Glycemic Risk Index in a Cohort of Patients with Type 1 Diabetes Mellitus Stratified by the Coefficient of Variation: A Real-Life Study.

Objective: To analyze the Glycemic Risk Index (GRI) and assess their possible differences according to coefficient of variation (CV) in a cohort of real-life type 1 diabetes mellitus (DM) patient users of intermittently scanned continuous glucose monitoring (isCGM). Patients and Methods: In total, 447 adult users of isCGM with an adherence ≥70% were included in a cross-sectional study. GRI was calculated with its hypoglycemia (CHypo) and hyperglycemia (CHyper) components. Multivariate linear regression analysis was performed to evaluate the factors associated with GRI. Results: Mean age was 44.6 years (standard deviation [SD] 13.7), 57.7% being male; age of DM onset was 24.5 years (SD 14.3) and time of evolution was 20.6 years (SD 12.3). In patients with CV >36% (52.8%) versus CV ≤36% (47.2%), differences were observed in relation to GRI (18.8% [SD 1.9]; P < 0.001), CHypo (2.9% [SD 0.3]; P < 0.001), CHyper (6.3% [SD 1.4]; P < 0.001), and all classical glucometric parameters except time above range level 1. The variables that were independently associated with GRI in patient with CV >36% were time in range (TIR) (ß = -1.49; confidence interval [CI:] 95% -1.63 to -1.37; P < 0.001), glucose management indicator (GMI) (ß = -7.22; CI: 95% -9.53 to -4.91; P < 0.001), and CV (ß = 0.85; CI: 95% 0.69 to 1.02; P < 0.001). However, in patients with CV ≤36%, the variables were age (ß = 0.15; CI: 95% 0.03 to 0.28; P = 0.019), age of onset (ß = -0.15; CI: 95% -0.28 to -0.02; P = 0.023), TIR (ß = -1.35; CI: 95% -1.46 to -1.23; P < 0.001), GMI (ß = -6.67; CI: 95% -9.18 to -4.15; P < 0.001), and CV (ß = 0.33; CI: 95% 0.11 to 0.56; P = 0.004). Conclusions: In this study, the factors independently associated with metabolic control according to GRI are modified by glycemic variability.

Nutritional Prescription in ICU Patients: Does it Matter?

Background: The nutritional status of the patients before critical illness and nutrition support given during the critical illness play an important role in the recovery. We aimed to evaluate the nutritional prescription and its effect on ICU mortality. Materials and methods: This was a prospective observational study conducted after institutional ethical committee approval (IEC 94/2018, CTRI/2018/06/014625) in a case-mixed (medical and surgical) ICU. Patients admitted to the ICU were enrolled within 24 hours of admission. The amount of calories and proteins prescribed and received by the patients was collected for 7 days. The primary outcome was ICU mortality. Results: A total of 100 patients were included. The mean age was 48.63 (16.25) years, and 62% were males. The acute physiology and chronic health evaluation (APACHE II), sequential organ failure assessment (SOFA), and modified Nutric (mNUTRIC) scores were comparable between the two groups. The ICU mortality was 30%. The calorie and protein deficits were comparable between survivors and non-survivors. Among the secondary outcomes, a significant time effect (p = 0.013) and interaction effect (p = 0.004) were noted for maximum glucose levels. The glucose variability calculated by coefficient of variation (CV) was significantly higher in non-survivors than survivors (p = 0.031). Conclusion: The calorie and protein deficits did not affect ICU mortality. The maximum glucose variability and CV were significant parameters associated with ICU mortality. How to cite this article: Havaldar AA, Selvam S. Nutritional Prescription in ICU Patients: Does it Matter? Indian J Crit Care Med 2024;28(7):657-661.

[Synchronies and asynchronies in the development of COVID-19 pandemic in Italy]. / Sincronie e asincronie negli sviluppi della pandemia di COVID-19 sul territorio.

