Objective: To identify influencing factors associated with the prognosis of sepsis patients receiving blood component transfusion, and to provide a more rational and scientific transfusion strategy for clinical management. Methods: Clinical data of 232 patients with sepsis treated at the Second Xiangya Hospital of Central South University between January 2022 and December 2023 were retrospectively analyzed. These patients were categorized into the transfusion group (n=64) and the non-transfusion group (n=168) based on whether they received transfusions, and the patients in the transfusion group were further divided into non-survivor group (n=26) and survivor group (n=38) based on their survival outcome. Baseline characteristics and clinical characteristics were compared between two groups. Factors impacting the prognosis of sepsis patients undergoing blood component transfusion were identified using logistic regression. Results: Compared to the non-transfusion group, the transfusion group showed significantly higher levels of coagulation indicators (prothrombin time, activated partial thromboplastin time, international normalized ratio, D-dimer) and inflammatory markers (C-reactive protein, procalcitonin, interleukin-6), while the level of hemoglobin, platelet, lymphocyte, fibrinogen, albumin, blood glucose, and oxygen saturation were significantly lower (P<0.05). The [M(P , P )] for C-reactive protein (mg/L), hemoglobin (g/L), and platelet count (×10 /L) in the transfusion vs non-transfusion groups were 178.0(156.1-178) vs 102.7(74.0-119.6), 88.5(72.3-113.0) vs 110.5(101-121.8), and 63.0(26.5-156.5) vs 202.5(108.3-286.8), respectively (all P<0.05). Logistic regression analysis revealed that hemoglobin level, platelet count, lactate concentration, and the storage duration of transfused red blood cells were independent risk factors affecting the survival outcomes of sepsis patients receiving transfusions (P<0.05). In septic transfusion patients, the [M(P , P )] lactate concentration (mmol/L) and RBC storage time (d) in the non-survivor vs survivor groups were 3.5(1.9-7.7) vs 2.1(1.3-3.5), 18.0 (13.0-18.0) vs 12.0(9.0-14.0), respectively (both P<0.05). Conclusion: Compared to non-transfused sepsis patients, those receiving transfusions exhibited poorer baseline conditions, more severe infections, and worse survival outcomes. More importantly, the study found that the timing of transfusion decisions and the quality control of blood products (such as storage duration) may directly impact patient prognosis, providing critical evidence for optimizing transfusion strategies in septicemia patients.

This study aims to screen the diagnostic biomarkers for fecal antigen of Echinococcus granulosus(E.granulosus)in dogs with high specificity and sensitivity.The sheep-derived EgPSC artificially infected dogs were collected,and the negative and positive fecal samples of dogs with E.granulosus were prepared by arecoline hydrobromide leakage method.Polyclonal antibody,negative fecal antigen-polyclonal antibody conjugates and positive fecal antigen-polyclonal antibody conju-gates were purified by ammonium sulfate precipitation and affinity chromatography,three groups of samples were detected by ELISA and Western blot,LC-MS/MS and bioinformatics analysis were performed on the three groups of samples.The positive fecal antigen-polyclonal antibody con-jugate was used as the treatment group,the polyclonal antibody and the negative fecal antigen-polyclonal antibody conjugates were used as the control groups to screen the unique peptides of the treatment group.ELISA and Western blot showed that only the positive fecal antigen-polyclonal antibody conjugates were positive.According to LC-MS/MS and bioinformatics analysis,11 unique peptides were screened out only in the treatment group.Among them,3 proteins were related to E.granulosus,namely dysferlin,integrator complex 9 and diagnostic antigen gp50,which were mem-brane-associated proteins,INT complex components and diagnostic antigens.This study has pre-liminarily screened out three candidate canine E.granulosus fecal antigen diagnostic markers,pro-viding a reference for further exploration of diagnostic standards for E.granulosus,screening of echinococcosis target genes,and vaccine development.

