Koumoutsakos, P. | bioRxiv Science

bioRxiv2024

Quantitative 3D histochemistry reveals region-specific amyloid-β reduction by the antidiabetic drug netoglitazone

A hallmark of Alzheimers disease (AD) is the extracellular aggregation of toxic amyloid-beta (A{beta}) peptides in form of plaques. Here, we identify netoglitazone, an antidiabetic compound previously tested in humans, as an A{beta} aggregation antagonist. Netoglitazone improved cognition and reduced microglia activity in a mouse model of AD. Using quantitative whole-brain three-dimensional histology (Q3D), we precisely identified brain regions where netoglitazone reduced the number and size of A{beta} plaques. We demonstrate the utility of Q3D in preclinical drug evaluation for AD by providing a high-resolution brain-wide view of drug efficacy. Applying Q3D has the potential to improve pre-clinical drug evaluation by providing information that can help identify mechanisms leading to brain region-specific drug efficacy. Significance statementAlzheimers disease (AD) is the most prevalent neurodegenerative disease. Its primary symptom is progressive cognitive decline, which impairs executive brain functions and deprives patients of their autonomy in life. Experimental and clinical evidence points to the critical pathophysiological role of the amyloid-beta (A{beta}) peptide. Despite some limited successes in AD immunotherapy targeting A{beta}, AD is still incurable. Here, we use an innovative pipeline for accurate whole-brain measurements of A{beta} load to test the efficacy of the antidiabetic compound, netoglitazone. We found that netoglitazone decreases A{beta} burden in certain brain areas but not in others. Region-specific assessment of anti-A{beta} efficacy may be useful in the development of effective drugs against Alzheimers disease.

neuroscience↗

bioRxiv2018

Detection of arterial wall abnormalities via Bayesian model selection

Patient-specific modeling of hemodynamics in arterial networks has so far relied on parameter estimation for inexpensive or small-scale models. We describe here a Bayesian uncertainty quantification framework which makes two major advances: an efficient parallel implementation, allowing parameter estimation for more complex forward models, and a system for practical model selection, allowing evidence-based comparison between distinct physical models. We demonstrate the proposed methodology by generating simulated noisy flow velocity data from a branching arterial tree model in which a structural defect is introduced at an unknown location; our approach is shown to accurately locate the abnormality and estimate its physical properties even in the presence of significant observational and systemic error. As the method readily admits real data, it shows great potential in patient-specific parameter fitting for hemodynamical flow models.

bioengineering↗