BACKGROUND: the study of the possible determinants of the rise and fall of infections can be of great relevance, as was experienced during the COVID-19 pandemic. One of the methods to understand whether determinants are simultaneous or develop through contiguity between different areas is the study of the diagnostic replication index RDt among regions. OBJECTIVES: to introduce the analysis of RDt variability and the subsequent application of a recently introduced functional clustering method as highly useful procedures for recognizing the presence of clusters with similar trends in epidemic curves. DESIGN: within the considered period, trends in regional RDt are analyzed in detail over four different time intervals. SETTING AND PARTICIPANTS: to exemplify this methodology, the study of variability in the period from the end of 2021 to the beginning of 2022 may be of interest. MAIN OUTCOMES MEASURES: the variability in the regional RDt indices is assessed by means of the correlation coefficient weighted with respect to the populations of the individual regions. The clustering procedure is applied to the time series of absolute RDt values. RESULTS: it emerges that the periods of increasing variability in the RDt correspond to the initial growth or decrease in the number of infections, while functional clustering identifies macro-areas in which the epidemic curves have had similar trends. What caused contagions to increase seems to relate to a factor that is not specific to certain areas, with the contribution in some cases of a contagion dynamic between adjacent areas. CONCLUSIONS: the variability in the trend of regional diagnostic replication indices, which are calculated with only a few days delay, is a further indicator for the early detection of major changes in the trend of epidemic curves. The clustering of epidemic index curves may be useful to determine whether determinants act simultaneously or by contiguity between adjacent areas.

Clinical-imaging metrics for the diagnosis of prostate cancer in PI-RADS 3 lesions.

OBJECTIVE: To explore the feasibility and efficacy of clinical-imaging metrics in the diagnosis of prostate cancer (PCa) and clinically significant prostate cancer (csPCa) in prostate imaging-reporting and data system (PI-RADS) category 3 lesions. METHODS: A retrospective analysis was conducted on lesions diagnosed as PI-RADS 3. They were categorized into benign, non-csPCa and csPCa groups. Apparent diffusion coefficient (ADC), T2-weighted imaging signal intensity (T2WISI), coefficient of variation of ADC and T2WISI, prostate-specific antigen density (PSAD), ADC density (ADCD), prostate-specific antigen lesion volume density (PSAVD) and ADC lesion volume density (ADCVD) were measured and calculated. Univariate and multivariate analyses were used to identify risk factors associated with PCa and csPCa. Receiver operating characteristic curve (ROC) and decision curves were utilized to assess the efficacy and net benefit of independent risk factors. RESULTS: Among 202 patients, 133 had benign prostate disease, 25 non-csPCa and 44 csPCa. Age, PSA and lesion location showed no significant differences (P > 0.05) among the groups. T2WISI and coefficient of variation of ADC (ADCcv) were independent risk factors for PCa in PI-RADS 3 lesions, yielding an area under the curve (AUC) of 0.68. ADC was an independent risk factor for csPCa in PI-RADS 3 lesions, yielding an AUC of 0.65. Decision curve analysis showed net benefit for patients at certain probability thresholds. CONCLUSIONS: T2WISI and ADCcv, along with ADC, respectively showed considerable promise in enhancing the diagnosis of PCa and csPCa in PI-RADS 3 lesions.

Numerical simulation and experimental verification of the velocity field in asymmetric circular bends.

To address the measurement accuracy challenges posed by the internal flow complexity in atypical circular bend pipes with short turning sections and without extended straight pipe segments, this study designed an experimental circular "S"-shaped bent pipe with a diameter of 0.4 m and a bending angle of 135°. Numerical analysis was used to determine the stable region for velocity distribution within the experimental segment. Furthermore, a novel evaluation method based on the coefficient of variation was proposed to accurately locate the optimal position for installing thermal mass flow meters on the test cross section. Additionally, a formula for calculating the pipeline flow rate based on velocity differences was derived. This formula considers pipeline flow as the dependent variable and uses the velocity at two points in the test cross section as the independent variable. Experimental validation on a primary standard test bench demonstrated that the flow rate calculated by this method had an error controlled within 0.625% compared to the standard flow rate, thus effectively verifying the method's high accuracy and engineering applicability. This research provides a new testing methodology and practical basis for flow measurement in complex pipeline systems, offering significant guidance for research and applications in related fields.

A simplified reliability-based design method for multiple design variables considering different limit states.

