Objective: To explore the association between the HLA antibody positivity rate in female blood donors and pregnancy history, number of pregnancies, interval from the last pregnancy to blood donation, and age, to identify associated variables using a univariate generalized additive model (GAM), and to further analyze the predictive role of characteristic variables for HLA antibody positivity using a random forest model. Methods: HLA antibody detection was performed on 391 female blood donors using the Luminex immunomagnetic bead method. The correlation between pregnancy-related factors and HLA antibodies was analyzed using the Chi-square test. Based on R software, a univariate GAM was first constructed to analyze the association types between characteristic variables and the HLA antibody positivity rate, followed by the construction of a random forest model to evaluate the predictive value of the variables. Results: Among the 391 female blood donors without a transfusion history, the overall HLA antibody positivity rate was 26.34%. The positivity rate in donors with a pregnancy history was significantly higher than that in those without (30.09% vs 9.72%, P<0.05), and HLA antibody positivity rate increased linearly with the number of pregnancies (P<0.05). In the univariate GAM, age and number of deliveries exhibited a non-linear association with the HLA antibody positivity rate (the positivity rate increased sharply between 25-35 years of age and stabilized after 3 deliveries). Besides, the interval from the last pregnancy to blood donation showed a linear association with the HLA antibody positivity rate, and the positivity rate decreased as the interval prolonged (P<0.05). In the random forest model, age (mean decrease gini=29.26) and interval from the last pregnancy to blood donation (mean decrease gini=22.02) were core predictive variables: age was more conducive to identifying positive samples, while the interval from the last pregnancy to blood donation was more helpful for excluding negative samples. The number of deliveries (mean decrease accuracy=16.98) made a significant contribution to predicting positive samples, whereas the number of abortions had no impact. The model had an AUC of 0.583 (95% CI: 0.593 8-0.770 2), indicating a certain predictive value. Conclusion: The associated variables identified by the univariate GAM model, including age, interval from the last pregnancy to blood donation, and number of deliveries, provide a basis for key variables in the random forest model. All three variables have predictive value for HLA antibody positivity, which can provide evidence-based support for personalized transfusion management and stratified screening of female blood donors in this region.

The Split-Cre system consists of two inactive polypeptides: NCre and CCre, which can be recombined into an active full-length Cre under certain conditions. This system is typically used with LoxP. To develop an efficient Split-Cre system, this study used Rma intein from Rhodothermus marinus to split Cre and screened out the split site S102 which could efficiently mediate the recombination of Cre in the "Traffic Light" reporter cell line. Moreover, the S102 Split-Cre system was delivered to mice by dual-adeno-associated virus (AAV), and it was demonstrated that the efficiency of the Rma intein-mediated S102 Split-Cre system was comparable to the full-length Cre in mice. This system lays a foundation for both basic and applied research on Split-Cre.
Inteins/genetics* ; Animals ; Integrases/biosynthesis* ; Mice ; Dependovirus/metabolism* ; Bacterial Proteins/genetics* ; Recombination, Genetic ; Humans

ObjectiveTo analyze the evolutionary characteristics and genetic variations of the HA (hemagglutinin) and NA (neuraminidase) genes of influenza A(H1N1) viruses in Shanghai during 2024, to investigate their transmission patterns, and to evaluate their potential impact on vaccine effectiveness. MethodsFrom January to October 2024, throat swab specimens were collected from influenza like illness (ILI) patients at 4 hospitals in Shanghai. Real-time fluorescence ploymerase chain reaction (RT-PCR) was used for virus detection and isolation of H1N1 influenza viruses. Forty influenza A(H1N1) virus strains were sequenced using Illumina NovaSeq 6000 platform, followed by phylogenetic analyses, genetic distance analysis, and amino acid variation analyses of HA and NA genes. ResultsPhylogenetic tree of the HA and NA genes revealed that the 40 influenza A(H1N1) virus strains circulating in Shanghai in 2024 exhibited no significant geographic clustering, with a broad origin of strains and complex transmission chains. Genetic distance analyses demonstrated that the average intra-group genetic distances of HA and NA genes among the Shanghai strains were 0.005 1±0.000 6 and 0.004 6±0.000 6, respectively, which were comparable to or higher than those observed in global surveillance strains. Both HA and NA genes displayed frequent mutations. Compared to the 2023‒2024 and 2024‒2025 Northern Hemisphere A(H1N1) vaccine strains (WHO-recommended), the HA proteins of 40 Shanghai strains exhibited amino acid substitutions at positions 120, 137, 142, 169, 216, 223, 260, 277, 356 and 451, with critical mutations at positions 137 and 142 located within the Ca2 antigenic determinant. Furthermore, mutations in the NA protein were observed at positions 13, 50, 200, 257, 264, 339 and 382. ConclusionThe genetic background of the 2024 Shanghai influenza A(H1N1) virus strains is complex and diverse, and antigenic variation may affect vaccine effectiveness. Therefore, it is recommended to enhance genomic surveillance of influenza viruses, evaluate vaccine suitability, and implement more targeted prevention and control strategies against imported influenza viruses.

