Methods of Determining Energy Expenditure in Individuals with Prader-Willi Syndrome: A Systematic Literature Review.

Prader-Willi syndrome (PWS) is a rare disorder characterised by varying nutritional phases that occur throughout the lifespan, ranging from failure to thrive to hyperphagia. If uncontrolled, the imbalance between energy intake and expenditure results in obesity development and increased morbidity and mortality risk. Although measures of energy requirements for accurate nutrition assessment are vital, the evidence appears sparse and heterogeneous; hence, the aim of this review was to examine the available literature on energy expenditure predicted or measured using various methods in individuals with PWS. Studies were sought that presented methods and results on resting energy expenditure or basal metabolic rate. A narrative synthesis was completed to present the study characteristics and results. Methods of determining energy requirements included predictive equations and indirect calorimetry. Differences amongst ages, growth hormone therapy, fasting status, and measures in which results were presented were limitations to appropriately summarising and identifying trends in energy expenditure. Indirect calorimetry was identified as the most accurate method; however, it is not widely available in all settings. Further research is encouraged to support the development of valid and reliable predictive equations that will better inform and improve the efficiency of clinical practice in supporting people with PWS.

Fat-free mass predictive equation using multifrequency bioelectrical impedance data in adolescent soccer athletes: development and cross-validation.

OBJECTIVES: This study aimed to develop and cross-validate a fat-free mass (FFM) predictive equation using multifrequency bioelectrical impedance analysis (BIA) data in adolescent soccer athletes. METHODS: Male adolescent soccer athletes (n = 149; 13-19 y old) were randomly sorted using Excel and independently selected for development group (n = 100) or cross-validation group (n = 49). The FFM reference values were determined using dual-energy X-ray absorptiometry. Single-frequency BIA was used to plot tolerance ellipses. Multifrequency-BIA raw data were used as independent variables in regression models. Student's independent t-test was used to compare development and cross-validation groups. Stepwise multiple regression was used to develop the FFM predictive equation. Bland-Altman plots, Lin's concordance correlation coefficient, according to McBride criteria, precision, accuracy, and standard error of estimate (SEE) were calculated to evaluate the concordance and reliability of estimates. Bioelectrical impedance vector analysis was plotted to assess hydration status. RESULTS: No differences (P > 0.05) were observed between development and validation groups in chronological age, anthropometric data, bioelectrical impedance data, and FFM values obtained using dual-energy X-ray absorptiometry. Bioelectrical impedance vector analysis tolerance showed that all participants presented adequate hydration status compared to the reference population. The new FFM predictive equation developed and validated: FFM (kg) = -7.064 + 0.592 × chronological age (y) + 0.554 × weight (kg) + 0.365 × height²/resistance (cm²/Ω), presented R² = 0.95; SEE = 1.76 kg; concordance correlation coefficient = 0.95, accuracy = 0.98, and strength of concordance = 0.99. CONCLUSIONS: The present study developed and cross-validated an FFM predictive equation based on multifrequency bioelectrical data providing substantial FFM accuracy for male adolescent soccer athletes.

Modified Squat Test for Predicting Knee Muscle Strength in Older Adults.

BACKGROUND: Methods for evaluating the strength of the knee extensor muscles play a vital role in determining the functionality of the lower limbs and monitoring any alterations that occur over time in older individuals. This study assessed the validity of the Modified Squat Test (MST) in predicting knee extensor muscle strength in older adults. METHODS: This study included a total of 110 older adults. We collected demographic information such as sex, age, body weight, height, and thigh circumference. Muscle strength was assessed by measuring the maximum voluntary isometric contraction of the knee extensors, and by performing the MST (5 and 10 repetitions) and single-leg standing balance test. Stepwise multiple linear regression analysis was used to investigate multiple factors impacting the prediction of knee extensor strength. RESULTS: Factors such as age, sex, thigh circumference, performance on the single-leg standing eye-open (SSEO) task, and the time required to complete the 10 MST repetitions together explained 77.8% of the variation in knee extensor muscle strength among older adults. We further developed a predictive equation to calculate strength as follows: strength = 36.78 - 0.24 (age) + 6.16 (sex) + 0.19 (thigh circumference) + 0.05 (SSEO) - 0.54 (time required to complete 10 MST repetitions) ± 5.51 kg. CONCLUSION: The 10-repetition MST is an invaluable instrument for establishing an equation to accurately predict lower limb muscle strength.

