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by Lei Guo, Jun Ge, Li Cheng, Xinyi Zhang, Zhengzheng Wu, Meili Liu, Hanmei Jiang, Wei Gong, Yi Liu
BackgroundThe incidence of ulcerative colitis (UC) remains high, with an increasing prevalence among elderly patients. Cellular senescence has been widely recognized as a contributor to UC susceptibility; however, the underlying molecular mechanisms remain incompletely understood. This study aimed to identify senescence-associated biomarkers in UC to provide new insight for diagnosis and treatment.
MethodsBy integrating transcriptomic data from UC patients with established aging-related databases, we identified aging-associated differentially expressed genes (DEGs). Using weighted gene co-expression network analysis (WGCNA) and Cytoscape, we pinpointed the core genes involved. A diagnostic model for UC was then developed based on these core genes, and their expression patterns were characterized at single-cell resolution. The roles of these genes were ultimately validated through in vitro and animal experiments.
ResultsWe identified 24 aging-related DEGs in UC, which were primarily implicated in inflammatory responses and cytokine-receptor interactions. Further analyses pinpointed three core genes (CXCL1, MMP9, and STAT1) that were predominantly expressed in macrophages. A diagnostic model constructed using these genes exhibited robust predictive performance. Experimental validation confirmed that the expression levels of all three core genes were significantly upregulated in both a UC mouse model and in macrophages compared to controls. Additionally, pathway analyses revealed elevated levels of CXCL12 and VEGFA in the enriched pathways.
DiscussionOur findings underscore the pivotal roles of CXCL1, MMP9, and STAT1 in UC-associated cellular senescence. The analysis positions these molecules as promising macrophage-mediated diagnostic biomarkers and therapeutic targets. Collectively, this work provides novel insights into UC pathogenesis and lays a foundation for developing precision medicine strategies that target senescence pathways.
by Ying Li, Jing Jia, Runze Lu, Liyan Dong, Lizhu Fang, Litao Sun, Zongyi Zhang, Qing Duan, Lijie Zhang, Kunzheng Lv, Huilai Ma
BackgroundQingdao, a historically high-risk area for hemorrhagic fever with renal syndrome (HFRS) in China, is undergoing agricultural mechanization and urbanization. However, the specific risk factors for HFRS in this context remain unclear. This study sought to determine the risk factors for HFRS in Qingdao.
MethodsCommunity-based, 1:2 case-control study. Each case was matched with two healthy neighborhood controls based on biological sex, age, and the same neighborhood or village. Univariate and multivariate conditional logistic regression analyses were performed. Furthermore, stratified analyses were performed to explore risk factor heterogeneity between the peak season for Hantaan virus (HTNV) type HFRS (October-January) and other months.
Results93 cases (73.2%, 93/127) reported from January 2022 to September 2023 and 186 controls completed this questionnaire. Farmers accounted for the highest proportion (68.8%, 64/93). In multivariate logistic regression analysis, there were three significant risk factors for HFRS: piles of firewood and/or grain in residential yards (odds ratio [OR]=3.75, 95% CI: 2.14–6.55), mite and/or flea bites (OR=1.83, 95% CI: 1.06–3.18) and contacting with rats and/or their excreta (OR=1.73, 95% CI: 1.09–2.74); three variables represented significant protective factors for HFRS: frequency of sun exposure for quilts and bedding (OR=0.41, 95% CI: 0.19–0.90), rodent control measures at home (OR=0.50, 95% CI: 0.30–0.81) and knowing the main sources of HFRS transmission (OR=0.58, 95% CI: 0.36–0.90). Stratified analysis revealed that the influence of these factors varied by season, with rodent contact and control measures being particularly salient during the HTNV peak season.
