PubMed Health⌕ Search

PubMed · 3077264

Early recognition of toxicity by assessing nervous system function.

Abstract

Many industrial substances, such as pesticides, solvents, and heavy metals, are toxic to the human nervous system. Nervous system function provides an excellent means for assessing the early effects of toxic substances. A large percentage of occupational chemicals that are recognized to produce adverse health effects in general also adversely affect the nervous system. Reliable and low-cost methods, including psychometric tests and nerve conduction velocity assessment, have been developed for evaluating nervous system function. By monitoring nervous system function in workers exposed to neurotoxic chemicals, early signs of chemical toxicity can be detected. Appropriate action following detection of early signs of neurotoxicity may then avert permanent and debilitating nervous system disorder and may help to limit exposure to toxic substances affecting other organ systems as well as the nervous system. A monitoring program along with its rationale is described in this paper.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

R Singer. 1988. Early recognition of toxicity by assessing nervous system function.. https://pubmed.ncbi.nlm.nih.gov/3077264/

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

KEEP EXPLORING

Related citations

A penalized likelihood approach to magnetic resonance image reconstruction.

Currently, images acquired via magnetic resonance imaging (MRI) and functional magnetic resonance imaging (fMRI) technology are reconstructed using the discrete inverse Fourier transform. While computationally convenient, this approach is not able to filter out noise. This is a serious limitation because the amount of noise in MRI and fMRI can be substantial. In this paper, we propose an alternative approach to reconstruction, based on penalized likelihood methodology. In particular, we focus on non-linear shrinkage estimators and show that this approach achieves a great reduction in integrated mean squared error (IMSE) of the estimated image with respect to the currently used estimator. This approach is extremely fast and easy to implement computationally. In addition, it can be combined with various alternative approaches to MR image reconstruction and can be easily adapted to other, non-MRI contexts, in which the observed data and the quantities of interest are related via a linear transform.

Brain Diseases↗