Informing clinical assessment by contextualizing post-hoc explanations of risk prediction models in type-2 diabetes.

Medical experts may use Artificial Intelligence (AI) systems with greater trust if these are supported by 'contextual explanations' that let the practitioner connect system inferences to their context of use. However, their importance in improving model usage and understanding has not been extensively studied. Hence, we consider a comorbidity risk prediction scenario and focus on contexts regarding the patients' clinical state, AI predictions about their risk of complications, and algorithmic explanations supporting the predictions.

Impact of the COVID-19 pandemic on treatment for mental health needs: a perspective on service use patterns and expenditures from commercial medical claims data.

Objective: To examine changes in use patterns, cost of healthcare services before and after the outbreak of the COVID-19 pandemic, and their impacts on expenditures for patients receiving treatment for depression, anxiety, eating disorders, and substance use.

Methods: This cross-sectional study employed statistical tests to analyze claims in MarketScan® Commercial Database in March 2020-February 2021 and quarterly from March 2020 to August 2021, compared to respective pre-pandemic periods. The analysis is based on medical episodes created by the Merative™ Medical Episode Grouper (MEG).

Human-centered explainability for life sciences, healthcare, and medical informatics.

Rapid advances in artificial intelligence (AI) and availability of biological, medical, and healthcare data have enabled the development of a wide variety of models. Significant success has been achieved in a wide range of fields, such as genomics, protein folding, disease diagnosis, imaging, and clinical tasks. Although widely used, the inherent opacity of deep AI models has brought criticism from the research field and little adoption in clinical practice.

Artificial Intelligence Clinical Evidence Engine for Automatic Identification, Prioritization, and Extraction of Relevant Clinical Oncology Research.

Purpose: We developed a system to automate analysis of the clinical oncology scientific literature from bibliographic databases and match articles to specific patient cohorts to answer specific questions regarding the efficacy of a treatment. The approach attempts to replicate a clinician's mental processes when reviewing published literature in the context of a patient case. We describe the system and evaluate its performance.

Applying Artificial Intelligence to Address the Knowledge Gaps in Cancer Care.

Background: Rapid advances in science challenge the timely adoption of evidence-based care in community settings. To bridge the gap between what is possible and what is practiced, we researched approaches to developing an artificial intelligence (AI) application that can provide real-time patient-specific decision support.

Materials And Methods: The Oncology Expert Advisor (OEA) was designed to simulate peer-to-peer consultation with three core functions: patient history summarization, treatment options recommendation, and management advisory.

Interrogation of Functional Cell-Surface Markers Identifies CD151 Dependency in High-Grade Serous Ovarian Cancer.

The degree of genetic aberrations characteristic of high-grade serous ovarian cancer (HGSC) makes identification of the molecular features that drive tumor progression difficult. Here, we perform genome-wide RNAi screens and comprehensive expression analysis of cell-surface markers in a panel of HGSC cell lines to identify genes that are critical to their survival. We report that the tetraspanin CD151 contributes to survival of a subset of HGSC cell lines associated with a ZEB transcriptional program and supports the growth of HGSC tumors.

An upstream insulator regulates DLK1 imprinting in AML.

DLK1 is an imprinted gene on chromosome 14. Using informative coding single nucleotide polymorphisms, we found DLK1 expression to be monoallelic in normal bone marrow, whereas it was biallelic in 76% of acute myeloid leukemia (AML) overexpressing DLK1 (61% of all AML). Quantitative methylation analysis of 7 cytosine-phosphate-guanosine-rich areas (3 upstream of or within DLK1, the putative intergenic-differentially methylated region and 3 upstream of or within MEG3) revealed a strong association between biallelic DLK1 expression and hypermethylation of a cytosine-phosphate-guanosine-rich region 18 kb upstream of DLK1.

Selective inhibition of histone deacetylases sensitizes malignant cells to death receptor ligands.

Evasion of death receptor ligand-induced apoptosis represents an important contributor to cancer development and progression. Therefore, molecules that restore sensitivity to death receptor stimuli would be important tools to better understand this biological pathway and potential leads for therapeutic adjuncts. Previously, the small-molecule 4-(4-chloro-2-methylphenoxy)-N-hydroxybutanamide (that we propose be named droxinostat) was identified as a chemical sensitizer to death receptor stimuli, decreasing the expression of the caspase-8 inhibitor FLIP.

A novel inhibitor of glucose uptake sensitizes cells to FAS-induced cell death.

Evasion of death receptor ligand-induced apoptosis is an important contributor to cancer development and progression. Therefore, molecules that restore sensitivity to death receptor stimuli would be important tools to better understand this biological pathway and potential leads for therapeutic adjuncts. Previously, the small-molecule N-[4-chloro-3-(trifluoromethyl)phenyl]-3-oxobutanamide (fasentin) was identified as a chemical sensitizer to the death receptor stimuli FAS and tumor necrosis factor apoptosis-inducing ligand, but its mechanism of action was unknown.

Quality of life beyond 6 months after diagnosis in older adults with acute myeloid leukemia.

Although intensive chemotherapy may improve survival in older people with acute myeloid leukemia (AML) without adverse cytogenetics, its impact on quality of life (QOL) is mixed and most patients complain of fatigue up to 6 months after diagnosis. Little information is available on longer-term QOL outcomes. We prospectively followed 20 patients age 60 or older with AML who provided QOL data more than 6 months after diagnosis.

Wilms' tumor 1 suppressor gene mediates antiestrogen resistance via down-regulation of estrogen receptor-alpha expression in breast cancer cells.

The antiestrogen tamoxifen has been used in the treatment of hormone-responsive breast cancer for over a decade. The loss of estrogen receptor (ER) expression is the most common mechanism for de novo antiestrogen resistance. Wilms' tumor 1 suppressor gene (WT1) is a clinically useful marker that is associated with poor prognosis in breast cancer patients; its high level expression is frequently observed in cases of breast cancer that are estrogen and progesterone receptor negative.

Lyl1 interacts with CREB1 and alters expression of CREB1 target genes.

The basic helix-loop-helix (bHLH) transcription factor family contains key regulators of cellular proliferation and differentiation as well as the suspected oncoproteins Tal1 and Lyl1. Tal1 and Lyl1 are aberrantly over-expressed in leukemia as a result of chromosomal translocations, or other genetic or epigenetic events. Protein-protein and protein-DNA interactions described so far are mediated by their highly homologous bHLH domains, while little is known about the function of other protein domains.