systemic energy homeostasis. The human GYS1 gene maps to chromosome 19q13.33, comprises 16 exons, and encodes a 737-amino-acid polypeptide that is highly conserved across mammals. GYS1 activity is subject to multilayered and precisely coordinated regulation. At the transcriptional level, the GYS1 promoter contains a hypoxia response element (HRE) that mediates HIF-1α-dependent induction under low-oxygen conditions, as well as a muscle-specific enhancer harboring MEF2 and MyoD binding sites that confers tissue-restricted expression. At the post-translational level, a hierarchical phosphorylation cascade serves as the primary activity switch: glycogen synthase kinase 3β (GSK3β) sequentially phosphorylates four C-terminal serine residues following casein kinase II priming, while protein kinase A (PKA) and AMP-activated protein kinase (AMPK) provide parallel inhibitory inputs at both N- and C-terminal sites. Dephosphorylation and reactivation are mediated by protein phosphatase 1 (PP1) through tissue-specific glycogen-targeting regulatory subunits such as PPP1R3A and PPP1R3B, which anchor PP1 to glycogen particles and direct its activity toward GYS1. The allosteric activator glucose-6-phosphate (G6P) binds at the dimer interface, simultaneously enhancing catalytic efficiency and promoting dephosphorylation susceptibility, thereby establishing a feed-forward activation loop that couples substrate availability to glycogen synthesis. Beyond phosphorylation, GYS1 is regulated by ubiquitination (mediated by the E3 ligase PJA1), acetylation, O-linked β-N-acetylglucosamine (O-GlcNAc) modification, and SUMOylation, which collectively modulate protein stability, subcellular localization, and protein-protein interactions. Epigenetic mechanisms, including CpG island methylation and histone acetylation dynamics, govern chromatin accessibility at the GYS1 locus, while muscle-specific microRNAs such as miR-1 and miR-206 fine-tune GYS1 expression at the post-transcriptional level. Dysregulation of GYS1 has been identified as a central pathogenic driver in a spectrum of human diseases. In inherited glycogen storage disorders—including Lafora disease, adult polyglucosan body disease (APBD), and Pompe disease—loss of upstream regulatory control leads to GYS1 hyperactivation and the accumulation of structurally abnormal or excessive glycogen, resulting in progressive neurodegeneration, myopathy, and multiorgan dysfunction. In type 2 diabetes mellitus (T2DM), impaired insulin signaling through the PI3K-AKT-GSK3β axis maintains GYS1 in a hyperphosphorylated inactive state in skeletal muscle, compromising postprandial glucose disposal and exacerbating hyperglycemia. In oncology, GYS1 exhibits context-dependent roles across multiple cancer types. In hepatocellular carcinoma, FMO2⁺ cancer-associated fibroblasts stabilize GYS1 by competitively inhibiting PJA1-mediated ubiquitination, and stabilized GYS1 subsequently activates NF‑κB/CCL19 signaling to promote tertiary lymphoid structure formation and enhance anti-PD-1 immunotherapy responsiveness. In clear cell renal cell carcinoma, GYS1 promotes tumor progression through non-canonical NF‑κB pathway activation via the scaffold protein RPS27A. In triple-negative breast cancer, GYS1 has been identified as a trigger of disulfidptosis and an activator of NF-κB signaling through non-enzymatic facilitation of IκBα degradation. In colorectal cancer, mitochondrial fission deficiency drives AMPK-dependent GYS1 upregulation and glycogen accumulation as a compensatory survival mechanism, while in cervical cancer, GYS1-maintained glycogen reserves fuel the pentose phosphate pathway to generate NADPH for ROS clearance, thereby conferring cisplatin resistance in cancer stem cells. Therapeutic strategies targeting GYS1 have gained substantial momentum across these disease contexts. For glycogen storage disorders, antisense oligonucleotides, small interfering RNAs (e.g., ABX1100), and small-molecule inhibitors (e.g., MZ-101) have demonstrated preclinical and early clinical efficacy in reducing pathological glycogen accumulation. For T2DM, pharmacological activation of GYS1 through GSK3β inhibition or enhancement of PP1-mediated dephosphorylation is being explored to restore insulin-stimulated glycogen synthesis. In cancer, GYS1-directed interventions—including targeted silencing to sensitize tumors to chemotherapy and immune microenvironment modulation to enhance immunotherapy—represent emerging precision oncology approaches. This review provides a comprehensive and integrated account of GYS1 gene structure, tissue-specific distribution, regulatory networks, and pathogenic roles in metabolic disorders and malignancies, with the aim of establishing a theoretical framework for the development of GYS1-targeted precision therapies.

