Determination of the best non-linear function to describe the growth of Kajli sheep

The aim of the present study was to determine the best non-linear growth function to describe the growth of Kajli sheep. For this aim, the Brody, von Bertalanffy, Logistic, and Gompertz models were used to describe the sigmoidal relationship between bodyweight and age of the Kajli sheep. The records obtained from the Livestock Experiment Station, Khushab, were collected between 2007 and 2018. The records comprised 9864 age-weight observations (300 for male, 9564 for female, 7392 for single, 2388 for twin, and 84 for triplet lambs), which extended from birth to 12 months old. Candidate non-linear functions were fitted and the curve parameters were estimated by nlsfit (fit non-linear models) function in R statistical package, version 3.6.1. Goodness of fit criteria that were used to evaluate predictive performances of candidate models were adjusted coefficient of determination (R2adj), Akaike’s information criterion (AIC), Bayesian information criterion (BIC) and root means square error (RMSE). The Brody model was the best non-linear function that described the biological growth pattern of all, male, female, single, twin, and triplet lambs. Differences in curve parameter estimates between male and female suggested a definite pattern of sexual dimorphism. Moreover, a higher estimate of rate of maturity in female lambs reflects their early maturity compared with male Kajli lambs. Similarly, the single-born Kajli animals with highest maturity rate were maturing at an earlier age than twins and triplets. This is the first report on the non-linear pattern of visible changes in bodyweight of Kajli sheep from birth to 12 months old.Key words: age, bodyweight, growth curves, regression, shee

Analysis of non-genetic and genetic influences underlying the growth of Kajli lambs

Data on 2931 Kajli lambs, born from 2007 to 2018, were used to quantify environmental and genetic effects on growth performance of Kajli sheep. Traits considered for evaluation were birth weight (BWT), 120-day adjusted weight (120DWT), 180-day adjusted weight (180DWT), 270-day adjusted weight (270DWT), and 365-day adjusted weight (365DWT). Fixed effects of year of birth, season of birth, sex, birth type, and dam age on these traits were evaluated using linear procedures of SAS, 9.1. Similarly, BWT, 120DWT, 180DWT, and 270DWT were used as fixed effects mixed model analyses. Variance components, heritability and breeding values were estimated by restricted maximum likelihood. The genetic trend for each trait was obtained by regression of the estimated breeding values (EBV) on year of birth. Analyses revealed substantial influence of birth year on all traits. Sex and birth type were the significant sources of variation for BWT and 120DWT. Season of birth did not influence birth weight meaningfully, but had a significant role in the expression of 120DWT, 180DWT, and 270DWT. Heritability estimates were generally low (0.003 ± 0.018 to 0.099 ± 0.067) for all traits. With the exception of the genetic correlation of 180DWT and 365DWT, the genetic correlations between trait were strong and positive. Only 365DWT had a positive genetic trend. Although the heritability estimates for almost all weight traits were low, high and positive genetic correlations between BWT and other weight traits suggest that selection based on BWT would result in the improvement of other weight traits as a correlated response.Keywords: bodyweight, breeding value, genetic correlation, shee

Reducing the environmental impact of surgery on a global scale: systematic review and co-prioritization with healthcare workers in 132 countries

Background Healthcare cannot achieve net-zero carbon without addressing operating theatres. The aim of this study was to prioritize feasible interventions to reduce the environmental impact of operating theatres. Methods This study adopted a four-phase Delphi consensus co-prioritization methodology. In phase 1, a systematic review of published interventions and global consultation of perioperative healthcare professionals were used to longlist interventions. In phase 2, iterative thematic analysis consolidated comparable interventions into a shortlist. In phase 3, the shortlist was co-prioritized based on patient and clinician views on acceptability, feasibility, and safety. In phase 4, ranked lists of interventions were presented by their relevance to high-income countries and low–middle-income countries. Results In phase 1, 43 interventions were identified, which had low uptake in practice according to 3042 professionals globally. In phase 2, a shortlist of 15 intervention domains was generated. In phase 3, interventions were deemed acceptable for more than 90 per cent of patients except for reducing general anaesthesia (84 per cent) and re-sterilization of ‘single-use’ consumables (86 per cent). In phase 4, the top three shortlisted interventions for high-income countries were: introducing recycling; reducing use of anaesthetic gases; and appropriate clinical waste processing. In phase 4, the top three shortlisted interventions for low–middle-income countries were: introducing reusable surgical devices; reducing use of consumables; and reducing the use of general anaesthesia. Conclusion This is a step toward environmentally sustainable operating environments with actionable interventions applicable to both high– and low–middle–income countries