ConclusionThis study provides the first comprehensive evidence of risk and protective factors for HFRS in Qingdao, highlighting the role of rodent control, promoting comprehensive health education, environmental management, and personal protection. However, the results should be interpreted considering the study’s limitations, including a 73.2% response rate and the potential for recall bias.
by Yizhe Yang, Ruifeng Liang, Yan Luo, Doudou Zhu, Yi Liu, Yuyan Guo, Jiafen Zhang, Qiao Niu
ObjectiveInvestigate the Knowledge-Attitude-Practice (KAP) of students from Medical College towards emerging infectious diseases, and assess their impact, can provide a scientific basis and practical guidance for enhancing medico’s prevention and control capabilities.
MethodsA total of 2,395 participants from various grades and majors at Medical University were randomly selected using a stratified cluster sampling method. This cross-sectional study was conducted between April 25 and May 31, 2020, using a self-administered questionnaire developed on the Wenjuanxing platform to assess COVID-19-related knowledge, attitudes, and practices (KAP) among medical students.
ResultsA total of 2,245 participants (aged 16–28 years) were included in the study, coming from five medical disciplines: Clinical Medicine, Preventive Medicine, Nursing, Clinical Pharmacy, Health Inspection and Quarantine. The average scores for the COVID-19 epidemiological knowledge and the control measures for the epidemic were 4.92 ± 1.03 and 4.50 ± 0.78, respectively. Among them, the scores of epidemiological knowledge exhibited significant differences in sex, nation, type of dwelling place, major, grade, annual per capita household income, and age. The scores of preventive knowledge significantly differed by sex, major, grade, physical condition, and age. Further, behavioral data indicated that 96.0% of the students thought the pandemic had severely affected their daily life, while >90% maintained consistent mask usage and >80% insisted on health-protective practices. Practice scores finally varied significantly by sex, family structure, and ethnicity.
ConclusionsAltogether, medical students possess certain basic knowledge in controlling emerging infectious diseases, but some still generally suffer from insufficient cognitive depth and anxiety. Colleges can systematically enhance students’ rational cognitive level which include offering specialized courses as well as promoting cutting-edge research achievements, and through standardized operations stabilize their psychological states.
by Chamberline E. Ozigbu, Zhenlong Li, Bankole Olatosi, James W. Hardin, Nicole L. Hair
While prior studies have identified sociodemographic correlates of zero-dose status within populations in sub-Saharan Africa (SSA), few have applied spatial regression techniques to explore geographic variability in these relationships. We aimed to address this gap using data from Demographic and Health Surveys conducted in SSA between 2010 and 2020. Our sample comprised children aged 12–59 months in 33 countries and 329 survey regions. Data were aggregated to the first-level administrative unit prior to analysis. First, using ordinary least squares regression, we documented global relationships between theoretically important sociodemographic characteristics and zero-dose prevalence. Next, we identified patterns, i.e., geographic clustering, of zero-dose prevalence. Finally, using multiscale geographically weighted regression, we described spatial variability in relationships between sociodemographic characteristics and zero-dose prevalence. We detected 27 regions with higher than expected concentrations of zero-dose children. All but one of these hot spots were observed in 7 Western and Central African countries; only 1 was located in an Eastern African country. Regions with higher proportions of mothers with no antenatal care visits were consistently found to have higher rates of zero-dose children. In contrast, relationships between zero-dose prevalence and indicators of religious affiliation, delivery site, maternal age, maternal education, and maternal employment were found to vary locally in terms of their strength and/or direction. Study findings underscore spatial disparities in zero-dose prevalence within SSA and, further, highlight the importance of geographically informed strategies to effectively address immunization gaps. Implementing targeted interventions based on regional sociodemographic dynamics is crucial for achieving comprehensive vaccination coverage in SSA.by Linna Zhao, Juanjuan Zhang, Weizhe Liu, Cheng Dai, Aiying Li
