Milk productivity of first-calf cows depending on the intensity of their breeding

The paper presents the results of studies of milk productivity of black-and-white cows depending on the age of the first insemination and live weight at the first calving in the conditions of the Irkutsk region. The dynamics of heifer rearing by year, milk productivity of cows in the 1st and 3rd lactations depending on the intensity of their development has been studied. The experiment was conducted on farm materials from the Irkutsk Region using generally accepted zootechnical, analytical, variation and statistical research methods from 2016 to 2020. Live weight of heifers at all ages met the requirements of the elite and elite-record classes. Analysis of the data showed that the growth rate of the animals increased during 5 years (6.0%, 6.8, 2.3 and 4.8% respectively with a significant difference p ≥ 0.90). With the increased intensity of heifer rearing, an increase in milk yield over 305 days of the first lactation was observed. The highest milk production was noted in the 1st (5309-5476 kg) and 3rd (5418-5817 kg) lactations in cows with the first fruitful insemination at 13-14 months. The lowest 1st and 3rd lactation yields are obtained from cows inseminated at 20 months of age or older. Higher milk production in the first and third lactations was obtained from cows with a live weight at first calving of 541-550 kg, 551 kg and higher (5197-5164, 5436-5545 kg respectively). Less milk production was obtained from cows with a live weight at first calving of up to 500 kg (4567-5122, 4943-5009 kg). The results obtained make it possible to reveal the influence of the intensity of rearing heifers on the productive qualities of cows.

Metabolite Ratios as Quality Indicators for Pre-Analytical Variation in Serum and EDTA Plasma

In clinical diagnostics and research, blood samples are one of the most frequently used materials. Nevertheless, exploring the chemical composition of human plasma and serum is challenging due to the highly dynamic influence of pre-analytical variation. A prominent example is the variability in pre-centrifugation delay (time-to-centrifugation; TTC). Quality indicators (QI) reflecting sample TTC are of utmost importance in assessing sample history and resulting sample quality, which is essential for accurate diagnostics and conclusive, reproducible research. In the present study, we subjected human blood to varying TTCs at room temperature prior to processing for plasma or serum preparation. Potential sample QIs were identified by Ultra high pressure liquid chromatography tandem mass spectrometry (UHPLC-MS/MS) based metabolite profiling in samples from healthy volunteers (n = 10). Selected QIs were validated by a targeted MS/MS approach in two independent sets of samples from patients (n = 40 and n = 70). In serum, the hypoxanthine/guanosine (HG) and hypoxanthine/inosine (HI) ratios demonstrated high diagnostic performance (Sensitivity/Specificity > 80%) for the discrimination of samples with a TTC > 1 h. We identified several eicosanoids, such as 12-HETE, 15-(S)-HETE, 8-(S)-HETE, 12-oxo-HETE, (±)13-HODE and 12-(S)-HEPE as QIs for a pre-centrifugation delay > 2 h. 12-HETE, 12-oxo-HETE, 8-(S)-HETE, and 12-(S)-HEPE, and the HI- and HG-ratios could be validated in patient samples.

Biological variation and reference change value data for serum copper, zinc and selenium in Turkish adult population

Objectives Calculation of biological variation (BV) components is very important in evaluating whether a test result is clinically significant. The aim of this study is to analyze BV components for copper, zinc and selenium in a cohort of healthy Turkish participants. Methods A total of 10 serum samples were collected from each of the 15 healthy individuals (nine female, six male), once a week, during 10 weeks. Copper, zinc and selenium levels were analyzed by atomic absorption spectrometer. BV parameters were calculated with the approach suggested by Fraser. Results Analytical variation (CVA), within-subject BV (CVI), between-subject BV (CVG) values were 8.4, 7.1 and 4.3 for copper; 4.2, 9.1 and 13.7 for zinc; 7.6, 2.5 and 6.9 for selenium, respectively. Reference change values (RCV) were 30.46, 27.56 and 22.16% for copper, zinc and selenium, respectively. The index of individuality (II) values were 1.65, 0.66 and 0.36 for copper, zinc and selenium, respectively. Conclusions According to the results of this study, traditional reference intervals can be used for copper but we do not recommend using it for zinc and selenium. We think that it would be more accurate to use RCV value for zinc and selenium in terms of following significant changes in recurrent results of a patient.