Fat-free mass predictive equation using bioelectrical impedance and maturity offset in adolescent athletes: Development and cross-validation.

OBJECTIVES: This is a cross-sectional study, aimed to develop and cross-validate a fat-free mass (FFM) predictive equation using single-frequency bioelectrical impedance (BIA), considering the predicted age at peak height velocity (PHV) as a variable. Additionally, the study aims to test the FFM-BIA obtained using a previous predictive equation that used skeletal maturity as a variable. METHOD: The participants (n = 169 male adolescent athletes) were randomly divided into two groups: development of a new predictive equation (n = 113), and cross-validation (n = 56). The concordance test between the FFM values obtained by Koury et al. predictive equation and DXA data was determined (n = 169). Bioelectrical data was obtained using a single-frequency analyzer. RESULTS: Among the models tested, the new predictive equation has resistance index (height2/resistance) and predictive age at PHV as variables and presented R2 = 0.918. The frequency of maturity status using skeletal maturity and PHV diagnosis was inadequate (Kappa = 0.4257; 95%CI = 0.298-0.553). Bland-Altman plots and concordance correlation coefficient showed substantial concordance between the FFM-DXA values (48.8 ± 11.2 kg) and the new predictive equation (CCC = 0.960). The results showed that the new equation performed better than the equation developed by Koury et al. (CCC = 0.901). CONCLUSIONS: Our results show that it is feasible to predict FFM in male adolescent athletes using predictive age at PHV, with moderate concordance. The calculation of FFM using more economical and less complex variables is viable and should be further explored.

Development of New Predictive Equations for the Resting Metabolic Rate (RMR) of Women with Lipedema.

This study aimed to develop a novel predictive equation for calculating resting metabolic rate (RMR) in women with lipedema. We recruited 119 women diagnosed with lipedema from the Angiology Outpatient Clinic at Wroclaw Medical University, Poland. RMR was assessed using indirect calorimetry, while body composition and anthropometric measurements were conducted using standardized protocols. Due to multicollinearity among predictors, classical multiple regression was deemed inadequate for developing the new equation. Therefore, we employed machine learning techniques, utilizing principal component analysis (PCA) for dimensionality reduction and predictor selection. Regression models, including support vector regression (SVR), random forest regression (RFR), and k-nearest neighbor (kNN) were evaluated in Python's scikit-learn framework, with hyperparameter tuning via GridSearchCV. Model performance was assessed through mean absolute percentage error (MAPE) and cross-validation, complemented by Bland-Altman plots for method comparison. A novel equation incorporating body composition parameters was developed, addressing a gap in accurate RMR prediction methods. By incorporating measurements of body circumference and body composition parameters alongside traditional predictors, the model's accuracy was improved. The segmented regression model outperformed others, achieving an MAPE of 10.78%. The proposed predictive equation for RMR offers a practical tool for personalized treatment planning in patients with lipedema.

High-Flow and Low-Flow Oxygen Delivery by Nasal Cannula Evaluated in Infant and Adult Airway Replicas.