Objective To analyze the reasons for the failure of subject screening in clinical trials of antineoplastic drugs and the impact of natriuretic criteria on the entry of subjects into clinical trials,to explore the strategies to improve the suc-cessful enrolment of screened subjects,and to provide reference bases for research institutes and sponsors in the formulation of na-triuretic criteria.Methods This study selected data from 40 drug clinical trials conducted at the Drug Clinical Trial Research Center of the Cancer Hospital of the Chinese Academy of Medical Sciences from January 1,2016,to June 30,2022.It statistically described the collected data on the frequency and percentage composition of screening failures among participants and the inclu-sion and exclusion criteria in the protocols.Results A total of 425 subjects were screened out of 40 clinical trial programmers covering 8 tumor types,with the majority being＜65 years of age(333,78.4％),of which the most important reasons included vol-untary withdrawal(71,16.7％),tumor metastasis(52,12.2％),failure to recover from treatment of pre-existing disease(38,8.9％),failure of bone marrow function(19,4.5％),and non-compliant liver function(15,3.5％).Among the nadir indicators,the age of the subjects(100％),ECOG score(97.5％),bone marrow function(ANC:95.0％,PLT:97.5％,HB:97.5％),liver function(T-BiL:95.0％,ALT:87.5％,AST:95.0％),renal function(CR:80.0％),and viral screening(HIV:80.0％,HBV:70.0％,HCV:62.5％)were relatively stringent.Conclusion The main reasons for subject screening failure in clinical trials in oncology in our hospital are voluntary withdrawal,brain metastasis,and failure of their biochemical test standards,which are close-ly related to the setting of clinical trial nadir criteria.Therefore,an in-depth understanding of subjects'characteristics,accurate set-ting of appropriate nadir criteria,continuous improvement of trial design,and strengthening of communication with subjects to pro-vide more relevant information will help to improve the screening success rate of clinical trials.

Glycoengineering was carried out in the mammalian cell line CHO for the production of pro-tein-based drugs.Firstly,the genome sequence of the Rosa26 locus of CHO cells was determined,the gRNA sequences were designed,and the landing pad was integrated into the Rosa26 locus of CHO cells by CRISPR/Cas9 technology.Three targeting vectors co-expressed by glycosyltransferases,which are β-1,4 galactosyltransferase(B4GALT1),α-2,6-sialyltransferase 1(ST6GAL1)and N-acetaminoglycosyl-transferase Ⅲ(GnT Ⅲ),were constructed by overlapping PCR and seamless ligation technology,and the three glycosyltransferase genes were integrated into the CHO Rosa26 locus by Cre enzyme-mediated cassette exchange technology.PCR confirmed that three glycosyltransferases had been successfully site-directed integrated into the Rosa26 site.The mRNA expression levels of the three glycosyltransferases were more than 50 000-fold by qRT-PCR,and the protein expression levels of the three glycosyltrans-ferases were more than 4-fold via western blotting(P＜0.001).A CHO-engineered cell line with three glycosyltransferases integrated into Rosa26 site was successfully constructed.

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.

