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Development of a stream DTM generation method using vegetation and morphology composite filters with SfM point clouds
Dynamic monitoring of M-protein quantification by immunotyping using capillary zone electrophoresis during the chemotherapy of patients with multiple myeloma
Modeling and performance evaluation of hybrid photovoltaic thermal, wind, and battery microgrids using optimization and dynamic simulation
Abstract This study aims to comprehensively develop a modeling framework to evaluate the dynamic performance of a photovoltaic/thermal (PV/T) system integrated with a hybrid off-grid microgrid. The advancements made by this research in investigating the optimal design of the PV/T system and dynamic performance assessment of the proposed hybrid microgrid are twofold. First, a nonlinear mathematical problem is formulated to determine the optimal system design that maximizes power, taking into account the thermo-electrical constraints. Secondly, the research highlights the development of a component mask with a user-defined functionality in MATLAB/Simulink using the optimal design parameters obtained from the optimization model. The developed PV/T component is then integrated with a wind turbine/ battery system. The resultant integrated energy system is then compared with a conventional PV/wind/battery microgrid system based on a 72-hour simulation. The outcomes showed that under cloudy, rainy, sunny, and windy conditions, the extra cumulative electricity generation from the PV/T system-based microgrid is 2.12, 2.74, 1.72, and 0.31% compared to the PV system-based microgrid. Additionally, improved battery system operation of nearly 1.8Wh was realized, signifying PV/T contribution to efficient microgrid operation.
A high-throughput, fully automated competition assay to evaluate SARS-CoV-2 neutralizing responses and epitope specificity in clinical samples
Abstract Coronavirus disease-2019 (COVID-19) remains a critical global health concern. We developed a fully automated, high-throughput competition immunoassay to elucidate how epitope recognition on the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spike receptor-binding domain (RBD) correlates with neutralizing activity. Analysis of clinical samples from both SARS-CoV-2-infected and vaccinated individuals revealed that vaccination elicits significantly higher antibody titers across multiple S1 subunit epitopes compared to natural infection. Notably, median antibody levels against the receptor-binding motif (RBM) exceeded 50% in both cohorts, highlighting the RBM as a key target for antibody induction irrespective of immune origin. Furthermore, the strongest correlation with neutralizing activity was observed for antibodies directed against the broader S1 subunit, indicating that epitopes outside the RBM also contribute to neutralization. These findings underscore the importance of both RBM- and non-RBM-directed antibodies in effective immune defense against SARS-CoV-2. Our assay enables large-scale, reliable quantification of neutralizing antibodies and provides critical insights for developing improved diagnostic antigens and vaccine strategies aimed at eliciting robust, multi-epitope immune responses.
Detection of pathogens associated with acute febrile illness in children under five years of age in rural Tanzania
Abstract Acute febrile illness (AFI) investigations are crucial for public health. They can provide data on disease prevalence, morbidity, and mortality, and improve treatment, management, control, and detection of outbreaks in areas with limited diagnostic tests. Current understanding of multiple causes of AFI in the paediatric population in Tanzania is limited. This study aimed to simultaneously detect 33 pathogens using TaqMan Array Card based real-time PCR. Whole blood samples were collected from a total of 247 children (2–59 months old) who presented with febrile illness at Dareda and Haydom hospitals in north-eastern Tanzania between November 2015 and March 2016. Overall, 50 (20.2%) and 8 (3.2%) of 247 children had at least one and more than one pathogen detected respectively. Bacterial zoonoses were frequently detected including Brucella spp. (n = 18, 7.3%), C. burnetii (n = 4, 1.6%), Bartonella spp. (n = 3, 1.2%), Rickettsia spp. (n = 3, 1.2%) and Leptospira spp. (n = 1, 0.4%). Dengue virus was detected in 14 (5.7%) individuals and Plasmodium spp. in 12 (4.9%) individuals. These findings reveal the potential clinical importance of zoonoses and arboviruses in febrile children in Tanzania and highlight the need to consider a broad range of pathogens in febrile illness diagnosis.
