Exploring the Relationship between Food Insecurity, Chronic Health Conditions, and Serious Mental Illness in the United States: Implications for Social Work.

Food insecurity (FI) is a modifiable social determinant of health that impacts approximately 10 percent of the U.S. population. FI has been linked to poorer health outcomes and higher healthcare costs. Given the prevalence of chronic health conditions in the United States, including serious mental illness (SMI), the current study aims to better understand the relationship between FI and chronic conditions, including SMI, in a nationally representative sample. Authors analyzed data from the 2016 Medical Expenditure Panel Survey household component and food security supplement. Findings indicate the prevalence of FI among those with diabetes, lung disease, stroke, and SMI is higher than among the general population, with the prevalence for those with SMI being particularly high (43 percent of the sample). Logistic regression models indicate strong, statistically significant relationships between FI and chronic conditions, including SMI, as well as FI and two or more chronic illnesses, even when controlling for sociodemographic and health factors. There are likely bidirectional relationships between FI and chronic conditions. Findings have implications for social workers, in relation to prevention and treatment of SMI and FI through direct care, advocacy, and integrated services in health, mental health, and social services.

An in vitro CRISPR screen of cell-free DNA identifies apoptosis as the primary mediator of cell-free DNA release.

ABTRACT: Clinical circulating cell-free DNA (cfDNA) testing is now routine, however test accuracy remains limited. By understanding the life-cycle of cfDNA, we might identify opportunities to increase test performance. Here, we profile cfDNA release across a 24-cell line panel and utilize a cell-free CRISPR screen (cfCRISPR) to identify mediators of cfDNA release. Our panel outlines two distinct groups of cell lines: one which releases cfDNA fragmented similarly to clinical samples and purported as characteristic of apoptosis, and another which releases larger fragments associated with vesicular or necrotic DNA. Our cfCRISPR screens reveal that genes mediating cfDNA release are primarily involved with apoptosis, but also identify other subsets of genes such as RNA binding proteins as potential regulators of cfDNA release. We observe that both groups of cells lines identified primarily produce cfDNA through apoptosis. These results establish the utility of cfCRISPR, genetically validate apoptosis as a major mediator of DNA release in vitro, and implicate ways to improve cfDNA assays.

FinaleMe: Predicting DNA methylation by the fragmentation patterns of plasma cell-free DNA.

Analysis of DNA methylation in cell-free DNA reveals clinically relevant biomarkers but requires specialized protocols such as whole-genome bisulfite sequencing. Meanwhile, millions of cell-free DNA samples are being profiled by whole-genome sequencing. Here, we develop FinaleMe, a non-homogeneous Hidden Markov Model, to predict DNA methylation of cell-free DNA and, therefore, tissues-of-origin, directly from plasma whole-genome sequencing. We validate the performance with 80 pairs of deep and shallow-coverage whole-genome sequencing and whole-genome bisulfite sequencing data.

FinaleMe: Predicting DNA methylation by the fragmentation patterns of plasma cell-free DNA.

Analysis of DNA methylation in cell-free DNA (cfDNA) reveals clinically relevant biomarkers but requires specialized protocols and sufficient input material that limits its applicability. Millions of cfDNA samples have been profiled by genomic sequencing. To maximize the gene regulation information from the existing dataset, we developed FinaleMe, a non-homogeneous Hidden Markov Model (HMM), to predict DNA methylation of cfDNA and, therefore, tissues-of-origin directly from plasma whole-genome sequencing (WGS). We validated the performance with 80 pairs of deep and shallow-coverage WGS and whole-genome bisulfite sequencing (WGBS) data.