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Haematological values in cattle reared in humid and subhumid tropics of Mexico.

Roberto González-GardúñoClaudia Zaragoza-VeraAlfonso J Chay-CanulEver Del Jesus Flores-Santiago
Published in: Tropical animal health and production (2023)
The objective of this study was to determine some factors that influence the haematological values of cattle reared in the humid and subhumid tropics of Mexico. Whole blood samples were taken from 1355 crossbred cattle in the years 2017 to 2019. Haematocrit (HTC, %), total plasma protein (TPP, g/dL) and peripheral eosinophils count (PEOS, × 10 3 /µL) were determined manually, and the main haematological variables were recorded with an automatic analyser. The statistical analysis considered as classification variables age, sex, season (cold, dry and rainy), year (2017, 2018 and 2019) and origin of the cattle. The mean of the haematological parameters was determined along with the confidence limits (CL) of the different categories of animals according to age. Calves younger than 1-year-old presented higher levels of HTC, red blood cell count (RBC), haemoglobin (HGB), red blood cell distribution width (RDW), platelet number (PLT), white blood cell count (WBC) and lymphocyte count (LYMF) than animals older than 2 years of age. However, they showed the lowest mean cell volume (MCV) and TPP values. In cows, the highest levels of PEOS, granulocytes (GRAN), MCV and medium cells (MID) were observed and the lowest HTC, RBC, RDW and WBC levels. Intervals were determined with the 1st quartile (Q1) or lower confidence interval (90% CI) as the minimum values and the 3rd quartile (Q3) or upper confidence interval (90% CI) as the maximum values. The haematological parameters of cattle reared in the Southeast of Mexico are significantly affected by age, sex and environmental conditions.
Keyphrases
  • red blood cell
  • peripheral blood
  • single cell
  • deep learning
  • machine learning
  • cell therapy
  • induced apoptosis
  • physical activity
  • oxidative stress
  • climate change
  • binding protein
  • risk assessment
  • neural network
  • life cycle