Biological variation of serum neurofilament light chain

Objectives The neurofilament light chain (NfL) has emerged as a versatile biomarker for CNS-diseases and is approaching clinical use. The observed changes in NfL levels are frequently of limited magnitude and in order to make clinical decisions based on NfL measurements, it is essential that biological variation is not confused with clinically relevant changes. The present study was designed to evaluate the biological variation of serum NfL. Methods Apparently healthy individuals (n=33) were submitted to blood draws for three days in a row. On the second day, blood draws were performed every third hour for 12 h. NfL was quantified in serum using the Simoa™ HD-1 platform. The within-subject variation (CVI) and between-subject variation (CVG) were calculated using linear mixed-effects models. Results The overall median value of NfL was 6.3 pg/mL (range 2.1–19.1). The CVI was 3.1% and the CVG was 35.6%. An increase in two serial measurements had to exceed 24.3% to be classified as significant at the 95% confidence level. Serum NfL levels remained stable during the day (p=0.40), whereas a minute variation (6.0–6.6 pg/mL) was observed from day-to-day (p=0.02). Conclusions Serum NfL is subject to tight homeostatic regulation with none or neglectable semidiurnal and day-to-day variation, but considerable between-subject variation exists. This emphasizes serum NfL as a well-suited biomarker for disease monitoring, but warrants caution when interpreting NfL levels in relation to reference intervals in a diagnosis setting. Furthermore, NfL’s tight regulation requires that the analytical variation is kept at a minimum.

167 Analysis of variation in net energy prediction of feed ingredients fed to swine

Precision of NRC and CVB net energy (NE) prediction equations was investigated by taking into account natural and analyzed variation of the chemical components (e.g., moisture, protein, starch, NDF, ADF, sugars, fat) for six feed ingredients used in swine diets. Precision is defined as the variation of the NE formula given the variation of its chemical components. Stochastic variables were substituted for the component values and the variation in nutrient composition was algebraically, not statistically, propagated to the NE value for both equations. This was done both, for the naturally occurring variation as published in CVB, and for a range of analytical variation values associated with wet chemistry analysis based on a range of values obtained by published ring-tests. Consequently, variation of the NE value and the contribution to variation of each chemical component in the NE equation were calculated. The variation of NE prediction using CVB is lower than using NRC. The main contributor for increased variation of the NRC NE is the NDF fraction. Whereas in CVB, this is replaced by the NSP fraction which is computed as a residue from the other chemical component values, forcing the sum of the composition to add to 100%. Furthermore, it was determined that analyzing all nutrients, in particular NDF and ADF, did not always reduce the variation of the NE equations. In conclusion, analytical variation, especially fiber analysis, must be critically examined and, preferably, sum to 100% to increase precision in the prediction of NE in feed ingredients. Otherwise, the use of a residue fraction, although nutritionally difficult to justify, actually increased precision in the NE equations. Note that it is unfair to compare both NE formulas based on precision alone. An interesting follow-up question is to take accuracy also into account when comparing CVB and NRC.

Best Practices for Human Milk Collection for COVID-19 Research

In addition to providing life-giving nutrients and other substances to the breastfed infant, human milk can also represent a vehicle of pathogen transfer. As such, when an infectious disease outbreak, epidemic, or pandemic occurs – particularly when it is associated with a novel pathogen – the question will naturally arise as to whether the pathogen can be transmitted via breastfeeding. Until high-quality data are generated to answer this question, abandonment of breastfeeding due to uncertainty can result. The COVID-19 pandemic, which was in full swing at the time this document was written, is an excellent example of this scenario. During these times of uncertainty, it is critical for investigators conducting research to assess the possible transmission of pathogens via milk, whether by transfer through the mammary gland or contamination from respiratory droplets, skin, breast pumps, and milk containers, and/or close contact between mother and infant. To promote the most rigorous science, it is critical to outline optimal methods for milk collection, handling, storage, and analysis in these situations, and investigators should openly share their methods in published materials. Otherwise, the risks of inconsistent test results from pre-analytical and analytical variation, false positives, and false negatives are unacceptably high and the ability to provide public health guidance poor. Here we provide “best practices” for collecting human milk samples for COVID-19 research with the intention that this will also be a useful guide for future pandemics.