Objective:To investigate the correlations between physical, psychological and social frailty in elderly patients with multimorbidity.Methods:This study utilized a mixed method. A questionnaire survey was conducted from February to June 2024, among elderly patients with multimorbidity attending 4 primary health care centers in urban Beijing selected by the convenience sampling method. The FRAIL Frailty Assessment Scale, WHO-5 Index of Well-Being Scale, and HALFT Scale were used to assess the patients′ physical, psychological, and social frailty, respectively. Spearman correlation analysis was used to analyze the correlation between different dimensions of frailty in elderly with multimorbidity. Logistic regression model was used to analyze the factors influencing physical, psychological and social frailty. The elderly with multimorbidity who were assessed to have at least 1 or more types of frailty in the quantitative study were selected for in-depth interviews in the form of online and offline combination. The topics of in-depth interview included the real experience of the different dimensions of frailty, the possible causes and the difficulties caused. The sample size was determined according to the principle of information saturation. Thematic analysis was used to summarize, code and analyze the interview data.Results:A total of 919 participants were included in the quantitative study, with a mean age of (74.09±6.03) years, 329(35.80%) were males and 590(64.20%) were females. The prevalence of physical, psychological, and social frailty was 17.85%(164/919), 21.44%(197/919), 11.21%(103/919), respectively. A total of 21 participants were included in the qualitative study, with a mean age (76.90±5.13)years, 5(23.81%) males and 16(76.19%) females. Spearman correlation analysis showed that physical and psychological frailty were moderately correlated ( r=0.311, P<0.001), psychological and social frailty were weakly correlated ( r=0.218, P<0.001), and physical and social frailty were weakly correlated ( r=0.267, P<0.001). Logistic regression analysis showed that the age, the number of multimorbidities, the psychological frailty and social frailty were the influencing factors for physical frailty (all P<0.05). The gender, number of multimorbidity, type of medication taken, physical frailty and social frailty were influencing factors of psychological frailty (all P<0.05). And age, number of multimorbidities, physical frailty and psychological frailty were influencing factors of social frailty (all P<0.05). A total of 3 themes were extracted through in-depth interviews, namely, "physical and psychological frailty are interrelated""physical and social frailty are interrelated", and "psychological and social frailty are interrelated". Conclusions:The physical, psychological, and social frailty in elderly patients with multimorbidity interacts with each other. Whereas the number of multimorbidities is a common risk factor for all three.

ObjectiveThis study aims to develop a prediction model for the compatibility of Chinese medicinal pairs based on Graph Convolutional Networks （GCN）， named HC-GCN. The model integrates the properties of herbs with modern pharmacological mechanisms to predict pairs with specific therapeutic effects. It serves as a demonstration by applying the model to predict and validate the efficacy of blood-activating herb pairs. MethodsThe training dataset for herb pair prediction was constructed by systematically collecting commonly used herb pairs along with their characteristic data， including Qi， flavor， meridian tropism， and target genes. Integrating traditional characteristics of herb with modern bioinformatics， we developed an efficacy-oriented herb pair compatibility prediction model （HC-GCN） using graph convolutional networks （GCN）. This model leverages machine learning to capture the complex relationships in herb pair compatibility， weighted by efficacy features. The performance of the HC-GCN model was evaluated using accuracy （ACC）， recall， precision， F1 score （F1）， and area under the ROC curve （AUC）. Its predictive effectiveness was then compared to five other machine learning models： eXtreme Gradient Boosting （XGBoost）， logistic regression （LR）， Naive Bayes， K-nearest neighbor （KNN）， and support vector machine （SVM）. ResultsUsing herb pairs with blood-activating effects as a demonstration， a prediction model was constructed based on a foundational dataset of 46 blood-activating herb pairs， incorporating their Qi， flavor， meridian tropism， and target gene characteristics. The HC-GCN model outperforms other commonly used machine learning models in key performance metrics， including ACC， recall， precision， F1 score， and AUC. Through the predictive analysis of the HC-GCN model， 60 herb pairs with blood-activating effects were successfully identified. Among of these potential herb pairs， 44 include at least one herb with blood-activating effects. ConclusionIn this study， we established an efficacy-oriented compatibility prediction model for herb pairs based on GCN by integrating the unique characteristics of traditional herbs with modern pharmacological mechanisms. This model demonstrated high predictive performance， offering a novel approach for the intelligent screening and optimization of traditional Chinese medicine prescriptions， as well as their clinical applications.

