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Drug discovery and clinical trial design for dementia have historically been challenging. In part these challenges have arisen from patient heterogeneity, length of disease course, and the tractability of a target for the brain. Applying big data analytics and machine learning tools for drug discovery and utilizing them to inform successful clinical trial design has the potential to accelerate progress. Opportunities arise at multiple stages in the therapy pipeline and the growing availability of large medical data sets opens possibilities for big data analyses to answer key questions in clinical and therapeutic challenges. However, before this goal is reached, several challenges need to be overcome and only a multi‐disciplinary approach can promote data‐driven decision‐making to its full potential. Herein we review the current state of machine learning applications to clinical trial design and drug discovery, while presenting opportunities and recommendations that can break down the barriers to implementation. [ABSTRACT FROM AUTHOR]

INTRODUCTION: The use of applied modeling in dementia risk prediction, diagnosis, and prognostics will have substantial public health benefits, particularly as "deep phenotyping" cohorts with multi‐omics health data become available. METHODS: This narrative review synthesizes understanding of applied models and digital health technologies, in terms of dementia risk prediction, diagnostic discrimination, prognosis, and progression. Machine learning approaches show evidence of improved predictive power compared to standard clinical risk scores in predicting dementia, and the potential to decompose large numbers of variables into relatively few critical predictors. RESULTS: This review focuses on key areas of emerging promise including: emphasis on easier, more transparent data sharing and cohort access; integration of high‐throughput biomarker and electronic health record data into modeling; and progressing beyond the primary prediction of dementia to secondary outcomes, for example, treatment response and physical health. DISCUSSION: Such approaches will benefit also from improvements in remote data measurement, whether cognitive (e.g., online), or naturalistic (e.g., watch‐based accelerometry). [ABSTRACT FROM AUTHOR]

Chemotaxis signals between hepatic stellate cells (HSC) and sinusoidal endothelial cells (SEC) maintain hepatic vascular homeostasis and integrity and also regulate changes in sinusoidal structure in response to liver injury. Our prior studies have demonstrated that the bidirectional chemotactic signaling molecules EphrinB2 and EphB4 are expressed in HSC. The aim of our present study was to explore whether and how the EphrinB2/EphB4 system in HSC could promote SEC recruitment, which is essential for sinusoidal structure and remodeling. Stimulation of human HSC (hHSC) with chimeric agonists (2 [micro]g/ml) of either EphrinB2 or EphB4 (EphrinB2 Fc or EphB4 Fc, respectively) significantly increased VEGF mRNA levels in hHSC as assessed by quantitative PCR, with respective small interfering RNAs for EphrinB2 and EphB4 inhibiting this increase (P < 0.05, n = 3). EphrinB2 agonist-induced increase in VEGF mRNA levels in hHSC was associated with increased phosphorylation of Erk and was significantly blocked by U0126 (20 [micro]M), an inhibitor of MEK, which is a kinase upstream from Erk (P < 0.05, n = 3). The EphB4 agonist also significantly increased human VEGF promoter activity (P < 0.05, n = 3) as assessed by promoter reporter luciferase assay in transfected LX2-HSC. This was associated with upregulation of the vasculoprotective transcription factor, Kruppel-like factor 2 (KLF2). In Boyden chamber assays, conditioned media from hHSC stimulated with agonists of EphrinB2 or EphB4 increased SEC chemotaxis in a VEGF-dependent manner, compared with control groups that included basal media with agonists of EphrinB2, EphB4, or HSC-conditioned media from HSC in absence of agonist stimulation (P < 0.05, n = 3). EphB4 expression was detected in situ within liver sinusoidal vessels of rats after carbon tetrachloride-induced liver injury. In summary, activation of the EphrinB2/EphB4 signaling pathway in HSC promotes chemotaxis of SEC through a pathway that involves Erk, KLF2, and VEGF. These studies identify EphrinB2 or EphB4 as a key intermediary that links HSC signal transduction pathways with angiogenesis and sinusoidal remodeling. hepatic stellate cell; sinusoidal endothelial cell; VEGF; ephrins doi: 10.1152/ajpgi.00510.2009.

Abstract Background Acute lung injury (ALI) is an example of a critical care syndrome with limited treatment options once the condition is fully established. Despite improved understanding of pathophysiology of ALI, the clinical impact has been limited to improvements in supportive treatment. On the other hand, little has been done on the prevention of ALI. Olmsted County, MN, geographically isolated from other urban areas offers the opportunity to study clinical pathogenesis of ALI in a search for potential prevention targets. Methods/Design In this population-based observational cohort study, the investigators identify patients at high risk of ALI using the prediction model applied within the first six hours of hospital admission. Using a validated system-wide electronic surveillance, Olmsted County patients at risk are followed until ALI, death or hospital discharge. Detailed in-hospital (second hit) exposures and meaningful short and long term outcomes (quality-adjusted survival) are compared between ALI cases and high risk controls matched by age, gender and probability of developing ALI. Time sensitive biospecimens are collected for collaborative research studies. Nested case control comparison of 500 patients who developed ALI with 500 matched controls will provide an adequate power to determine significant differences in common hospital exposures and outcomes between the two groups. Discussion This population-based observational cohort study will identify patients at high risk early in the course of disease, the burden of ALI in the community, and the potential targets for future prevention trials.