Effects of estradiol treatments on PGF2α release in beef heifers submitted to estrous resynchronization 14 days after timed-AI.

The effects of 17ß-estradiol (E2) or estradiol benzoate (EB) on PGF2α release were studied in bred-non-pregnant and pregnant Nelore beef heifers. The day of timed artificial insemination (TAI) was designated day 0 (D0), and a single treatment was given on D14. All heifers also received an intravaginal P4 device on D14, and were randomly assigned to three groups: Control (C, P4 device only, n = 12); E2 (1 mg E2 + 9 mg P4, n = 10); or EB (1 mg, n = 10). Blood samples were collected hourly for 8 hours after treatment (Hours 0-8) to measure plasma concentrations (pg/mL) of a PGF2α metabolite (PGFM). The P4 device was removed on D22 and pregnancy was diagnosed on D28. Pregnancy rate was not different among groups (C, n = 7/12; E2, n = 5/10; EB, n = 5/10). More (P < 0.05) heifers had a CV-identified prominent PGFM pulse (peak of > 100 pg/mL) in E2 group (6/10) than in EB (1/10) and C (0/12) groups. Hourly concentration of PGFM for Hours 0 to 8 showed significant effects of group and hour and an interaction of group by hour but did not show an interaction of group or hour with pregnancy status. In preliminary post-hoc analyses, PGFM concentrations during Hours 0 to 8 and pulse characteristics were analyzed within each pregnancy status. For the non-pregnant heifers, a group-by-hour interaction was detected tentatively indicating an increase (P < 0.005) in PGFM concentrations in E2 group from Hours 4 to 6 and in EB group at Hours 5 and 6. Maximum PGFM concentration during Hours 0 to 8 did not differ (P > 0.1) between E2 (124 ± 23) and EB (110 ± 30) groups, but was greater (P < 0.05) in each group than in C (32 ± 3). Furthermore, PGFM concentrations of pulses at the peak, amplitude, and area under pulse curve (pg/mL/h) were greater (P < 0.05) in E2 group than in C group whereas the EB group did not differ (P > 0.1) from the other groups. For pregnant heifers, no effects of group, hour, or their interaction were detected in PGFM concentrations during the hourly sessions, except that maximum PGFM concentration was greater (P < 0.05) in E2 than in EB and C groups. In addition, the number of prominent pulses was greater in E2 group than in Control or EB groups. In conclusion, PGFM increased earlier and in greater concentration combined for bred-non-pregnant and pregnant heifers treated 14 days after TAI with 1 mg E2 plus 9 mg P4 than with 1 mg EB. Tentatively, a positive effect for each of E2 and EB on PGFM concentrations was attenuated in pregnant heifers.

Death trends based on autopsy data compared to the beginning of the coronavirus pandemic in Brazil.

The novel Coronavirus disease (COVID-19) is responsible for thousands of deaths worldwide, especially in Brazil, currently one of the leading countries in number of infections and deaths. The beginning of the COVID-19 epidemic in Brazil is uncertain due to the low number of tests done in the country. The excess number of deaths can suggest the beginning of the pandemic in this context. In this article, we used an autoregressive integrated moving average (ARIMA) model to investigate possible excesses in the number of deaths processed by the São Paulo Autopsy Service according to different causes of deaths: all-cause, cardiovascular, and pulmonary causes. We calculated the expected number of deaths using data from 2019 to 2020 (n=17,011), and investigated different seasonal patterns using harmonic dynamic regression with Fourier terms with residuals modeled by an ARIMA method. We did not find any abnormalities in the predicted number of deaths and the real values in the first months of 2020. We found an increase in the number of deaths only by March 20, 2020, right after the first COVID-19 confirmed case in the city of São Paulo, which occurred on March 16, 2020.

Death trends based on autopsy data compared to the beginning of the coronavirus pandemic in Brazil

The novel Coronavirus disease (COVID-19) is responsible for thousands of deaths worldwide, especially in Brazil, currently one of the leading countries in number of infections and deaths. The beginning of the COVID-19 epidemic in Brazil is uncertain due to the low number of tests done in the country. The excess number of deaths can suggest the beginning of the pandemic in this context. In this article, we used an autoregressive integrated moving average (ARIMA) model to investigate possible excesses in the number of deaths processed by the São Paulo Autopsy Service according to different causes of deaths: all-cause, cardiovascular, and pulmonary causes. We calculated the expected number of deaths using data from 2019 to 2020 (n=17,011), and investigated different seasonal patterns using harmonic dynamic regression with Fourier terms with residuals modeled by an ARIMA method. We did not find any abnormalities in the predicted number of deaths and the real values in the first months of 2020. We found an increase in the number of deaths only by March 20, 2020, right after the first COVID-19 confirmed case in the city of São Paulo, which occurred on March 16, 2020.