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Phenotypic and molecular characterization of extended-spectrum β-lactamase producing Escherichia coli and Klebsiella pneumoniae isolates from communities in South Western Uganda
Development of a pH-sensitive magnetic hydrogel based on chitosan for synergistic chemo/hyperthermia treatment of MCF-7 breast cancer cells
Numerical investigation of SWCNT–H₂O nanofluid and core topology effects on the thermal and moisture performance of sandwich panels
Modeling the enablers and barriers to circular economy integration in Nigeria’s construction industry: a structural equation approach
Grain texture and starch physicochemical properties of waxy maize in response to contrasting ecological conditions
Quadrature solution of fractional coupled burgers and plankton-oxygen dynamics under climate change
An intelligent approach for automated vehicle damage classification
Abstract Manual examination using imitative assessment methods requires considerable investment in time, effort, resources, and funds. Furthermore, these assessments often exhibit inconsistencies. Therefore, this study introduces a framework for accurately detecting and classifying vehicle damage and estimating repair costs using deep learning and machine learning approaches. In this study, YOLOv5 and YOLOv8 models were applied to detect and classify vehicle damage types, such as broken lamps, glass shatters, cracks, scratches, and dents. Repair costs were predicted using the XGBoost machine learning model. The dataset used for damage detection, classification, and cost prediction was created from scratch. It was developed by identifying the types of damage and generating bounding box coordinates around damaged areas. After extracting the bounding box coordinates and damage types, additional features were incorporated. The YOLOv8 model outperformed YOLOv5 in both detecting and classifying vehicle damage, achieving a precision of 87% on the validation dataset and mean absolute precision 90.7% on the test dataset. The XGBoost model achieved an R² score of 97.28% and a Mean Absolute Error (MAE) of 128.50. These results confirm that the proposed framework enhances the precision of vehicle damage detection and classification while accelerating assessment and ensuring its effectiveness in real-world scenarios.
Educational inequalities in disability among older adults: a trend analysis in 15 European countries (2002 to 2023)
Abstract The aim of this study was to examine the magnitude and trends of educational inequalities in disability among older men and women (≥ 60 years) in 15 European countries. We used data of 11 waves of the European Social Survey (ESS, 2002 to 2023, N = 98,300). Disability was assessed by a variant of the Global Activity Limitation Indicator. In the latest wave of the ESS (2023), there was an educational gradient in the prevalence of disability in the 15 European countries. In terms of time trends among men, disability significantly declined in high education groups in ten countries while it increased among those with low education in three countries. Trends in prevalence were quite different among women. There was a significant decline among low as well as high education groups in five countries and prevalences were stable in eight countries. Overall, among men, there was a significant increase of inequalities in disability between low and high educational groups from 2002 to 2023, while this trend was not significant among women. The mechanisms behind these inequalities in disability are not yet fully understood. Future studies should investigate to what extent behavioural, psychosocial, and material factors contribute to their explanation. A better understanding of these explanatory factors is needed to develop and implement interventions for a reduction of inequalities in disability in aging societies.
Development of a novel phage-displayed fusion protein of newcastle disease virus as a highly specific antigen for serological assays
A time-frequency collaborative cross-device bearing fault diagnosis model based on supervised transfer learning with limited data
Synergistic effect of Cu metal-organic framework grafted polyaniline (Cu-MOF@PANI) on adsorptive removal of anionic dye
Correction: An open carbon–phenolic ablator for scientific exploration
Anonymous transaction amount verification system based on zero knowledge range proof
Efficacy and safety of pilocarpine for the treatment of presbyopia: a systematic review and meta‑analysis of randomized controlled trials
Molecular MRI monitoring of cyclodextrin therapy in murine abdominal aortic aneurysms
Abstract Abdominal aortic aneurysms (AAAs) are characterized by progressive extracellular matrix (ECM) degradation and inflammation of the aortic wall. 2-Hydroxypropyl-β-cyclodextrin (cyclodextrin) has shown potential in attenuating AAA progression via activation of transcription factor EB. This study evaluated whether molecular magnetic resonance imaging (MRI) enables non-invasive monitoring of therapeutic effects in a murine AAA model. Thirty-two male apolipoprotein-E knockout mice with angiotensin II–induced AAAs received either cyclodextrin ( n = 8) or saline ( n = 10) for three weeks. A dual molecular MRI approach was applied using a gadolinium-based elastin-specific probe to assess ECM integrity and ultrasmall superparamagnetic iron oxide particles (USPIO, iron oxide particles) to evaluate macrophage-driven inflammation. MRI was performed at pre-treatment baseline and at 2 and 3 weeks after treatment initiation. Cyclodextrin-treated animals demonstrated significantly smaller aortic cross-sectional areas (2.31 ± 0.36 mm² vs. 2.96 ± 0.62 mm²; p = 0.018) and lower elastin-specific signal enhancement (1.56 ± 0.26 vs. 2.15 ± 0.69; p = 0.038), indicating preserved ECM integrity. In contrast, no significant differences in iron oxide particles-related signal changes were observed between groups ( p = 0.441), consistent with histological and molecular findings. This study shows that elastin-specific molecular imaging non-invasively monitors cyclodextrin’s effects in AAA, whereas iron oxide particle imaging revealed no significant in vivo signal changes, consistent with ex vivo findings and indicating limited detectable inflammatory modulation associated with cyclodextrin treatment under the conditions of this study.
Unveiling wolf dietary specialization on free ranging horses using forensic genetics and field monitoring
Healthcare waste management practice, associated factors and barriers among waste handlers in healthcare facilities of Gondar City, Ethiopia
Abstract Healthcare waste poses significant risks to human health and the environment when improperly managed. In low-income countries such as Ethiopia, healthcare waste management remains a major public health concern, particularly among waste handlers who are directly exposed during waste handling processes. This study aimed to assess healthcare waste management practices, associated factors, and barriers among waste handlers in healthcare facilities in Gondar City, Northwest Ethiopia. An institution-based mixed-methods study was conducted from May 30 to June 30, 2025, among 372 waste handlers selected using multistage sampling techniques. Quantitative data were collected using structured questionnaires and analyzed using Bivariable and multivariable logistic regression analyses. Qualitative data were collected through a descriptive phenomenological approach and analyzed thematically. The prevalence of good healthcare waste management practices was 39.4%(95% CI:34.3,44.5). Type of healthcare facility, work section, training, disposal system type, availability of personal protective equipment (AOR:5.63;95%CI:2.64,12.02), knowledge (AOR:4.44;95%CI:2.38,8.26), and attitude (AOR:7.86;95%CI:4.05,15.28) were significantly associated with healthcare waste management practices. Qualitative findings revealed major barriers including shortage of human resources, poor disposal systems, weak institutional supervision and also insufficient availability of personal protective equipment was a key barrier that limited waste handlers’ ability to perform their work safely and effectively. Healthcare waste management practices among waste handlers in Gondar City were inadequate. Strengthening regular training programs, ensuring adequate provision of personal protective equipment, improving waste disposal systems, and enhancing institutional supervision are essential to promote safe waste management practices and reduce occupational health risks.