Fractional modelling of COVID-19 transmission incorporating asymptomatic and super-spreader individuals.

The COVID-19 pandemic has presented unprecedented challenges worldwide, necessitating effective modelling approaches to understand and control its transmission dynamics. In this study, we propose a novel approach that integrates asymptomatic and super-spreader individuals in a single compartmental model. We highlight the advantages of utilizing incommensurate fractional order derivatives in ordinary differential equations, including increased flexibility in capturing disease dynamics and refined memory effects in the transmission process.

IgE to wheat, prick test, and Patch test among children with celiac disease.

Introduction And Aim: Celiac disease is one of the most common autoimmune disorders. This study aimed to evaluate the relationship between celiac disease and wheat sensitization.

Subjects And Methods: In the current study, children aged < 18 years with confirmed celiac disease were included.

Early-Frame [F]Florbetaben PET/MRI for Cerebral Blood Flow Quantification in Patients with Cognitive Impairment: Comparison to an [O]Water Gold Standard.

Cerebral blood flow (CBF) may be estimated from early-frame PET imaging of lipophilic tracers, such as amyloid agents, enabling measurement of this important biomarker in participants with dementia and memory decline. Although previous methods could map relative CBF, quantitative measurement in absolute units (mL/100 g/min) remained challenging and has not been evaluated against the gold standard method of [O]water PET. The purpose of this study was to develop and validate a minimally invasive quantitative CBF imaging method combining early [F]florbetaben (eFBB) with phase-contrast MRI using simultaneous PET/MRI.

Generative Adversarial Network-Enhanced Ultra-Low-Dose [F]-PI-2620 τ PET/MRI in Aging and Neurodegenerative Populations.

Background And Purpose: With the utility of hybrid τ PET/MR imaging in the screening, diagnosis, and follow-up of individuals with neurodegenerative diseases, we investigated whether deep learning techniques can be used in enhancing ultra-low-dose [F]-PI-2620 τ PET/MR images to produce diagnostic-quality images.

Materials And Methods: Forty-four healthy aging participants and patients with neurodegenerative diseases were recruited for this study, and [F]-PI-2620 τ PET/MR data were simultaneously acquired. A generative adversarial network was trained to enhance ultra-low-dose τ images, which were reconstructed from a random sampling of 1/20 (approximately 5% of original count level) of the original full-dose data.