Size-resolved gas-particle partitioning of polycyclic aromatic hydrocarbons in a large temperature range of 50 ℃.

Size-resolved gas-particle partitioning of semi-volatile organic compounds (SVOCs) can affect their environmental behaviors and health effects, which has not been widely studied in comparing with the gas-total suspended particle partitioning. Herein, the size-resolved gas-particle partitioning quotient (KPi) of polycyclic aromatic hydrocarbons (PAHs) in a large temperature range (-20.6 â„ƒ ∼ 29.4 â„ƒ) was firstly comprehensively studied. The log KPi values of PAHs related to fine particles were significantly higher than those related to coarse particles. When the logarithm of subcooled liquid-vapor pressure (log PL0) ∈ [-7, -1), the regression slopes of log KPi vs log PL0 related to the particle size > 1.0 µm were shallower than those with the particle size range of 0.10-1.0 µm, which indicated the influence of particle size on KPi. Among the three previous prediction equations of gas-particle partitioning quotient, the empirical equation based on the ambient temperature matched better with the measured log KPi. Therefore, a new prediction equation including ambient temperature and particle size as the two major parameters was established. For most particle size ranges, the new equation showed better prediction performance than the three previous equations. In summary, this study provided new insights for the size-resolved gas-particle partitioning mechanism and quotient.

Determination and prediction of the net energy content of wheat bran for pregnant sow.

Two experiments were conducted to determine net energy (NE) values of wheat bran ingredients and develop a prediction equation for NE of wheat bran. In each experiment, 12 multiparous pregnant sows were allocated to two 3 × 6 Youden squares with three consecutive periods and six diets in each square. The study consisted of six diets, including a corn-soybean meal basal diet and five diets formulated with 29.2% wheat bran. Each period lasted for 10 d, with 5 d allocated for adaptation and followed by 5 d for heat production measurement. Sows were provided feed at 604 kJ/kg BW0.75·d-1. On day 10, sows underwent fasting to measure fasting heat production. Results indicated that the inclusion of wheat bran in the diets significantly reduced digestibility of energy and nutrients in (p < 0.05). The average net energy (NE) content of wheat bran was determined to be 8.8 MJ/kg DM. A regress equation NE = 7.968 + 0.28 × CP + 0.607 × EE - 0.782 × ash - 0.05 × hemicellulose (R2 = 0.98, p < 0.01) was found to accurately predit the NE value when feeding pregnant sows with wheat bran-based diets. In conclusion, the net energy content of wheat bran fed to pregnant sows ranged from 7.24 to 10.67 MJ/kg DM and can be effectively estimated using proximate analysis methods.

Developing normative values and predictive models for the 6-minute walk test across diverse adolescent developmental stages.

The six-minute walking test (6MWT) is commonly used to measure functional capacity in field settings, primarily through the distance covered. This study aims to establish reference curves for the six-minute walking distance (6MWD) and peak heart rate (PHR) and develop a predictive equation for cardiovascular capacity in Tunisian children and adolescents. A total of 1501 participants (706 boys and 795 girls), aged 10-18 years, were recruited from schools in Tunisia. The Lambda (L), Mu (M), and Sigma (S) methods (LMS method) were employed to develop smoothed percentile curves for 6MWD and PHR. Multivariate linear regression was utilized to formulate a prediction equation for 6MWD. Smoothed percentiles (3rd, 10th, 25th, 50th, 75th, 90th, and 97th) for 6MWD and PHR were presented with age. All variables showed a strong positive correlation (p < 0.001) with a six-minute walking distance (r ranged from 0.227 to 0.558 for girls and from 0.309 to 0.610 for boys), except resting heart rate, which showed a strong negative correlation (girls: r = -0.136; boys: r = -0.201; p < 0.001). Additionally, PHR showed a weak correlation (p > 0.05). The prediction equations, based on age as the primary variable, were established for both genders. For boys: 6MWD = 66.181 + 38.142 × Age (years) (R2 = 0.372; Standard Error of Estimate (SEE) = 122.13), and for girls: 6MWD = 105.535 + 28.390 × Age (years) (R2 = 0.312; SEE = 103.66). The study provides normative values and predictive equations for 6MWD and PHR in Tunisian children and adolescents. These findings offer essential tools for identifying, monitoring, and interpreting cardiovascular functional deficits in clinical and research settings.

Oxygen uptake efficiency plateau is unaffected by fitness level - the NOODLE study.