Objective: To study the correlation between short sleep duration and screening myopia among primary and middle school students in Beijing, so as to provide a scientific basis for the comprehensive prevention and control of myopia among students. Methods: Using a stratified cluster random sampling, 25 593 primary and middle school students from 16 districts of Beijing were selected from September to November 2023. The National Common Diseases and Health Influencing Factors Monitoring Survey Questionnaire was used to conduct a questionnaire survey, and visual acuity was tested according to the Specification for the Screening of Refractive Error in Primary and Middle School Students. The reporting rates of short sleep duration and detection rates of screening myopia among primary and middle school students were compared using the Chi square test. Binary Logistic regression was used to analyze the correlation between short sleep duration and screening myopia. Results: About 68.63% of students reported short sleep duration. There was a statistically significant difference in the reporting rate of short sleep duration among students in different school stages ( χ 2=981.18, P <0.01), with the lowest reporting rate of vocational high school students (47.07%) and the highest reporting rate of ordinary high school students (76.17%). The detection rates of screening myopia among primary school students ( 57.09% ) and middle school students (76.53%) who reported short sleep duration were higher than those who reported enough sleep duration (52.65%, 71.94%), with satistically significant differences ( χ 2=14.83, 17.96, P <0.01). The results of binary Logistic regression analysis showed that primary and middle school students with short sleep duration had a higher risk of developing screening myopia, compared to students with enough sleep duration ( OR =1.25); after adjusting for confounding factors such as educational stage, gender, region, boarding situation, primary and secondary school students with short sleep duration still had a higher risk of screening myopia ( OR =1.26) ( P <0.01). The analysis results stratified by educational stage showed that primary school students from grades 4-6 and middle school students with short sleep duration had a higher risk of screening myopia ( OR=1.18, 1.20, P <0.01). Conclusions Primary and secondary school students in Beijing with short sleep duration sleep have a higher risk of developing screening myopia. Families, schools, and society should ensure enough sleep duration to reduce the occurrence of myopia among students.

Objective: To identify influencing factors associated with the prognosis of sepsis patients receiving blood component transfusion, and to provide a more rational and scientific transfusion strategy for clinical management. Methods: Clinical data of 232 patients with sepsis treated at the Second Xiangya Hospital of Central South University between January 2022 and December 2023 were retrospectively analyzed. These patients were categorized into the transfusion group (n=64) and the non-transfusion group (n=168) based on whether they received transfusions, and the patients in the transfusion group were further divided into non-survivor group (n=26) and survivor group (n=38) based on their survival outcome. Baseline characteristics and clinical characteristics were compared between two groups. Factors impacting the prognosis of sepsis patients undergoing blood component transfusion were identified using logistic regression. Results: Compared to the non-transfusion group, the transfusion group showed significantly higher levels of coagulation indicators (prothrombin time, activated partial thromboplastin time, international normalized ratio, D-dimer) and inflammatory markers (C-reactive protein, procalcitonin, interleukin-6), while the level of hemoglobin, platelet, lymphocyte, fibrinogen, albumin, blood glucose, and oxygen saturation were significantly lower (P<0.05). The [M(P , P )] for C-reactive protein (mg/L), hemoglobin (g/L), and platelet count (×10 /L) in the transfusion vs non-transfusion groups were 178.0(156.1-178) vs 102.7(74.0-119.6), 88.5(72.3-113.0) vs 110.5(101-121.8), and 63.0(26.5-156.5) vs 202.5(108.3-286.8), respectively (all P<0.05). Logistic regression analysis revealed that hemoglobin level, platelet count, lactate concentration, and the storage duration of transfused red blood cells were independent risk factors affecting the survival outcomes of sepsis patients receiving transfusions (P<0.05). In septic transfusion patients, the [M(P , P )] lactate concentration (mmol/L) and RBC storage time (d) in the non-survivor vs survivor groups were 3.5(1.9-7.7) vs 2.1(1.3-3.5), 18.0 (13.0-18.0) vs 12.0(9.0-14.0), respectively (both P<0.05). Conclusion: Compared to non-transfused sepsis patients, those receiving transfusions exhibited poorer baseline conditions, more severe infections, and worse survival outcomes. More importantly, the study found that the timing of transfusion decisions and the quality control of blood products (such as storage duration) may directly impact patient prognosis, providing critical evidence for optimizing transfusion strategies in septicemia patients.

