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.

ObjectiveTo analyze the epidemiological characteristics of latent tuberculosis infection (LTBI) among newly detained populations in eastern China, to identify high-risk groups, and to provide a scientific basis for formulating tuberculosis prevention and control strategies in the prison system. MethodsA cross-sectional study was conducted among the newly admitted detainees in two prisons in eastern China in 2022. Data on demographic characteristics, behavioral risk factors and previous disease history of the research subjects were collected through a structured questionnaire survey. The LTBI status of the detainees was determined using the QuantiFERON-TB Gold In-Tube (QFT-GIT) method. Lasso regression was used to screen variables, followed by multivariate logistic regression analysis to investigate the influencing factors of LTBI. ResultsA total of 305 detainees were included in the study. The median age of detainees was 35 (31, 43) years. The study population was predominantly male (67.21%), of Han ethnicity (95.41%), had a junior or senior high school education (59.34%), and was unemployed (31.80%). A history of smoking was reported by 52.79% of participants, while 57.70% reported no alcohol consumption. The majority had no history of hypertension (85.90%), diabetes mellitus (93.77%), human immunodeficiency virus (HIV) infection (97.38%), familial genetic diseases (95.08%), surgery or trauma (73.77%), drug use (92.79%), or hepatitis (93.77%). The LTBI rate was 14.75%. After comparing the demographic characteristics of LTBI and non-infected groups, it was found that smoking history (χ²=7.40, P=0.025), drug use history (χ²=5.49, P=0.019), and HIV infection (χ²=8.12, P=0.004) were statistically correlated with LTBI infection. The results of multivariate logistic regression analyses showed that smoking [adjusted odds ratio (aOR)=4.08, 95%CI: 1.60‒10.42, P=0.003], HIV infection (aOR=11.57, 95%CI: 2.50‒53.51, P=0.002) and drug use (aOR=3.04, 95%CI: 1.02‒9.09, P=0.046) were risk factors for LTBI. ConclusionThe LTBI rate among newly detainees in two prisons in eastern China is slightly lower than that among long-term detainees. Early screening and intervention should be implemented for newly detainees, with particular attention focused on high-risk groups such as those with a history of smoking, HIV infection, or drug use.

With the accelerating trend of population aging, the number of elderly patients with lung cancer continues to rise, and the disease burden is becoming increasingly heavy. The clinical management of these patients faces severe challenges due to their decreased physiological reserve, complex comorbidities, and significant individual heterogeneity. Consequently, under traditional diagnosis and treatment models, doctors often struggle to identify the individualized risks of elderly patients in a timely and comprehensive manner, which can easily lead to decision biases such as undertreatment or overtreatment. In view of this, this study advocates for the establishment of an umbrella decision-making model specifically tailored for elderly lung cancer patients. Grounded in a multidisciplinary team (MDT) platform, this model deeply integrates oncological indicators with the comprehensive geriatric assessment (CGA) system. By holistically considering multidimensional variables including tumor burden, organ function, frailty index, cognitive status, and social support, the model establishes an operational mechanism characterized by "single entry, precise stratification, and targeted selection". Accordingly, patients can be scientifically triaged into distinct intervention tiers, such as active surveillance, minimally invasive surgery, drug therapy, radiotherapy, and best supportive care, thereby achieving real-time alignment between treatment intensity and patient fitness. This article elaborates on the construction logic and key operational procedures of this novel decision-making framework, aiming to guide clinical practice beyond the limitations of a tumor-centric perspective toward a holistic, dynamic, whole-course management strategy. This transition seeks to ensure optimal quality of life and clinical net benefit for elderly patients alongside survival prolongation.

Digital transformation is reshaping disease monitoring. We assessed the real-world utility of linking hospital information systems with the foodborne disease surveillance network in Shenzhen. Analysing data from 53 sentinel hospitals (2024–2025) and surveying 263 staff members, we found that integration triggered a 125% surge in case reports. However, regional performance varied sharply (Nanshan +161% vs Pingshan -44%), and personnel training frequency remained low (3.08/5). While automation solves the volume problem, it exposes a technology capacity gap that requires urgent human-centric intervention.