BACKGROUND: The nasal cannula is widely regarded as a safe and effective means of administering low- and high-flow oxygen to patients irrespective of their age. However, variability in delivered oxygen concentration (FDO2 FDO2 ) via nasal cannula has the potential to pose health risks. The present study aimed to evaluate predictive equations for FDO2 over a large parameter space, including variation in breathing, oxygen flow, and upper-airway geometry representative of both young children and adults. METHODS: Realistic nasal airway geometries were previously collected from medical scans of adults, infants, and neonates. Nasal airway replicas based on these geometries were used to measure the FDO2 for low-flow oxygen delivery during simulated spontaneous breathing. The present study extends previously published data sets to include higher oxygen flows. The extended data sets included nasal cannula oxygen flows that ranged from 6 to 65 L/min for the adult replicas, and from 0.5 to 6 L/min for the infant replicas. For both age groups, FDO2 was measured over a range of breathing frequencies, inspiratory to expiratory time ratios, and tidal volumes. Measured FDO2 values were compared with values predicted by using a previously derived flow-weighted equation. RESULTS: For both age groups, FDO2 was observed to increase nonlinearly with the ratio between oxygen flow supplied to the nasal cannula and the average inhalation flow. The previously derived flow-weighted equation over-predicted FDO2 at higher oxygen flows. A new empirical equation, therefore, was proposed to predict FDO2 for either age group as a function of nasal cannula flow, tidal volume, and inspiratory time. Predicted FDO2 values matched measured values, with average relative errors of 2.4% for infants and 4.3% for adults. CONCLUSIONS: A new predictive equation for FDO2 was obtained that accurately matched measured data in both adult and infant airway replicas for low- and high-flow regimens.

Strength Characteristics, Ultrasonic Wave Velocity, and the Correlation between Them in Clay Bricks under Dry and Saturated Conditions.

One of the methods used to discover the development of deterioration in bricks used as a construction material in a building is the monitoring of the bricks' strength characteristics over time. However, measuring the strength characteristics of bricks used in a building requires sampling for performing laboratory tests, which is not possible in some cases. As an alternative, ultrasonic wave velocity can be a useful, nondestructive tool for the indirect assessment of the strength characteristics of the bricks. In the present study, six different samples of clay bricks before utilization as construction materials in buildings located in Khorramabad City (Lorestan Province, western Iran) were collected. The mineralogical composition of the samples was studied using X-ray diffraction (XRD) analysis. As one common physical characteristic of the construction materials, the porosity (n) of the samples was measured. Next, the strength characteristics, including uniaxial compressive strength (UCS), Brazilian tensile strength (BTS), and P-wave velocity (Vp), of the samples under dry and saturated conditions were determined. It was found that after the saturation of the samples, considerable decreases in the UCS and BTS and increases in the Vp occurred, respectively. By comparing the values of the UCS, BTS, and Vp of the samples under dry and saturated conditions, we found that the integrity loss for the UCS and BTS was higher than for the Vp. Results showed that the integrity loss of the UCS, BTS, and Vp was significantly affected by the n and clay mineral (CM) content of the samples. Considering the dry or saturated condition of the samples, there are good correlations with acceptable accuracy levels between the Vp and the UCS and BTS, with coefficients of determination (R2) varying from 0.95 to 0.98. Consequently, our findings showed that establishing UCS and BTS predictive equations for bricks before their use as a construction material can be a worthy, practical tool for monitoring the deterioration of bricks over time after their utilization in a building.

Estimates of resting energy expenditure using predictive equations in adults with severe burns: A systematic review and meta-analysis.

BACKGROUND: Many equations to estimate the resting energy expenditure (REE) of patients with burns are currently available, but which of them provides the best guide to optimize nutrition support is controversial. This review examined the bias and precision of commonly used equations in patients with severe burns. METHODS: A systematic search of the PubMed, Web of Science, Embase, and Cochrane Library databases was undertaken on June 1, 2023, to identify studies comparing predicted REE (using equations) with measured REE (by indirect calorimetry [IC]) in adults with severe burns. Meta-analyses of bias and calculations of precisions were performed in each predictive equation, respectively. RESULTS: Nine eligible studies and 12 eligible equations were included. Among the equations, the Toronto equation had the lowest bias (26.1 kcal/day; 95% CI, -417.0 to 469.2), followed by the Harris-Benedict equation × 1.5 (1.5HB) and the Milner equation. The Ireton-Jones equation (303.4 kcal/day; 95% CI, 224.5-382.3) acceptably overestimated the REE. The accuracy of all of the equations was <50%. The Ireton-Jones equation had the relatively highest precision (41.2%), followed by the 1.5HB equation (37.0%) and the Toronto equation (34.7%). CONCLUSION: For adult patients with severe burns, all of the commonly used equations for the prediction of REE are inaccurate. It is recommended to use IC for accurate REE measurements and to use the Toronto equation, 1.5HB equation, or Ireton-Jones equation as a reference when IC is not available. Further studies are needed to propose more accurate REE predictive models.

