Biomedical subjects

Paul R Nelson

Publications and source records attributed to Paul R Nelson.

2 recordsLinked to original sources

PubMed2006

DBA/2 mouse skin is unresponsive to dermal tumor promotion by cigarette smoke condensate.

Previous studies demonstrated that repetitive application of cigarette smoke condensate (CSC) to 7,12-dimethylbenz[a]anthracene (DMBA)-initiated SENCAR mouse skin for 29 weeks at doses of 10, 20 and 40 mg "tar"/application results in time- and dose-dependent dermal tumor formation. To evaluate CSC-induced tumor promotion in other mouse skin models, male DBA/2 mice were treated with N-methyl-N'-nitro-N-nitrosoguanidine (MNNG) (300 microg) or DMBA (75 or 150 microg) followed by promotion with 1R4F CSC at concentrations ranging from 9 to 45 mg "tar"/application. Both MNNG and DMBA have previously been shown to adequately initiate tumor development. Study end-points included clinical signs, body weights, and mass tracking. Neither the DMBA-initiated/acetone-promoted control groups, nor DMBA-initiated/CSC-promoted groups produced grossly observable skin tumors. For MNNG-initiated groups, a total of four tumors were observed. Based on these findings, it would appear the DBA/2 mouse was unresponsive to CSC dermal tumor promotion. It is not possible, based on the study design employed, to determine the underlying basis for the apparent resistance exhibited by this mouse strain to CSC-induced tumor promotion.

9,10-Dimethyl-1,2-benzanthracene↗

PubMed2005

Effective analysis of genomic data.

High-throughput biotechnology has enabled genome-wide investigation of gene expression and has the potential to identify genes that have a role to play in focal cerebral ischemia, as well as many other interventions. The advent of this technology has also led to the generation of large amounts of expensive and complex expression data. One of the major problems with the generation of so much data is locating and extracting the relevant information to aid target identification and interpretation effectively and reliably. Statistical involvement is vital. Not only does it help to ensure effective extraction of information from the data, it also increases the likelihood that the data collected will embody the information about the differential expression of interest in the first place. The goal of this chapter is to recommend an effective process for investigating gene expression data. There are five stages in this process that we believe lead to reliable results when routinely applied to an expression dataset, once it has been appropriately generated and collected: (1) biological problem definition and design selection; (2) data examination, "preprocessing," and reexamination; (3) data analysis step I: screening for differentially expressed genes; (4) data analysis step II: verifying differential expression; and (5) biological verification, interpretation, and communication.

Computational Biology↗