scholarly journals Local MRI-based measures of thigh adipose and muscle tissue are highly responsive to bidirectional change in body weight – data from the osteoarthritis initiative

2018 ◽  
Vol 26 ◽  
pp. S240-S241
Author(s):  
E. Steidle-Kloc ◽  
T. Dannhauer ◽  
W. Wirth ◽  
F. Eckstein
Keyword(s):  
Body Weight ◽  
Muscle Tissue ◽  
Body Weight Data ◽  
Osteoarthritis Initiative ◽  
Weight Data

scholarly journals Estimating my equilibrium energy intake during lockdown: very introspective study

BMJ ◽  
2020 ◽  
pp. m4561
Author(s):  
R A Lewis
Keyword(s):  
Body Weight ◽  
Energy Intake ◽  
Outcome Measures ◽  
Dietary Energy ◽  
Saturday Morning ◽  
Dietary Energy Intake ◽  
Body Weight Data ◽  
Tuesday Afternoon ◽  
Weight Data ◽  
At Home

AbstractObjectiveTo estimate the daily dietary energy intake for me to maintain a constant body weight. How hard can it be?DesignVery introspective study.SettingAt home. In lockdown. (Except every Tuesday afternoon and Saturday morning, when I went for a run.)ParticipantsMe. n=1.Main outcome measuresMy weight, measured each day.ResultsSleeping, I shed about a kilogram each night (1.07 (SD 0.25) kg). Running 5 km, I shed about half a kilogram (0.57 (SD 0.15) kg). My daily equilibrium energy intake is about 10 000 kJ (10 286 (SD 201) kJ). Every kJ above (or below) 10 000 kJ adds (or subtracts) about 40 mg (35.4 (SD 3.2) mg).ConclusionsBody weight data show persistent variability, even when the screws of control are tightened and tightened.

scholarly journals Comparison of a soluble co-formulation of insulin degludec/insulin aspart vs biphasic insulin aspart 30 in type 2 diabetes: a randomised trial

2012 ◽  
Vol 167 (3) ◽  
pp. 453 ◽  
Author(s):  
Leo Niskanen ◽  
Lawrence A Leiter ◽  
Edward Franek ◽  
Jianping Weng ◽  
Taner Damci ◽  
...  
Keyword(s):  
Body Weight ◽  
Insulin Aspart ◽  
Randomised Trial ◽  
Baseline Hba1c ◽  
Body Weight Data ◽  
Biphasic Insulin Aspart 30 ◽  
Article Type ◽  
Full Analysis ◽  
Weight Data

The journal and the authors apologise for errors in Table 2 of this article that was published in the August issue (vol 167, pp 287–294). The n values were incorrectly published. The correct values are presented below and the table is published in full below.Table 2Observed mean changes from baseline HbA1c, FPG and body weight. Data are observed as mean (s.d.) for all randomised subjects (full analysis set).nBaselineaWeek 16bChange from baselineHbA1c (%) IDegAsp618.5 (1.2)6.7 (1.0)−1.8 (1.1)c AF598.5 (0.9)6.6 (0.6)−1.9 (1.1)c BIAsp 30628.6 (1.0)6.7 (0.7)−1.8 (0.9)cFPG (mmol/l) IDegAsp6111.5 (2.6)6.4 (2.2)−5.1 (2.9) AF5911.8 (2.9)6.5 (1.9)−5.3 (3.0) BIAsp 306211.7 (3.1)7.5 (2.1)−4.3 (3.0)Body Weight (kg) IDegAsp6187.5 (16.3)88.6 (16.9)1.1 (2.8) AF5984.9 (14.3)85.6 (14.9)0.7 (2.5) BIAsp 306291.8 (13.5)93.2 (13.1)1.4 (3.2)aValues at randomisation.bLast observation carried forward.c% points.

Discussion of Calculation of Power Function with Special Reference to Respiratory Metabolism in Fish

1969 ◽  
Vol 26 (10) ◽  
pp. 2643-2650 ◽  
Author(s):  
Norman R. Glass
Keyword(s):  
Body Weight ◽  
Oxygen Consumption ◽  
Power Function ◽  
Body Weight Data ◽  
Fitting Method ◽  
Wide Range ◽  
Curve Fitting Method ◽  
Parameter Values ◽  
Weight Data ◽  
Range Of Values

The rationale for employing a nonlinear iterative least-squares technique for fitting the well-known power function to oxygen consumption–body weight data is set forth. Twenty-six sets of routine or standard metabolism data from six authors were used to demonstrate the relative merits of two methods of calculating parameter values for the power function. The conclusion was reached that if accuracy in predicting oxygen consumption over a wide range of values of body weight is desired, an iterative curve fitting method may be superior to the much used technique of performing a linear regression on logarithmically transformed data.