BACKGROUND: Endurance athletes (EA) are an emerging population of focus for cardiovascular health. The oxygen uptake efficiency plateau (OUEP) is the levelling-off period of ratio between oxygen uptake (VO2) and ventilation (VE). In the cohort of EA, we externally validated prediction models for OUEP and derived with internal validation a new equation. METHODS: 140 EA underwent a medical assessment and maximal cycling cardiopulmonary exercise test. Participants were 55% male (N = 77, age = 21.4 ± 4.8 years, BMI = 22.6 ± 1.7 kg·m- 2, peak VO2 = 4.40 ± 0.64 L·min- 1) and 45% female (N = 63, age = 23.4 ± 4.3 years, BMI = 22.1 ± 1.6 kg·m- 2, peak VO2 = 3.21 ± 0.48 L·min- 1). OUEP was defined as the highest 90-second continuous value of the ratio between VO2 and VE. We used the multivariable stepwise linear regression to develop a new prediction equation for OUEP. RESULTS: OUEP was 44.2 ± 4.2 mL·L- 1 and 41.0 ± 4.8 mL·L- 1 for males and females, respectively. In external validation, OUEP was comparable to directly measured and did not differ significantly. The prediction error for males was - 0.42 mL·L- 1 (0.94%, p = 0.39), and for females was + 0.33 mL·L- 1 (0.81%, p = 0.59). The developed new prediction equation was: 61.37-0.12·height (in cm) + 5.08 (for males). The developed model outperformed the previous. However, the equation explained up to 12.9% of the variance (R = 0.377, R2 = 0.129, RMSE = 4.39 mL·L- 1). CONCLUSION: OUEP is a stable and transferable cardiorespiratory index. OUEP is minimally affected by fitness level and demographic factors. The predicted OUEP provided promising but limited accuracy among EA. The derived new model is tailored for EA. OUEP could be used to stratify the cardiorespiratory response to exercise and guide training.

Prediction models for basal endogenous losses of crude protein and amino acids in pigs.

Objective: The objectives were to validate a previously published equation for estimating basal endogenous losses (BEL) of crude protein (CP) in pigs fed nitrogen-free diets and to develop prediction equations for BEL of CP and amino acids (AA). Methods: A total of 139 observations from 123 experiments in 117 papers that determined the BEL of CP and AA in pigs were collected. For the validation of the previous equation for the BEL of CP, 94 observations that were not used for developing the previous equation were used. All observations were used to develop novel equations for estimating BEL of CP in pigs based on the initial body weight (IBW). Results: The validation study indicated that the slope for BEL of CP, representing a linear bias, was less than zero (-0.56; SE = 0.130; p<0.001). The intercept for BEL of CP, representing a mean bias, was less than zero (-3.21; SE = 0.488; p<0.001). The models for estimating BEL of CP (g/kg dry matter intake) in pigs fed a nitrogen-free diet were developed: 20.36 - 0.077 × IBW with R2 = 0.11 and p<0.001 and 20.80 × e(-0.00475 × IBW) with R2 = 0.12 and p<0.001. Novel linear models for estimating BEL of AA were developed using BEL of CP as the independent variable. Conclusion: The accuracy of the previous equation for estimating BEL of CP in pigs has been improved by reflecting additional data from recent publications. In the novel linear models for estimating BEL of AA of pigs, BEL of CP was used as an independent variable.

Determination and prediction of standardized ileal amino acid digestibility of wheat bran in broiler chickens.

OBJECTIVE: The objective of this experiment was to determine the standard ileal digestibility (SID) of amino acid (AA) in 10 different sources of wheat bran fed to broilers and establish the SID AA prediction based on the chemical composition. METHODS: A total of 660 1-day-old broilers were randomly divided into 11 treatments with 6 replicates of 10 chickens each. Diets included 10 semi-purified mash diets and 1 nitrogenfree diet. Titanium dioxide (TiO2) 0.50% was used as an indigestible index. On day 13, 6 chickens from each replicate were selected for slaughter to collect ileal contents. On day 28, 4 chickens from each replicate were selected for slaughter to collect ileal contents. RESULTS: Results showed that the coefficient of variation of the conventional nutrients (except for gross energy, and dry matter) and all AAs was greater than 8.00%. The average SID of essential AA in wheat bran for 13-day-old broilers was 37.24% and the average SID of nonessential AA was 42.02%; the average SID of essential AA for 28-day-old broilers was 67.13% and the average SID of nonessential AA was 69.51%. A correlation was observed (p<0.05) between most SID AA and crude protein (CP), crude fiber (CF), acid detergent fiber (ADF), and ash at day 13. A correlation was observed (p<0.05) between most SID AA and CF, and ADF at day 28. The R2 value of stepwise regression equations for predicting the SID AA at day 13 and day 28 was best for glutamic acid (R2 = 0.97 using CP, ash, CF, ether extract (EE), and neutral detergent fiber [NDF]) and lysine (R2 = 0.74 using ash, ADF, EE, and NDF), respectively. CONCLUSION: In conclusion, broiler age had a significant effect on the SID AA values of wheat bran. The chemical composition of wheat bran varied widely between sources, and CP, CF, ADF, NDF, and ash were reasonable predictors of the SID AA of wheat bran.