Blended nutrition education with real-life scenarios enhances learning and nutritional counseling capabilities in nursing students
HIF-1 regulates mitochondrial function in bone marrow-derived macrophages but not in tissue-resident alveolar macrophages
Author Correction: An open-access dashboard to interrogate the genetic diversity of Mycobacterium tuberculosis clinical isolates
Tire-road surface characteristics estimation for skid-steered wheeled vehicle
A quantum inspired machine learning approach for multimodal Parkinson’s disease screening
Abstract Parkinson’s disease, currently the fastest-growing neurodegenerative disorder globally, has seen a 50% increase in cases within just two years. As disease progression impairs speech, memory, and motor functions over time, early diagnosis is crucial for preserving patients’ quality of life. Although machine-learning-based detection has shown promise for detecting Parkinson’s disease, most studies rely on a single feature for classification and can be error-prone due to the variability of symptoms between patients. To address this limitation we utilized the mPower dataset, which includes 150,000 samples across four key biomarkers: voice, gait, tapping, and demographic data. From these measurements, we extracted 64 features and trained a baseline Random Forest model to select the features above the 80th percentile. For classification, we designed a simulatable quantum support vector machine (qSVM) that detects high-dimensional patterns, leveraging recent advancements in quantum machine learning. With this novel and simulatable architecture that can be run on standard hardware rather than resource-intensive quantum computers, our model achieves an accuracy of 90%, F-1 score of 0.90, and an AUC of 0.98—surpassing benchmark models. Utilizing an innovative classification framework built on a diverse set of features, our model offers a pathway for accessible global Parkinson’s screening.
Influence of preoperative anti TNF alpha antibody therapy on postoperative recurrence of Crohn’s disease
Mapping relationships among gross motor skills in 16,989 children using network analysis
Abstract The development of gross motor skills during childhood is crucial for shaping more complex movements and laying the groundwork for physical activity, and subsequently lifelong health and enhanced well-being. Performance in motor skills improves throughout development, with the greatest improvements occurring during childhood. Understanding the relationships between developing gross motor skills is essential for informing educational and intervention practices. A total of 16,989 children aged 3–11 years underwent assessment of gross motor skills. Using network analysis, gross motor skills networks were constructed for the entire sample, and stratified by age and sex. The accuracy and stability of the networks were assessed, and centrality and bridge statistics were estimated for each node. The results indicated that running and two-hand catching exhibited higher centrality and bridge statistics compared to the other nodes in the all-sample network. Additionally, it was observed that the strength between nodes decreased and their distance increased with age. These results highlight the importance of specific gross motor skills due to their significant role in relation to other skills within the network. Gross motor skills progress towards increased independence and specialisation during development, indicating the importance of early educational interventions where children could benefit from educational practices focused on catching and running.
How to evaluate the quality of the clinical learning environment in health professions education? Protocol of a systematic review
Background Internships can constitute up to one third of the curriculum and during these internships, the foundation for developing specific health professional competencies is formed. The clinical learning environment (CLE) is a critical determinant of the overall quality of internships in health profession education, shaping students’ professional competencies and experiences. Objective This systematic review aims to identify and categorize assessment tools available for evaluating the quality of the CLE in health professions education. Methods This in the International Database of Education Systematic Reviews preregistered systematic review [IDESR000098] will consider peer-reviewed articles in English where instruments are developed and validated to illustrate the quality of the CLE in higher education health professions students. The search strategy will encompass multiple electronic databases, including MEDLINE, EMBASE, the Cochrane Library, ERIC, Education Research Complete, Education Database, and CINAHL. Studies will be independently assessed for risk of bias using the COSMIN Risk of Bias checklist for systematic reviews of PROMs. We will summarize and tabulate the basic characteristics of each identified tool and via a comprehensive table we will summarize the reported psychometric properties. Discussion This systematic review protocol will outline a comprehensive approach to identifying and evaluating assessment tools for measuring the quality of the CLE in health profession students. It is assumed that the findings will offer several notable advantages and impacts, which could significantly influence the quality of clinical education for health profession students.
