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Introduction COVID-19 is an international public health crisis with more than 132 million infections worldwide. Beyond acute infection, emerging data indicate patients diagnosed with COVID-19 may experience persistent sequelae similar to survivors of sepsis or acute respiratory syndromes, including mobility limitations and fatigue. However, there is limited evidence on the trajectory of functional recovery in those hospitalised with COVID-19. The primary aim of the Coronavirus Registry Functional Recovery (COREG-FR) study is to understand the trajectory of functional recovery among individuals hospitalised for COVID-19 over the medium (up to 6 months) and longer term (6–12 months) that will guide clinical care and optimal management of serious COVID-19 illness and recovery. Methods and analysis COREG-FR is a multicentre longitudinal cohort study. We will enrol a minimum of 211 adults age 18 years and older with COVID-19 from five hospitals. Participants will be followed from admission to hospital as an inpatient, to hospital discharge, and at 3-month, 6-month, 9-month and up to 12-month post-hospital discharge. We will conduct telephone interviews at ward admission and discharge, and telephone interviews plus in-person assessments of physical function and lung function at all remaining follow-ups. Our primary outcome is the Activity Measure for Post-Acute Care mobility scale measured at all time points. We will conduct linear mixed effects regression analyses to explore determinants of functional outcomes after COVID-19 illness. Subgroup analyses based on age (≤65 vs >65 years), frailty status (Clinical Frailty Scale score ≤4 vs >5) and variants of concern will be conducted. Ethics and dissemination COREG-FR has been approved by Research Ethics Boards at participating sites. We will disseminate this work through peer-reviewed manuscripts, presentations at national and international meetings and through the established COREG website (www.coregontario.ca). COREG-FR is designed as a data platform for future studies evaluating COVID-19 recovery. Trial registration number NCT04602260; Pre-results.

Connectomics has demonstrated that synaptic networks and their topologies are precise and directly correlate with physiology and behavior. The next extension of connectomics is pathoconnectomics: to map neural network synaptology and circuit topologies corrupted by neurological disease in order to identify robust targets for therapeutics. In this report, we characterize the first pathoconnectome, in this case, generated from a retina with photoreceptor degeneration. We observe aberrant connectivity in the rod-network pathway and novel synaptic connections deriving from neurite sprouting. These observations reveal principles of neuron responses to the loss of network components and can be extended to other neurodegenerative diseases.

Glia play important roles in neural function, including but not limited to amino acid recycling, ion homeostasis, glucose metabolism, and waste removal. During retinal degeneration and subsequent retinal remodeling, Müller cells (MCs) are the first cells to show metabolic and morphological alterations in response to stress. Metabolic alterations in MCs chaotically progress in retina undergoing photoreceptor degeneration; however, what relationship these alterations have with neuronal stress, synapse maintenance, or glia-glia interactions is currently unknown. The work described here reconstructs a MC from a pathoconnectome of early retinal remodeling retinal pathoconnectome 1 (RPC1) and explores relationships between MC structural and metabolic phenotypes in the context of neighboring neurons and glia. Here we find variations in intensity of osmication inter- and intracellularly, variation in small molecule metabolic content of MCs, as well as morphological alterations of glial endfeet. RPC1 provides a framework to analyze these relationships in early retinal remodeling through ultrastructural reconstructions of both neurons and glia. These reconstructions, informed by quantitative metabolite labeling via computational molecular phenotyping (CMP), allow us to evaluate neural-glial interactions in early retinal degeneration with unprecedented resolution and sensitivity.