Pittsburg State University

The International Dyslexia Association's current definition of dyslexia was approved by its Board of Directors on November 12, 2002. After two decades of scientific inquiry into the nature of dyslexia, it is time to reconsider and potentially revise the definition in light of what has been learned. We propose a definition of dyslexia based on its essential nature. Dyslexia is a specific learning disability in reading at the word level. It involves difficulty with accurate and/or fluent word recognition and/or pseudoword reading. We also suggest that the definition should focus solely on dyslexia's core features and should not include risk factors, potential secondary consequences, or other characteristics. Until those factors can reliably differentiate between those with and without dyslexia at an individual level, they should not be included in the definition.

This article provides a brief overview of the incidence, pathophysiology, diagnosis, and management of neonatal abstinence syndrome. Areas of discussion include genetics, pathophysiology, diagnostic tools, and pharmacologic and nonpharmacologic management of neonatal abstinence syndrome.

Metal-organic frameworks (MOFs) have emerged as promising materials for the remediation and sensing of per- and polyfluoroalkyl substances (PFAS), which are persistent environmental contaminants with serious health implications.This review comprehensively examines MOF-based strategies for PFAS removal and detection, focusing on fundamental aspects of MOF design, adsorption mechanisms, and sensor technologies.The unique properties of MOFs, including high porosity, tunable surface chem., and selective binding interactions, facilitate effective PFAS capture through electrostatic, hydrogen bonding, and fluorophilic interactions.Advances in MOF-polymer and MOF-carbon composites have improved structural stability, recyclability, and catalytic degradation, while MOF-based photocatalysts and electrocatalysts offer promising pathways for PFAS degradationEmerging artificial intelligence (AI)-driven design approaches further accelerate the discovery of optimized MOFs for ultrasensitive PFAS detection via optical, electrochem., and field-effect transistor (FET) sensors.Despite their potential, challenges related to aqueous stability, regeneration efficiency, and scalability remain.This review highlights current breakthroughs, identifies critical knowledge gaps, and outlines future research directions aimed at the practical implementation of MOF-based technologies for PFAS remediation and sensing, ultimately contributing to improved water quality and public health.Integrating insights from materials science, environmental engineering, and AI, this review offers a multidimensional perspective to guide future scalable and sustainable PFAS remediation solutions for widespread implementation.