Diabetes mellitus (DM) is identified as a potential modifier of clinical outcomes in acute heart failure (AHF), yet its prognostic impact is not fully determined. This systematic review and meta-analysis aimed to assess the prognostic impact of DM on survival outcomes in AHF patients by synthesizing evidence from 26 studies involving 326,928 subjects collected from Cochrane Library, PubMed, Web of Science, and Embase databases up to 1 June 2024. Both prospective/retrospective cohort and case-control studies published since 2000 were included, with outcomes evaluated through multivariate, univariate, and binary analyses using the Newcastle-Ottawa Scale for quality assessment. Multivariate analysis indicated that DM significantly increased the risk of all-cause mortality in AHF patients (cohort studies: HR = 1.21, 95%CI (1.13, 1.29), OR=1.15, 95%CI (1.05, 1.26); case-control studies: HR = 1.39, 95%CI (1.26, 1.53), OR=1.43, 95%CI (1.10, 1.84)]. Univariate analysis confirmed this finding in case-control studies [HR = 1.30, 95%CI (1.01, 1.67)], but not in cohort studies. In both cohort [RR = 1.27, 95%CI (1.12, 1.43)] and case-control [OR=1.21, 95%CI (1.08, 1.35)] studies, DM increased the risk of all-cause mortality. AHF patients with DM had a higher risk of cardiovascular mortality [cohort studies: HR = 1.85, 95%CI (1.46, 2.33); case-control: OR=1.70, 95%CI (1.17, 2.47)]. While multivariate analysis showed no association between DM and in-hospital mortality, case-control studies indicated an increased risk [OR=1.21, 95%CI (1.03, 1.42)]. DM also increased the risk of readmission [cohort studies: HR = 1.32, 95%CI (1.14, 1.53); case-control studies: HR = 1.44, 95%CI (1.23, 1.69); binary data: OR=1.19, 95%CI (1.07, 1.31)].This updated meta-analysis demonstrates that DM imposes significant adverse effects on all-cause mortality, cardiovascular-related mortality, and readmission risk in AHF patients. However, no significant connection was found between diabetes and survival outcomes with respect to the co-endpoint of death or readmission and the endpoint of in-hospital mortality. These findings underscore the necessity for implementing targeted diabetes management within AHF care protocols to enhance clinical outcomes, an essential consideration for future practice.by Georgia Black, Reena Besa, Daniel Blumberger, Heather Brooks, Graham Collingridge, John Georgiou, Evelyn K. Lambe, Clement Ma, Bernadette Mdawar, Tarek K. Rajji, Sanjeev Sockalingam, Cara Sullivan, Quincy Vaz, Zhengbang Yao, Branka Agic
IntroductionIntegrated knowledge translation (iKT) is a collaborative research approach that emphasizes the meaningful and active participation of knowledge users throughout the research process. Evidence suggests that integrated knowledge translation has the potential to increase the relevance, applicability, and use of research findings. This approach has been increasingly utilized in health research in recent years. However, the extent to which it has been applied in preclinical research and its effectiveness are unknown. To address this gap, we will conduct a scoping review to map the current use, potential benefits, and challenges of iKT in preclinical research.
MethodsGuided by a modified Arksey and O’Malley’s scoping review framework, we will systematically search reference lists and key research databases including Medline, Embase, PsycINFO, Cochrane CENTRAL, Cochrane Database of Systematic Reviews, and Web of Science. Peer-reviewed articles written or translated in English that focus on iKT or approaches that align with iKT within the context of preclinical research will be included. This review will be conducted as part of the Improving Neuroplasticity through Spaced Prefrontal intermittent-Theta-Beta-Stimulation REfinement in Depression (INSPiRE-D) project, which features preclinical research from mouse models to human work (Grant number CAMH File No.22-060). The project’s multidisciplinary team and knowledge user advisory committee will be consulted at key points throughout the scoping review process. A person with lived experience co-chairs the project advisory committee, co-authored this manuscript, and will be routinely included in the decision-making process of the scoping review.
by Lei Wang, Chang Han Ma, Si Yuan Yang, Zheng Gang Zhang
BackgroundHigh-dose intravenous iron supplementation offers substantial hematologic protective benefits in clinical practice; however, its efficacy in enhancing blood protection during cardiac surgery remains uncertain. The present study aimed to investigate the effects and safety of high-dose intravenous iron as an optimal blood management strategy for patients undergoing cardiac procedures.
