Glycemic index, glycemic load, and risk of digestive tract neoplasms: a systematic review and meta-analysis

Background: Habitual consumption of diets with a high glycemic index (GI) and a high glycemic load (GL) may influence cancer risk via hyperinsulinemia and the insulin-like growth factor axis.
Objective: The objective was to conduct a systematic review to assess the association between GI, GL, and risk of digestive tract cancers.
Design: Medline and Embase were searched for relevant publications from inception to July 2008. When possible, adjusted results from a comparison of cancer risk of the highest compared with the lowest category of GI and GL intake were combined by using random-effects meta-analyses.
Results: Cohort and case-control studies that examined the risk between GI or GL intake and colorectal cancer (n = 12) and adenomas (n = 2), pancreatic cancer (n = 6), gastric cancer (n = 2), and squamous-cell esophageal carcinoma (n = 1) were retrieved. Most case-control studies observed positive associations between GI and GL intake and these cancers. However, pooled cohort study results showed no associations between colorectal cancer risk and GI intake [relative risk (RR): 1.04; 95% CI: 0.92, 1.12; n = 7 studies] or GL intake (RR: 1.06; 95% CI: 0.95, 1.17; n = 8 studies). Furthermore, no significant associations were observed in meta-analyses of cohort study results of colorectal cancer subsites and GI and GL intake. Similarly, no significant associations emerged between pancreatic cancer risk and GI intake (RR: 0.99; 95% CI: 0.83, 1.19; n = 5 studies) or GL intake (RR: 1.01; 95% CI: 0.86, 1.19; n = 6 studies) in combined cohort studies.
Conclusions: The findings from our meta-analyses indicate that GI and GL intakes are not associated with risk of colorectal or pancreatic cancers. There were insufficient data available regarding other digestive tract cancers to make any conclusions about GI or GL intake and risk.

A Census-based longitudinal study of variations in survival amongst residents of nursing and residential homes in Northern Ireland

Background: despite the intensive services provided to residents of care homes, information on death rates is not routinely available for this population in the UK. Objective: to quantify mortality rates across the care home population of Northern Ireland, and assess variation by type of care home and resident characteristics. Design: a prospective, Census-based cohort study, with 5-year follow-up. Participants: all 9,072 residents of care homes for people aged 65 and over at the time of the 2001 census with a special emphasis on the 2,112 residents admitted during the year preceding census day. Measurements: age, sex, self-reported health, marital status, residence (not in care home, residential home, dual registered home, nursing home), elderly mentally infirm care provision. Results: the median survival among nursing home residents was 2.33 years (95% CI 2.25–2.59), for dual registered homes 2.75 (95% CI 2.42–3.17) and for residential homes 4.51 (95% CI 3.92–4.92) years. Age, sex and self-reported health showed weaker associations in the sicker populations in nursing homes compared to those in residential care or among the non-institutionalised. Conclusions: the high mortality in care homes indicates that places in care homes are reserved for the most severely ill and dependent. Death rates may not be an appropriate care quality measure for this population, but may serve as a useful adjunct for clinical staff and the planning of care home provision.

NADPH oxidase-4 mediates protection against chronic load-induced stress in mouse hearts by enhancing angiogenesis

Cardiac failure occurs when the heart fails to adapt to chronic stresses. Reactive oxygen species (ROS)-dependent signaling is implicated in cardiac stress responses but the role of different ROS sources remains unclear. Here, we report that NADPH oxidase-4 (Nox4) facilitates cardiac adaptation to chronic stress. Unlike other Nox proteins, Nox4 activity is regulated mainly by its expression level which increased in cardiomyocytes during stresses such as pressure overload or hypoxia. To investigate the functional role of Nox4 during the cardiac response to stress, we generated mice with a genetic deletion of Nox4 or a cardiomyocyte-targeted overexpression of Nox4. Basal cardiac function was normal in both models but Nox4-null animals developed exaggerated contractile dysfunction, hypertrophy and cardiac dilatation during exposure to chronic overload whereas Nox4-transgenic mice were protected. Investigation of mechanisms underlying this protective effect revealed a significant Nox4-dependent preservation of myocardial capillary density after pressure overload. Nox4 enhanced stress-induced activation of cardiomyocyte Hif1 and the release of VEGF, resulting in an increased paracrine angiogenic activity. These data indicate that cardiomyocyte Nox4 is a novel inducible regulator of myocardial angiogenesis, a key determinant of cardiac adaptation to overload stress. Our results also have wider relevance to the use of non-specific antioxidant approaches in cardiac disease and may provide an explanation for the failure of such strategies in many settings.

Short, medium and long term load forecasting model and virtual load forecaster based on radial basis neural networks

Artificial neural networks (ANNs) can be easily applied to short-term load forecasting (STLF) models for electric power distribution applications. However, they are not typically used in medium and long term load forecasting (MLTLF) electric power models because of the difficulties associated with collecting and processing the necessary data. Virtual instrument (VI) techniques can be applied to electric power load forecasting but this is rarely reported in the literature. In this paper, we investigate the modelling and design of a VI for short, medium and long term load forecasting using ANNs. Three ANN models were built for STLF of electric power. These networks were trained using historical load data and also considering weather data which is known to have a significant affect of the use of electric power (such as wind speed, precipitation, atmospheric pressure, temperature and humidity). In order to do this a V-shape temperature processing model is proposed. With regards MLTLF, a model was developed using radial basis function neural networks (RBFNN). Results indicate that the forecasting model based on the RBFNN has a high accuracy and stability. Finally, a virtual load forecaster which integrates the VI and the RBFNN is presented.