Validation of a Risk Prediction Equation for Incident Chronic Kidney Disease in a Hypertensive Non-Diabetes Cohort in Singapore Primary Care Patients.

BACKGROUND: Accurate identification of individuals at risk of developing chronic kidney disease (CKD) may improve clinical care. Nelson et al. developed prediction equations to estimate the risk of incident eGFR of less than 60 mL/min/1.73 m2 in diabetic and non-diabetes patients using data from 34 multinational cohorts. We aim to validate the non-diabetes equation in our local multi-ethnic cohort and develop further prediction models. METHODS: Demographics, clinical and laboratory data of hypertensive non-diabetes patients with baseline eGFR ≥60 mL/min/1.73 m2 on follow-up with primary care clinics between 2010 and 2015 were collected. Follow-up was 5 years from entry to study. We validated Nelson's equation and developed our own model which we subsequently validated. The developmental cohort included patients between 2010 and 2014 while the validation cohort included patients in 2015. Variables included age, sex, eGFR, history of cardiovascular disease, ever smoker, body mass index, albuminuria, cholesterol, and treatment. Primary outcome was incident eGFR <60/min/1.73 m2 within 5 years. Model performance was evaluated by C-statistics and calibration was assessed. RESULTS: In the developmental cohort of 27,800 patients, 2823 (10.2%) developed the outcome during a mean follow-up of 4.4 years while 638 (12.8%) patients developed the outcome in the validation cohort of 4,994 patients. Applicability of Nelson's equation was limited by missing albuminuria, absence of black race, and exclusion of non-hypertensive patients in our cohort. Nonetheless, the modified Nelson's model demonstrated C-statistic of 0.85 (95% CI: 0.84-0.86). The C-statistic of our bespoke model was 0.85 (0.85-0.86) and 0.87 (0.85-0.88) for the developmental cohort and validation cohort, respectively. Calibration was suboptimal as the predicted risk exceeded the observed risk. CONCLUSIONS: The modified Nelson's equation and our locally derived novel model demonstrated high discrimination. Both models may potentially be used in predicting risk of CKD in hypertensive patients who are managed in primary care, allowing for early interventions in high-risk population.

Development and validation of age-specific predictive equations for total energy expenditure and physical activity levels for older adults.

BACKGROUND: Predicting energy requirements for older adults is compromised by the underpinning data being extrapolated from younger adults. OBJECTIVES: To generate and validate new total energy expenditure (TEE) predictive equations specifically for older adults using readily available measures (age, weight, height) and to generate and test new physical activity level (PAL) values derived from 1) reference method of indirect calorimetry and 2) predictive equations in adults aged ≥65 y. METHODS: TEE derived from "gold standard" methods from n = 1657 (n = 1019 females, age range 65-90 y), was used to generate PAL values. PAL ranged 1.28-2.05 for males and 1.26-2.06 for females. Physical activity (PA) coefficients were also estimated and categorized (inactive to very active) from population means. Nonlinear regression was used to develop prediction equations for estimating TEE. Double cross-validation in a randomized, sex-stratified, age-matched 50:50 split, and leave one out cross-validation were performed. Comparisons were made with existing equations. RESULTS: Equations predicting TEE using the Institute of Medicine method are as follows: For males, TEE = -5680.17 - 17.50 × age (years) + PA coefficient × (6.96 × weight [kilograms] + 44.21 × height [centimeters]) + 1.13 × resting metabolic rate (RMR) (kilojoule/day). For females, TEE = -5290.72 - 8.38 × age (years) + PA coefficient × (9.77 × weight [kilograms] + 41.51 × height [centimeters]) + 1.05 × RMR (kilojoule/day), where PA coefficient values range from 1 (inactive) to 1.51 (highly active) in males and 1 to 1.44 in females respectively. Predictive performance for TEE from anthropometric variables and population mean PA was moderate with limits of agreement approximately ±30%. This improved to ±20% if PA was adjusted for activity category (inactive, low active, active, and very active). Where RMR was included as a predictor variable, the performance improved further to ±10% with a median absolute prediction error of approximately 4%. CONCLUSIONS: These new TEE prediction equations require only simple anthropometric data and are accurate and reproducible at a group level while performing better than existing equations. Substantial individual variability in PAL in older adults is the major source of variation when applied at an individual level.

