Biology subjects

Yada, Y.

Publications and source records attributed to Yada, Y..

3 recordsLinked to original sources

bioRxiv2023

Leveraging Machine Learning to Uncover the Hidden Links between Trusting Behavior and Biological Markers

Understanding the decision-making mechanisms underlying trust is essential, particularly for patients with mental disorders who experience difficulties in developing trust. We aimed to explore biomarkers associated with trust-based decision-making by quantitative analysis. However, quantification of decision-making properties is difficult because it cannot be directly observed. Here, we developed a machine learning method based on Bayesian hierarchical model to quantitatively decode the decision-making properties from behavioral data of a trust game. By applying the method to data of patients with MDD and healthy controls, we estimated model parameters regulating trusting decision-making. The estimated model was able to predict behaviors of each participant. Although there is no difference of the estimated parameters between MDD and healthy controls, several biomarkers were associated with the decision-making properties in trusting behavior. Our findings provide valuable insights into the trusting decision-making, offering a basis for developing targeted interventions to improve their social functioning and overall well-being.

bioinformatics↗

bioRxiv2023

Chronic BCR signaling generates and maintains age-associated B cells from anergic B cells

Accumulation of age-associated B cells (ABCs) with autoreactive properties contributes to the pathogenesis of autoimmune diseases1-5. However, the mechanisms whereby ABCs are generated and maintained are not understood1, 2, 4. Here, we show that continuous stimulation of the B-cell receptor (BCR) with self-antigens plays a crucial role in ABC generation from anergic B cells and that this signal is vital for sustaining ABCs during aging and autoimmunity. In ABCs, BCR signaling was constitutively activated and the surface BCR was internalized in vivo, as occurs in autoreactive B cells chronically exposed to self-antigens6. With aging, ABCs were generated from autoreactive anergic B cells, but not from B cells expressing non-self-reactive BCR. In vitro stimulation of anergic B cells with self-antigen, interleukin-21, and Toll-like receptor 7/9 agonists promoted their differentiation to ABCs. Furthermore, the cellular phenotype of ABCs in Bm12-induced lupus mice7, 8 resembled that of ABCs in aged mice, showing activation of BCR signaling, expression of activation markers, and BCR internalization. Importantly, Btk was persistently activated in ABCs of aged/autoimmune mice and humans with lupus. Pharmacological Btk inhibition resulted in a marked reduction in the number of ABCs and pathogenicity in lupus mice. Our findings have implications for accumulating ABCs and developing therapies for autoimmune diseases.

immunology↗

bioRxiv2022

Prediction of amyloid β accumulation from multiple biomarkers using a hierarchical Bayesian model

Accumulation of amyloid-beta (A{beta}) in the brain is associated with neurodegeneration in Alzheimers disease and can be an indicator of early disease progression. Thus, the non-invasively and inexpensively observable features related to A{beta} accumulation are promising biomarkers. However, in the experimental discovery of biomarkers in preclinical models, A{beta} and biomarker candidates are usually not observed in identical sample populations. This study established a hierarchical Bayesian model that predicts A{beta} accumulation level solely from biomarker candidates by integrating incomplete information. The model was applied to 5xFAD mouse behavioral experimental data. The predicted A{beta} accumulation level obeyed the observed amount of A{beta} when multiple features were used for learning and prediction. Based on the evaluation of predictability, the results suggest that the proposed model can contribute to discovering novel biomarkers, that is, multivariate biomarkers relevant to the accumulation state of abnormal proteins.

neuroscience↗