The association between meal-based diet quality index-international (DQI-I) with obesity in adults.

BACKGROUND AND OBJECTIVE: Due to the growing global trend of obesity, it is necessary to study the diet quality as a modifiable factor to reduce the dangerous consequences of obesity. Therefore, the aim of this study was to evaluate the association between meal-based diet quality index-international (DQI-I) with obesity in adults. METHODS: This cross-sectional study was performed on 850 men and women in Tehran (aged 20-59 y). Dietary intakes were assessed using three 24-h dietary recalls. Meal-based Diet quality was assessed based on the construction of DQI-I. The total DQI-I score ranged from 0 to 100, with higher scores denoting better diet quality. Multiple linear regression analysis was used to examine the association of DQI-I and BMI in each meal and Logistic regression analysis was used to examine the association of DQI-I and obesity in each meal. RESULTS: The mean (± SD) of age, body mass index (BMI), waist circumference (WC) and waist to hip ratio (WHR) were 42.35(± 10.90) years, 27.32(± 5.61) kg/m2, 89.09 (± 12.04) cm and 0.86 (± 0.11), respectively. In none of the meals, after adjusting for confounders, no significant difference in BMI was observed in the both women and men groups. After controlling of confounders, there was not any relationship between meal-based DQI-I and BMI resulted from multiple linear regression analysis also there was not any significant association between meal-based DQI-I and obesity resulted from Logistic regression analysis. CONCLUSION: In this study, we did not find any significant association between meal-specified DQI with obesity. To reach the better evaluation, more prospective studies with large sample size are needed.

Graph-based abstractive biomedical text summarization.

Summarization is the process of compressing a text to obtain its important informative parts. In recent years, various methods have been presented to extract important parts of textual documents to present them in a summarized form. The first challenge of these methods is to detect the concepts that well convey the main topic of the text and extract sentences that better describe these essential concepts. The second challenge is the correct interpretation of the essential concepts to generate new paraphrased sentences such that they are not exactly the same as the sentences in the main text. The first challenge has been addressed by many researchers. However, the second one is still in progress. In this study, we focus on the abstractive summarization of biomedical documents. In this regard, for the first challenge, a new method is presented based on the graph generation and frequent itemset mining for generating extractive summaries by considering the concepts within the biomedical documents. Then, to address the second challenge, a transfer learning-based method is used to generate abstractive summarizations from extractive summaries. The efficiency of the proposed solution has been evaluated by conducting several experiments over BioMed Central and NLM's PubMed datasets. The obtained results show that the proposed approach admits a better interpretation of the main concepts and sentences of biomedical documents for the abstractive summarization by obtaining the overall ROUGE of 59.60%, which, on average, is 17% better than state-of-the-art summarization techniques. The source code, datasets, and results are available in GitHub1.

The Neural Engine: A Reprogrammable Low Power Platform for Closed-Loop Optogenetics.

Brain-machine Interfaces (BMI) hold great potential for treating neurological disorders such as epilepsy. Technological progress is allowing for a shift from open-loop, pacemaker-class, intervention towards fully closed-loop neural control systems. Low power programmable processing systems are therefore required which can operate within the thermal window of 2° C for medical implants and maintain long battery life. In this work, we have developed a low power neural engine with an optimized set of algorithms which can operate under a power cycling domain. We have integrated our system with a custom-designed brain implant chip and demonstrated the operational applicability to the closed-loop modulating neural activities in in-vitro and in-vivo brain tissues: the local field potentials can be modulated at required central frequency ranges. Also, both a freely-moving non-human primate (24-hour) and a rodent (1-hour) in-vivo experiments were performed to show system reliable recording performance. The overall system consumes only 2.93 mA during operation with a biological recording frequency 50 Hz sampling rate (the lifespan is approximately 56 hours). A library of algorithms has been implemented in terms of detection, suppression and optical intervention to allow for exploratory applications in different neurological disorders. Thermal experiments demonstrated that operation creates minimal heating as well as battery performance exceeding 24 hours on a freely moving rodent. Therefore, this technology shows great capabilities for both neuroscience in-vitro/in-vivo applications and medical implantable processing units.

