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| http://dx.doi.org/10.1136/bmj-2021-067663 | DOI Listing |
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Integration of clinical, pathological, radiological, and transcriptomic data improves prediction for first-line immunotherapy outcome in metastatic non-small cell lung cancer.
Nat Commun
January 2025
Bioinformatics and computational systems biology of cancer, Institut Curie, Inserm U900, PSL Research University, Paris, France.
Immunotherapy is improving the survival of patients with metastatic non-small cell lung cancer (NSCLC), yet reliable biomarkers are needed to identify responders prospectively and optimize patient care. In this study, we explore the benefits of multimodal approaches to predict immunotherapy outcome using multiple machine learning algorithms and integration strategies. We analyze baseline multimodal data from a cohort of 317 metastatic NSCLC patients treated with first-line immunotherapy, including positron emission tomography images, digitized pathological slides, bulk transcriptomic profiles, and clinical information.
View Article and Find Full Text PDFLiver function and Alzheimer's brain pathologies: A longitudinal study: Liver and Alzheimer's pathologies.
J Prev Alzheimers Dis
January 2025
Department of Neuropsychiatry, Seoul National University Hospital, Seoul, 03080, Republic of Korea; Department of Psychiatry, Seoul National University College of Medicine, Seoul, 03080, Republic of Korea; Institute of Human Behavioral Medicine, Medical Research Center Seoul National University, Seoul, 03080, Republic of Korea; Interdisciplinary Program of Cognitive Science, Seoul National University College of Humanities, Seoul, 08826, Republic of Korea. Electronic address:
Importance: The neuropathological links underlying the association between changes in liver function and AD have not yet been clearly elucidated.
Objective: We aimed to examine the relationship between liver function markers and longitudinal changes in Alzheimer's disease (AD) core pathologies.
Design: Data from the Korean Brain Aging Study for the Early Diagnosis and Prediction of Alzheimer's Disease, a longitudinal cohort study initiated in 2014, were utilized.
Expert opinion on Centiloid thresholds suitable for initiating anti-amyloid therapy. Summary of discussion at the 2024 spring Alzheimer's Association Research Roundtable.
J Prev Alzheimers Dis
January 2025
Memory & Aging Center, Departments of Neurology, Radiology & Biomedical Imaging, University of California San Francisco, San Francisco, California, USA.
A 24-30 Centiloid (CL) threshold was collectively considered by a group of global dementia experts as a practical and implementable cut-off for anti-amyloid therapy intervention, in Alzheimer's disease patients who have been diagnosed at the mild cognitive impairment or mild dementia stage of their disease. Though additional validation is needed, knowledge of this threshold would be valuable to those involved in diagnosing and treating patients in the new AD care pathways, as well as entry into clinical trials. Therapy monitoring to determine future treatment response and assess amyloid clearance can be accomplished with amyloid PET with some technical details still to be elucidated.
View Article and Find Full Text PDFINTERCEPT-AD, a phase 1 study of intravenous sabirnetug in participants with mild cognitive impairment or mild dementia due to Alzheimer's disease.
J Prev Alzheimers Dis
January 2025
CenExel iResearch, Atlanta, GA, USA.
Background: Soluble species of multimeric amyloid-beta including globular amyloid-beta oligomers (AβOs) and linear amyloid-beta protofibrils are toxic to neurons. Sabirnetug (ACU193) is a humanized monoclonal antibody, raised against globular species of soluble AβO, that has over 650-fold greater binding affinity for AβOs over monomers and appears to have relatively little binding to amyloid plaque.
Objectives: To assess safety, pharmacokinetics, and exploratory measures including target engagement, biomarker effects, and clinical efficacy of sabirnetug in participants with early symptomatic Alzheimer's disease (AD; defined as mild cognitive impairment and mild dementia due to AD).
End-to-end deep-learning model for the detection of coronary artery stenosis on coronary CT images.
Open Heart
January 2025
Department of Molecular and Clinical Medicine, University of Gothenburg Institute of Medicine, Gothenburg, Sweden.
Purpose: We examined whether end-to-end deep-learning models could detect moderate (≥50%) or severe (≥70%) stenosis in the left anterior descending artery (LAD), right coronary artery (RCA) or left circumflex artery (LCX) in iodine contrast-enhanced ECG-gated coronary CT angiography (CCTA) scans.
Methods: From a database of 6293 CCTA scans, we used pre-existing curved multiplanar reformations (CMR) images of the LAD, RCA and LCX arteries to create end-to-end deep-learning models for the detection of moderate or severe stenoses. We preprocessed the images by exploiting domain knowledge and employed a transfer learning approach using EfficientNet, ResNet, DenseNet and Inception-ResNet, with a class-weighted strategy optimised through cross-validation.
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