Multi-omics analysis in mouse primary cortical neurons reveals complex positive and negative biological interactions between constituent compounds in Centella asiatica.

Background: A water extract of the Ayurvedic plant (CAW) improves cognitive function in mouse models of aging and Alzheimer's disease, and affects dendritic arborization, mitochondrial activity and oxidative stress in mouse primary neurons. Triterpenes (TT) and caffeoylquinic acids (CQA) are constituents associated with these bioactivities of CAW although little is known about how interactions between these compounds contribute to the plant's therapeutic benefit.

Methods: Mouse primary cortical neurons were treated with CAW, or equivalent concentrations of four TT combined, eight CQA combined, or these twelve compounds combined (TTCQA).

Multiomic profiling identifies predictors of survival in African American patients with acute myeloid leukemia.

Genomic profiles and prognostic biomarkers in patients with acute myeloid leukemia (AML) from ancestry-diverse populations are underexplored. We analyzed the exomes and transcriptomes of 100 patients with AML with genomically confirmed African ancestry (Black; Alliance) and compared their somatic mutation frequencies with those of 323 self-reported white patients with AML, 55% of whom had genomically confirmed European ancestry (white; BeatAML). Here we find that 73% of 162 gene mutations recurrent in Black patients, including a hitherto unreported PHIP alteration detected in 7% of patients, were found in one white patient or not detected.

Upregulation of HOXA3 by isoform-specific Wilms tumour 1 drives chemotherapy resistance in acute myeloid leukaemia.

Upregulation of the Wilms' tumour 1 (WT1) gene is common in acute myeloid leukaemia (AML) and is associated with poor prognosis. WT1 generates 12 primary transcripts through different translation initiation sites and alternative splicing. The short WT1 transcripts express abundantly in primary leukaemia samples.

Data Valuation with Gradient Similarity.

High-quality data is crucial for accurate machine learning and actionable analytics, however, mislabeled or noisy data is a common problem in many domains. Distinguishing low- from high-quality data can be challenging, often requiring expert knowledge and considerable manual intervention. algorithms are a class of methods that seek to quantify the of each sample in a dataset based on its contribution or importance to a given predictive task.