Application of near-infrared spectroscopy for hay evaluation at different degrees of sample preparation.

OBJECTIVE: A study was conducted to quantify the performance differences of the nearinfrared spectroscopy (NIRS) calibration models developed with different degrees of hay sample preparations. METHODS: A total of 227 imported alfalfa (Medicago sativa L.) and another 360 imported timothy (Phleum pratense L.) hay samples were used to develop calibration models for nutrient value parameters such as moisture, neutral detergent fiber, acid detergent fiber, crude protein, and in vitro dry matter digestibility. Spectral data of hay samples prepared by milling into 1-mm particle size or unground were separately regressed against the wet chemistry results of the abovementioned parameters. RESULTS: The performance of the developed NIRS calibration models was evaluated based on R2, standard error, and ratio percentage deviation (RPD). The models developed with ground hay were more robust and accurate than those with unground hay based on calibration model performance indexes such as R2 (coefficient of determination), standard error, and RPD. Although the R2 of calibration models was mainly greater than 0.90 across the feed value indexes, the R2 of cross-validations was much lower. The R2 of cross-validation varies depending on feed value indexes, which ranged from 0.61 to 0.81 in alfalfa, and from 0.62 to 0.95 in timothy. Estimation of feed values in imported hay can be achievable by the calibrated NIRS. However, the NIRS calibration models must be improved by including a broader range of imported hay samples in the modeling. CONCLUSION: Although the analysis accuracy of NIRS was substantially higher when calibration models were developed with ground samples, less sample preparation will be more advantageous for achieving rapid delivery of hay sample analysis results. Therefore, further research warrants investigating the level of sample preparations compromising analysis accuracy by NIRS.

Optimizing the hydraulic performance of a baffled horizontal subsurface flow constructed wetland through computational fluid dynamics modelling.

Baffled constructed wetlands (CWs) offer a promising solution to address low hydraulic efficiency in traditional CWs. However, there is a research gap in the field regarding the optimal length and quantity of baffles, and their comprehensive effects on hydraulic efficiency. This study is the first CFD-based assessment to comprehensively investigate the combined influence of baffle length and the number of baffles on the hydraulic efficiency of CWs. Using OpenFOAM simulations at a laboratory scale, various baffle configurations were examined with lengths ranging from 0.4 m to 0.58 m and baffle numbers varying from 0 to 11. Experimental tracer tests were conducted to validate the simulations. The high correlation coefficient (R2) between the tracer test results and simulations (ranging between 0.84 and 0.93) further underscores the reliability of the findings. Residence time distributions (RTDs) were derived from the temporal evolution of the outlet concentration of a tracer. The results indicate that augmenting the number of baffles under a fixed baffle length has a greater impact on the RTD curves, causing a backward displacement of the peak time. However, when the number of baffles is three or fewer, extending the baffle length does not significantly affect the RTD. When the baffle length is held constant at 0.58 m, there is a 58% enhancement in hydraulic efficiency as the number of baffles increases from 0 to 5. However, when maintaining a constant number of 11 baffles, increasing the baffle length from 0.4 to 0.5 m results in only a 5.5% improvement in hydraulic efficiency. Moreover, a generalized predictive equation for hydraulic efficiency was derived based on the CFD results and dimensional analysis. The study enhances the optimization of constructed wetland design by providing greater understanding of hydrodynamic behavior, leading to improved performance and applicability in practical environmental engineering.

