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Ovulation

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Defective secretion of Prostaglandin F2α during development of idiopathic persistent corpus luteum in mares.

Domestic animal endocrinology
Five mares that developed idiopathic persistent corpus luteum (PCL) were compared with 5 mares with apparently normal interovulatory intervals (IOIs). Progesterone (P4) and a metabolite of prostaglandin F2α (PGFM) were assayed daily beginning on the ...

Stimulation of regressing subordinate follicles of wave 2 with a gonadotropin product in heifers.

Domestic animal endocrinology
The recovery of regressing wave-2 subordinate follicles was studied by treating heifers with a gonadotropin product that had about 84% and 16% of follicle-stimulating hormone and luteinizing hormone activity, respectively. A treated group (n = 8) rec...

Changes in brain ribonuclease (BRB) messenger RNA in granulosa cells (GCs) of dominant vs subordinate ovarian follicles of cattle and the regulation of BRB gene expression in bovine GCs.

Domestic animal endocrinology
Brain ribonuclease (BRB) is a member of the ribonuclease A superfamily that is constitutively expressed in a range of tissues and is the functional homolog of human ribonuclease 1. This study was designed to characterize BRB gene expression in granul...

Preovulatory progesterone levels are the top indicator for ovulation prediction based on machine learning model evaluation: a retrospective study.

Journal of ovarian research
BACKGROUND: Accurately predicting ovulation timing is critical for women undergoing natural cycle-frozen embryo transfer. However, the precise predicting of the ovulation timing remains challenging due to the lack of consensus among different clinics...

Improved clinical pregnancy rates in natural frozen-thawed embryo transfer cycles with machine learning ovulation prediction: insights from a retrospective cohort study.

Scientific reports
This study aims to develop physician support software for determining ovulation time and assess its impact on pregnancy outcomes in natural cycle frozen embryo transfers (NC-FET). To develop, assess, and validate an ovulation prediction model, three ...

Machine learning model for menstrual cycle phase classification and ovulation day detection based on sleeping heart rate under free-living conditions.

Computers in biology and medicine
The accurate classification of menstrual cycle phases and detection of ovulation is critical for women's health management, particularly in addressing infertility, alleviating premenstrual syndrome, and preventing hormone-related disorders. However, ...