A generalized reliability model comprising the objective, constraint, and judgment functions is established for the reliability index approach (RIA), taking parameters' properties of engineering practice and negative reliability index into consideration. Based on this, the reliability-based design (RBD) problem with multiple design variables is translated into the solution to the nonlinear equations, and a simplified method consisting of a simple variant of the Newton iteration method and the finite difference method (FDM) is proposed. Numerical examples are presented to verify the efficiency of the proposed reliability approach and to determine the incremental step size for FDM. RBD of a simply supported beam is illustrated and the variabilities of design variables are investigated considering the uncertainties in the manufacturing process and practical operations. Results reveal that the variations of the design variables should not be ignored. Moreover, analysis results show that the design value might not intuitively increase with the increase of its coefficient of variation (CoV), and it might not increase with the increasing reliability requirement for problems involving multiple variables. The reasons for this phenomenon are very complicated, and it is a systematic problem. One should be aware of this phenomenon, and specific analysis is required for specific problems.

Bayesian sample size determination for coefficient of variation of normal distribution.

Sample size determination is an active area of research in statistics. Generally, Bayesian methods provide relatively smaller sample sizes than the classical techniques, particularly average length criterion is more conventional and gives relatively small sample sizes under the given constraints. The objective of this study is to utilize major Bayesian sample size determination techniques for the coefficient of variation of normal distribution and assess their performance by comparing the results with the freqentist approach. To this end, we noticed that the average coverage criterion is the one that provides relatively smaller sample sizes than the worst outcome criterion. By comparing with the existing frequentist studies, we show that a smaller sample size is required in Bayesian methods to achieve the same efficiency.

TCS-Fall: Cross-individual fall detection system based on channel state information and time-continuous stack method.

Background: Falls pose a serious health risk for the elderly, particular for those who are living alone. The utilization of WiFi-based fall detection, employing Channel State Information (CSI), emerges as a promising solution due to its non-intrusive nature and privacy preservation. Despite these advantages, the challenge lies in optimizing cross-individual performance for CSI-based methods. Objective: This study aimed to develop a resilient real-time fall detection system across individuals utilizing CSI, named TCS-Fall. This method was designed to offer continuous monitoring of activities over an extended timeframe, ensuring accurate and prompt detection of falls. Methods: Extensive CSI data on 1800 falls and 2400 daily activities was collected from 20 volunteers. The grouped coefficient of variation of CSI amplitudes were utilized as input features. These features capture signal fluctuations and are input to a convolutional neural network classifier. Cross-individual performance was extensively evaluated using various train/test participant splits. Additionally, a user-friendly CSI data collection and detection tool was developed using PyQT. To achieve real-time performance, data parsing and pre-processing computations were optimized using Numba's just-in-time compilation. Results: The proposed TCS-Fall method achieved excellent performance in cross-individual fall detection. On the test set, AUC reached 0.999, no error warning ratio score reached 0. 955 and correct warning ratio score reached of 0.975 when trained with data from only two volunteers. Performance can be further improved to 1.00 when 10 volunteers were included in training data. The optimized data parsing/pre-processing achieved over 20× speedup compared to previous method. The PyQT tool parsed and detected the fall within 100 ms. Conclusions: TCS-Fall method enables excellent real-time cross-individual fall detection utilizing WiFi CSI, promising swift alerts and timely assistance to elderly. Additionally, the optimized data processing led to a significant speedup. These results highlight the potential of our approach in enhancing real-time fall detection systems.

The effect of lithium variation coefficient on the risk of attack in patients with bipolar disorder: A pilot study.

BACKGROUND: This study examines the association between the coefficient of variation (%CV) of lithium levels and episode risk and frequency in bipolar patients maintaining serum lithium levels within the therapeutic range. METHODS: We retrospectively reviewed patients with bipolar disorder under care from 2018 to 2022. Inclusion criteria were at least 2 years of follow-up, a minimum of three annual lithium level measurements within the therapeutic range. Patients were categorized based on seizure status. We calculated mean lithium levels, standard deviation (SD), and %CV. RESULTS: The study included 75 patients (patients with-without episodes, 39-36). Demographic data revealed no significant differences. While mean lithium levels showed no significant disparity between groups, SD and %CV were notably higher in patients with episodes (P < .05). ROC analysis demonstrated AUC values of 0.722 (95% CI: 0.607-0.836 P = .001) for %CV and 0.709 (95% CI: 0.593-0.826; P = .002) for SD. The optimal %CV cutoff was 17.39, with 67% sensitivity and 69% specificity. A weak correlation was found between %CV and the number of episodes (P = .001, r = 0.376). The post-hoc power analysis for this study was 0.78. CONCLUSIONS: Despite acceptable lithium levels, patients with recent episodes exhibited significant lithium level fluctuations. Integrating %CV with real-time lithium measurements during bipolar disorder follow-up may enhance clinical monitoring and seizure prediction.