Predictive Equation for Basal Metabolic Rate in Normal-Weight Chinese Adults.

This study aimed to develop a predictive equation for basal metabolic rate (BMR) in normal-weight Chinese adults and provide a reference for establishing the national recommended dietary energy intake. A new equation for BMR was derived from a sample of 516 normal-weight Chinese adults (men = 253, women = 263), and this sample was collected from two previous studies. Furthermore, the accuracy of this new equation and eight other previous predictive equations was reviewed. The agreement and reliability were compared in terms of bias, accuracy, the intraclass correlation coefficient, and Bland-Altman plots between predictive equations. In addition, the newly developed equation was further verified using a small independent sample, which contained 41 healthy Chinese adults (men = 21, women = 20). The measured BMR (mBMR) of all participants, measured using indirect calorimetry, was 1346.2 ± 358.0 kcal/d. Thirty participants were excluded based on Cook's distance criteria (Cook's distance of ≥0.008). Previous equations developed by Henry, Schofield, Harris-Benedict (H-B), Yang, and Hong overestimated the BMR of healthy Chinese adults. The present equation displayed the smallest average bias (0.2 kcal/d) between the mBMR and predicted basal metabolic rate (pBMR). The limits of agreement of the present equation from Bland-Altman plots were -514.3 kcal/d and 513.9 kcal/d, which is the most narrow and balanced limit of agreement. Moreover, in the verification of the testing database, the pBMR of the new equation was not significantly different from the mBMR, and the accuracy was 75.6%. Compared with pre-existing equations, the present equation is more applicable to the prediction of BMR in healthy Chinese adults. However, further studies are required to verify the accuracy of this new equation.

Prediction of body fat percentage: Development and validation of new anthropometric equations.

INTRODUCTION: The aim of this study was to test the validity of existing equations, retrieved from the literature, in the Algerian adult population. To develop, and validate, new predictive equations for body fat percentage (%BF) using simple and easy-to-measure anthropometric parameters. METHODS: This is a cross-sectional study including 877 Algerian adults who underwent a body composition assessment by the direct segmental multi-frequency bioelectrical impedance technique (Inbody-770). Participants were randomly divided into two groups: the development group (n = 577) and the validation group (n = 300). To develop the equations, multiple linear regression models were analyzed. The predictive performance of the developed equations was compared with the direct technique. The following validation tests were used: Student's t-test for paired samples, correlation, and Bland-Altman diagram. Diagnostic accuracy has also been assessed. RESULTS: Four existing equations were tested, and all showed statically significant bias. Four new equations were developed; all had satisfactory predictive performance, with a correlation coefficient ranging from 0.72 to 0.94 in men and 0.87 to 0.93 in women. The best-fitting equation was based on body mass index, waist-to-hip ratio, and chest circumference. The diagnostic accuracy of this equation was 96.7% in men and 95.3% in women. CONCLUSION: The newly developed equations based on anthropometric parameters can serve as a simple tool for the accurate prediction of BF% in adult subjects, at both individual and epidemiological levels.

Validation of predictive equations for resting energy expenditure in children and adolescents with different body mass indexes.

