ABSTRACT INTRODUCTION Early detection of cognitive decline in Alzheimer's disease (AD), particularly in preclinical stages, is critical for evaluating therapeutic interventions. Traditional cognitive assessments often require lengthy in‐person visits, and may therefore limit scalability for younger, trial‐ready populations for primary and secondary prevention studies. We evaluated the feasibility, reliability, and validity of high‐frequency remote digital cognitive assessments in individuals with autosomal dominant AD (ADAD). METHODS One hundred twenty‐three mutation carriers and non‐carriers from the Dominantly Inherited Alzheimer Network Trials Unit from 20 international sites (Ages 19–66 years) completed remote assessments via personal smartphones, prompted four times daily for 7 days (≈ 3 minutes per session), and conventional in‐clinic cognitive testing at baseline. Adherence, between‐person reliability, test–retest reliability (intraclass correlation coefficients [ICCs]), construct validity (confirmatory factor analysis), and sensitivity to clinical disease progression were evaluated. RESULTS Remote measures demonstrated excellent reliability (ICCs > 0.90 after just 10 sessions) and strong construct validity, with tasks loading onto memory, attention, and executive function domains. A composite of the remote tests slightly outperformed a traditional composite at separating mutation carriers from non‐carriers. Average adherence to the study protocol was 42%, lower that observed in previous studies of older adults. DISCUSSION High‐frequency remote cognitive assessment is feasible, reliable, and valid in a relatively young international ADAD cohort. This approach offers substantial utility for clinical trials, including improved accessibility and enhanced reliability, supporting its integration into early‐intervention studies.
A. Aschenbrenner, H. Wilks, Matthew S. Welhaf et al.· Alzheimer's & Dementia· 1 citation
GeneLLM is presented, a Transformer-based model that directly processes the nucleotide sequences of human-mapped cfRNA reads to identify cancer-indicative signatures and allows accurate cancer classification from plasma biopsies, suggesting that sequence-level modelling of plasma cfRNA can capture diagnostically relevant information beyond annotation-dependent approaches.
Siwei Deng, Lei Sha, Yongcheng Jin et al.· Nature Communications· 0 citations
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