Appropriate Endotracheal Tube Position for Percutaneous Dilatational Tracheostomy: A Single-Center Observational Study.

Aim This study aimed to investigate the appropriate endotracheal tube (ETT) position during percutaneous dilatational tracheostomy (PDT). Methods This single-center observational study included hospitalized patients who underwent surgical tracheostomy (ST) between August 2021 and October 2022. During ST, the trachea was opened, and the ETT was pulled out visually. It stopped when the ETT was no longer visible, and the tracheostomy tube was placed in the trachea. The ETT position was measured by considering the ETT position during ST to be the appropriate position during PDT. The correlation between the measured ETT position and patient characteristics was evaluated. A prediction equation for the ETT position was derived from the derivation group, and validation of the prediction equation was evaluated by the validation group. Results Forty-six and 15 patients were in the derivation and validation groups, respectively. Weight, duration of intubation, and in-hospital mortality were significantly different between the two groups. The measured ETT position correlated with body height (r=0.60, p<0.001) and sex (r=0.45, p=0.002), while the ETT position before ST showed a weak correlation (r=0.34, p=0.020). The predicted and measured values in the validation group correlated with each other (r=0.58, p=0.024). Conclusion The appropriate ETT position for PDT correlates with body height, and the equation "body height×0.112-0.323 cm" was derived. This predictive equation may be useful as a guide for ETT positioning during PDT puncture.

Below or all the way to the peak? Oxygen uptake efficiency slope as the index of cardiorespiratory response to exercise-the NOODLE study.

Background: The ratio of oxygen uptake (VO2) to minute ventilation (VE) is described as the oxygen uptake efficiency slope (OUES). OUES has been suggested as a valuable submaximal cardiorespiratory index; however, its characteristics in endurance athletes remain unknown. In this study, we a) investigated OUES between different time intervals, b) assessed their prediction power for VO2peak, and c) derived new prediction equations for OUES tailored for well-trained individuals. Materials and Methods: A total of 77 male (age = 21.4 ± 4.8 yrs; BMI = 22.1 ± 1.6 kg·m-2; peak oxygen uptake = 4.40 ± 0.64 L·min-1) and 63 female individuals (age = 23.4 ± 4.3 yrs; BMI = 23.1 ± 1.6 kg·m-2; peak oxygen uptake = 3.21 ± 0.48 L·min-1) underwent the cycling cardiopulmonary exercise test. OUES was measured at 75%, 90%, and 100% of exercise duration. Prediction power and new models were derived with the multiple linear regression method. Results: In male subjects, OUES [mL·min-1/L·min-1] from 75% = 4.53 ± 0.90, from 90% = 4.52 ± 0.91, and from 100% = 4.41 ± 0.87. In female subjects, OUES [mL·min-1/L·min-1] from 75% = 3.50 ± 0.65, from 90% = 3.49 ± 0.62, and from 100% = 3.41 ± 0.58. OUES did not differ between time intervals in male (p = 0.65) and female individuals (p = 0.69). OUES strongly predicts peak VO2 independently from the measuring interval (ß = 0.71-0.80; R 2 = 0.50-0.63). The prediction model designed for elite athletes was OUES [mL·min-1/L·min-1] = -1.54 + 2.99; BSA [m2]-0.0014; (age [in years]; sex [1 = male, 2 = female]) (R 2 = 0.36). Conclusion: OUES enables an accurate prediction of peak cardiorespiratory fitness in elite endurance athletes. OUES is a feasible alternative to maximal exercise testing. A new prediction equation should be used for highly trained individuals. Physicians should understand OUES physiology to properly assess the cardiorespiratory response to exercise in athletic cohorts.

Is the Ventilatory Efficiency in Endurance Athletes Different?-Findings from the NOODLE Study.

