The angular spectrum of the scattering coefficient map reveals subsurface colorectal cancer

Abstract Colorectal cancer diagnosis currently relies on histological detection of endoluminal neoplasia in biopsy specimens. However, clinical visual endoscopy provides no quantitative subsurface cancer information. In this ex vivo study of nine fresh human colon specimens, we report the first use of quantified subsurface scattering coefficient maps acquired by swept-source optical coherence tomography to reveal subsurface abnormities. We generate subsurface scattering coefficient maps with a novel wavelet-based-curve-fitting method that provides significantly improved accuracy. The angular spectra of scattering coefficient maps of normal tissues exhibit a spatial feature distinct from those of abnormal tissues. An angular spectrum index to quantify the differences between the normal and abnormal tissues is derived, and its strength in revealing subsurface cancer in ex vivo samples is statistically analyzed. The study demonstrates that the angular spectrum of the scattering coefficient map can effectively reveal subsurface colorectal cancer and potentially provide a fast and more accurate diagnosis

Dietary effects of arachidonate-rich fungal oil and fish oil on murine hepatic and hippocampal gene expression

BACKGROUND: The functions, actions, and regulation of tissue metabolism affected by the consumption of long chain polyunsaturated fatty acids (LC-PUFA) from fish oil and other sources remain poorly understood; particularly how LC-PUFAs affect transcription of genes involved in regulating metabolism. In the present work, mice were fed diets containing fish oil rich in eicosapentaenoic acid and docosahexaenoic acid, fungal oil rich in arachidonic acid, or the combination of both. Liver and hippocampus tissue were then analyzed through a combined gene expression- and lipid- profiling strategy in order to annotate the molecular functions and targets of dietary LC-PUFA. RESULTS: Using microarray technology, 329 and 356 dietary regulated transcripts were identified in the liver and hippocampus, respectively. All genes selected as differentially expressed were grouped by expression patterns through a combined k-means/hierarchical clustering approach, and annotated using gene ontology classifications. In the liver, groups of genes were linked to the transcription factors PPARα, HNFα, and SREBP-1; transcription factors known to control lipid metabolism. The pattern of differentially regulated genes, further supported with quantitative lipid profiling, suggested that the experimental diets increased hepatic β-oxidation and gluconeogenesis while decreasing fatty acid synthesis. Lastly, novel hippocampal gene changes were identified. CONCLUSIONS: Examining the broad transcriptional effects of LC-PUFAs confirmed previously identified PUFA-mediated gene expression changes and identified novel gene targets. Gene expression profiling displayed a complex and diverse gene pattern underlying the biological response to dietary LC-PUFAs. The results of the studied dietary changes highlighted broad-spectrum effects on the major eukaryotic lipid metabolism transcription factors. Further focused studies, stemming from such transcriptomic data, will need to dissect the transcription factor signaling pathways to fully explain how fish oils and arachidonic acid achieve their specific effects on health

The limit fold change model: A practical approach for selecting differentially expressed genes from microarray data

BACKGROUND: The biomedical community is developing new methods of data analysis to more efficiently process the massive data sets produced by microarray experiments. Systematic and global mathematical approaches that can be readily applied to a large number of experimental designs become fundamental to correctly handle the otherwise overwhelming data sets. RESULTS: The gene selection model presented herein is based on the observation that: (1) variance of gene expression is a function of absolute expression; (2) one can model this relationship in order to set an appropriate lower fold change limit of significance; and (3) this relationship defines a function that can be used to select differentially expressed genes. The model first evaluates fold change (FC) across the entire range of absolute expression levels for any number of experimental conditions. Genes are systematically binned, and those genes within the top X% of highest FCs for each bin are evaluated both with and without the use of replicates. A function is fitted through the top X% of each bin, thereby defining a limit fold change. All genes selected by the 5% FC model lie above measurement variability using a within standard deviation (SD(within)) confidence level of 99.9%. Real time-PCR (RT-PCR) analysis demonstrated 85.7% concordance with microarray data selected by the limit function. CONCLUSION: The FC model can confidently select differentially expressed genes as corroborated by variance data and RT-PCR. The simplicity of the overall process permits selecting model limits that best describe experimental data by extracting information on gene expression patterns across the range of expression levels. Genes selected by this process can be consistently compared between experiments and enables the user to globally extract information with a high degree of confidence

Associations of combined physical activity and body mass index groups with colorectal cancer survival outcomes

BACKGROUND: Physical activity and BMI have been individually associated with cancer survivorship but have not yet been studied in combinations in colorectal cancer patients. Here, we investigate individual and combined associations of physical activity and BMI groups with colorectal cancer survival outcomes. METHODS: Self-reported physical activity levels (MET hrs/wk) were assessed using an adapted version of the International Physical Activity Questionnaire (IPAQ) at baseline in 931 patients with stage I-III colorectal cancer and classified into \u27highly active\u27 and\u27not-highly active\u27(≥ / \u3c 18 MET hrs/wk). BMI (kg/m RESULTS: \u27Not-highly active\u27 compared to \u27highly active\u27 and \u27overweight\u27/ \u27obese\u27 compared to \u27normal weight\u27 patients had a 40-50% increased risk of death or recurrence (HR: 1.41 (95% CI: 0.99-2.06), p = 0.03; HR: 1.49 (95% CI: 1.02-2.21) and HR: 1.51 (95% CI: 1.02-2.26), p = 0.04, respectively). \u27Not-highly active\u27 patients had worse disease-free survival outcomes, regardless of their BMI, compared to \u27highly active/normal weight\u27 patients. \u27Not-highly active/obese\u27 patients had a 3.66 times increased risk of death or recurrence compared to \u27highly active/normal weight\u27 patients (HR: 4.66 (95% CI: 1.75-9.10), p = 0.002). Lower activity thresholds yielded smaller effect sizes. CONCLUSION: Physical activity and BMI were individually associated with disease-free survival among colorectal cancer patients. Physical activity seems to improve survival outcomes in patients regardless of their BMI