MethodsMajor databases, including PubMed, Embase, and Cochrane, were searched on June 20, 2025, for randomized controlled trials (RCTs) comparing red blood cell transfusion rates in adult patients undergoing high-dose intravenous iron supplementation versus those receiving control therapy (placebo) following cardiac surgery. The secondary outcome measures included postoperative hemoglobin levels, length of hospital stay, and incidence of adverse events.
ResultsSeven RCTs involving 975 subjects were identified in the database search. Compared with the control group (placebo), high-dose intravenous iron significantly decreased the rate of postoperative red blood cell transfusion among patients undergoing cardiac surgery (risk ratio 0.69, 95% confidence interval [CI] 0.52–0.91, P = 0.009, I2 = 61%, n = 975, certainty of evidence: moderate). Furthermore, one week or more following surgery, administration of high-dose intravenous iron resulted in a significant increase in postoperative hemoglobin levels (mean difference 0.71, 95% CI 0.41 to 1.01, P 2 = 63%, certainty of evidence: moderate). Significant differences between the groups were not observed for the other outcome measures, including mortality, infection rates, and cardiovascular events.
ConclusionsHigh-dose intravenous iron supplementation during the perioperative period of cardiac surgery significantly reduces the risk of red blood cell transfusion and enhances postoperative hemoglobin levels. Although the present study demonstrated a favorable safety profile for intravenous iron administration, the limitations of the present meta-analysis necessitate continued vigilance regarding potential drug-related risks associated with intravenous iron therapy. Systematic review protocol: CRD420251069827 (PROSPERO).
by Weixin Chen, Jiaming Yan, Zhenxing Kong, Yuliang Sun, Wenfei Zhu, Jie Kang
ObjectivesAir quality in China has become an increasing concern, its association with physical fitness remains unclear. This study represents one of the largest nationwide investigations of this association, leveraging data from 174,246 students aged 7–22 years across 30 provinces.
MethodsAnnual concentrations of PM₂.₅, PM₁₀, SO₂, NO₂, CO, O₃, and the Air Quality Index (AQI) were obtained from the Tracking of Atmospheric Pollution in China dataset. Physical fitness was evaluated through a comprehensive set of field-based tests covering anthropometric, cardiopulmonary, flexibility, muscular strength, and endurance. Associations were examined using generalized linear models with progressive adjustments: Model 1 controlled for demographic factors (age, sex, residence, province), Model 2 additionally accounted for physical activity and parental factors, and Model 3 further incorporated temperature and humidity.
ResultsAfter adjusting for covariates, each 1 μg/m³ increase in PM₂.₅ and PM₁₀ was associated with decreases in physical fitness scores of 0.18 [95% CI: −0.22, −0.14] and 0.12 [−0.16, −0.08] points, respectively. SO₂, O₃, and CO showed similar negative associations, with reductions of 0.42 [−0.47, −0.38], 0.21 [−0.25, −0.16], and 0.16 [−0.20, −0.11] points, respectively. In contrast, NO₂ exhibited a positive association, with an increase of 0.29 [0.25, 0.33] points per 1 μg/m³. AQI was also inversely related to fitness, decreasing scores by 0.17 [−0.21, −0.13] points per 1-unit increase.
ConclusionsAmbient air pollution is adversely associated with physical fitness among Chinese children, adolescents, and young adults, highlighting the importance of air quality improvement strategies for youth health. Future longitudinal studies are warranted to strengthen causal inference.