Evaluation of Alternative Methods to Analyze Pig Body Weight Data from Animal Sorting Technologies

2009 ◽  
Vol 25 (6) ◽  
pp. 654-662 ◽  
Author(s):  
A.P. Schinckel ◽  
M. Wastell ◽  
M.E. Einstein ◽  
J.T. Hubbs ◽  
P.V. Preckel
Keyword(s):  
Body Weight ◽  
Alternative Methods ◽  
Body Weight Data ◽  
Weight Data

scholarly journals Brain weight predicts yawn duration across domesticated dog breeds

2019 ◽  
Vol 66 (4) ◽  
pp. 401-405
Author(s):  
Andrew C Gallup ◽  
Lea Moscatello ◽  
Jorg J M Massen
Keyword(s):  
Body Weight ◽  
Large Scale ◽  
Brain Temperature ◽  
Single Species ◽  
Brain Weight ◽  
Canis Lupus Familiaris ◽  
Taxonomic Rank ◽  
Body Weight Data ◽  
Final Sample ◽  
Weight Data

Abstract Previous research shows that yawning enhances intracranial circulation and regulates brain temperature. Consistent with these functional outcomes, yawn duration correlates positively with interspecies variation in brain weight across mammals, with robust relationships documented at both the taxonomic rank of class and the more restricted scale of family (e.g., Felidae). This study provides the first investigation into whether differences in brain weight within a single species, domesticated dogs Canis lupus familiaris, can predict intraspecific variation in yawn duration. Measures of yawn duration were obtained from public videos available online and then paired with previously published brain and body weight data of different dog breeds. The final sample consisted of 272 yawns from 198 dogs across 23 breeds. Consistent with recent studies, we find robust correlations between yawn duration and brain weight across breeds. Moreover, these correlations remain significant after controlling for differences in body weight across breeds. These findings replicate and extend upon past work in this area and provide further support that yawns evolved to serve an important and large-scale neurophysiologic function.

scholarly journals Successful Treatment of a Pacemaker Infection with Intraperitoneal Daptomycin

2016 ◽  
Vol 36 (1) ◽  
pp. 114-117 ◽  
Author(s):  
Anne B. Taegtmeyer ◽  
Nina Kononowa ◽  
Dominique Fasel ◽  
Manuel Haschke ◽  
Felix Burkhalter
Keyword(s):  
Body Weight ◽  
Peak Concentration ◽  
Single Case ◽  
Intraperitoneal Administration ◽  
Therapeutic Drug ◽  
Serum Drug Concentration ◽  
Dose Administration ◽  
Pacemaker Infection ◽  
Body Weight Data ◽  
Weight Data

We investigated the pharmacokinetics of intraperitoneal administration of daptomcyin in a peritoneal dialysis (PD) patient treated for a pacemaker infection with Staphylococcus epidermidis. After initial start of intravenous daptomycin at 9 mg/kg body weight every 48 hours, the therapy was switched to intraperitoneal administration of 5.3 mg/kg body weight in 1 L icodextrin 7.5% with a dwell time of 12 hours overnight every 48 hours. Therapeutic drug monitoring (TDM) was performed at 4 hours and 24 hours after dose administration. Due to high peak concentration above target peak concentration, the dose was reduced to a final maintenance dose of 3.2 mg/kg body weight. Data from this single case suggest that serum drug concentration above the minimal inhibitory concentration (MIC) can be easily achieved with intraperitoneal administration of daptomycin every 48 hours even with a lower dose, as recommended for the intravenous administration, but measurement of serum concentration and dose adjustments are mandatory in such cases.

scholarly journals PSV-20 The effect of differing implant strategies on the galanin receptor 2 genotype on feed intake, efficiency, and feeding behavior of crossbred Angus finishing steers