BACKGROUND: Accurate estimation of resting energy expenditure (REE) in children and adolescents is important to establish estimated energy requirements. The objective of this study was to assess the validity of existing equations in literature and a newly developed equation in estimating REE in children and adolescents. METHODS: 275 participants (148 boys, 127 girls) aged 6-18 years included in the study were classified as normal-weighted, overweight, obese based on BMI z-scores for age according to WHO-2007 growth curves for 5-19 years of age. REEs were measured using an indirect calorimeter, with various equations, and a newly established equation [REE = 505.412+(24.383*FFM);Adjusted R2 = 0.649] were compared with REE measured using Bland-Altman and further validation parameters. RESULTS: When the predicted REEs were compared with the measured REEs, the highest prediction accuracy was achieved using the new Eq. (64.8%) and IOM (63.8%) for normal-weight participants, Müller FFM and new Eq. (59.6%) for overweight participants and Lazzer (44.9%) for obese participants. In normal and overweight participants, lowest root mean squared error (RMSE) values were acquired from Schmelzle's equation (respectively 136.2;159.9 kcal/d), and the highest values were found in Kim's Eq. (315.2; 295.2 kcal/d respectively). RMSE value of the new equation was 174.7 kcal/d for normal-weight children and adolescents, and 201.9 kcal/d for overweight ones. In obese participants, the lowest RMSE value was obtained from Schmelzle's Eq. (305.4 kcal/d) and the new Eq. (317.4 kcal/d), while the highest value was obtained from IOM Eq. (439.9 kcal/d). RMSE was higher in obese groups compared to the other BMI groups. CONCLUSION: Indirect-calorimeter is the most suitable method for REE measurement in especially obese children and adolescents. The new equation and Schmelzle's equation appear to be most accurate equations for normal and overweight children and adolescents.

Machine learning models using non-linear techniques improve the prediction of resting energy expenditure in individuals receiving hemodialysis.

PURPOSE: Approximately 700,000 people in the USA have chronic kidney disease requiring dialysis. Protein-energy wasting (PEW), a condition of advanced catabolism, contributes to three-year survival rates of 50%. PEW occurs at all levels of Body Mass Index (BMI) but is devastating for those people at the extremes. Treatment for PEW depends on an accurate understanding of energy expenditure. Previous research established that current methods of identifying PEW and assessing adequate treatments are imprecise. This includes disease-specific equations for estimated resting energy expenditure (eREE). In this study, we applied machine learning (ML) modelling techniques to a clinical database of dialysis patients. We assessed the precision of the ML algorithms relative to the best-performing traditional equation, the MHDE. METHODS: This was a secondary analysis of the Rutgers Nutrition and Kidney Database. To build the ML models we divided the population into test and validation sets. Eleven ML models were run and optimized, with the best three selected by the lowest root mean squared error (RMSE) from measured REE. Values for eREE were generated for each ML model and for the MHDE. We compared precision using Bland-Altman plots. RESULTS: Individuals were 41.4% female and 82.0% African American. The mean age was 56.4 ± 11.1 years, and the median BMI was 28.8 (IQR = 24.8 - 34.0) kg/m2. The best ML models were SVR, Linear Regression and Elastic net with RMSE of 103.6 kcal, 119.0 kcal and 121.1 kcal respectively. The SVR demonstrated the greatest precision, with 91.2% of values falling within acceptable limits. This compared to 47.1% for the MHDE. The models using non-linear techniques were precise across extremes of BMI. CONCLUSION: ML improves precision in calculating eREE for dialysis patients, including those most vulnerable for PEW. Further development for clinical use is a priority.

Potentially impacting millions of patients worldwide, our continuing goal is to understand energy expenditure (EE) across the spectrum of CKD (stages 1­5) in adults and children being treated with dialysis or transplantation, with the intent of providing tools for the health professional that will improve the delivery of quality care.In past research, we have identified and focused on disease-specific variables which account for 60% of the variance in predicting EE in individuals receiving dialysis, but many questions remain unanswered.Our hypotheses are that (1) there are determinants of EE specific to CKD and, (2) predicting EE for individuals may be greatly advanced using sophisticated models that combine these determinants. In this study, we applied machine learning (ML) with linear and non-linear techniques to our existing dataset. The best models demonstrated improved precision in predicting EE for all individuals in the validation group.

Predicting resting energy expenditure among athletes: a systematic review.

