Diet and cancer prevention

Auteur(s) :
Greenwald P., Basilakos S., Clifford CK., Plionis M., Rowan-robinson M., Milner JA.
Date :
Mai, 2001
Source(s) :
EUROPEAN JOURNAL OF CANCER. #37:8 p948-965
Adresse :
"GREENWALD P,NCI,DIV CANC PREVENT NIH BLACKETT LAB ASTROPHYS GRP;BLDG 31,ROOM 10A52,31 CTR DR,MSC 2580; BETHESDA MD 20892, USA.pg37g@nih.gov"

Sommaire de l'article

Research from several sources provides strong evidence that vegetables, fruits, and whole grains, dietary fibre, certain micronutrients. some fatty acids and physical activity protect against some cancers. In contrast, other factors, such as obesity, alcohol, some fatty acids and food preparation methods may increase risks. Unravelling the multitude of plausible mechanisms for the effects of dietary factors on cancer risk will likely necessitate that nutrition research moves beyond traditional epidemiological and metabolic studies. Nutritional sciences must build on recent advances in molecular biology and genetics to move the discipline from being largely ‘observational’ to focusing on ’cause and effect’. Such basic research is fundamental to cancer prevention strategies that incorporate effective dietary interventions for target populations. Crown Copyright (C) 2001 Published by Elsevier Science Ltd. hll rights reserved.We study the possibility of correctly identifying, from the smooth galaxy density field of the PSCz flux-limited catalogue, high-density regions (superclusters) and recovering their true shapes in the presence of a bias introduced by the coupling between the selection function and the constant radius smoothing. We quantify such systematic biases in the smoothed PSCz density field and after applying the necessary corrections we study supercluster multiplicity and morphologies using a differential geometry definition of shape. Our results strongly suggest that filamentary morphology is the dominant feature of PSCz superclusters. Finally, we compare our results with those expected in three different cosmological models and find that the Lambda cold dark matter (CDM) model (Omega (Lambda) = 1 – Omega (m) = 0.7) performs better than Omega (m) = 1 CDM models.

Source : Pubmed
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