A multi-cohort study of longitudinal and cross-sectional Alzheimer's disease biomarkers in cognitively unimpaired older adults.

Introduction: The generalizability of neuroimaging and cognitive biomarkers in their sensitivity to detect preclinical Alzheimer's disease (AD) and power to predict progression in large, multisite cohorts remains unclear.

Method: Longitudinal demographics, T1-weighted magnetic resonance imaging (MRI), and cognitive scores of 3036 cognitively unimpaired (CU) older adults (amyloid beta [Aβ]-negative/positive [A-/A+]: 1270/1558) were included. Cross-sectional and longitudinal cognition and medial temporal lobe (MTL) structural measures were extracted.

Patient-reported outcomes following ciltacabtagene autoleucel or standard of care in patients with lenalidomide-refractory multiple myeloma (CARTITUDE-4): results from a randomised, open-label, phase 3 trial.

Background: In CARTITUDE-4, ciltacabtagene autoleucel (cilta-cel) significantly improved progression-free survival (primary endpoint; previously reported) versus standard of care in patients with relapsed, lenalidomide-refractory multiple myeloma. We report here patient-reported outcomes.

Methods: In the ongoing, phase 3, open-label CARTITUDE-4 study, patients were recruited from 81 sites in the USA, Europe, Asia, and Australia, and were randomly assigned 1:1 to cilta-cel (target, 0·75 × 10 CAR-T cells/kg) or standard of care (daratumumab, pomalidomide, and dexamethasone; pomalidomide, bortezomib, and dexamethasone).

Determining the time to cholangiocarcinoma in pediatric-onset PSC-IBD.

Primary sclerosing cholangitis (PSC) is a risk factor for cholangiocarcinoma. When a child is diagnosed with both PSC and inflammatory bowel disease (IBD), evidence-based information on counseling families and risk management of developing cholangiocarcinoma is limited. In this case series (PubMed/collaborators), we included patients with PSC-IBD who developed cholangiocarcinoma and contacted authors to determine an event curve specifying the time between the second diagnosis (IBD or PSC) and a diagnosis of cholangiocarcinoma.

Random survival forest model for early prediction of Alzheimer's disease conversion in early and late Mild cognitive impairment stages.

With a clinical trial failure rate of 99.6% for Alzheimer's Disease (AD), early diagnosis is critical. Machine learning (ML) models have shown promising results in early AD prediction, with survival ML models outperforming typical classifiers by providing probabilities of disease progression over time.