Premature and early menopause among US women with or at risk for HIV

Objective:Little is known about the prevalence and treatment of premature and early menopause among people with HIV. We described premature and early menopause and subsequent hormonal treatment in a longitudinal cohort of women living with or at risk for HIV in the US.Methods:Data from the Women's Interagency HIV Study between 2008 and 2020 were analyzed to describe premature and early menopause among cohort participants under the age of 51.Results:Of 3,059 eligible women during the study period, 1% (n=35) underwent premature menopause before age 41, 3% (n=101) underwent menopause between ages 41 and 46, and 21% (n=442) underwent menopause between ages 46 and 50, inclusive. Of participants who experienced menopause before age 41, between age 41 and 45, and between ages 46 and 50, 51%, 24%, and 7% (respectively) received either menopausal hormone therapy or hormonal contraception.Conclusion:These findings suggest that disparities in receipt of recommended hormone therapy for premature and early menopause may contribute, in part, to evident health disparities, such as cardiovascular disease, osteoporosis, and overall mortality. They also suggest a substantial need for education among people experiencing early menopause and their providers, with the goal of improving access to hormone therapy based on guidelines to address health disparities and minimize future health consequences

Use of machine learning algorithms to classify binary protein sequences as highly-designable or poorly-designable

<p>Abstract</p> <p>Background</p> <p>By using a standard Support Vector Machine (SVM) with a Sequential Minimal Optimization (SMO) method of training, Naïve Bayes and other machine learning algorithms we are able to distinguish between two classes of protein sequences: those folding to highly-designable conformations, or those folding to poorly- or non-designable conformations.</p> <p>Results</p> <p>First, we generate all possible compact lattice conformations for the specified shape (a hexagon or a triangle) on the 2D triangular lattice. Then we generate all possible binary hydrophobic/polar (H/P) sequences and by using a specified energy function, thread them through all of these compact conformations. If for a given sequence the lowest energy is obtained for a particular lattice conformation we assume that this sequence folds to that conformation. Highly-designable conformations have many H/P sequences folding to them, while poorly-designable conformations have few or no H/P sequences. We classify sequences as folding to either highly – or poorly-designable conformations. We have randomly selected subsets of the sequences belonging to highly-designable and poorly-designable conformations and used them to train several different standard machine learning algorithms.</p> <p>Conclusion</p> <p>By using these machine learning algorithms with ten-fold cross-validation we are able to classify the two classes of sequences with high accuracy – in some cases exceeding 95%.</p