Objective To investigate the differences in oral and gut microbiota composition among children aged 3-5 years with varying body mass index(BMI)levels in Urumqi,and to provide a scientific basis for early microbiological warning and intervention strategies for childhood obesity.Methods A total of 40 children aged 3-5 years were enrolled.Based on their BMI percentiles,the participants were divided into 4 groups,including the underweight,normal weight,overweight,and obesity groups(n=10 per group).A total of 80 saliva and fecal samples were collected.Microbial community structures were analyzed using 16S rRNA gene sequencing,followed by bioinformatics and statistical analyses.Results Oral microbiota richness,as measured by Chao1 and observed-species indices,differed significantly among the four groups(P=0.004 7 and P=0.005 4,respectively),whereas no significant difference in gut microbiota diversity was observed(P>0.05).Beta diversity analysis revealed a distinct separation in oral microbiota between the normal-weight weight and other groups.At the genus level,obese children exhibited increased abundance in oral Leptotrichia,underweight children showed enrichment of gut Bacteroides,and overweight children showed increased abundance in gut Faecalibacterium and Blautia.Linear discriminant analysis effect size(LEfSe)analysis identified multiple biomarkers,including Prevotellaceae in the oral microbiota of normal-weight children,Catonella in the oral microbiota of obese children,and Clostridiales,Lachnospiraceae,and Hungatella in the gut microbiota of underweight children.Metabolic pathways related to lipopolysaccharide synthesis and amino acid metabolism were significantly upregulated in the microbiota of overweight and obese children.Conclusion Significant differences are observed in the oral and gut microbiota composition among children aged 3-5 years of different BMI levels in Urumqi.Oral microbiota show greater sensitivity to BMI changes.Specific genera,such as Catonella,Leptotrichia,and Prevotellaceae,may be involved in the development of obesity.The microbiota metabolic pathways in children with high BMI are characterized by the core features of inflammation activation and lipid metabolism dysregulation.

The research on the cardiovascular toxicity of air pollutants is in urgent need of collaborative innovation across multiple models. This paper systematically reviewed the advantages and limitations of four principal research models of cardiotoxicity, including epidemiological model, mammalian model, zebrafish model, and in vitro model. Epidemiological models have been used to demonstrate a significant correlation between exposure to PM_2.5 and both the incidence and mortality of cardiovascular diseases within populations; however, these models face challenges in establishing causal inferences and interpreting individual mechanisms. Mammalian models have been applied to elucidate the pathogenic mechanisms of PM_2.5 at both the systemic and organ-specific levels, yet they encounter difficulties related to interspecies differences and throughput constraints. Zebrafish models, with their transparent embryos and observable development, offer a distinctive opportunity for high-throughput screening and mechanistic investigation of PM_2.5-induced cardiac developmental toxicity. Nonetheless, their cardiac physiological structure diverges from that of mammals, limiting their capacity to accurately model chronic conditions such as coronary heart disease. In vitro models, particularly human heart organoids and chip technologies, have provided profound insights into the direct toxic mechanisms of PM_2.5, including disruptions in calcium homeostasis, cellular senescence, and electrophysiological irregularities at the cellular and molecular levels. Despite these advancements, the complexity and developmental maturity of these models present challenges to their broader application. This paper proposed that the key to overcoming the bottlenecks of single models lies in the construction of an integrated evaluation system that combines “epidemiological studies, mammalian models, zebrafish models, and in vitro models”. By focusing on three aspects, namely model integration, technological convergence, and policy support, it is intended to collaboratively address issues such as standardization of multi-model data, simulation of complex exposure scenarios and susceptible life stages, and transformation pathways. This will provide innovative methodological support for the analysis of the cardiotoxic mechanisms of air pollutants, the assessment of environmental health impacts, and the formulation of precise prevention and control strategies.

