Perceptual decision making refers to the process by which individuals make choices and judgments based on sensory information, serving as a fundamental ability for human adaptation to complex environments. While traditional research has focused on perceptual decision making in isolated contexts, growing evidence highlights the profound influence of social contexts prevalent in real-world scenarios. As a crucial factor supporting individual survival and development, social context not only provides rich information sources but also shapes perceptual decision making through top-down processing mechanisms, prompting researchers to recognize the inherently social nature of human decisions. Empirical studies have demonstrated that social information, such as others’ choices or group norms, can systematically bias individuals’ perceptual decisions, often manifesting as conformity behaviors. Social influence can also facilitate performance under certain conditions, particularly when individuals can accurately identify and adopt high-quality social information. The impact of social context on perceptual decisions is modulated by a variety of external and internal factors, including group characteristics(e.g., group size, response consistency), attributes of peers (e.g., familiarity, social status, distinctions between human and artificial agents), as well as individual differences such as confidence, personality traits, and developmental stage. The motivations driving social influence encompass three primary mechanisms: improving decision accuracy through informational influence, gaining social acceptance through normative influence, and maintaining positive self-concept. Recent computational approaches have employed diverse theoretical frameworks to provide valuable insights into the cognitive mechanisms underlying social influence in perceptual decision making. Reinforcement learning models demonstrate how social feedback shapes future choices through reward-based updating. Bayesian inference frameworks describe how individuals integrate personal beliefs with social information based on their respective reliabilities, dynamically updating beliefs to optimize decisions under uncertainty. Drift diffusion models offer powerful tools to decompose social influence into distinct cognitive components, allowing researchers to differentiate between changes in perceptual processing and shifts in decision criteria. Collectively, these models establish a comprehensive methodological foundation for disentangling the multiple pathways by which social context shapes perceptual decisions. Neuroimaging and electrophysiological studies provide converging evidence that social context influences perceptual decision making through multi-level neural mechanisms. At early perceptual processing stages, social influence modulates sensory evidence accumulation in parietal cortex and directly alters primary visual cortex activity, while guiding selective attention to stimulus features consistent with social norms through attentional alignment mechanisms. At higher cognitive levels, the reward system (ventral striatum, ventromedial prefrontal cortex) is activated during group-consistent decisions; emotion-processing networks (anterior cingulate cortex, insula, amygdala) regulate experiences of social acceptance and rejection; and mentalizing-related brain regions (dorsomedial prefrontal cortex, temporoparietal junction) support inference of others’ mental states and social information integration. These neural circuits work synergistically to achieve top-down multi-level modulation of perceptual decision making. Understanding the mechanisms by which social context shapes perceptual decision making has broad theoretical and practical implications. These insights inform the optimization of collective decision-making, the design of socially adaptive human-computer interaction systems, and interventions for cognitive disorders such as autism spectrum disorder and anorexia nervosa. Future studies should combine computational modeling and neuroimaging approaches to systematically investigate the multi-level and dynamic nature of social influences on perceptual decision making.

Objective To investigate the attributable risk(AR)of Acinetobacter baumannii(AB)infection in criti-cally ill patients.Methods A multicenter retrospective cohort study was conducted among adult patients in inten-sive care unit(ICU).Patients with AB isolated from sterile body fluid and confirmed with AB infection in each cen-ter were selected as the infected group.According to the matching criteria that patients should be from the same pe-riod,in the same ICU,as well as with similar APACHE Ⅱ score(±5 points)and primary diagnosis,patients who did not infect with AB were selected as the non-infected group in a 1:2 ratio.The AR was calculated.Results The in-hospital mortality of patients with AB infection in sterile body fluid was 33.3％,and that of non-infected group was 23.1％,with no statistically significant difference between the two groups(P=0.069).The AR was 10.2％(95％CI:-2.3％-22.8％).There is no statistically significant difference in mortality between non-infected pa-tients and infected patients from whose blood,cerebrospinal fluid and other specimen sources AB were isolated(P＞0.05).After infected with AB,critically ill patients with the major diagnosis of pulmonary infection had the high-est AR.There was no statistically significant difference in mortality between patients in the infected and non-infec-ted groups(P＞0.05),or between other diagnostic classifications.Conclusion The prognosis of AB infection in critically ill patients is highly overestimated,but active healthcare-associated infection control for AB in the ICU should still be carried out.