Resting energy expenditure (REE) is often estimated in athletes using equations developed from the general population however, the application in athletic-specific populations is questionable. The aim of this systematic review was to compare measured REE and estimations of REE obtained from non-sport participants and athletes. Inclusion criteria met PICO criteria: population - participants involved in organized sport; intervention - resting energy expenditure was obtained by calorimetry; comparator - equations to estimate REE; outcomes - comparisons between measured REE and predicted REE. The search was conducted in Web of Science all databases, PubMed and Scopus. Comparisons between measured REE and predicted REE as well the potential models to estimate REE developed among athletes were summarized. Allowing for variation among studies, equations developed within general populations were not comparable to REE measured by calorimetry in athletes. Equations across athletic samples were obtained but, few studies tested their validity across independent samples of sport participants. Nevertheless, equations developed within athlete populations seem to be widely unused in sports nutrition literature and practice. De Lorenzo and ten Haaf equations appear to present an acceptable agreement with measured REE. Finally, equations used among adults should not be generalised for youth sport participants.

Prediction of Rowing Functional Threshold Power Using Body Mass, Blood Lactate and GxT Peak Power Data.

Functional Threshold Power (FTP) is a validated index of a maximal quasi steady-state cycling intensity. The central component of the FTP test is a maximal 20-min time-trial effort. A model to predict FTP from a cycling graded exercise test (m-FTP) was published that estimated FTP without the requirement of the exhaustive 20-min time-trial. The predictive model (m-FTP) was trained (developed to find the best combination of weights and bias) on a homogenous group of highly-trained cyclists and triathletes. This investigation appraised the external validity of the m-FTP model vis-à-vis the alternate modality of rowing. The reported m-FTP equation purports to be sensitive to both changing levels of fitness, and exercise capacity. To assess this claim, eighteen (7 female, 11 male) heterogeneously-conditioned rowers were recruited from regional rowing clubs. The first rowing test was a 3-min graded incremental test with a 1-min break between increments. The second test was a rowing adapted FTP test. There were no significant differences between rowing FTP (r-FTP) and m-FTP (230 ± 64 versus 233 ± 60 W, respectively, F = 1.13, P = 0.80). Computed Bland-Altman 95% LoA between r-FTP and m-FTP were (-18 W to + 15 W), sy.x was 7 W, and 95 %CI of regression were 0.97 to 0.99. The r-FTP equation was demonstrated to be effective in predicting a rowers 20-min maximum power; further appraisal of the physiological response to rowing for 60-min at the corresponding calculated FTP requires investigation.

Six-minute walking distance reference value for healthy Indonesian children: A cross-sectional study from the largest country in South East Asia.

Introduction: There is no global reference value for the 6-minute walking distance (6MWD) in paediatric populations, as it can vary greatly depending on local characteristics and anthropometric measures. This study aimed to identify a 6MWD reference value that could be applied in both local and regional settings. Method: This cross-sectional multicentre study investigated a healthy paediatric population aged 4-18 years in Indonesia. The 6-minute walk test (6MWT) was conducted in accordance with the American Thoracic Society guidelines. Data were presented as the 6MWD according to age and sex per year. Univariate and multivariate analyses were conducted on the basis of the 6MWDpred Rizky formula. Results: A total of 634 participants were included in this study. Age, sex, weight, leg length and height affected the 6MWD (P<0.001). In the regression model, sex and height were the predictors of 6MWD, with height as the best single predictor. Conclusion: The reference charts and 6MWDpred Rizky formula are applicable in multi-ethnic paediatric Indonesian populations but in limited settings.

Prediction of fresh and ripened cheese yield using detailed milk composition and udder health indicators from individual Brown Swiss cows.