Prediction of Efficacy for Atezolizumab/Bevacizumab in Unresectable Hepatocellular Carcinoma with Hepatobiliary-Phase Gadolinium Ethoxybenzyl-Diethylenetriaminepentaacetic Acid MRI.

BACKGROUND: This study aimed to examine whether the coefficient of variation (CV) in the hepatobiliary-phase (HBP) of Gd-EOB-DTPA-MRI could be an independent predictive factor for tumor progression. METHODS: Patients who underwent Gd-EOB-DTPA-MRI before Atezolizumab/bevacizumab therapy at six affiliated institutions between 2018 and 2022 were included. CV for each patient was calculated as the mean value for up to five tumors larger than 10 mm, and CV of the whole tumor was calculated using LIFEx software. The tumor response was evaluated within 6-10 weeks. The primary endpoint was to investigate the predictive factors, including CV, related to tumor progression using logistic regression analysis. The secondary endpoints were tumor response rate and progression-free survival (PFS) based on CV. RESULTS: Of the 46 enrolled patients, 13 (28.3%) underwent early progressive disease. Multivariate analysis revealed that a high CV (≥0.22) was an independent predictive factor for tumor progression (p = 0.043). Patients with a high CV had significantly frequent PD than those with a low CV (43.5 vs. 13.0%, p = 0.047). Patients with a high CV tended to have shorter PFS than those with a low CV (3.5 vs. 6.7 months, p = 0.071). CONCLUSION: Quantitative analysis using CV in the HBP of Gd-EOB-DTPA-MRI may be useful for predicting tumor progression for atezolizumab/bevacizumab therapy.

Using Publicly Available RNA-seq Data for Expression Analysis of Genes of Interest.

RNA-seq data in publicly available repositories enable the efficient reanalysis of transcript abundances in existing experiments. Graphical user interfaces usually only allow the visual inspection of a single gene and of predefined experiments. Here, we describe how experiments are selected from the Sequence Read Archive or the European Nucleotide Archive, how data is efficiently mapped onto a reference transcriptome, and how global transcript abundances and patterns are inspected. We exemplarily apply this analysis pipeline to study the expression of photorespiration-related genes in photosynthetic organisms, such as cyanobacteria, and to identify conditions under which photorespiratory transcript abundances are enhanced.

The Influence of Tacrolimus Exposure and Metabolism on the Outcomes of Kidney Transplants.

Tacrolimus (TAC) has a narrow therapeutic window and patient-specific pharmacokinetic variability. In our study, we analyzed the association between TAC exposure, metabolism, and kidney graft outcomes (function, rejection, and histological lesions). TAC trough (C0), coefficient of variation (TAC CV), concentration/dose ratio (C/D), and biomarkers related to kidney injury molecule-1 (KIM-1) and neutrophil gelatinase lipocalin (NGAL) were analyzed. We examined 174 patients who were subjected to a triple immunosuppressive regimen and underwent kidney transplantation between 2017 and 2022. Surveillance biopsies were performed at the time of kidney implantation and at three and twelve months after transplantation. We classified patients based on their Tac C/D ratios, classifying them as fast (C/D ratio < 1.05 ng/mL × 1/mg) or slow (C/D ratio ≥ 1.05 ng/mL × 1/mg) metabolizers. TAC exposure/metabolism did not significantly correlate with interstitial fibrosis/tubular atrophy (IF/TA) progression during the first year after kidney transplantation. TAC CV third tertile was associated with a higher chronicity score at one-year biopsy. TAC C/D ratio at three months and Tac C0 at six months were associated with rejection during the first year after transplantation. A fast TAC metabolism at six months was associated with reduced kidney graft function one year (OR: 2.141, 95% CI: 1.044-4.389, p = 0.038) and two years after transplantation (OR: 4.654, 95% CI: 1.197-18.097, p = 0.026), and TAC CV was associated with reduced eGFR at three years. uNGAL correlated with IF/TA and chronicity scores at three months and negatively correlated with TAC C0 and C/D at three months and one year. Conclusion: Calculating the C/D ratio at three and six months after transplantation may help to identify patients at risk of suffering acute rejection and deterioration of graft function.