by Xu Jia, Jiaojiao Peng, Junhong Lv, Yuanting Li, Ziren Luo, Jing Xiang, Yaqin Hou, Qian Zheng, Bin Han
The presence of substantial quantities of antibiotics and their metabolites in hospital wastewater can lead to the accumulation of antibiotic-resistant bacteria (ARB) and antibiotic resistance genes (ARGs). Research on the influent and effluent sewage of hospitals is crucial for understanding the effectiveness of wastewater treatment systems in inactivating ARB and ARGs. Key features of microbial communities and ARGs in influent and effluent wastewater – including taxonomic diversity and relative abundance – were assessed via metagenomic sequencing. The treatment process resulted in a reduction of the overall bacterial count in hospital wastewater. However, a notable increase in relative abundance was observed for three phyla, 16 genera, and 21 species post-treatment. Bacteria harboring ARGs were predominantly identified as belonging to Pseudomonadota and Bacillota. A total of 354 ARGs were detected in the influent, while 331 were identified in the effluent samples, with a general decrease in absolute abundance. Nevertheless, the relative abundance of certain ARGs, such as mphG, fosA8, and soxR, was found to increase in the effluent across all samples. Seasonal fluctuations also played a role in the distribution of microbial communities and ARGs. These findings underscore the role of hospital wastewater treatment systems in reducing the discharge of ARB and ARGs into the environment, while also revealing potential shortcomings in the wastewater treatment process that necessitate further improvement for more effective removal of these ARGs.by Xu Cai, Xiaoqing Jiang, Wenbin Nan, Zhenyu Peng, Chenlu Wu, Kui Tang
BackgroundAcute pancreatitis is a prevalent and severe digestive disease with significant mortality. Early identification of high-risk patients is essential for improving outcomes. The ratio of blood urea nitrogen to albumin (BAR) has emerged as a potential prognostic predictor in various critical illnesses. This study explores the associations between BAR and 28-day and 90-day mortality in acute pancreatitis patients.
MethodsThis retrospective cohort study enrolled patients diagnosed with acute pancreatitis from the Medical Information Mart for Intensive Care IV (MIMIC-IV) database. BAR was calculated using initial blood urea nitrogen and serum albumin. Patients were categorized into three groups according to the tertiles (T1-T3) of BAR values. Kaplan-Meier survival analysis and restricted cubic spline (RCS) curve assessed the impact of BAR on overall survival. Multivariate Cox regression analysis was used to determine association of BAR with 28-day and 90-day mortality in acute pancreatitis. The receiver operating characteristic (ROC) curve was employed to assess the predictive value of BAR for 28-day and 90-day mortality in acute pancreatitis.
ResultsIn this study, 452 patients with acute pancreatitis were analyzed, with 28-day mortality rate of 11.7% and 90-day mortality rate of 13.7%. Kaplan-Meier survival analysis indicated a notable increase in mortality rates at 28 days and 90 days among patients with elevated BAR levels (Log-rank P Conclusion
Higher BAR values were significantly associated with increased 28-day and 90-day mortality in acute pancreatitis patients. Moreover, BAR may serve as a simple and effective tool for identifying higher death risk of patients with acute pancreatitis.
by Haoyang Zhou, Jinfeng Yang, Na Li, Jinying Li, Jianxin Ran, Yan Zheng, Yifan Long, Fang Cheng, Yuanpeng Liao
BackgroundSarcopenia is an age-associated disorder characterized by a progressive decline in skeletal muscle mass, strength, and physical function. The condition is linked to low levels of anabolic hormones such as insulin-like growth factor 1 (IGF-1), with its downstream phosphatidylinositol 3 kinase (PI3K)/ protein kinase B (AKT)/ forkhead box protein O3 (FOXO3) signaling pathway. There is growing evidence that resistance training (RT) or vibration training (VT) could improve physical functioning in individuals with sarcopenia. However, the related physiological influence of exercise on sarcopenia remains elusive.
MethodThis prospective randomized controlled trial will be conducted among 96 participants, aged between 65 and 80 years. In participants, sarcopenia diagnosis will be confirmed based on the Asian Working Group for Sarcopenia criteria, and participants will be randomized into either control, RT, VT, or RVT (combined RT and VT) groups. The intervention will last 12 weeks, with assessments performed at baseline, 12 weeks (after intervention), and 24 weeks (follow-up). The primary outcomes will include skeletal muscle mass, handgrip strength, and gait speed. Secondary outcomes comprise IGF-1 concentrations, PI3K/AKT and FOXO3 protein activity, quality of life, and timed-up-and-go test performance assessments.
DiscussionThis clinical study aims to elucidate the potential modulation of molecular mechanisms in vivo for combined RT and VT in sarcopenia patients and to identify the effects of the intervention on physical function.
