scholarly journals How do nutritional intake targets and requirements change during development of a generalist invertebrate herbivore?

2021 ◽  
Vol 35 (S1) ◽  
Author(s):  
Stav Talal ◽  
Ruth Farington ◽  
Jon Harrison ◽  
Arianne Cease
Keyword(s):  
Nutritional Intake ◽  
Requirements Change

Health and Nutritional Status of Orphan Children’s Living in Orphanages with Special Reference to District Anantnag of Jammu and Kashmir

2016 ◽  
Vol 3 (2) ◽  
Author(s):  
Mashkoor Ahmad Lone ◽  
Dr. P. Ganesan
Keyword(s):  
Age Groups ◽  
Indian Academy ◽  
Dental Enamel ◽  
Nutritional Intake ◽  
Age Group ◽  
Asian Countries ◽  
Jammu And Kashmir ◽  
Material Resources ◽  
Height For Age ◽  
Weight For Age

The practice of placing deprived children having least or no emotional and material resources, in orphanages has since long been prevailing in socio -economically poor Asian countries. A sample of 30 children residing in orphanage in district Anantnag in the age group of 13-18 years was selected for the present study. Most of the children were found socially and psychologically disturbed. As per Indian Academy Paediatrics (IAP) classification with respect to weight for age the condition was not bad that as approximately 67% percent of the children were found to be normal. In the same way height for age as per Waterloo’s classification shown that more than half of the children were normal. On clinical examination approximately 47% of children were normal, while as rest were suffering from dispigmentation of hair, moon face, xerosis of skin cheilosis, magenta tongue, spongy bleeding gums, oedema, conjuctival xerosis, and mottled dental enamel. The findings indicated that nutritional intake was deficient for all nutrients when compared to, Recommended Daily Allowances Chart (RDA) for all age groups which may be linked to poor planning of menus in orphanages.

Stress is Associated with Nutritional Intake and Metabolic Syndrome in Urban Middle-Aged Women

2019 ◽  
Vol 48 (8) ◽  
pp. 802-810
Author(s):  
Suji Byeon ◽  
Yoonjin Shin ◽  
Jungwon Yoon ◽  
Sooa Kim ◽  
Yangha Kim
Keyword(s):  
Metabolic Syndrome ◽  
Nutritional Intake ◽  
Middle Aged

23 Nutritional Intake and Weight Change in Severely Burned Patients

2021 ◽  
Vol 42 (Supplement_1) ◽  
pp. S20-S21
Author(s):  
Sandrine O Fossati ◽  
Beth A Shields ◽  
Renee E Cole ◽  
Adam J Kieffer ◽  
Saul J Vega ◽  
...  
Keyword(s):  
Wound Healing ◽  
Mass Loss ◽  
Performance Improvement ◽  
Body Mass ◽  
Total Body Surface Area ◽  
Nutritional Intake ◽  
Energy Deficit ◽  
Body Mass Loss ◽  
Improvement Project ◽  
Burn Care

Abstract Introduction Nutrition is crucial for recovery from burn injuries, as severe weight (wt.) loss can lead to impaired immunity and wound healing, infections, skin graft failure, and mortality. Previous studies recommended avoiding more than 10% wt. loss, as this level resulted in increased infection rates. However, wt. loss is often not quantifiable during the critical illness phase, with severe edema masking non-fluid related body wt. changes. Energy (kcal) deficits can be used to estimate wt. loss until the edema has resolved, but previous studies in non-burn patients indicate that actual wt. loss is less than the commonly used 3500 kcal per pound of fat (7700 kcal per kg of fat). The objective of this performance improvement project was to evaluate nutritional intake and the resulting dry wt. change in severely burned patients. Methods This performance improvement project was approved by our regulatory compliance division. We performed a retrospective evaluation on patients with at least 20% total body surface area (TBSA) burns admitted for initial burn care to our intensive care unit over a 7-year period. Patients who died or who had major fascial excisions or limb amputations were excluded. Patients who did not achieve a recorded dry wt. after wound healing were not included in this analysis. Retrospective data were collected, including sex, age, burn size, kcal intake, kcal goal per the Milner equation using activity factor of 1.4, admission dry wt., dry wt. after wound healing (defined as less than 10% TBSA open wound), and days to dry wt. after wound healing. Descriptive statistics and linear regression were performed using JMP. Significance was set at p< 0.05. Results The 30 included patients had the following characteristics: 90% male, 30 ± 11 years old, 45% ± 15% TBSA burn. They received 2720 ± 1092 kcal/day, meeting 68% ± 24% kcal goal, and took approximately 53 ± 30 days from injury to achieve dry wt. after wound healing. These patients had wt. loss of 8 ± 8 kg from the kcal deficit of 69,819 ± 51,704 during this time period. The kcal deficit was significantly associated with wt. change [p < 0.001, R2 = 0.49, wt. change in kg = (-0.000103 x kcal deficit) – 1]. This translates to one kg of body wt. loss resulting from 9709 kcal deficit. Conclusions This performance improvement project found that an energy deficit of approximately 9700 kcal in our patients equates to 1 kg of body mass loss (4400 kcal deficit equates to 1 pound of body mass loss). These findings are similar to wt. loss studies in other patient populations and contrary to the commonly used 3500 kcal per pound of fat (7700 kcal per kg of fat).