Alzheimer's disease (AD) is characterized by cognitive and functional deterioration, with pathological features such as amyloid-beta (Aβ) aggregates in the extracellular spaces of parenchymal neurons and intracellular neurofibrillary tangles formed by the hyperphosphorylation of tau protein. Despite a thorough investigation, current treatments targeting the reduction of Aβ production, promotion of its clearance, and inhibition of tau protein phosphorylation and aggregation have not met clinical expectations, posing a substantial obstacle in the development of drugs for AD. Recently, artificial intelligence (AI), computational biology (CB), and systems biology (SB) have emerged as promising methodologies in AD research. Their capacity to analyze extensive and varied datasets facilitates the identification of intricate patterns, thereby enriching our comprehension of AD pathology. This paper provides a comprehensive examination of the utilization of AI, CB, and SB in the diagnosis of AD, including the use of imaging omics for early detection, drug discovery methods such as lecanemab, and complementary therapies like phototherapy. This review offers novel perspectives and potential avenues for further research in the realm of translational AD studies.

Objective To develop a risk assessment scale for immune checkpoint inhibitor (ICI)-associated myocarditis based on multidisciplinary collaboration, and to evaluate its diagnostic performance. Methods Based on multidisciplinary cooperation, integrating clinical experience from oncology and cardiology, literature data, and patient conditions, a risk assessment scale for ICI-associated myocarditis was developed. A total of 101 patients with malignancies who received immunotherapy at Zhongshan Hospital, Fudan University, from October 2020 to October 2024 were included as the validation cohort. Patients were stratified into low-risk (0-1 point), medium-risk (2-4 points), and high-risk (≥5 points) groups based on their scale scores. The association between pretictive risk stratifications and actual assessment results was assessed using the Cox proportional hazards regression model. The predictive value of the scale for ICI-associated myocarditis was evaluated using receiver operating characteristic (ROC) curve. Agreement between the scale scores and actual assessment results was assessed using Cohen’s Kappa coefficient. Results Based on the scale pretictive results, 28(27.7%), 8(7.9%), 65(64.4%) patients were at low risk, medium risk, and high risk for ICI-related myocarditis, respectively; however, 46(45.5%), 8(7.9%), 47(46.5%) were at low risk, medium risk, and high risk actually. Kaplan-Meier survival analysis showed that the cumulative incidence of ICI-related myocarditis in the high-risk group was significantly higher than that in the medium- and low-risk groups (P＜0.05). In the multivariable-adjusted Cox proportional hazards model, the ICI-related myocarditis risk in high-risk group was about 4 times that in the low-risk group. ROC curve analysis demonstrated that the average area under the curve (AUC) for predicting ICI-related myocarditis was 0.81, with an accuracy of 0.74. The Cohen’s Kappa coefficient was 0.55, indicating moderate agreement. In the actual high-risk group, no patient was predicted to be at low risk; in the actual low-risk group, 16 patients were predicted to be at high risk. Conclusions This risk assessment scale for ICI-associated myocarditis shows high predictive performance. It provides oncologists with a simple yet effective multidisciplinary diagnostic reference tool, potentially enhancing early identification of ICI-associated myocarditis.

Objective:To explore the molecular epidemiological characteristics of human metapneumovirus (HMPV) in children with acute respiratory infection (ARI) in Beijing from 2023 to 2024.Methods:In the longitudinal study, 9 834 children with ARI were enrolled from August 2023 to December 2024, including the influenza-like illness (ILI) group from emergency and outpatient department receiving influenza virus (Flu) and HMPV test and the ARI inpatient group for 13 common respiratory pathogen screening test including HMPV, Flu, respiratory syncytial virus, and so on. All respiratory samples positive with HMPV were genotyped by amplifying and sequencing of G gene and further phylogenetic analysis. The χ2 test and Wilcoxon rank-sum test were used to compare the positive rate and basic clinical data of the 2 groups. Results:Among 9 834 enrolled patient, there were 5 276 male and 4 558 female children, with age 5.4 (1.9, 8.2) years. In ILI group of 1 460 patients, there were 83 cases (5.7%) positive for HMPV, with the age 4.9 (3.6, 6.6) years and children under 6.0 years old 59 cases (71.1%). Among 8 374 ARI inpatients, there were 256 cases (3.1%) positive for HMPV, with age 3.5 (1.3, 6.4) years and children under 6.0 years old 188 cases (73.4%). The HMPV positive rate and the age of children positive for HMPV in ARI inpatient group were significantly lower than that in ILI group (both P<0.001). In December, 2024, the HMPV positive rates of ILI and ARI inpatient group (21.3% (17/80), 15.0% (47/314)) were significantly higher than the total positive rates of each group (both P<0.001). Among 279 subtyped specimens, there were 155 cases (55.6%) belonging to genotype A and 124 cases (44.4%) belonging to genotype B. Sub-lineage A2.2.2 containing 111nt-insertions was predominate one in 2023 with positive ratio 89.2% (91/102), and B2 was predominate in 2024 with positive ratio 64.4% (114/177). Conclusions:From 2023 to 2024, the positive rate of HMPV in the ILI group was higher than that in the ARI inpatient group, suggesting a common epidemic of HMPV infection. Children positive for HMPV in the ARI inpatient group were younger than that in the ILI group. A severe epidemic of HMPV was observed in the winter of 2024, which requires attention. Sub-lineage A2.2.2 with 111nt-duplicate insertions and B2 were the predominant epidemic strains in 2023 and 2024, respectively.