Trial registrationChiCTR, ChiCTR2400083643. Registered on April 29, 2024.
by Mengzhen Qin, Mengyuan Qiao, Yuying Dong, Haiyan Wang
ObjectiveCompared with those without such impairment, middle-aged and older adults with sensory impairment (SI) demonstrate a greater prevalence and severity of depressive symptoms, significantly affecting their mental health. We aimed to develop and validate a depression risk prediction model for middle-aged and elderly individuals with SI.
MethodsData from the 2018 China Health and Retirement Longitudinal Study were randomly partitioned into training and validation sets at a 7:3 ratio. Within the training set, least absolute shrinkage and selection operator (LASSO) regression analysis and binary logistic regression were used to identify predictor variables, and a risk prediction column‒line graph was subsequently developed, with depression status among middle-aged and elderly individuals with SI as the dependent variable. Predictive performance of the training and validation sets was assessed via receiver operating characteristic (ROC) curves, calibration plots, and decision curve analysis.
ResultsIn total, 5308 middle-aged and older adults with SI were included, with 50.1% (n = 2657) developing depression. Multifactorial logistic regression analysis identified several depression predictors, including sex, education level, place of residence, marital status, self-rated health, life satisfaction, pension insurance status, nighttime sleep duration, functional impairment status, and pain (all P CI = 0.783–0.811) and 0.778 (95% CI = 0.755–0.800), respectively. The Hosmer–Lemeshow values were P = 0.176 and P = 0.606 (P > 0.05), and the calibration curves revealed significant agreement between the model and actual observations. ROC and DCA curves indicated good predictive performance for the column‒line graph.
ConclusionThis study presents a reliable, validated, and acceptable predictive model for depression risk in middle-aged and elderly individuals with SI, and the identified predictors have potential applications in public health policy and clinical practice.
by Mengqi Yuan, Yajing Yuan, Xiangqun Zhang, Zhenghao Zhu, Chenxi Zhao, Xiangqian Gao, Genyuan Du
Millimeter-wave (mmWave) radar has become an important research direction in the field of object detection because of its characteristics of all-time, low cost, strong privacy and not affected by harsh weather conditions. Therefore, the research on millimeter wave radar object detection is of great practical significance for applications in the field of intelligent security and transportation. However, in the multi-target detection scene, millimeter wave radar still faces some problems, such as unable to effectively distinguish multiple objects and poor performance of detection algorithm. Focusing on the above problems, a new target detection and classification framework of S2DB-mmWave YOLOv8n, based on deep learning, is proposed to realize more accuracy. There are three main improvements. First, a novel backbone network was designed by incorporating new convolutional layers and the Simplified Spatial Pyramid Pooling - Fast (SimSPPF) module to strengthen feature extraction. Second, a dynamic up-sampling technique was introduced to improve the model’s ability to recover fine details. Finally, a bidirectional feature pyramid network (BiFPN) was integrated to optimize feature fusion, leveraging a bidirectional information transfer mechanism and an adaptive feature selection strategy. A publicly available 5-class object mmWave radar heatmap dataset, including 2,500 annotated images, were selected for data modeling and method evaluation. The results show that the mean average precision (mAP), precision and recall of the S2DB-mmWave YOLOv8n model were 93.1% mAP@0.5, 55.8% mAP@0.5:0.95, 89.4% and 90.6%, respectively, which is 3.3, 1.6, 4.5 and 7.7 percentage points higher than the baseline YOLOv8n network without increasing the parameter count.by Chenyu Zheng, Ming Fang, Yue Zhang, Xinyu Liu, Zhihuan Huang
Exposure to natural landscapes has been shown to affect both physiological and psychological well-being, with the extent of these effects varying across different landscape types. However, the underlying mechanisms remain poorly understood. The association among stress reduction, environments characteristics and individual differences requires further investigation, particularly considering the complexity of landscape attributes and the variability of personal responses. In this study, 98 university students participated in a survey to evaluate the effects of different landscape types on visual preference and fatigue recovery. Physiological data (blood pressure, heart rate), psychological data (Perceived Restorative Scale), and visual preferences were analyzed before and after participants viewed the images of eight representative landscape space types: mountain, field, waterscape, lawn, desert, forest, artificial nature, plant. The results indicated that landscape type significantly influenced both physiological responses and emotional states, as well as participants’ perceived recovery from stress. Among the eight landscape spaces, water features and forests were reported to be the most restorative. Compared to freshmen, juniors exhibited greater improvements in physical and psychological recovery, alongside more positive evaluations of the environments. Notably, the desert landscape elicited varied responses depending on participants’ grade level and gender, suggesting that restoration effects may be modulated by individual characteristics. This may reflect an evolutionary predisposition to prefer natural features that enhance survival. These findings contribute to environmental psychology and provide valuable insights for educational practice and environmental design.by Yiming Jin, Rong Lu, Mingyuan Wang, Zihao Xu, Zhen Liu, Shuhong Xie, Yu Zhang
ObjectiveIn this study, we aimed to analyze the blood screening detection strategies employed for voluntary blood donation in a specific region of East China and evaluate the efficacy of the blood safety detection system.
