Association between empirically derived dietary patterns with blood lipids, fasting blood glucose and blood pressure in adults - the India migration study.

BACKGROUND: Dietary patterns (DPs) in India are heterogenous. To date, data on association of indigenous DPs in India with risk factors of nutrition-related noncommunicable diseases (cardiovascular disease and diabetes), leading causes of premature death and disability, are limited. We aimed to evaluate the associations of empirically-derived DPs with blood lipids, fasting glucose and blood pressure levels in an adult Indian population recruited across four geographical regions of India. METHODS: We used cross-sectional data from the Indian Migration Study (2005-2007). Study participants included urban migrants, their rural siblings and urban residents and their urban siblings from Lucknow, Nagpur, Hyderabad and Bangalore (n = 7067, mean age 40.8 yrs). Information on diet (validated interviewer-administered, 184-item semi-quantitative food frequency questionnaire), tobacco consumption, alcohol intake, physical activity, medical history, as well as anthropometric measurements were collected. Fasting-blood samples were collected for estimation of blood lipids and glucose. Principal component analysis (PCA) was used to identify major DPs based on eigenvalue> 1 and component interpretability. Robust standard error multivariable linear regression models were used to investigate the association of DPs (tertiles) with total cholesterol (TC), low density lipoprotein-cholesterol (LDL-C), high density lipoprotein-cholesterol (HDL-C), triglycerides, fasting-blood glucose (FBG), systolic and diastolic blood pressure (SBP and DBP) levels. RESULTS: Three major DPs were identified: 'cereal-savoury' (cooked grains, rice/rice-based dishes, snacks, condiments, soups, nuts), 'fruit-vegetable-sweets-snacks' (Western cereals, vegetables, fruit, fruit juices, cooked milk products, snacks, sugars, sweets) and 'animal food' (red meat, poultry, fish/seafood, eggs) patterns. High intake of the 'animal food' pattern was positively associated with levels of TC (β = 0.10 mmol/L; 95% CI: 0.02, 0.17 mmol/L; p-trend = 0.013); LDL-C (β = 0.07 mmol/L; 95% CI: 0.004, 0.14 mmol/L; p-trend = 0.041); HDL-C (β = 0.02 mmol/L; 95% CI: 0.004, 0.04 mmol/L; p-trend = 0.016), FBG: (β = 0.09 mmol/L; 95% CI: 0.01, 0.16 mmol/L; p-trend = 0.021) SBP (β = 1.2 mm/Hg; 95% CI: 0.1, 2.3 mm/Hg; p-trend = 0.032); DBP: (β = 0.9 mm/Hg; 95% CI: 0.2, 1.5 mm/Hg; p-trend = 0.013). The 'cereal-savoury' and 'fruit-vegetable-sweets-snacks' patterns showed no association with any parameter except for a positive association with diastolic blood pressure for high intake of 'fruits-vegetables-sweets-snacks' pattern. CONCLUSION: Our results indicate positive associations of the 'animal food' pattern with cardio-metabolic risk factors in India. Further longitudinal assessments of dietary patterns in India are required to validate the findings

Guidelines for the use and interpretation of assays for monitoring autophagy (4th edition)1.

In 2008, we published the first set of guidelines for standardizing research in autophagy. Since then, this topic has received increasing attention, and many scientists have entered the field. Our knowledge base and relevant new technologies have also been expanding. Thus, it is important to formulate on a regular basis updated guidelines for monitoring autophagy in different organisms. Despite numerous reviews, there continues to be confusion regarding acceptable methods to evaluate autophagy, especially in multicellular eukaryotes. Here, we present a set of guidelines for investigators to select and interpret methods to examine autophagy and related processes, and for reviewers to provide realistic and reasonable critiques of reports that are focused on these processes. These guidelines are not meant to be a dogmatic set of rules, because the appropriateness of any assay largely depends on the question being asked and the system being used. Moreover, no individual assay is perfect for every situation, calling for the use of multiple techniques to properly monitor autophagy in each experimental setting. Finally, several core components of the autophagy machinery have been implicated in distinct autophagic processes (canonical and noncanonical autophagy), implying that genetic approaches to block autophagy should rely on targeting two or more autophagy-related genes that ideally participate in distinct steps of the pathway. Along similar lines, because multiple proteins involved in autophagy also regulate other cellular pathways including apoptosis, not all of them can be used as a specific marker for bona fide autophagic responses. Here, we critically discuss current methods of assessing autophagy and the information they can, or cannot, provide. Our ultimate goal is to encourage intellectual and technical innovation in the field

Optimal navigation for vehicles with stochastic dynamics

This brief presents a framework for input-optimal navigation under state constraints for vehicles exhibiting stochastic behavior. The resulting stochastic control law is implementable in real time on vehicles with limited computational power. When control actuation is unconstrained, then convergence with probability 1 can be theoretically guaranteed. When inputs are bounded, the probability of convergence is quantifiable. The experimental implementation on a 5.5 g, 720-MHz processor that controls a bioinspired crawling robot with stochastic dynamics, corroborates the design framework.by Shridhar K. Shah, Herbert G. Tanner and Chetan D. Pahlajan

Artificial Intelligence in CT and MR Imaging for Oncological Applications

Cancer care increasingly relies on imaging for patient management. The two most common cross-sectional imaging modalities in oncology are computed tomography (CT) and magnetic resonance imaging (MRI), which provide high-resolution anatomic and physiological imaging. Herewith is a summary of recent applications of rapidly advancing artificial intelligence (AI) in CT and MRI oncological imaging that addresses the benefits and challenges of the resultant opportunities with examples. Major challenges remain, such as how best to integrate AI developments into clinical radiology practice, the vigorous assessment of quantitative CT and MR imaging data accuracy, and reliability for clinical utility and research integrity in oncology. Such challenges necessitate an evaluation of the robustness of imaging biomarkers to be included in AI developments, a culture of data sharing, and the cooperation of knowledgeable academics with vendor scientists and companies operating in radiology and oncology fields. Herein, we will illustrate a few challenges and solutions of these efforts using novel methods for synthesizing different contrast modality images, auto-segmentation, and image reconstruction with examples from lung CT as well as abdome, pelvis, and head and neck MRI. The imaging community must embrace the need for quantitative CT and MRI metrics beyond lesion size measurement. AI methods for the extraction and longitudinal tracking of imaging metrics from registered lesions and understanding the tumor environment will be invaluable for interpreting disease status and treatment efficacy. This is an exciting time to work together to move the imaging field forward with narrow AI-specific tasks. New AI developments using CT and MRI datasets will be used to improve the personalized management of cancer patients