Ankylosing spondylitis (AS) combined with lower cervical fracture is often categorized into unstable fracture, with a high incidence of neurological injury and a high rate of disability and morbidity. As factors such as shoulder occlusion may affect the accuracy of X-ray imaging diagnosis, it is often easily misdiagnosed at the primary diagnosis. Non-operative treatment has complications such as bone nonunion and the possibility of secondary neurological damage, while the timing, access and choice of surgical treatment are still controversial. Currently, there are no clinical practice guidelines for the treatment of AS combined with lower cervical fracture with or without dislocation. To this end, the Spinal Trauma Group of Orthopedics Branch of Chinese Medical Doctor Association organized experts to formulate Clinical guidelines for the treatment of ankylosing spondylitis combined with lower cervical fracture in adults ( version 2024) in accordance with the principles of evidence-based medicine, scientificity and practicality, in which 11 recommendations were put forward in terms of the diagnosis, imaging evaluation, typing and treatment, etc, to provide guidance for the diagnosis and treatment of AS combined with lower cervical fracture.

AIM To investigate the variation rules of main secondary metabolites in Hedysari Radix before and after rubbing strip.METHODS UPLC-MS/MS was adopted in the content determination of formononetin,ononin,calycosin,calycosin-7-glucoside,medicarpin,genistein,luteolin,liquiritigenin,isoliquiritigenin,vanillic acid,ferulic acid,γ-aminobutyric acid,adenosine and betaine,after which cluster analysis,principal component analysis and orthogonal partial least squares discriminant analysis were used for chemical pattern recognition to explore differential components.RESULTS After rubbing strip,formononetin,calycosin,liquiritigenin and γ-aminobutynic acid demonstrated increased contents,along with decreased contents of ononin,calycosin-7-glucoside and vanillic acid.The samples with and without rubbing strip were clustered into two types,calycosin-7-glucoside,formononetin,γ-aminobutynic acid,vanillic acid,calycosin-7-glucoside and formononetin were differential components.CONCLUSION This experiment clarifies the differences of chemical constituents in Hedysari Radix before and after rubbing strip,which can provide a reference for the research on rubbing strip mechanism of other medicinal materials.

Objective To construct a machine learning prediction model for postoperative liver injury in patients with non-liver surgery based on preoperative and intraoperative medication indicators.Methods A case-control study was conducted on 315 patients with liver injury after non-liver surgery selected from the databases developed by 3 large general hospitals from January 2014 to September 2022.With the positive/negative ratio of 1 ∶3,928 cases in corresponding period with non-liver surgery and without liver injury were randomly matched as negative control cases.These 1243 patients were randomly divided into the modeling group(n=869)and the validation group(n=374)in a ratio of 7∶3 using the R language setting code.Preoperative clinical indicators(basic information,medical history,relevant scale score,surgical information and results of laboratory tests)and intraoperative medication were used to construct the prediction model for liver injury after non-liver surgery based on 4 machine learning algorithms,k-nearest neighbor(KNN),support vector machine linear(SVM),logic regression(LR)and extreme gradient boosting(XGBoost).In the validation group,receiver operating characteristic(ROC)curve,precision-recall curve(P-R),decision curve analysis(DCA)curve,Kappa value,sensitivity,specificity,Brier score,and F1 score were applied to evaluate the efficacy of model.Results The model established by 4 machine learning algorithms to predict postoperative liver injury after non-liver surgery was optimal using the XGBoost algorithm.The area under the receiver operating characteristic curve(AUROC)was 0.916(95%CI:0.883～0.949),area under the precision-recall curve(AUPRC)was 0.841,Brier score was 0.097,and sensitivity and specificity was 78.95%and 87.10%,respectively.Conclusion The postoperative liver injury prediction model for non-liver surgery based on the XGBoost algorithm has effective prediction for the occurrence of postoperative liver injury.