The composition of raw milk is of major importance for dairy products, especially fat, protein, and casein (CN) contents, which are used worldwide in breeding programs for dairy species because of their role in human nutrition and in determining cheese yield (%CY). The aim of the study was to develop formulas based on detailed milk composition to disentangle the role of each milk component on %CY traits. To this end, 1,271 individual milk samples (1.5 L/cow) from Brown Swiss cows were processed according to a laboratory model cheese-making procedure. Fresh %CY (%CYCURD), total solids and water retained in the fresh cheese (%CYSOLIDS and %CYWATER), and 60-days ripened cheese (%CYRIPENED) were the reference traits and were used as response variables. Training-testing linear regression modeling was performed: 80% of observations were randomly assigned to the training set, 20% to the validation set, and the procedure was repeated 10 times. Four groups of predictive equations were identified, in which different combinations of predictors were tested separately to predict %CY traits: (i) basic composition, i.e., fat, protein, and CN, tested individually and in combination; (ii) udder health indicators (UHI), i.e., fat + protein or CN + lactose and/or somatic cell score (SCS); (iii) detailed protein profile, i.e., fat + protein fractions [CN fractions, whey proteins, and nonprotein nitrogen (NPN) compounds]; (iv) detailed protein profile + UHI, i.e., fat + protein fractions + NPN compounds and/or UHI. Aside from the positive effect of fat, protein, and total casein on %CY, our results allowed us to disentangle the role of each casein fraction and whey protein, confirming the central role of ß-CN and κ-CN, but also showing α-lactalbumin (α-LA) to have a favorable effect, and ß-lactoglobulin (ß-LG) a negative effect. Replacing protein or casein with individual milk protein and NPN fractions in the statistical models appreciably increased the validation accuracy of the equations. The cheese industry would benefit from an improvement, through genetic selection, of traits related to cheese yield and this study offers new insights into the quantification of the influence of milk components in composite selection indices with the aim of directly enhancing cheese production.

Comparison of international and Japanese predictive equations for maximal respiratory mouth pressures.

Respiratory muscle weakness has attracted attention because sarcopenia and respiratory muscle dysfunction may play a key role in the development of respiratory failure. To evaluate respiratory muscle strength appropriately, individual factors such as sex, age, body size, and ethnicity should be considered. This study aimed to compare equations available in Japan and other countries for predicting respiratory muscle strength. We tested 21 equations for maximal inspiratory pressure (MIP) and 17 for maximal expiratory pressure (MEP) for each sex (76 equations in total) in 159 normal, healthy subjects. We observed wide variations in the overall agreement among the MIP and MEP equations. Some equations showed a proper normal distribution, with median values of almost 100%, and the Japanese equations released in 1997 generally showed the best distributions of both %MIP and %MEP. We can conclude that it is better to use Japanese equations when evaluating respiratory muscle strength in Japanese subjects.

Energy expenditure in COVID-19 mechanically ventilated patients: A comparison of three methods of energy estimation.

BACKGROUND: Indirect calorimetry (IC) is the gold standard for measuring resting energy expenditure. Energy expenditure (EE) estimated by ventilator-derived carbon dioxide consumption (EEVCO2 ) has also been proposed. In the absence of IC, predictive weight-based equations have been recommended to estimate daily energy requirements. This study aims to compare simple predictive weight-based equations with those estimated by EEVCO2 and IC in mechanically ventilated patients of COVID-19. METHODS: Retrospective study of a cohort of critically ill adult patients with COVID-19 requiring mechanical ventilation and artificial nutrition to compare energy estimations by three methods through the calculation of bias and precision agreement, reliability, and accuracy rates. RESULTS: In 58 mechanically ventilated patients, a total of 117 paired measurements were obtained. The mean estimated energy derived from weight-based calculations was 2576 ± 469 kcal/24 h, as compared with 1507 ± 499 kcal/24 h when EE was estimated by IC, resulting in a significant bias of 1069 kcal/day (95% CI [-2158 to 18.7 kcal]; P < 0.001). Similarly, estimated mean EEVCO2 was 1388 ± 467 kcal/24 h when compared with estimation of EE from IC. A significant bias of only 118 kcal/day (95% CI [-187 to 422 kcal]; P < 0.001), compared by the Bland-Altman plot, was noted. CONCLUSION: The energy estimated with EEVCO2 correlated better with IC values than energy derived from weight-based calculations. Our data suggest that the use of simple predictive equations may potentially lead to overfeeding in mechanically ventilated patients with COVID-19.