scholarly journals Classification and Prediction on the Effects of Nutritional Intake on Overweight/Obesity, Dyslipidemia, Hypertension and Type 2 Diabetes Mellitus Using Deep Learning Model: 4–7th Korea National Health and Nutrition Examination Survey

2021 ◽  
Vol 18 (11) ◽  
pp. 5597
Author(s):  
Hyerim Kim ◽  
Dong Hoon Lim ◽  
Yoona Kim
Keyword(s):  
Diabetes Mellitus ◽  
Logistic Regression ◽  
Decision Tree ◽  
Prediction Accuracy ◽  
Nutritional Intake ◽  
Nutrition Examination Survey ◽  
Learning Models ◽  
Health And Nutrition ◽  
Machine Learning Models

Few studies have been conducted to classify and predict the influence of nutritional intake on overweight/obesity, dyslipidemia, hypertension and type 2 diabetes mellitus (T2DM) based on deep learning such as deep neural network (DNN). The present study aims to classify and predict associations between nutritional intake and risk of overweight/obesity, dyslipidemia, hypertension and T2DM by developing a DNN model, and to compare a DNN model with the most popular machine learning models such as logistic regression and decision tree. Subjects aged from 40 to 69 years in the 4–7th (from 2007 through 2018) Korea National Health and Nutrition Examination Survey (KNHANES) were included. Diagnostic criteria of dyslipidemia (n = 10,731), hypertension (n = 10,991), T2DM (n = 3889) and overweight/obesity (n = 10,980) were set as dependent variables. Nutritional intakes were set as independent variables. A DNN model comprising one input layer with 7 nodes, three hidden layers with 30 nodes, 12 nodes, 8 nodes in each layer and one output layer with one node were implemented in Python programming language using Keras with tensorflow backend. In DNN, binary cross-entropy loss function for binary classification was used with Adam optimizer. For avoiding overfitting, dropout was applied to each hidden layer. Structural equation modelling (SEM) was also performed to simultaneously estimate multivariate causal association between nutritional intake and overweight/obesity, dyslipidemia, hypertension and T2DM. The DNN model showed the higher prediction accuracy with 0.58654 for dyslipidemia, 0.79958 for hypertension, 0.80896 for T2DM and 0.62496 for overweight/obesity compared with two other machine leaning models with five-folds cross-validation. Prediction accuracy for dyslipidemia, hypertension, T2DM and overweight/obesity were 0.58448, 0.79929, 0.80818 and 0.62486, respectively, when analyzed by a logistic regression, also were 0.52148, 0.66773, 0.71587 and 0.54026, respectively, when analyzed by a decision tree. This study observed a DNN model with three hidden layers with 30 nodes, 12 nodes, 8 nodes in each layer had better prediction accuracy than two conventional machine learning models of a logistic regression and decision tree.

Requirements change: Fears dictate the must haves; desires the won’t haves

2007 ◽  
Vol 80 (3) ◽  
pp. 328-355 ◽  
Author(s):  
Johan F. Hoorn ◽  
Elly A. Konijn ◽  
Hans van Vliet ◽  
Gerrit van der Veer
Keyword(s):  
Requirements Change

Nutritional intake and bone health

2021 ◽  
Author(s):  
René Rizzoli ◽  
Emmanuel Biver ◽  
Tara C Brennan-Speranza
Keyword(s):  
Bone Health ◽  
Nutritional Intake
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