Results 81 to 90 of about 1,469,254 (314)

The Australian Longitudinal Study on Women's Health: 1989-95 cohort Core Data Release, Survey 6 data, 2018

open access: yes, 2022
The Australian Longitudinal Study on Women's Health (ALSWH) is a longitudinal population-based survey, first funded in 1995, which examines the health of over 57,000 Australian women.
Mishra, Gita   +4 more
core   +1 more source

Linkage analysis of longitudinal data [PDF]

open access: yesBMC Genetics, 2003
Abstract Background We propose a statistical model for linkage analysis of the longitudinal data. The proposed model is a mixed model based on the new Haseman and Elston model and allows several random effects.
Suh, Young Ju   +2 more
openaire   +2 more sources

Developmental programmes drive cellular plasticity, disease progression and therapy resistance in lung adenocarcinoma

open access: yesMolecular Oncology, EarlyView.
This study shows that lung adenocarcinomas exploit developmental branching morphogenesis to acquire a therapy resistant basal‐like tumour cell state. This process was found to be regulated by combined TP53 loss‐of‐function and type‐I interferon signalling, identifying a novel axis for biomarker and therapeutic target discovery.
Kamila J Bienkowska   +13 more
wiley   +1 more source

Sex-differences in age-related grip strength decline: A 10-year longitudinal study of community-living middle-aged and older Japanese

open access: yesJournal of Physical Fitness and Sports Medicine, 2016
The purpose of this study was to estimate sex differences in age-related grip strength decline and describe the course of decline in grip strength from age 40 to 89 years by a longitudinal epidemiological study.
Rumi Kozakai   +5 more
doaj   +1 more source

Harmonised Height, Weight and BMI in Five Longitudinal Cohort Studies: Avon Longitudinal Study of Parents and Children: Special Licence Access

open access: yes, 2017
copyright UK Data Service and data collection copyright owner.The Cohort and Longitudinal Studies Enhancement Resources (CLOSER) project aims to maximise the use, value and impact of longitudinal research. It brings together leading longitudinal studies,
Cohort and Longitudinal Studies Enhancement Resources
core  

Loss of proton‐sensing TDAG8 increases tumor progression in mouse models of colon cancer

open access: yesMolecular Oncology, EarlyView.
Loss of the pH‐sensing receptor TDAG8 accelerates colorectal cancer progression in mice. Animals lacking TDAG8 expression had increased tumor growth, DNA damage, and recruitment of tumor‐associated immune cells, including macrophages, neutrophils, and monocytes.
Ermanno Malagola   +11 more
wiley   +1 more source

Integrated noninvasive diagnostics for prediction of survival in immunotherapy

open access: yesImmuno-Oncology and Technology
Background: Integrating complementary diagnostic data sources promises enhanced robustness in the predictive performance of artificial intelligence (AI) models, a crucial requirement for future clinical validation/implementation.
M. Yeghaian   +10 more
doaj   +1 more source

The Australian Longitudinal Study on Women's Health: 1946-51 cohort Core Data Release (Version 2), Survey 8 data, 2016

open access: yes, 2020
The Australian Longitudinal Study on Women's Health (ALSWH) is a longitudinal population-based survey, first funded in 1995, which examines the health of over 57,000 Australian women.
Mishra, Gita   +4 more
core   +1 more source

Epigenetic heterogeneity and plasticity in therapy‐induced tumor states through single‐cell multi‐omics

open access: yesMolecular Oncology, EarlyView.
Single‐cell multi‐omics reveals epigenetic heterogeneity across therapy‐adaptive tumor states, including quiescent/dormant, drug‐tolerant persister, and EMT‐like phenotypes. By linking regulatory features with state‐associated biomarkers, these approaches inform biomarker‐guided therapeutic strategies for evolving tumors.
Hee Jung Kim   +3 more
wiley   +1 more source

Model-Free Feature Screening Based on Data Aggregation for Ultra-High-Dimensional Longitudinal Data

open access: yesStats
Ultra-high dimensional longitudinal data feature screening procedures are widely studied, but most require model assumptions. The screening performance of these methods may not be excellent if we specify an incorrect model.
Junfeng Chen   +3 more
doaj   +1 more source

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