2020 ◽  
Vol 98 (Supplement_3) ◽  
pp. 159-159
Author(s):  
Jerica R Hall ◽  
Mara Hirchert ◽  
Kasey R Maddock Carlin ◽  
Carl R Dahlen ◽  
Alison K Ward
Keyword(s):  
Body Weight ◽  
Feeding Behavior ◽  
Rfid Tags ◽  
Galanin Receptor ◽  
Future Studies ◽  
Body Weight Data ◽  
Finishing Steers ◽  
Main Effect ◽  
N 93 ◽  
Weight Data

Abstract Abstract: The objective of this study was to determine how the interaction different implant strategies with the galanin receptor 2 (GALR2) would influence intake, efficiency, and feeding behavior of finishing steers along with carcass characteristics. Angus crossbred steers (n = 93) were selected for this study based on their GALR2 genotype (GG, TT, and TG) and weaning weight with 19 GG, 36 TT, and 38 TG. Calves were blocked by initial body weight and fed a standard feedlot ration for 166 d or 202 d. Body weight data and blood samples were collected every 28 d. Steers were randomly assigned to one of two implant strategies; 1) a single Revalor-S (Merk, New Jersey) on d 77, or 2) a Revalor-S with on d 0 and another on d 77. Intake and feeding behavior data were recorded using RFID tags and Insentec feeders (Hokofarm Group, B.V., Marknesse, the Netherlands). Total DMI and feeding behavior were summarized following procedures described by (Swanson et al. 2014). Data were analyzed using the mixed procedure in SAS with a 2 x 3 factorial arrangement of treatments. The interaction of genotype x implant had a significant effect on minutes spent eating/visit (P = 0.02). The main effect of genotype had a significant effect on DMI (P = 0.05), number of meals/day (P 0.0001), and intake/minute (P = 0.002). While there was a greater DMI with the GG genotype we do not see the benefit of improved feed efficiency. The main effect of implant had a significant effect on ADG (P = 0.0008) and intake/visit (P 0.0012). Based on this data we conclude that there is no interaction between GALR2 genotype and implant strategy on efficiency or intake in finishing steers, however there may be benefit to future studies focusing on the GALR2 genotype and intake.

scholarly journals Data Imputation and Body Weight Variability Calculation Using Linear and Nonlinear Methods in Data Collected From Digital Smart Scales: Simulation and Validation Study (Preprint)

2020 ◽  
Author(s):  
Jake Turicchi ◽  
Ruairi O'Driscoll ◽  
Graham Finlayson ◽  
Cristiana Duarte ◽  
A L Palmeira ◽  
...  
Keyword(s):  
Body Weight ◽  
Missing Data ◽  
Moving Average ◽  
Simulated Data ◽  
Correct Identification ◽  
Data Imputation ◽  
Nonlinear Methods ◽  
Body Weight Data ◽  
Linear And Nonlinear ◽  
Weight Data

BACKGROUND Body weight variability (BWV) is common in the general population and may act as a risk factor for obesity or diseases. The correct identification of these patterns may have prognostic or predictive value in clinical and research settings. With advancements in technology allowing for the frequent collection of body weight data from electronic smart scales, new opportunities to analyze and identify patterns in body weight data are available. OBJECTIVE This study aims to compare multiple methods of data imputation and BWV calculation using linear and nonlinear approaches METHODS In total, 50 participants from an ongoing weight loss maintenance study (the NoHoW study) were selected to develop the procedure. We addressed the following aspects of data analysis: cleaning, imputation, detrending, and calculation of total and local BWV. To test imputation, missing data were simulated at random and using real patterns of missingness. A total of 10 imputation strategies were tested. Next, BWV was calculated using linear and nonlinear approaches, and the effects of missing data and data imputation on these estimates were investigated. RESULTS Body weight imputation using structural modeling with Kalman smoothing or an exponentially weighted moving average provided the best agreement with observed values (root mean square error range 0.62%-0.64%). Imputation performance decreased with missingness and was similar between random and nonrandom simulations. Errors in BWV estimations from missing simulated data sets were low (2%-7% with 80% missing data or a mean of 67, SD 40.1 available body weights) compared with that of imputation strategies where errors were significantly greater, varying by imputation method. CONCLUSIONS The decision to impute body weight data depends on the purpose of the analysis. Directions for the best performing imputation methods are provided. For the purpose of estimating BWV, data imputation should not be conducted. Linear and nonlinear methods of estimating BWV provide reasonably accurate estimates under high proportions (80%) of missing data.

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