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We conducted the current analysis to determine the potential role of measles vaccination in the context of the spread of COVID-19. Data were extracted from the World Health Organization's (WHO) Global Health Observatory data repository about the measles immunization coverage estimates and correlated to overall morbidity and mortality for COVID-19 among different countries. Data were statistically analyzed to calculate the Spearman rank correlation coefficient (rho). There was a significant positive correlation between the vaccine coverage (%) and new cases per one million populations (rho = 0.24; p-value = 0.025); however, this correlation was absent in deaths per one million populations (rho = 0.17; p-value = 0.124). On further analysis of the effect of first reported year of vaccination policy, there was no significant correlation with both of total cases per one million populations (rho = 0.11; p-value = 0.327) and deaths per one million populations (rho = -0.02; p-value = 0.829). Claims regarding the possible protective effect of measles vaccination seem to be doubtful.
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http://dx.doi.org/10.1007/s11356-021-14980-6 | DOI Listing |
Ann Acad Med Singap
August 2025
Dementia Research Centre (Singapore), Lee Kong Chian School of Medicine, Nanyang Technology University, Singapore.
Introduction: Interpretation and analysis of magnetic resonance imaging (MRI) scans in clinical settings comprise time-consuming visual ratings and complex neuroimage processing that require trained professionals. To combat these challenges, artificial intelligence (AI) techniques can aid clinicians in interpreting brain MRI for accurate diagnosis of neurodegenerative diseases but they require extensive validation. Thus, the aim of this study was to validate the use of AI-based AQUA (Neurophet Inc.
View Article and Find Full Text PDFEur J Nucl Med Mol Imaging
September 2025
Nuclear Medicine and Molecular Imaging, Department of Imaging and Pathology, KU Leuven, Leuven, Belgium.
Purpose: Cardiac noradrenergic denervation visualized by meta-[I]iodobenzylguanidine ([I]MIBG) imaging supports the diagnosis of Parkinson's disease (PD). Recently, meta-[F] fluorobenzylguanidine ([F]MFBG) PET demonstrated favorable imaging characteristics compared with [I]MIBG scintigraphy for neuroendocrine tumors. We assessed [F]MFBG dosimetry and myocardial pharmacokinetics in healthy controls and PD patients.
View Article and Find Full Text PDFCancer Biol Med
September 2025
State Key Laboratory of Holistic Integrative Management of Gastrointestinal Cancers, Peking University Cancer Hospital & Institute, Beijing 100142, China.
Objective: The key molecular events signifying the -induced gastric carcinogenesis process are largely unknown.
Methods: Bulk tissue-proteomics profiling were leveraged across multi-stage gastric lesions from Linqu ( = 166) and Beijing sets ( = 99) and single-cell transcriptomic profiling ( = 18) to decipher key molecular signatures of -related gastric lesion progression and gastric cancer (GC) development. The association of key proteins association with gastric lesion progression and GC development were prospectively studied building on follow-up of the Linqu set and UK Biobank ( = 48,529).
Thyroid Res
September 2025
Department of Translational Medical Sciences, University of Naples "Federico II", Via S. Pansini, Naples, 5 - 80131, +39817462038, Italy.
Objectives: Serum thyroglobulin (Tg) is a key biomarker in the post-surgical monitoring of differentiated thyroid cancer (DTC). However, inter-assay variability among different immunoassay platforms can impact clinical interpretation, particularly at low Tg concentrations. This study aimed to compare the analytical performance and concordance of three widely used Tg immunoassays, Access (Beckman Coulter, Tg-B), Atellica (Siemens, Tg-A), and Liaison (Diasorin, Tg-L), with a focus on their agreement across clinically relevant Tg ranges.
View Article and Find Full Text PDFNAR Cancer
September 2025
Institute of Pathology, Charité-Universitätsmedizin Berlin, 10117 Berlin, Germany.
Personalized treatment selection is crucial for cancer patients due to the high variability in drug response. While actionable mutations can increasingly inform treatment decisions, most therapies still rely on population-based approaches. Here, we introduce neural interaction explainable AI (NeurixAI), an explainable and highly scalable deep learning framework that models drug-gene interactions and identifies transcriptomic patterns linked with drug response.
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