Background: Ventilatory efficiency (VE/VCO2) is a strong predictor of cardiovascular diseases and defines individuals' responses to exercise. Its characteristics among endurance athletes (EA) remain understudied. In a cohort of EA, we aimed to (1) investigate the relationship between different methods of calculation of VE/VCO2 and (2) externally validate prediction equations for VE/VCO2. Methods: In total, 140 EA (55% males; age = 22.7 ± 4.6 yrs; BMI = 22.6 ± 1.7 kg·m-2; peak oxygen uptake = 3.86 ± 0.82 L·min-1) underwent an effort-limited cycling cardiopulmonary exercise test. VE/VCO2 was first calculated to ventilatory threshold (VE/VCO2-slope), as the lowest 30-s average (VE/VCO2-Nadir) and from whole exercises (VE/VCO2-Total). Twelve prediction equations for VE/VCO2-slope were externally validated. Results: VE/VCO2-slope was higher in females than males (27.7 ± 2.6 vs. 26.1 ± 2.0, p < 0.001). Measuring methods for VE/VCO2 differed significantly in males and females. VE/VCO2 increased in EA with age independently from its type or sex (ß = 0.066-0.127). Eleven equations underestimated VE/VCO2-slope (from -0.5 to -3.6). One equation overestimated VE/VCO2-slope (+0.2). Predicted and observed measurements differed significantly in nine models. Models explained a low amount of variance in the VE/VCO2-slope (R2 = 0.003-0.031). Conclusions: VE/VCO2-slope, VE/VCO2-Nadir, and VE/VCO2-Total were significantly different in EA. Prediction equations for the VE/VCO2-slope were inaccurate in EA. Physicians should be acknowledged to properly assess cardiorespiratory fitness in EA.

Prediction equations of the metabolizable energy in corn developed by chemical composition and enzymatic hydrolysate gross energy for roosters.

Two experiments were conducted to establish the prediction equations for AME and TME of corn based on chemical composition and enzymatic hydrolysate gross energy (EHGE) in roosters. In experiment 1, eighty 32-wk-old Hy-line Brown roosters with an average body weight of 2.55 ± 0.21 kg were randomly assigned to 10 diet treatments in a completely randomized design to determine AME and TME by the force-feeding method. Each treatment had 8 replicates with 1 bird per replicate. The 10 test diets used in the experiment were formulated with corn (including 96.10%) as the sole source of energy. In experiment 2, the EHGE of 14 corn samples was measured by the computer-controlled simulated digestion system (CCSDS) with 5 replicates of each sample. The average AME and TME values of corn were 14.58 and 16.46 MJ/kg DM, respectively. The EHGE of 14 corn samples ranged from 14.66 to 15.89 (the mean was 15.24) MJ/kg DM. The best-fit equations for corn based on chemical composition were AME (MJ/kg DM) = 14.5504 + 0.1166 × ether extract (EE) + 0.5058 × Ash - 0.0957 × neutral detergent fiber (NDF) (R2 = 0.8194, residual standard deviation (RSD) = 0.0860, P < 0.01) and TME (MJ/kg DM) = 16.0625 + 0.1314 × EE + 0.4725 × Ash - 0.0872 × NDF (R2 = 0.7867, RSD = 0.0860, P < 0.01). The best-fit equations for corn based on EHGE were AME (MJ/kg DM) = 7.8883 + 0.4568 × EHGE (R2 = 0.8587, RSD = 0.0693, P < 0.01) and TME (MJ/kg DM) = 10.0099 + 0.4228 × EHGE (R2 = 0.8720, RSD = 0.0608, P < 0.01). The differences between determined and predicted values from equations established based on EHGE were lower than those observed from chemical composition equations. These results indicated that EHGE measured with CCSDS could predict the AME and TME of corn for roosters with high accuracy.

A Reference Equation for Peak Oxygen Uptake for Pediatric Patients Who Undergo Treadmill Cardiopulmonary Exercise Testing.

Pediatric patients are often referred to cardiopulmonary exercise testing (CPET) laboratories for assessment of exercise-related symptoms. For clinicians to understand results in the context of performance relative to peers, adequate fitness-based prediction equations must be available. However, reference equations for prediction of peak oxygen uptake (VO2peak) in pediatrics are largely developed from field-based testing, and equations derived from CPET are primarily developed using adult data. Our objective was to develop a pediatric reference equation for VO2peak. Clinical CPET data from a validation cohort of 1,383 pediatric patients aged 6 to 18 years who achieved a peak respiratory exchange ratio ≥1.00 were analyzed to identify clinical and exercise testing factors that contributed to the prediction of VO2peak from tests performed using the Bruce protocol. The resultant prediction equation was applied to a cross-validation cohort of 1,367 pediatric patients. Exercise duration, gender, weight, and age contributed to the prediction of VO2peak, generating the following prediction equation: (R2 = 0.645, p <0.001, standard error of the estimate = 6.19 ml/kg/min): VO2peak (ml/kg/min) =16.411+ 3.423 (exercise duration [minutes]) - 5.145 (gender [0 = male, 1 = female]) - 0.121 (weight [kg]) + 0.179 (age [years]). This equation was stable across the age range included in the present study, with differences ≤0.5 ml/kg/min between mean measured and predicted VO2peak in all age groups. In conclusion, this study represents what we believe is the largest pediatric CPET-derived VO2peak prediction effort to date, and this VO2peak prediction equation provides clinicians who perform and interpret exercise tests in pediatric patients with a resource with which to better quantify fitness when CPET is not available.

