This study developed a mathematical model using hair biomarkers (levels of Mg, K, Fe, Al, Cr) to noninvasively predict iron content in Hereford cattle muscle tissue, potentially improving livestock management and meat quality.
This study evaluated machine-learning models to predict PCOS among reproductive-aged women in Bangladesh, finding that the XGBoost model achieved high accuracy (99.63%) and effectiveness, particularly when prioritizing clinical features over psychological ones in the predictive process.
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March 2022 in “Frontiers in Endocrinology” This study developed a mathematical model and online tool using serum AMH, androstenedione levels, UML, and BMI to screen for undiagnosed PCOS, particularly useful for Asian populations.
November 2025 in “Frontiers in Animal Science” This study established a predictive model for water intake in hair sheep, finding significant associations with dry matter and its intake but not with sex classes, suggesting a more accurate and efficient way to predict and manage water use in these animals.
January 2025 in “Epsilon Open Archive (Sveriges lantbruksuniversitet biblioteket (Swedish University of Agricultural Sciences))” This study developed a prediction model using data from 185 hair sheep, finding that daily water intake is best predicted by dry matter intake and establishing a widely applicable equation for these animals, validated to enhance the efficient use of water.