Donors and MethodsA total of 539,117 whole blood samples were collected from voluntary blood donors between January 2018 and July 2021, as well as in 2023 and 2024. The samples were screened for hepatitis B surface antigen (HBsAg), hepatitis C virus (HCV) antibodies, human immunodeficiency virus antibodies/antigen (HIV Ab/Ag), and Treponema pallidum (TP) antibodies using enzyme-linked immunosorbent assay (ELISA). Alanine aminotransferase (ALT) levels were measured using a rapid method. Chemiluminescence immunoassay technology was used to detect five hepatitis B virus (HBV) markers. Polymerase chain reaction was employed to detect HBV DNA, HCV RNA, and HIV RNA. The reactivity rates of each marker were analyzed.
ResultsThe overall positivity rate for blood testing among donors in this region was 0.76% (4,078/539,117). The positivity rates for the individual markers were as follows: anti-TP (0.20%)> HBsAg (0.18%)> ALT (0.13%)> anti-HCV (0.085%)> nucleic acid testing (0.080%)> HIV antigen/anti-HIV (0.079%). No significant differences were observed (P > 0.05). Before 2023, the positivity rates for ALT and HBsAg exhibited occasional fluctuations, followed by a significant decline. Conversely, in 2024, a slight upward trend in the HIV positivity rate was noted.
ConclusionThe current multitiered blood screening and detection strategy in this region exhibits complementary advantages, ensuring effective blood safety. However, the observed slight upward trend in the HIV positivity rate among voluntary blood donors highlights the necessity for enhanced pre-donation counseling and risk assessment for key populations.
by Ruibo Sha, Zhifeng Zhang, Xiao Cui, Qingzheng Mu
Sewer pipeline defect detection is a critical task for ensuring the normal operation of urban infrastructure. However, the sewer environment often presents challenges such as multi-scale defects, complex backgrounds, lighting changes, and diverse defect morphologies. To address these issues, this paper proposes a lightweight cross-scale feature fusion model based on YOLOv8. First, the C2f module in the backbone network is replaced with the C2f-FAM module to enhance multi-scale feature extraction capabilities. Second, the HS-BiFPN module is adopted to replace the original structure, leveraging cross-layer semantic fusion and feature re-weighting mechanisms to improve the model’s ability to distinguish complex backgrounds and diverse defect morphologies. Finally, DySample is introduced to replace traditional sampling operations, enhancing the model’s ability to capture details in complex environments. This study uses the Sewer-ML dataset to train and evaluate the model, selecting 1,158 images containing six types of typical defects (CK, PL, SG, SL, TL, ZW), and expanding the dataset to 1,952 images through data augmentation. Experimental results show that compared to the YOLOv8n model, the improved model achieves a 3.8% increase in mAP, while reducing the number of parameters by 35%, floating-point operations by 21%, and model size by 33%. By improving detection accuracy while achieving model lightweighting, the model demonstrates potential for application in pipeline defect detection.
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