The Effects of Melatonin Supplementation on Parameters of Mental Health, Glycemic Control, Markers of Cardiometabolic Risk, and Oxidative Stress in Diabetic Hemodialysis Patients: A Randomized, Double-Blind, Placebo-Controlled Trial.

OBJECTIVE: This study evaluated the effects of melatonin supplementation on parameters of mental health, glycemic control, markers of cardiometabolic risk, and oxidative stress in diabetic hemodialysis (HD) patients. DESIGN: A randomized, double-blind, placebo-controlled clinical trial was conducted in 60 diabetic HD patients, 18-80 years of age. Participants were randomly divided into 2 groups to take either melatonin (2 x 5mg/day) (n = 30) or placebo (n = 30) 1 hour before bedtime for 12 weeks. The effects of melatonin on mental health, metabolic status, and gene expression related to metabolic status were assessed using multiple linear regression adjusting for age and BMI. RESULTS: Melatonin supplementation significantly decreased Pittsburgh Sleep Quality Index (P = .007), Beck Depression Inventory index (P = .001), and Beck Anxiety Inventory index (P = .01) compared with the placebo. Additionally, melatonin administration significantly reduced fasting plasma glucose (ß = -21.77 mg/dL, 95% CI -33.22 to -10.33, P < .001), serum insulin levels (ß = -1.89 µIU/mL, 95% CI -3.34 to -0.45, P = .01), and homeostasis model of assessment-insulin resistance (ß = -1.45, 95% CI -2.10 to -0.80, P < .001), and significantly increased the quantitative insulin sensitivity check index (ß = 0.01, 95% CI 0.007-0.02, P < .001) compared with placebo treated subjects. In addition, melatonin administration resulted in a significant reduction in serum high sensitivity C-reactive protein (ß = -1.92 mg/L, 95% CI -3.02 to -0.83, P = .001) and plasma malondialdehyde (ß = -0.21 µmol/L, 95% CI -0.36 to -0.06, P = .005); also, significant rises in plasma total antioxidant capacity (ß = 253.87 mmol/L, 95% CI 189.18-318.56, P < .001) and nitric oxide levels (ß = 2.99 µmol/L, 95% CI 0.71-5.28, P = .01) were observed compared with the placebo. CONCLUSION: Overall, melatonin supplementation for 12 weeks to diabetic HD patients had beneficial effects on mental health, glycemic control, inflammatory markers, and oxidative stress.

Design Considerations for Artefact-Free Optoelectronic Systems.

This paper proposes design considerations that need to be followed in order to eliminate potential sources of artefact that could distort a recorded neural signal. The artefact that appears in a recorded signal has a combination of potential sources each of which contributes towards its formation. As such, these sources of artefact have been addressed in three main categories: a) electronics artefact, b) encapsulation artefact and c) interface artefact. Each source (component) is analyzed further and appropriate design techniques and considerations are suggested towards its mitigation.

On-Probe Neural Interface ASIC for Combined Electrical Recording and Optogenetic Stimulation.

Neuromodulation technologies are progressing from pacemaking and sensory operations to full closed-loop control. In particular, optogenetics-the genetic modification of light sensitivity into neural tissue allows for simultaneous optical stimulation and electronic recording. This paper presents a neural interface application-specified integrated circuit (ASIC) for intelligent optoelectronic probes. The architecture is designed to enable simultaneous optical neural stimulation and electronic recording. It provides four low noise (2.08  µV) recording channels optimized for recording local field potentials (LFPs) (0.1-300 Hz bandwidth, 5 mV range, sampled 10-bit@4 kHz), which are more stable for chronic applications. For stimulation, it provides six independently addressable optical driver circuits, which can provide both intensity (8-bit resolution across a 1.1 mA range) and pulse-width modulation for high-radiance light emitting diodes (LEDs). The system includes a fully digital interface using a serial peripheral interface (SPI) protocol to allow for use with embedded controllers. The SPI interface is embedded within a finite state machine (FSM), which implements a command interpreter that can send out LFP data whilst receiving instructions to control LED emission. The circuit has been implemented in a commercially available 0.35  µm CMOS technology occupying a 1.95 mm 1.10 mm footprint for mounting onto the head of a silicon probe. Measured results are given for a variety of bench-top, in vitro and in vivo experiments, quantifying system performance and also demonstrating concurrent recording and stimulation within relevant experimental models.