Evaluation of consumer digital radon measurement devices: a comparative analysis.

Kansas State University (KSU) Engineering Extension conducted an abridged evaluation of eight consumer grade digital radon monitors. Using the KSU secondary radon chamber, these devices were exposed to three different radon concentrations for 7 d in average household temperature and relative humidity conditions. The three different radon concentration ranges used were: 12.8 pCi L-1to 15.5 pCi L-1(473.6 Bq m-3-573.5 Bq m-3), 27.7 pCi L-1to 29.4 pCi L-1(1024.9-10 857.8 Bq m-3), and ambient room level average radon concentration of 0.6 pCi L-1(22.2 Bq m-3). The American National Standards Institute/American Academy of Radon Scientists and Technologists Performance Specifications for Instrumentation Systems Designed to Measure Radon Gas in Air (ANSI/AARST MS-PC) (ANSI/AARST MS-PC 2022Performance Specifications for Instrumentation Systems Designed to Measure Radon Gas in Air(AARST Radon Standards)) minimum performance metrics were used to evaluate the accuracy and precision of each model type for each radon concentration tested. The eight different device models performed within the 0 ± 25% requirement for the individual percent error (IPE) for radon concentrations between 27.7 pCi L-1and 29.4 pCi L-1(1024.9-10 857.8 Bq m-3). For radon concentrations between 12.8 pCi L-1and 15.5 pCi L-1(444-592 Bq m-3) seven of the eight monitors fell within the IPE requirement and for ambient room radon concentrations six of the eight monitors fell within the IPE requirement for the ANSI/AARST MS-PC minimum performance requirement (ANSI/AARST MS-PC 2022Performance Specifications for Instrumentation Systems Designed to Measure Radon Gas in Air(AARST Radon Standards)) ranges. All eight device models fell within the ± 15% ANSI/AARST MS-PC minimum performance requirement (ANSI/AARST MS-PC 2022Performance Specifications for Instrumentation Systems Designed to Measure Radon Gas in Air(AARST Radon Standards)) coefficient of variation (CV) range for radon concentrations between 12.8 pCi L-1and 15.5 pCi L-1(444-592 Bq m-3) and for radon concentrations between 27.7 pCi L-1and 29.4 pCi L-1(1024.9-10 857.8 Bq m-3). In the future, evaluating the performance of these models over time to observe their long term accuracy and precision is anticipated.

New adaptive EWMA CV control chart with application to the sintering process.

This research presents a new adaptive exponentially weighted moving average control chart, known as the coefficient of variation (CV) EWMA statistic to study the relative process variability. The production process CV monitoring is a long-term process observation with an unstable mean. Therefore, a new modified adaptive exponentially weighted moving average (AAEWMA) CV monitoring chart using a novel function hereafter referred to as the "AAEWMA CV" monitoring control chart. the novelty of the suggested AAEWMA CV chart statistic is to identify the infrequent process CV changes. A continuous function is suggested to be used to adapt the plotting statistic smoothing constant value as per the process estimated shift size that arises in the CV parametric values. The Monte Carlo simulation method is used to compute the run-length values, which are used to analyze efficiency. The existing AEWMA CV chart is less effective than the proposed AAEWMA CV chart. An industrial data example is used to examine the strength of the proposed AAEWMA CV chart and to clarify the implementation specifics which is provided in the example section. The results strongly recommend the implementation of the proposed AAEWMA CV control chart.

Compensation effect of mortality is a challenge to substantial lifespan extension of humans.