Evaluation of radiographic knee OA progression after arthroscopic meniscectomy compared with IACI for degenerative meniscus tear
Supervised Machine Learning and Physics Machine Learning approach for prediction of peak temperature distribution in Additive Friction Stir Deposition of Aluminium Alloy
Additive friction stir deposition (AFSD) is a novel solid-state additive manufacturing technique that circumvents issues of porosity, cracking, and properties anisotropy that plague traditional powder bed fusion and directed energy deposition approaches. However, correlations between process parameters, thermal profiles, and resulting microstructure in AFSD still need to be better understood. This hinders process optimization for properties. This work employs a framework combining supervised machine learning (SML) and physics-informed neural networks (PINNs) to predict peak temperature distribution in AFSD from process parameters. Eight regression algorithms were implemented for SML modeling, while four PINNs leveraged governing equations for transport, wave propagation, heat transfer, and quantum mechanics. Across multiple statistical measures, ensemble techniques like gradient boosting proved superior for SML, with the lowest MSE of 165.78. The integrated ML approach was also applied to classify deposition quality from process factors, with logistic regression delivering robust accuracy. By fusing data-driven learning and fundamental physics, this dual methodology provides comprehensive insights into tailoring microstructure through thermal management in AFSD. The work demonstrates the power of bridging statistical and physics-based modeling for elucidating AM process-property relationships.
Author Correction: Assessment of heavy metals and microbial loads in Nile tilapia (Oreochromis niloticus) from different farms and rivers
Exploring the impact of opioid use on outcomes in allogeneic hematopoietic stem cell transplantation
Introduction Hematological malignancies and allogeneic hematopoietic cell transplantation (alloHCT) often necessitate the use of opioids due to significant pain. This study aimed to investigate the impact of opioid use on the clinical outcomes of patients undergoing alloHCT. Methods A retrospective cohort study was conducted by merging data from our local transplant database with anonymized pharmacy records obtained from the Institute for Clinical Evaluative Sciences (ICES). We analyzed 681 patients who underwent alloHCT at Princess Margaret Cancer Centre between January 2010 and December 2019. Patients who initiated opioid use within one-year post-alloHCT and had opioid prescriptions for more than 30 days were categorized as intense opioid users (IOU). Additionally, patients who started opioids before or within one-year post-alloHCT and had opioid prescriptions for less than 30 days but died while on opioids were also classified as IOU. The analytical code used for the analysis is available in the Supporting Information file. Results Among the 681 patients, 51 were identified as IOU. The two-year overall survival (OS) was significantly lower in the IOU group, with 29.4% survival compared to 53% in non-IOU (HR 1.77, 95% CI 1.26–2.48, p = 0.0008). Multivariate analysis indicated that IOU status was associated with a 2.32 times higher instantaneous rate of death compared to non-IOU (HR 2.32, 95% CI 1.5–3.5, p = 0.002). The median time for relapse was 147 days in the IOU group (range 52–393) and 209 day for non-IOU (range 96–1793), p = 0.0082. Furthermore, the relapse rate at two years was notably higher in the IOU group (31.4% vs. 16.4%, p = 0.0049). The analysis of factors independently associated with relapse-free survival (LFS) showed that IOU status, age, donor type, and cytogenetic risk were significant predictors. At two years, relapse-free survival was 29.4% in the IOU group compared to 52.5% in the non-IOU group (p < 0.001). Conclusions In our study, we found a correlation between intense opioid use in alloHCT patients and worse overall survival, particularly concerning higher relapse rates. These findings highlight the need for further research into pain management strategies to improve outcomes and reduce potential toxicity.