Background Work-related musculoskeletal disorders (WMSDs) are one of the major occupational health problems faced by bus drivers and should receive special attention. Objective To explore the associations of weekly working hours and sleep quality with neck and lower back WMSDs among bus drivers, as well as assess the potential mediating role of sleep quality. Methods From June to December 2022, we recruited bus drivers from 5 subsidiaries of the Shenzhen Bus Group by convenient sampling method. Demographic characteristics, lifestyles, and work-related features of the bus drivers were collected through a questionnaire survey. The Pittsburgh Sleep Quality Index (PSQI) scale and the Musculoskeletal Disorders Survey Questionnaire were used to assess sleep quality and WMSDs respectively. Logistic regression models were applied to analyze the associations of weekly working hours and sleep quality with WMSDs in neck and lower back. Furthermore, mediation analysis was performed to investigate the role of sleep quality in the associations between weekly work hours and neck and lower back WMSDs. Results A total of 1792 valid questionnaires were collected, with a response rate of 95.07%. Among these, 89.3% of the bus drivers worked more than 40 h per week, and the positive rate of poor sleep quality was 28.2%. The overall positive rate of WMSDs within one year before the survey was 69.12%. Among different body regions, the leading positive rates were identified in neck and lower back regions, at 55.92% and 53.68%, respectively. The logistic regression model showed that compared to the bus drivers with weekly work hours ≤40 h, those weekly worked (40~48] h (OR=1.43, 95%CI: 1.02, 2.01; OR=1.65, 95%CI: 1.17, 2.34), (48~56] h (OR=2.69, 95%CI: 1.89, 3.83; OR=2.30, 95%CI: 1.62, 3.29) and ＞56 h (OR=2.63, 95%CI: 1.86, 3.74; OR=3.23, 95%CI: 2.27, 4.62) had significantly increased risk of WMSDs in neck and lower back. Additionally, when compared to those with good sleep quality, bus drivers with poor sleep quality had higher risks of both neck and lower back WMSDs (OR=4.08, 95%CI: 3.20, 5.23; OR=4.15, 95%CI: 3.26, 5.30, respectively). Further mediation analysis indicated that sleep quality partially mediated the associations between weekly working hours and both neck and lower back WMSDs, with 36.15% and 33.68% as the respective proportion of mediation. Conclusion The positive rate of WMSDs is high among bus drivers, and the most common WMSDs occur in neck and lower back. Long working hours and poor sleep quality are significantly associated with increased risks of neck and lower back WMSDs. Sleep quality may mediate the associations of weekly working hours with neck and lower back WMSDs.

Objective To explore the correlation between stroke etiology and clinical and imaging features in patients with acute ischemic stroke(AIS)due to large vessel occlusion treated by intravascular thrombectomy.Methods A total of 213 patients with AIS and endovascular embolectomy in our hospital from Oct.2016 to Jun.2018 were enrolled retrospectively.According to the etiological classification criteria of Trial of Org 10172 in Acute Stroke Treatment(TOAST),there were 116 cases of cardioembolism and 97 cases of non-cardioembolism.Multivariate logistic regression analysis was used to screen the clinical and imaging characteristics for identifying cardioembolism and non-cardioembolism.Results Compared with non-cardioembolism AIS,cardioembolism AIS was associated with higher NIHSS scores(adjusted odds ratio[OR]=1.09,95%confidence interval[95%CI]1.01-1.18,P=0.02),atrial fibrillation(adjusted OR=76.46,95%CI 26.75-218.51,P＜0.01),absence of hypertension(adjusted OR=0.32,95%CI 0.12-0.84,P=0.02),antiplatelet drug use(adjusted OR=5.03,95%CI 1.22-20.63,P=0.03),shorter onset-to-puncture time(adjusted OR=0.998,95%CI 0.996-1.000,P=0.04),and presence of hyperdense artery sign(HAS)(adjusted OR=4.45,95%CI 1.47-13.49,P=0.01).Conclusion There are some differences in clinical and imaging characteristics between patients with cardioembolism and non-cardioembolism AIS.The occurrence of HAS suggests a higher probability of cardioembolism in AIS patients.

As one of the primary causes of death in burn patients, sepsis presents challenges in early warning and diagnosis, mainly due to its nonspecific clinical manifestations and the limitations of traditional biomarker detection efficiency. As an important carrier of intercellular information transfer, exosomes and their contents (RNAs, proteins, and metabolites) can reflect the pathophysiological status of the body, thus attracting significant attention in the field of disease diagnosis. This review aims to summarize the research advances of exosomal multi-omics (transcriptomics, proteomics, metabolomics, etc.) analysis technologies in the warning and diagnosis of burn sepsis, and explore their application potential in revealing disease mechanisms, screening specific early biomarkers, and integrating emerging bioinformatics technologies. The goal is to provide new strategies and directions for achieving the precision diagnosis and treatment of burn sepsis.

