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This study aimed to compare the accuracy of IFN-τ stimulated gene abundance (ISGs) in peripheral blood mononuclear cells (PBMCs), CL blood perfusion by Doppler ultrasound (Doppler-US), plasma concentration of P4 on Day 21 and pregnancy-associated glycoproteins (PAGs) test on Day 25 after timed-artificial insemination (TAI) for early pregnancy diagnosis in dairy cows and heifers. Holstein cows (n = 140) and heifers (n = 32) were subjected to a hormonal synchronization protocol and TAI on Day 0. On Day 21 post-TAI, blood samples were collected for PBMC isolation and plasma concentration of P4. The CL blood perfusion was evaluated by Doppler-US. Plasma samples collected on Day 25 were assayed for PAGs. The abundance of ISGs (ISG15 and RSAD2) in PBMCs was determined by RT-qPCR. Pregnancy was confirmed on Days 32 and 60 post-TAI by B-mode ultrasonography. Statistical analyses were performed by ANOVA using the MIXED procedure and GLIMMIX in SAS software. The pregnancy biomarkers were used to categorize the females as having undergone late luteolysis (LL); early embryonic mortality (EEM); late embryonic mortality (LEM); or late pregnancy loss (LPL). The abundance of ISGs, CL blood perfusion by Doppler-US, and concentrations of P4 on Day 21, and PAGs test on Day 25 were significant (P < 0.05) predictors of early pregnancy in dairy cows and heifers. Dairy cows had a greater (P = 0.01) occurrence of LL than heifers, but there was no difference (P > 0.1) for EEM, LEM, and LPL in heifers compared to cows. Cows with postpartum reproductive issues had a greater (P = 0.008) rate of LEM and a lesser (P = 0.01) rate of LPL compared to cows without reproductive issues. In summary, the CL blood perfusion by Doppler-US had the highest accuracy and the least number of false negatives, suggesting it is the best predictor of pregnancy on Day 21 post-TAI. The PAGs test was the most reliable indicator of pregnancy status on Day 25 post-TAI in dairy heifers and cows. The application of machine learning, specifically the MARS algorithm, shows promise in enhancing the accuracy of predicting early pregnancies in cows., Competing Interests: Declaration of competing interest The authors declare no conflicts of interest., (Copyright © 2024 Elsevier Inc. All rights reserved.)

Objectives: Being one of the main causes of morbidity and mortality in developed countries, ischaemic heart disease's (IHD) incidence and mortality present clear differences between and within countries. Several authors already proposed possible explanations based on the demography, environmental factors, diet and level of urbanisation. This study reflects the Portuguese reality concerning IHD, by analysing the geographical distribution of hospital admissions and mortality due to this condition, in Portugal, and its association with demography, economical factors and the distribution of healthcare resources at the regional level., Design: Ecological study., Setting: Data from all Portuguese Public Hospitals were obtained using the National Registry of Hospital Admissions, between 2000 and 2007, and data on demography, economical factors and health resources distribution were obtained from the National Institute of Statistics., Participants: Aggregated statistics on hospital admissions and mortality were computed for 278 counties based on almost 200 000 admissions., Primary and Secondary Outcome Measures: Mortality rate; hospital admissions rate., Results: The geographical distribution of non-adjusted mortality and hospital admission showed an inner/coastal pattern but no North/South gradient was clear. Counties with higher economical development had significantly higher mortality and admission rates. However, healthcare resources distribution was not significantly associated with IHD hospital admission and mortality. When adjusted for age, gender, economic development and health resources distribution, there was still unexplained geographical variation both in hospital admissions and mortality rates., Conclusion: A pattern in the geographic distribution of incidence and mortality of IHD was clear even after the adjustment for age and gender. Economical variables were the ones presenting the strongest association. These types of analysis may be very helpful for the definition of health policies, in particular to identify priority regions for disease prevention and guidelines for healthcare resources distribution.