Despite frequent claims regarding radical extensions of human lifespan in the near future, many pragmatic scientists caution against excessive and baseless optimism on this front. In this study, we examine the compensation effect of mortality (CEM) as a potential challenge to substantial lifespan extension. The CEM is an empirical mortality regularity, often depicted as relative mortality convergence at advanced ages. Analysis of mortality data from 44 human populations, available in the Human Mortality Database, demonstrated that CEM can be represented as a continuous decline in relative mortality variation (assessed through the coefficient of variation and the standard deviation of the logarithm of mortality) with age, reaching a minimum corresponding to the species-specific lifespan. Through this method, the species-specific lifespan is determined to be 96-97 years, closely aligning with estimates derived from correlations between Gompertz parameters (95-98 years). Importantly, this representation of CEM can be achieved non-parametrically, eliminating the need for estimating Gompertz parameters. CEM is a challenge to lifespan extension, because it suggests that the true aging rate in humans (based on loss of vital elements, e.g., functional cells) remains stable at approximately 1% per year in the majority of human populations and is not affected by environmental or familial longevity factors. Given this rate of functional cell loss, one might anticipate that the total pool of functional cells could be entirely depleted by the age of 115-120 years creating physiological limit to human lifespan. Mortality pattern of supercentenarians (110 + years) aligns with this prediction.

Machine learning approach for predicting post-intubation hemodynamic instability (PIHI) index values: towards enhanced perioperative anesthesia quality and safety.

BACKGROUND: Adequate preoperative evaluation of the post-intubation hemodynamic instability (PIHI) is crucial for accurate risk assessment and efficient anesthesia management. However, the incorporation of this evaluation within a predictive framework have been insufficiently addressed and executed. This study aims to developed a machine learning approach for preoperatively and precisely predicting the PIHI index values. METHODS: In this retrospective study, the valid features were collected from 23,305 adult surgical patients at Peking Union Medical College Hospital between 2012 and 2020. Three hemodynamic response sequences including systolic pressure, diastolic pressure and heart rate, were utilized to design the post-intubation hemodynamic instability (PIHI) index by computing the integrated coefficient of variation (ICV) values. Different types of machine learning models were constructed to predict the ICV values, leveraging preoperative patient information and initiatory drug infusion. The models were trained and cross-validated based on balanced data using the SMOTETomek technique, and their performance was evaluated according to the mean absolute error (MAE), root mean square error (RMSE), mean absolute percentage error (MAPE) and R-squared index (R2). RESULTS: The ICV values were proved to be consistent with the anesthetists' ratings with Spearman correlation coefficient of 0.877 (P < 0.001), affirming its capability to effectively capture the PIHI variations. The extra tree regression model outperformed the other models in predicting the ICV values with the smallest MAE (0.0512, 95% CI: 0.0511-0.0513), RMSE (0.0792, 95% CI: 0.0790-0.0794), and MAPE (0.2086, 95% CI: 0.2077-0.2095) and the largest R2 (0.9047, 95% CI: 0.9043-0.9052). It was found that the features of age and preoperative hemodynamic status were the most important features for accurately predicting the ICV values. CONCLUSIONS: Our results demonstrate the potential of the machine learning approach in predicting PIHI index values, thereby preoperatively informing anesthetists the possible anesthetic risk and enabling the implementation of individualized and precise anesthesia interventions.

Uncertainty Analysis of Time-Integrated Activity Coefficient in Single-Time-Point Dosimetry Using Bayesian Fitting Method.

Purpose: Calculation of the uncertainty of the individual time-integrated activity coefficient (TIACs) is desirable in molecular radiotherapy. However, the calculation of TIAC's uncertainty in single-time-point (STP) method has never been reported in the literature. This study presents a method based on the Bayesian fitting (BF) to calculate the standard deviation (SD) of individual TIACs in the STP dosimetry. Methods: Biokinetic data of 177Lu-DOTATATE in kidneys were obtained from PMID33443063. BF methods with extended objective function, which optimize the fitting using prior knowledge of the function's parameters, were used. Reference TIACs (rTIACs) were calculated by fitting a mono-exponential function to the all-time-point data. The goodness of fit was checked based on the visual inspection and the coefficient of variations (CV) of the fitted parameters < 0.5. BF with relative (BFr) and absolute-based (BFa) variance methods were used to obtain the calculated TIACs (cTIACs) from the STP dosimetry. Performance of the STP method was obtained by calculating the relative deviation (RD) between cTIACs and rTIACs. Results: Visual inspection showed a good fit for all patients with CV of fitted parameters less than 50%. The mean ± SD of cTIAC's %RD were 7.0 ± 25.2 for BFr and 2.6 ± 8.9 for BFa. The range of %CV of the individual cTIAC's SD for BFr and BFa methods was 36-78% and 22-33%, respectively, while the %CV of the rTIAC SD was 0.8-49%. Conclusion: We introduce the BF method to calculate the SD of individual TIACs in STP dosimetry. The presented method might be used as an alternative method for uncertainty analysis in STP dosimetry. Supplementary Information: The online version contains supplementary material available at 10.1007/s13139-024-00851-8.

