Rotavirus A (RVA) remains one of the most widespread causes of diarrheal disease and mortality in piglets despite decades of research and efforts to boost lactogenic immunity for passive protection. Genetic changes at B cell epitopes (BCEs) may be driving failure of lactogenic immunity, which relies on production of IgA antibodies to passively neutralize RVA within the piglet gut, yet little research has mapped epitopes to swine-specific strains of RVA. Here we describe a bioinformatic approach to predict BCEs on the VP7 outer capsid protein using sequence data alone. We first validated the approach using a previously published dataset of VP7-specific cross-neutralization titers, and found that amino acid changes at predicted BCEs on the VP7 protein allowed for accurate recapitulation of antigenic relationships among the strains. Applying the approach to a dataset of swine RVA sequences identified 9 of the 11 known BCEs previously mapped to swine strains, indicating that epitope prediction can identify sites that are known to drive neutralization escape . Additional genotype-specific BCEs were also predicted that may be the cause of antigenic differences among strains of RVA on farms and should be targeted for further confirmatory work. The results of this work lay the groundwork for high throughput, immunologically-relevant analysis of swine RVA sequence data, and provide potential sites that can be targeted with vaccines to reduce piglet mortality and support farm health.
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http://dx.doi.org/10.3389/fgene.2020.00828 | DOI Listing |
Diagn Progn Res
January 2025
Department of Applied Health Sciences, College of Medicine and Health, University of Birmingham, Edgbaston, Birmingham, UK.
Background: Pressure injuries (PIs) place a substantial burden on healthcare systems worldwide. Risk stratification of those who are at risk of developing PIs allows preventive interventions to be focused on patients who are at the highest risk. The considerable number of risk assessment scales and prediction models available underscores the need for a thorough evaluation of their development, validation, and clinical utility.
View Article and Find Full Text PDFInt J Retina Vitreous
January 2025
Department of Retina and Vitreous, Narayana Nethralaya, #121/C, 1st R Block, Chord Road, Rajaji Nagar, Bengaluru, 560010, India.
Purpose: To evaluate the predictive accuracy of various machine learning (ML) statistical models in forecasting postoperative visual acuity (VA) outcomes following macular hole (MH) surgery using preoperative optical coherence tomography (OCT) parameters.
Methods: This retrospective study included 158 eyes (151 patients) with full-thickness MHs treated between 2017 and 2023 by the same surgeon and using the same intraoperative surgical technique. Data from electronic medical records and OCT scans were extracted, with OCT-derived qualitative and quantitative MH characteristics recorded.
J Transl Med
January 2025
School of Clinical Laboratory Science, Guizhou Medical University, Guiyang, Guizhou, 550000, China.
Background: Human kinesin family member 11 (KIF11) plays a vital role in regulating the cell cycle and is implicated in the tumorigenesis and progression of various cancers, but its role in endometrial cancer (EC) is still unclear. Our current research explored the prognostic value, biological function and targeting strategy of KIF11 in EC through approaches including bioinformatics, machine learning and experimental studies.
Methods: The GSE17025 dataset from the GEO database was analyzed via the limma package to identify differentially expressed genes (DEGs) in EC.
Syst Rev
January 2025
Statistical Laboratory, Department of Pure Mathematics and Mathematical Statistics, University of Cambridge, Cambridge, UK.
Background: Scientific papers increasingly put forward scientific-based policy recommendations (SPRs) as a means of closing the circle of science, policy and practice. Assessing the quality of such SPRs is crucial, especially within the context of a systematic review. Here, we present ECR-P (Evidence Communication Rules for Policy)-a critical appraisal tool that we have developed, which can be used in assessing not only the quality of SPRs but also the quality of their evidence base and how effectively these have both been communicated.
View Article and Find Full Text PDFBMC Musculoskelet Disord
January 2025
Department of Regional Health Research, University of Southern Denmark, Campusvej 55, Odense M, 5230, Denmark.
Background: The Knee Outcome Survey - Activities of Daily Living Scale (KOS-ADLS) is a patient-reported outcome measure (PROM) developed to assess symptoms and functional limitations in patients with various knee disorders. The aim of this study was to translate and culturally adapt the KOS-ADLS to Danish and to evaluate the psychometric properties of the Danish version (KOS-ADLS-DK) in patients with anterior cruciate ligament (ACL) injury.
Methods: The KOS-ADLS was translated and culturally adapted to Danish in accordance with recommended guidelines.
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