Estimation of peak oxygen pulse from body mass, resting heart rate, age, gender and systolic blood pressure in Chinese adults aged 20-39.

Background: Peak oxygen pulse (O2Ppeak) can reflect the condition of cardiovascular function and provide supplementary information for maximal oxygen uptake, but its direct measurement requires the precise instruments under the guidance of professionals, and the subjects should strive to the state of exhaustion. Objectives: The aim of the present cross-sectional study was to establish a prediction equation to estimate O2Ppeak of Chinese adults aged 20-39, from routine measures of anthropometry and cardiovascular function. Methods: 252 adults (20-39 years old) were recruited and randomly allocated to the validation group (n = 226) and the cross-validation group (n = 26). To be included in the study, subjects were required to be healthy, none-professional sports experience (healthy individuals who are not athletes or have had experience as athletes), and no medication taken recently. Subjects with cardiovascular diseases, lung disease and musculoskeletal diseases were excluded. The subjects' anthropometric and cardiovascular variables were measured and each subject performed a maximal exercise test on an electromagnetic cycle ergometer. Results: The O2Ppeak estimated equation was developed using multiple linear regression models, O2Ppeak = 30.394 + 0.083 x body mass (kg) - 0.090 x resting heart rate (bpm) - 0.157 x age (years) - 2.710 x gender (1 = male, 2 = female) - 0.035 x systolic blood pressure (mmHg). The equation had the coefficient of determination (R2) = 0.804 and the standard error of estimate (SEE) = 1.619 ml/beat. An ANOVA and Akaike's information criterion (AIC) were tested. Bland-Altman graphs were plotted to examine the distribution of bias. Cross-validation estimated O2Ppeak and directly measured O2Ppeak did not show significant difference while had a strong positive correlation (r = 0.89, p < 0.001). Conclusions: The established equation has high effectiveness and reliability to predict O2Ppeak of adults aged 20-39.

Normative data in resting and maximum heart rates and a prediction equation for young Tunisian soccer players: a cross-sectional study.

Heart rate (HR) is an important indicator of work intensity during physical activity. Maximum heart rate (MHR) is a physiological measure that is frequently used as a benchmark for maximal exercise intensity. The aim of this study was to establish reference curves for maximum heart rate (MHR) and resting heart rate (RHR) and to develop an estimated equation for Tunisian adolescent footballers. The study involved 801 adolescent players, aged 11 to 18, who belonged to five Tunisian first-division soccer teams. The LMS method was used for smoothing the curves and the multivariate linear regression to develop a prediction equation of MHR. Our results showed that MHR and RHR reference curves decrease with age. The values of the median curves of MHR and RHR ranged from 208.64 bpm (11 years) to 196.93 (18 years) and 73.86 (11 years) to 63.64 (18 years), respectively. The prediction equation obtained from the model was MHR= 225.08 - 1.55 X Age (years) (R2 = 0.317; P < 0.001; standard error of the estimate (SEE) = 5.22). The comparisons between the estimated values and the measured values have found that our model (- 0.004 ±5.22 bpm) was to be more accurate than two other widely known models. BOX's equation underestimates the measured MHR values by -3.17 ± 5.37 bpm and TANAKA's equation overestimates by + 4.33 ±5.5 bpm. The reference curves can be used by coaches and physical trainers to classify the resting heart rate (RHR) and maximum heart rate (MHR) of each adolescent player, track their evolution over time, and design tailored training programs with specific intensities for Tunisian soccer players.

A Maximal Rowing-Ergometer Protocol to Predict Maximal Oxygen Uptake in Female Rowers.

BACKGROUND: Laboratory assessment of maximal oxygen uptake (V˙O2max) is physically and mentally draining for the athlete and requires expensive laboratory equipment. Indirect measurement of V˙O2max could provide a practical alternative to laboratory testing. PURPOSE: To examine the relationship between the maximal power output (MPO) in an individualized 7 × 2-minute incremental test (INCR-test) and V˙O2max and to develop a regression equation to predict V˙O2max from MPO in female rowers. METHODS: Twenty female club and Olympic rowers (development group) performed the INCR-test on a Concept2 rowing ergometer to determine V˙O2max and MPO. A linear regression analysis was used to develop a prediction of V˙O2max from MPO. Cross-validation analysis of the prediction equation was performed using an independent sample of 10 female rowers (validation group). RESULTS: A high correlation coefficient (r = .94) was found between MPO and V˙O2max. The following prediction equation was developed: V˙O2max (mL·min-1) = 9.58 × MPO (W) + 958. No difference was found between the mean predicted V˙O2max in the INCR-test (3480 mL·min-1) and the measured V˙O2max (3530 mL·min-1). The standard error of estimate was 162 mL·min-1, and the percentage standard error of estimate was 4.6%. The prediction model only including MPO, determined during the INCR-test, explained 89% of the variability in V˙O2max. CONCLUSION: The INCR-test is a practical and accessible alternative to laboratory testing of V˙O2max.