Technical note: utilization of various allotment strategies to evaluate variation and replications required to detect statistical significance in nursery pig research.

A total of 720 barrows (line 200 × 400, DNA genetics) were used in two 42-d nursery trials (initially 6.20 ±â€…0.12 kg and 5.63 ±â€…0.16 kg, respectively) to evaluate strategies for allotting pigs to pens in randomized controlled trials. At placement, the population was split into three cohorts with similar average weight and standard deviation and randomly assigned to one of the three allotment strategies. Strategy 1 (random) utilized a simple randomization strategy with each pig randomized to pens independent of all other pigs. Strategy 2 (body weight [BW] distribution) sorted each pig within the cohort into one of the five BW groups. One pig from each weight group was then randomly assigned to a pen such that distribution of BW within pen was uniform across pens. Strategy 3 (BW grouping) sorted pigs within the cohort into 3 BW categories: light, medium, and heavy. Within each BW category, pigs were randomized to pen to create pens of pigs from each BW category. Within each experiment, there were 72 pens with five pigs per pen and 24 pens per allotment strategy. For all strategies, once pigs were allotted to pens, pens were allotted to one of the two treatments for a concurrent trial. In experiment 1, environmental enrichment using ropes tied near the pan of the feeder was compared to a control with no enrichment. In experiment 2, treatment diets consisted of basal levels of Zn and Cu from the trace mineral premix for the duration of the study (110 and 17 mg/kg, respectively; control), or diets (supplemented control) with carbadox (50 g/ton; Mecadox, Phibro Animal Health, Teaneck, NJ) fed in phase 1 (days 0 to 22) and 2 (days 22 to 43), pharmacological levels of Zn and Cu (2,414 mg/kg Zn from ZnO; 168 mg/kg Cu from CuSO4) fed in phase 1, and only pharmacological levels of Cu (168 mg/kg Cu from CuSO4) fed in phase 2. These treatment designs were used to determine the impact on coefficient of variation (CV) and to estimate the number of replications required to find significant treatment differences based on allotment strategy. There were no meaningful allotment strategy × treatment interactions for either study. For between-pen CV, pigs allotted using BW distribution and BW grouping strategies had the lowest CV at allotment and final weight in both trials. For overall average daily gain in experiments 1 and 2 in experiment 2, the BW distribution strategy required the fewest replications to detect differences in performance. However, there is no meaningful difference between allotment strategies in replications required to detect significant differences for gain:feed ratio.

Decreasing variation between experimental units increases the likelihood of finding a statistically significant difference if one exists. Assignment of animals to experimental units (pens) may contribute to that variation. Therefore, the purpose of this trial was to investigate the effect that different methods of allotting pigs to pens (experimental unit) have on variation and in turn, the number of replications required to detect a significant difference of a given amount between treatments. The random strategy assigned pigs to pens in a completely random fashion. The body weight (BW) distribution strategy ordered pigs from lightest to heaviest and created five groups based on BW. Each pen was randomly assigned one pig from each of the five groups. The BW grouping strategy again ordered pigs from lightest to heaviest but split pigs into three groups based on BW and each pen was randomly assigned pigs from only one BW group such that there were pens of light pigs, pens of medium pigs, and pens of heavy pigs. Ultimately, the best allotment strategy depends on the parameter of interest. For final BW and overall ADG, the BW grouping method required the fewest pens to detect statistically significant differences.