A probabilistic framework for non-cancer risk assessment.

James J Chen; Hojin Moon; Ralph L Kodell

doi:10.1016/j.yrtph.2006.10.008

Home/Archive/PubMed

PubMed · 17166641

A probabilistic framework for non-cancer risk assessment.

James J Chen·Hojin Moon·Ralph L Kodell

View original source ↗Export metadata ↓

Abstract

Risk assessment involves an analysis of the relationship between exposure and health related outcomes to derive an allowable exposure level or to estimate a low-dose risk. Acceptable levels of human exposure for non-cancer effects generally are derived by dividing an experimental no-observed-adverse-effect-level or a lower confidence limit benchmark dose by a product of several uncertainty factors. This paper presents a hierarchical modeling framework for a probabilistic approach to non-cancer risk assessment. The hierarchical model integrates the distributions of uncertainty factors and the distribution of the actual exposure level to construct the dose-response model for the proportion of population at risk and the dose-response model for the expected proportion of population at risk for a given exposure distribution. The proposed approach is based on the use of the BMDL (lower confidence limit on the benchmark dose) as a POD (point of departure) for risk assessment of non-cancer effects.

Explore related subjects

Animals Dose-Response Relationship, Drug Environmental Exposure Environmental Health Humans Likelihood Functions Models, Statistical Models, Theoretical No-Observed-Adverse-Effect Level Probability Risk Assessment Journal Article

Keep this discovery

Explore connections, maps & timelines

BibTeX RIS

James J Chen, Hojin Moon, Ralph L Kodell. 2006-12-12. A probabilistic framework for non-cancer risk assessment.. https://doi.org/10.1016/j.yrtph.2006.10.008

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related citations

PubMed2027

Protocol for Detecting and Sequencing Chikungunya Virus from Field-Collected Mosquitoes.

Arboviral diseases represent a major public health challenge, especially in tropical regions where environmental conditions may favor the proliferation and spread of mosquito vectors. Thus, early and accurate detection of chikungunya virus (CHIKV) in mosquito populations can be a valuable tool for effective surveillance of circulating variants and for identifying new viral introductions. Given the challenges of detecting arboviruses in field-captured mosquitoes, we describe an integrated workflow for CHIKV molecular detection and whole-genome sequencing. This protocol includes mosquito homogenization using a bead-based mechanical disruptor, RNA extraction using TRIzol reagent with minor modifications, molecular screening using CHIKV-specific RT-qPCR, and whole-genome amplification followed by sequencing on Illumina platforms. Despite the protocol being optimized for individual mosquitoes, it results in high-quality RNA suitable for both entomological surveillance and genomic analysis. As this protocol allows recovery of complete CHIKV genomes from mosquito specimens, it can serve as a basis for genomic epidemiology studies, enabling monitoring of viral diversity and lineage dynamics, and facilitating early detection of emerging variants to support timely and targeted public health interventions in endemic and at-risk regions.

Animals↗

PubMed2027

Genomic Profiling of Chromatin State Using CUT&Tag.

Alterations in chromatin state, mediated through histone modifications and the incorporation of histone variants, are fundamental to establishing transcriptional networks and cell identity. Recent advances in low-input epigenome profiling methods, such as CUT&Tag and CUT&RUN, have enabled the study of chromatin states from very limited starting materials. In this chapter, we describe procedures for generating CUT&Tag libraries to profile histone modifications and histone variants in early-developing zebrafish embryos.

Animals↗

PubMed2026

Relaxin-2: Shaping the Proteomic Landscape of Skeletal Muscle Physiology, Glucose Trafficking, and Mitochondrial Function in Rat.

Relaxin-2 is a hormone with robust beneficial effects on the heart and blood vessels and potential as a therapy for cardiovascular (CV) disease. Considering the interorgan communication between skeletal muscle and heart, and the relation between muscle quality/composition and CV events, we hypothesize that relaxin-2 may regulate skeletal muscle physiology and metabolism. We aim to evaluate the impact of relaxin-2 on the proteome of skeletal muscle from healthy Sprague-Dawley rats. Animals were treated with 0.4 mg/kg/day of serelaxin (recombinant form of human relaxin-2) or vehicle (PBS) for 2 weeks employing subcutaneous osmotic minipumps. Skeletal muscle protein identification and quantification were performed by LC-MS/MS using a Data-Independent Acquisition (DIA)-Sequential Window Acquisition of All Theoretical Fragment Ion Spectra (SWATH) method. SWATH/MS quantitative analysis identified that relaxin-2 significantly decreased 95 proteins and significantly increased 32 proteins in rat skeletal muscle when compared to control rats. From these, 34 proteins were associated with muscle function, myogenesis, muscle differentiation and/or regeneration, 20 are mitochondrial proteins (six from the complexes of the electron transport chain), and 10 proteins participate in glucose metabolism. Qualitative data-dependent workflow analysis identified 35 proteins exclusive to the skeletal muscle of the relaxin-2-treated group: eight proteins related to processes of skeletal muscle function (size, ion homeostasis or organization of caveolae structures and cytoskeleton) and myogenesis, and two proteins involved in muscle differentiation. Our work highlighted for the first time the role of relaxin-2 in crucial processes of muscle physiology and energetic metabolism, which could influence several processes involved in myopathy and CV.

Animals↗