ObjectiveAlveolar macrophages (AMs) are critical for maintaining the homeostasis of pulmonary microenvironment. They process surfactants to ensure alveoli patency, and also serve as the first line of immune defense against pathogen invasion. Available studies have shown that monocyte-derived AMs continuously release pro-inflammatory cytokines and chemokines, recruiting other immune cells to the damaged area during pulmonary fibrosis. These monocyte-derived AMs maintains and amplifies inflammation, playing a negative role in pulmonary fibrosis progression. Current researches have predominantly focused on the gene expression levels of AMs in pulmonary fibrosis microenvironment, with less emphasis on the function and regulation of proteins. This study aims to investigate the differentially expressed proteins (DEPs) of AMs under normal physiological conditions and after pulmonary fibrosis, in order to gain a more comprehensive understanding of the role of AMs in the progression of pulmonary fibrosis. MethodsFirstly, the construction of bleomycin-induced pulmonary fibrosis mouse models was evaluated through using measurements such as body mass, lung coefficient, lungwet-to-dry mass ratio, H&E staining and Masson staining. Subsequently, AMs from both the saline controls and the pulmonary fibrosis models (2.5×10⁵ cells per sample) were collected using FACS sorting, and protein expression profiles of these cells were obtained through label-free proteomics approach. The quality of the proteomic data was assessed by comparing our saline control proteomic data with public proteomic data of physiological AMs. Thirdly, DEPs analysis between the saline controls and the bleomycin groups was carried out using R package Prostar. Functional enrichment analyses of significantly upregulated DEPs were performed using R package Clusterprofiler for GO and KEGG pathways. Finally, the STRING database was used to explore the protein-protein interaction networks related to phagocytosis regulation, inflammatory response regulation, andI-κB/NF-κB signaling pathway. The expression levels of Tlr2 and Pycard were detected respectively by FACS and western blotting. ResultsCompared to the saline controls, mice in the bleomycin groups exhibited a lower average body mass, extensive infiltration of inflammatory cells, and deposition of collagen in the lungs. This indicates that bleomycin successfully induced pulmonary fibrosis in mouse models. The proteomic data of AMs obtained from these models was of high quality and fulfilled the research requirements. A comprehensive analysis showed that 778 proteins were upregulated in pulmonary fibrosis groups compared with control groups. Moreover, the signal pathways enriched in up-regulated DEPs were related to the I‑κB/NF‑κB pathway, inflammatory response regulation, phagocytosis regulation, TGF-β signaling, and HIF-1 pathway, indicating that AMs in pulmonary fibrosis microenvironment exerted pro-inflammatory and pro-fibrotic functions. Protein-protein interaction network analysis of the DEPs suggested that the interactions between Tlr2 and Pycard were control nodes for the pro-inflammatory phenotype of AMs, thereby contributing to pulmonary fibrosis progression. Further validation by FACS and Western blotting respectively confirmed that the expression levels of Tlr2 and Pycard in AMs were significantly increased after pulmonary fibrosis. ConclusionThis study investigates the changes in the protein expression profile of AMs in the pulmonary fibrosis microenvironment. The results show that AMs notably enhanced the activity of various pathways associated with inflammation and fibrosis, suggesting that the interaction between Tlr2 and Pycard serves as a key control node for the highly pro-inflammatory behavior of AMs.

In recent years, the research focus on health-related behavior and chronic non-communicable diseases has shifted from the analysis on independent effects of multiple causes on a single outcome to the evaluation the complex relationships between multiple causes and multiple effects. Complex network theory, an important branch of system science, considers the relationships among factors in a network and can reveal how health-related behaviors interact with chronic diseases through a series of complex network models and indicators. This paper summarizes the definition and development of complex network theory and its commonly used models, indicators, and case studies in the field of health-related behavior and chronic disease to promote the application of complex network theory in the field of health and provide reference and tools for future research of the relationship between health-related behavior and chronic disease.

To investigate the effects of ORF9 gene of porcine circovirus type 2(PCV2)on PK-15,eu-karyotic expression plasmid was constructed and transfected into PK-15 cells,and the effects of overexpression of ORF9 on proliferation,apoptosis and immunization of PK-15 cells were exam-ined by flow cytometry and qRT-PCR.The results showed that ORF9 gene overexpression signifi-cantly up-regulated the expression levels of the ER stress marker gene GRP78,increased the num-ber of S phase cells,accelerated cell cycle progression,increased the apoptosis rate of PK-15 cells,up-regulated the expression levels of apoptosis-related genes caspase-3,caspase-8,caspase-9,p53 and Bax(P＜0.01),down-regulated the expression levels of apoptosis-related genes Bcl-2,up-reg-ulated the expression levels of immune-related genes 1L-8,IL-10,NF-κB and TNF-α(P＜0.01),and down-regulated the expression levels of immune-related genes IL-2,IFN-β and IL-12(P＜0.01).The above results indicate that ORF9 gene may promote the proliferation and apoptosis of PK-15 cells and play a role in the escape process of PK-15 cells.

