An IoT-Based Framework for Automated Assessing and Reporting of Light Sensitivities in Children with Autism Spectrum Disorder.

Identification of light sensitivities, manifesting either as hyper-sensitive (over-stimulating) or hypo-sensitive (under-stimulating) in children with autism spectrum disorder (ASD), is crucial for the development of personalized sensory environments and therapeutic strategies. Traditional methods for identifying light sensitivities often depend on subjective assessments and manual video coding methods, which are time-consuming, and very keen observations are required to capture the diverse sensory responses of children with ASD. This can lead to challenges for clinical practitioners in addressing individual sensory needs effectively.

A plasmonic fiberoptic absorbance biosensor for mannose-capped lipoarabinomannan based tuberculosis diagnosis.

Tuberculosis (TB) is a resurgent infectious disease affecting a large number of people in the developing countries. An on-site, affordable diagnostic screening at an early-stage for an immediate anti-TB treatment is known to tremendously minimize the high mortality rates. Lipoarabinomannan (LAM), a surface glycolipid, has been identified as a potential TB biomarker present in urine at ultra-low concentrations of a few fg/mL.