Thalamic atrophy and dysconnectivity are associated with cognitive impairment in a multi-center, clinical routine, real-word study of people with relapsing-remitting multiple sclerosis.

Background: Prior research has established a link between thalamic pathology and cognitive impairment (CI) in people with multiple sclerosis (pwMS). However, the translation of these findings to pwMS in everyday clinical settings has been insufficient.

Objective: To assess which global and/or thalamic imaging biomarkers can be used to identify pwMS at risk for CI and cognitive worsening (CW) in a real-world setting.

Concurrent administration of serotonergic antidepressants and ozanimod in participants with relapsing multiple sclerosis from the open-label extension DAYBREAK trial.

Background: Monoamine oxidase (MAO) inhibitors can interact with selective serotonin reuptake inhibitors (SSRIs)/serotonin-norepinephrine reuptake inhibitors (SNRIs). There is clinical interest surrounding use of ozanimod with SSRIs/SNRIs because the major metabolites of ozanimod are weak inhibitors of MAO-B in vitro.

Objective: To evaluate the incidence of treatment-emergent adverse events (TEAEs) potentially related to serotonin accumulation (SA) during concomitant ozanimod and SSRI/SNRI use by performing analyses of data from an open-label, oral ozanimod 0.

Therapy effect on AI-derived thalamic atrophy using clinical routine MRI protocol: A longitudinal, multi-center, propensity-matched multiple sclerosis study.

Background: The effect of disease modifying therapies (DMTs) on brain atrophy in persons with multiple sclerosis (pwMS) is typically investigated in highly standardized clinical trial settings or single-center academic institutions. We aimed at utilizing artificial intelligence (AI)-based volumetric analysis on routine unstandardized T2-FLAIR scans in determining the effect of DMTs on lateral ventricular volume (LVV) and thalamic volume (TV) changes in pwMS.

Methods: The DeepGRAI (Deep Gray Rating via Artificial Intelligence) registry is a multi-center, longitudinal, observational, real-word study with a convenience sample of 1002 relapsing-remitting (RR) pwMS from 30 United States sites.

Thalamic atrophy measured by artificial intelligence in a multicentre clinical routine real-word study is associated with disability progression.

Background: The thalamus is a key grey matter structure, and sensitive marker of neurodegeneration in multiple sclerosis (MS). Previous reports indicated that thalamic volumetry using artificial intelligence (AI) on clinical-quality T2-fluid-attenuated inversion recovery (FLAIR) images alone is fast and reliable.

Objective: To investigate whether thalamic volume (TV) loss, measured longitudinally by AI, is associated with disability progression (DP) in patients with MS, participating in a large multicentre study.

Comparative analysis of genome code complexity and manufacturability with engineering benchmarks.

When knowledge has advanced to a state that includes a predictive understanding of the relationship between genome sequence and organism phenotype it will be possible for future engineers to design and produce synthetic organisms. However, the possibility of synthetic biology does not necessarily guarantee its feasibility, in much the same way that the possibility of a brute force attack fails to ensure the timely breaking of robust encryption. The size and range of natural genomes, from a few million base pairs for bacteria to over 100 billion base pairs for some plants, suggests it is necessary to evaluate the practical limits of designing genomes of similar complexity.