In situ bioimaging is a powerful tool for directly observing the localization, expression, and interactions of nucleic acids or protein targets within cells, providing essential insights into cell function and disease mechanisms. In recent years, the CRISPR/Cas9 system, a revolutionary gene-editing tool, has been applied to develop efficient in situ imaging techniques. This paper reviews recent CRISPR/Cas9-based imaging methods utilizing Cas9 protein, engineered single-guide RNA (sgRNA), and coupled fluorescent tags, and compares their application in living and fixed cells. It focuses on the specificity, signal amplification efficiency, and multi-modal imaging capabilities of these methods, with further discussion based on current research, aiming to offer a comprehensive overview of CRISPR/Cas9-based in situ bioimaging techniques, with some valuable reference and guidance for research in related fields.

Objective: To explore the association between CDKAL1 rs35261542, FAIM2 rs 3205718, and VGLL4 rs 2574704 polymorphisms with childhood obesity and related metabolic phenotypes to provide evidence for personalized prevention and management strategies. Methods: Based on the 2023 Long term Nutritional Health Effects of Early Childhood Nutrition Package Intervention project, the study enrolled 1 078 children aged 5-7 years from four counties in Henan (Songxian and Ruyang countries) and Guizhou (Guiding and Fuquan countries) provinces. Using BMI Z scores, 87 overweight and obese(OVOB) children were selected and matched by sex, age, and BMI Z score with 117 normal weight controls. Participants were further stratified into four metabolic phenotype groups: metabolically healthy normal weight (MHNW, n =51), metabolically unhealthy normal weight (MUNW, n =66), metabolically healthy obesity (MHO, n =31) and metabolically unhealthy obesity (MUO, n =56) based on four conventional cardiometabolic risk factor (CR) criteria. Data were collected through questionnaires, anthropometric measurements, serum biochemical tests, and KASP genotyping. The distribution of three genetic polymorphisms ( CDKAL1 rs35261542, FAIM2 rs3205718, VGLL4 rs 2574704) across metabolic subgroups was analyzed. Multivariate Logistic regression models assessed associations between these polymorphisms and obesity/metabolic phenotypes. Results: Multivariate Logistic regression analysis showed that Homozygous mutant AA genotype of CDKAL1 rs 35261542 was positively associated with OVOB( OR =3.63), MHO ( OR =11.04), MUO ( OR = 4.88 ) ( P <0.05). Homozygous TT genotype of FAIM2 rs 3205718 increased OVOB risk ( OR =4.44, P <0.05) but showed no association with metabolic phenotypes ( P >0.05). Homozygous mutant TT of VGLL4 rs 2574704 reduced the risks of MHO and MUO ( OR = 0.30, 0.24， P <0.05). Cumulative genetic effects analysis demonstrated carriers of 1 or 2 risk genotypes of rs 35261542 and rs 3205718 had progressively higher OVOB risk ( OR =2.53, 20.79), and the combination of rs 35261542 and rs 2574704 increased risks for both MHO ( OR =8.50) and MUO ( OR =5.00) ( P <0.05). Conclusions The AA genotype of rs 35261542 ( CDKAL1 ) positively correlates with childhood obesity and metabolic abnormalities. The TT genotype of rs 3205718 ( FAIM 2) increases obesity risk but not metabolic phenotypes. The TT genotype of rs 2574704 ( VGLL 4) shows protective effects against metabolic dysfunction. Risk genotypes exhibit dosedependent cumulative effects on obesity and metabolic outcomes.