An update of the predicted lean yield equation for the Destron PG-100 optical grading probe.

The objective was to update the equation used for prediction of pork carcass leanness with the Destron PG-100 optical grading probe. A recent cutout study (completed in 2020-2021) consisting of 337 pork carcasses was used for this research. An updated equation was generated using a calibration dataset (N = 188 carcasses) and prediction precision and prediction accuracy of the new equation was evaluated using a validation dataset (N = 149 carcasses). The updated equation was generated using forward stepwise multiple regression selection techniques in PROC REG of SAS, and the same parameters as the existing equation were used to fit the model. The updated Destron equation [89.16298 - (1.63023 × backfat thickness) - (0.42126 × muscle depth) + (0.01930 × backfat thickness2) + (0.00308 × muscle depth2) + (0.00369 × backfat thickness × muscle depth)] and the existing Destron equation [68.1863 - (0.7833 × backfat thickness) + (0.0689 × muscle depth) + (0.0080 × backfat thickness2) - (0.0002 × muscle depth2) + (0.0006 × backfat thickness × muscle depth)] were similar in their prediction precision for determination of carcass lean yield (LY), with the updated equation R2 = 0.75 and root mean square error (RMSE) = 1.97 and the existing equation R2 = 0.75 and RMSE = 1.94. However, when prediction accuracy was evaluated using the variance explained by predictive models based on cross-validation (VEcv) and Legates and McCabe's efficiency coefficient (E1), the updated equation (VEcv = 67.97%; E1 = 42.41%) was much more accurate compared with the existing equation (VEcv = -117.53%; E1 = -69.24%). Furthermore, when accuracy was evaluated by separating carcasses into 3% carcass LY groupings ranging from less than 50% LY to greater than 62% LY, the existing equation correctly estimated carcass LY 8.1% of the time, while the updated equation correctly estimated carcass LY 47.7% of the time. In an effort to further compare the abilities of the updated equation, comparisons were made with an advanced automated ultrasonic scanner (AutoFom III), which scans the entire carcass. The prediction precision of the AutoFom III was R2 = 0.83 and RMSE = 1.61, while the AutoFom III correctly estimated carcass LY 38.2% of the time and prediction accuracy calculations for the AutoFom III were VEcv = 44.37% and E1 = 21.34%). Overall, refinement of the Destron PG-100 predicted LY equation did not change prediction precision, but substantially improved prediction accuracy.

In the swine industry, optical grading probes are used to collect measurements at one location on the carcass (i.e., the grading site); this information is used to predict carcass leanness with an established multiple regression equation. For the Destron PG-100 optical probe, the equation currently used by the industry was established using the 1992 Canadian National Cutout Study. The objective of the current study was to update the equation by utilizing a recent cutout study consisting of 337 pork carcasses (N = 188 for the calibration dataset; N = 149 for the validation dataset). A multiple regression equation was generated with the calibration dataset using stepwise techniques and the same parameters as the existing equation were used to fit the model. When the validation dataset was tested for prediction precision, the existing Destron predicted lean yield (LY) equation (R2 = 0.75) and the updated Destron predicted LY equation (R2 = 0.75) were strikingly similar in their abilities to predict carcass LY. However, when prediction accuracy was evaluated by separating carcasses into six 3% LY groupings, the existing Destron predicted LY equation correctly estimated LY 8.1% of the time, while the updated Destron predicted LY equation correctly estimated LY 47.7% of the time.

Skinfold-based-equations to assess longitudinal body composition in children from birth to age 5 years.