Objective To analyze the core syndromes of patients with generalized anxiety disorder(GAD),explore the core pathogenesis,and offer innovative perspectives and practical strategies for the traditional Chinese medicine(TCM)diagnosis and treatment of GAD.Methods The basic information of GAD patients was collected,and depression symptoms were evaluated with Hamilton Anxiety Scale to evaluate anxiety symptoms,Hamilton Depression Scale,and the TCM psychiatric and somatic symptoms were evaluated with Traditional Chinese Medicine Symptom Observation Form.Based on the data collected from the Traditional Chinese Medicine symptom observation table,the systematic clustering method was used to cluster the symptoms with a frequency greater than 10％,determine the disease type syndrome and disease location syndrome,and form a syndrome symptom relationship table.According to this table,the traditional Chinese medicine syndrome score of each patient is calculated.The complex network analysis was carried out to evaluate core syndromes and analyze the relationships between core syndromes and psychiatric symptoms and core syndromes and other syndromes.Results A total of 517 patients with GAD were included.There were 81 symptoms with a frequency of more than 10％,including 21 psychological symptoms and 60 physical symptoms.The clustering analysis led to a total of 12 syndromes,including 6 pathological syndromes,namely yin deficiency,heat,phlegm dampness,qi stagnation,blood stasis,and qi deficiency,and 6 disease location syndromes,namely liver,spleen,kidney,gallbladder,stomach,and heart.The results of complex network analysis show that the core pathological syndrome of GAD is kidney,and the core pathological syndrome is yin deficiency.The joint analysis of pathological syndrome and pathological syndrome network suggests that yin deficiency is the core of the integrated network.The relationship between yin deficiency syndrome and various organs is in the order of kidney,spleen,gallbladder,liver,heart,and stomach.The syndrome element of yin deficiency has the highest correlation with being easily frightened,excessive thinking,indecisiveness,repetitive behavior,and groundless worry.The kidney syndrome has the highest correlation with the symptoms such as being easily scared,unfounded worry,repetitive actions,excessive rumination,and restlessness.Conclusion The core pathological pattern of GAD is kidney and the core pathological pattern is yin deficiency.Kidney yin deficiency may be the core pathogenesis of GAD.

Objective To investigate the internalized weight bias and weight control beliefs regarding the current status,influencing factors,and impacts on eating behaviors of women losing weight. Methods A convenient sampling method was used to select the females who underwent physical examination in the Peking Union Medical College Hospital from August to December 2023.The general information questionnaire,Weight Bias Internalization Scale,Weight Control Belief Questionnaire,and Chinese version of Three-Factor Eating Questionnaire were utilized for investigation.Latent class analysis was conducted to explore the potential categories based on the characteristics of internalized weight bias and weight control beliefs.Univariate analysis and multiple-factor linear regression analysis were performed to explore influencing factors of potential categories and their effects on eating behaviors. Results A total of 518 subjects were included in this study.The internalized weight bias and weight control beliefs could be classified into three potential categories: low weight bias tolerance type(53.7%),medium weight bias persistence type(29.5%),and high weight bias overcritical type(16.8%).Logistic regression analysis showed that body mass index,unmarriage,and divorce were the influencing factors of the high weight bias overcritical type.The scores of non-control eating and emotional eating in medium weight bias persistence type and high weight bias overcritical type were higher than those in low weight bias tolerance type(all P<0.001). Conclusions The attitudes of Chinese women towards body weight can be categorized into low weight bias tolerance type,medium weight bias persistence type,and high weight bias overcritical type.The high weight bias overcritical type is characterized by high weight bias and high personal responsibility attribution,and it is closely associated with negative eating behaviors.A high body mass index,unmarriage,and divorce are high-risk factors for developing the high weight bias overcritical type.
Humans ; Female ; Feeding Behavior/psychology* ; Surveys and Questionnaires ; Adult ; Body Mass Index ; Weight Loss ; Body Weight ; Body Image ; Middle Aged ; Weight Prejudice