Objective:To investigate the differences in acute intestinal injury and regulatory mechanisms in mice following carbon ion FLASH radiotherapy (FLASH-RT) and conventional dose rate radiotherapy (CONV-RT).Methods:Healthy C57BL/6J mice were randomly divided into three groups: control group, FLASH-RT group (100 Gy/s), and CONV-RT group (0.1 Gy/s), with 9 mice in each group. All mice received carbon ion whole abdominal radiotherapy. DNA double-strand breaks (DSB) and cell proliferation were evaluated by measuring the expression of phosphorylated histone H2AX (γ-H2AX) and nuclear-associated antigen 67 (Ki67) using immunohistochemistry; apoptosis was analyzed using terminal deoxynucleotidyl transferase-mediated dUTP nick end labeling (TUNEL); transcriptome sequencing was used to analyze the differences in molecular pathways between FLASH-RT and CONV-RT.Results:Compared with the CONV-RT group, the FLASH-RT group showed significantly reduced intestinal γ-H2AX signal at 3 h after radiotherapy ( t=3.80, P<0.01), significantly increased expression of Ki67 at the base of intestinal crypts at 6 h after radiotherapy ( t=4.30, P<0.001), and a significantly decreased number of TUNEL-positive cells at 12 h after radiotherapy ( t=3.08, P<0.01). Transcriptome sequencing analysis showed that FLASH-RT specifically activated the insulin-like growth factor (IGF) pathway, avoiding the excessive activation of CONV-RT-induced nuclear factor-κB and B cell receptor inflammatory pathways as well as the inhibition of energy metabolism. Conclusions:Compared with CONV-RT, carbon ion FLASH-RT can reduce DSB damage, preserve the proliferative activity of intestinal stem cells, activate the IGF pathway, and regulate inflammatory, immune, and metabolic pathways, thereby significantly alleviating acute intestinal epithelial injury. Specifically, the regulation of repair pathways mediated by reduced DSB and the inhibition of inflammatory pathways are potential protective mechanisms for normal tissues.

Objective:To analyze the influencing factors of China′s DRG payment system reform(DRG reform) and its hierarchical relationship, for references for the in-depth promotion of China′s medical insurance payment reform.Methods:Relevant literature on DRG reform in China from databases such as CNKI, Wanfang Database, Pubmed, etc, were obtained. Content analysis method was used to extract the influencing factors of DRG reform. The correlation between each influencing factor was determined through expert discussion. An interpretive structural model(ISM) was constructed to analyze the hierarchical relationship of factors influencing DRG reform.Results:After analysis, the influencing factors(12) of DRG reform in China were included such as medical level, hospital management, and medical staff′s cognition and behavior. Among them, the local situation was the deep-level factor affecting DRG reform, 9 factors such as data quality assurance and policy design/implementation were the middle-level factors, and patients′ interests/needs and disease grouping were the surface-level factors.Conclusions:There were many influencing factors on the reform of China′s DRG payment system. It was suggested that relevant management departments in various regions should focus on the actual situation of the locality, take data quality and policy design and implementation as the key points of reform, formulate a scientific and reasonable DRG grouping scheme, safeguard the interests of patients, so as to promote the deepening of DRG reform.

Primers and probes were designed based on the conserved regions of Senecavirus A(SVA),foot-and-mouth disease virus(FMDV),and porcine teschovirus(PTV)and used to devel-op a TaqMan fluorescent quantitative PCR method for detecting the above-mentioned three viru-ses.The triplex fluorescent quantitative PCR system was developed using recombinant positive plasmids containing conserved sequences of the three viruses as templates.After optimizing the conditions,the specificity,sensitivity,repeatability,standard curve,and mixed infection model were evaluated,and the constructed triplex fluorescent quantitative PCR was used for clinical detection.The results showed that this method could specifically detect SVA,FMDV and PTV without cross-reactivity with other pathogens with the minimal detection concentrations for SVA,FMDV,and PTV as low as 1X101 copies/μL,respectively.The coefficients of variation within and between groups were less than 5％.Furthermore,none of the three viruses were detected in 126 samples.The above results indicate that this method is highly specific,sensitive,and stable,making it suit-able for clinical detection.