Objective:To construct fused cancer cell/neutrophil membrane-coated polydopamine nanoparticles chelated with manganese ions (Ⅱ) (PMNP@FMs) and explore the potential for targeted pancreatic cancer fluorescence imaging and MRI.Methods:Cancer cell membranes fused with neutrophil membranes were encapsulated on the surface of polydopamine nanoparticles chelated with manganese ions (Ⅱ) (PMNPs) to prepare PMNP@FMs. The morphology, structure, and MRI performance of the product were characterized. The cytotoxicity of PMNP@FMs towards human pancreatic cancer cells (PANC-1) and normal human pancreatic ductal epithelial cells (hTERT-HPNE) was evaluated using cell counting kit (CCK)-8, and in vivo toxicity was assessed in healthy mice. PANC-1 pancreatic cancer xenograft nude mouse models were established for in vivo fluorescence imaging and MRI. Data were analyzed using the independent-sample t test, repeated measures analysis of variance and the least significance difference method. Results:PMNP@FMs exhibited a core-shell structure with a diameter of (112.81±8.64) nm, negative surface charge, and good dispersibility. The T 1 relaxivity of PMNPs was 18.81±0.22, which was 4.1 times higher than that of gadopentetate dimeglumine (Gd-DTPA) (4.55±0.24; t=75.54, P<0.001). Co-culture of PMNPs and PMNP@FMs with hTERT-HPNE and PANC-1 cells for 24 h resulted in cell viability above 90% within the concentration range of 0-500 μg/ml. PMNP@FMs did not affect mouse survival and showed no apparent organ damage. In vivo fluorescence imaging and MRI revealed that PMNP@FMs accumulated highly in tumors and reached the peak 24 h post intravenous administration (relative MR signal: 1.35±0.01, fluorescence intensity: (1.20±0.25)×10 10), surpassing the peak observed in the control group (1.22±0.01, (3.87±0.50)×10 9;F values: 11.03-188.01, t values: 18.20, 5.64, all P<0.05), with hepatic metabolism being the primary route of clearance. Conclusion:PMNP@FMs demonstrate a potential for targeted pancreatic cancer fluorescence imaging and MRI, offering promising prospect for precise diagnosis of early-stage pancreatic cancer.

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Objective To explore the factors influencing cognitive frailty in elderly inpatients with chronic heart failure(CHF)during hospitalisation,8 prediction models were developed with various machine learning algorithms to identify the best model as a guidance for medical staff on clinical interventions.Methods Convenience sampling method was used to select 650 elderly CHF inpatients who stayed in our hospital between September 2023 and June 2024 as the study objects in the cross-sectional investigation.A total of 607 patients had completed the study.The patients were divided into a cognitive frailty group and a non-cognitive frailty group according to the presence or absence of cognitive frailty.Variables were initially screened using univariate analysis and stepwise Logistic regression.The total sample was then randomly divided into a training set(n=424)and a testing set(n=183)of a 7:3 ratio.Eight predictive models were created using the algorithms of neural network(NN),k-nearest neighbour(KNN),linear discriminant analysis(LDA),support vector machine(SVM),naive Bayes(NB),logistic regression,decision tree(DT)and random forest(RF)on the training set.The predictive performance of the models was compared using the data of the testing set.Results The prevalence of cognitive frailty in elderly CHF inpatients was 48.3%.Results of Logistic regression showed that age,marital status,education,body mass index,multi-morbidity,nutritional status,medication,frequency of weekly exercise and the living conditions were the key factors(P<0.05).The overall accuracy in classification of the eight predictive models ranged from 0.803 to 0.847,with F1-values of 0.778 to 0.833,precision of 0.848 to 0.897,and recall rate of 0.700 to 0.778.The area under the receiver operating characteristic curve was 0.820 to 0.901.Conclusion Of the eight predictive models,the prediction model created with LDA shows the best performance and prediction in terms of comprehensive prediction metrics,while the prediction model created with NN shows the worst performance in comprehensive prediction.

Objective To explore and analyze the adverse drug event(ADE)signals associated with pembrolizumab and to provide a reference for the real-world safety of drug use on elderly patients.Methods ADE reports of pembrolizumab in the general population and elderly population were collected from the FDA Adverse Event Reporting System(FAERS)for the period between January 1st,2019,and June 30th,2024.Multiple signal detection methods(ROR,PRR,MHRA)were employed for data mining.Classification and statistical analysis were performed using the System Organ Class(SOC)and Preferred Term(PT)from the MedDRA(Version 27.1)dictionary.Results A total of 966 signals were identified in the general population,spanning 24 SOCs,while 593 signals were detected in the elderly population,spanning 23 SOCs.Comparative visualization analysis of critical PTs under four major SOCs revealed no significant abnormal signals in elderly patients.Conclusion A comprehensive and multidimensional analysis of the ADE data from the FAERS database indicates that pembrolizumab appears to be relatively safe for use in elderly patients.