In order to understand the genomic characteristics and genetic variation and strain type of infectious bursal disease virus(IBDV)isolate GZGY2022,which caused the death of chickens in Guizhou farm,primers were designed to amplify the whole genome of the isolate,and genetic evo-lution and strain type analysis were performed after cloning and sequencing.The results showed that the A and B segments of IBDV genome were 3 260,2 827 bp,respectively,encoding VP2-VP5 and VP1 genes.The nucleotide sequence homology between the A and B segments of this strain and the VvIBDV were 96.2％-98.7％and 87.7％-98.9％,respectively,which is the highest with NN1172 strain,83.1％-94.7％and 90.1％-91.0％with other strains.The results of genetic evolution and strain type study showed that IBDV strains can be divided into 6 branches according to antigen and virulence,and the A and B segments of the strain were clustered in the evolutionary branch of VvIBDV,and the strain was A3B3 genotype according to the new genotype classification method.The results of amino acid sequence analysis showed that there were 3 and 7 unique amino acid site variations in the A and B segments of the strain,respectively,and 13 unique characteristic amino acid sites in the coding region of the full-length genome were consistent with VvIBDV.The VP2 sequence of segment A has 19 characteristic amino acid identical with VvIBDV,among which hyper variable regions 222A,242I,253Q,256I,279D,284A,294I and 299S were characteristic ami-no acid sites of the VvIBDV,and the heptapeptide region sequence SWSASGS was consistent with the virulent strain.The VP1 sequence of segment B has 10 characteristic amino acid identical with VvIBDV,among which 61I,145T and 287A were the characteristic amino acid sites of the VvIB-DV.In addition,the nucleotide sequence GGTGCC of 777-782 did not form the restriction endo-nuclease site of Kpn Ⅰ,and combined with the triplet site 145/146/147(TEG),the segment B was consistent with the NN1172 strain,showed that its virulence was slightly weaker than that of the B2 strain of VvIBDV.The results of recombination analysis showed that there were no breaks and recombination sites in the sequence of the strain,and no recombination event occurred.In summa-ry,this study found that GZGY2022 strain belonged to the A3B3 genotype non-recombinant VvIB-DV strain,and its special amino acid sites were consistent with the molecular characteristics of VvIBDV.This study lays the foundation for further exploring the genomic characteristics and path-ogenicity of VvIBDV.

Objective To investigate the effect of interaction between serum neutrophil gelatinase-associat-ed lipid carrier protein(NGAL)and complications after percutaneous coronary intervention(PCI)on poor prognosis in patients with ST-segment elevation myocardial infarction(STEMI),and to provide reference for improving prognosis.Methods From January 2017 to January 2020,a total of 400 STEMI patients admitted to this hospital were selected and divided into good prognosis group(n=378)and poor prognosis group(n=22)according to their 2-year prognosis.Logistic regression equations were used to analyze the prognostic fac-tors of STEMI patients,and multiplicative and additive effects were established to analyze the effect of the in-teraction between serum NGAL and post-PCI complications on poor prognosis.Results Gender,age,heart rate,Gensini score,number of lesions ≥2,Killip grade,complications after PCI and NGAL level after PCI were associated with poor prognosis in STEMI patients(P＜0.05).Gender(female),age,heart rate,Gensini score,number of lesions ≥2,Killip grade,complications after PCI and NGAL level after PCI were all inde-pendent influencing factors of poor prognosis in STEMI patients(P＜0.05).There was interaction between serum NGAL after PCI and complications after PCI,both of which had an adverse effect on the prognosis of STEMI patients(P＜0.05).There was no multiplicative interaction between serum NGAL and complications after PCI(P＞0.05).When serum NGAL and complications after PCI were exposed at the same time,the risk of poor prognosis in STEMI patients was higher than the sum of the two exposures alone,and the interaction was 2.611 times the sum of the effects of the two exposures alone.Conclusion Serum NGAL and postopera-tive complications after PCI are independent factors influencing poor prognosis in STEMI patients.There is a additive interaction between the two factors,and exposure can increase the risk of poor prognosis.