BACKGROUND & AIMS: In order to identify children at risk for excess adiposity, it is important to determine body composition longitudinally throughout childhood. However, most frequently used techniques in research are expensive and time-consuming and, therefore, not feasible for use in general clinical practice. Skinfold measurements can be used as proxy for adiposity, but current anthropometry-based-equations have random and systematic errors, especially when used longitudinally in pre-pubertal children. We developed and validated skinfold-based-equations to estimate total fat mass (FM) longitudinally in children aged 0-5 years. METHODS: This study was embedded in the Sophia Pluto study, a prospective birth cohort. In 998 healthy term-born children, we longitudinally measured anthropometrics, including skinfolds and determined FM using Air Displacement Plethysmography (ADP) by PEA POD and Dual energy X-ray Absorptiometry (DXA) from birth to age 5 years. Of each child one random measurement was used in the determination cohort, others for validation. Linear regression was used to determine the best fitting FM-prediction model based on anthropometric measurements using ADP and DXA as reference methods. For validation, we used calibration plots to determine predictive value and agreement between measured and predicted FM. RESULTS: Three skinfold-based-equations were developed for adjoined age ranges (0-6 months, 6-24 months and 2-5 years), based on FM-trajectories. Validation of these prediction equations showed significant correlations between measured and predicted FM (R: 0.921, 0.779 and 0.893, respectively) and good agreement with small mean prediction errors of 1, 24 and -96 g, respectively. CONCLUSIONS: We developed and validated reliable skinfold-based-equations which may be used longitudinally from birth to age 5 years in general practice and large epidemiological studies.

Enteric Methane Emissions Prediction in Dairy Cattle and Effects of Monensin on Methane Emissions: A Meta-Analysis.

Greenhouse gas emissions, such as enteric methane (CH4) from ruminant livestock, have been linked to global warming. Thus, easily applicable CH4 management strategies, including the inclusion of dietary additives, should be in place. The objectives of the current study were to: (i) compile a database of animal records that supplemented monensin and investigate the effect of monensin on CH4 emissions; (ii) identify the principal dietary, animal, and lactation performance input variables that predict enteric CH4 production (g/d) and yield (g/kg of dry matter intake DMI); (iii) develop empirical models that predict CH4 production and yield in dairy cattle; and (iv) evaluate the newly developed models and published models in the literature. A significant reduction in CH4 production and yield of 5.4% and 4.0%, respectively, was found with a monensin supplementation of ≤24 mg/kg DM. However, no robust models were developed from the monensin database because of inadequate observations under the current paper's inclusion/exclusion criteria. Thus, further long-term in vivo studies of monensin supplementation at ≤24 mg/kg DMI in dairy cattle on CH4 emissions specifically beyond 21 days of feeding are reported to ensure the monensin effects on the enteric CH4 are needed. In order to explore CH4 predictions independent of monensin, additional studies were added to the database. Subsequently, dairy cattle CH4 production prediction models were developed using a database generated from 18 in vivo studies, which included 61 treatment means from the combined data of lactating and non-lactating cows (COM) with a subset of 48 treatment means for lactating cows (LAC database). A leave-one-out cross-validation of the derived models showed that a DMI-only predictor model had a similar root mean square prediction error as a percentage of the mean observed value (RMSPE, %) on the COM and LAC database of 14.7 and 14.1%, respectively, and it was the key predictor of CH4 production. All databases observed an improvement in prediction abilities in CH4 production with DMI in the models along with dietary forage proportion inclusion and the quadratic term of dietary forage proportion. For the COM database, the CH4 yield was best predicted by the dietary forage proportion only, while the LAC database was for dietary forage proportion, milk fat, and protein yields. The best newly developed models showed improved predictions of CH4 emission compared to other published equations. Our results indicate that the inclusion of dietary composition along with DMI can provide an improved CH4 production prediction in dairy cattle.

[Prediction equation of physical activity level based on step counts for Chinese adults: development and validation].

OBJECTIVE: To develop a prediction equation for estimation physical activity level(PAL) based on the average daily steps in Chinese adults and to verify its prediction power. METHODS: A total of 800 adults with no restriction on physical activity were recruited. Excluding those who wore Actigragh WGT3X-BT accelerometer continuously for less than 3 days, 743 subjects(324 males, 419 females, mean age 31.4 years) were finally included. Daily steps, time of low intensity physical activity(LPA), moderate physical activity(MPA) and vigorous physical activity(VPA) and physical activity energy expenditure(PAEE) were measured by the accelerometer. The method of external validation was adopted. The measured PAL calculated by basal metabolic rate(BMR) measured by indirect thermography(IC) and total energy expenditure(TEE) measured by doubly labeled water method was used as the gold standard. The consistency of the prediction formula was verified by Bland-Altman analysis and the accuracy was verified by the accurate. RESULTS: The prediction equation was developed: PAL=1.17 + 0.000028 Steps. In the external verification, the mean difference was 0.05, the 95% consistency limit was(-0.26, 0.36), and the accuracy was 73%, indicating a good consistency. CONCLUSION: The prediction formula of PAL for Chinese adults based on the steps has good performance and can be applied to the management of physical activity and the prevention of obesity in Chinese adults.