Patient Factors That Matter in Predicting Hip Arthroplasty Outcomes: A Machine-Learning Approach.

Background: Despite the success of total hip arthroplasty (THA), approximately 10%-15% of patients will be dissatisfied with their outcome. Identifying patients at risk of not achieving meaningful gains postoperatively is critical to pre-surgical counseling and clinical decision support. Machine learning has shown promise in creating predictive models.

Ex vivo HIV entry into blood CD4+ T cells does not predict heterosexual HIV acquisition in women.

Background: A blood-based assay that could quantify HIV susceptibility would be very valuable for HIV prevention research. Previously, we developed and validated an ex vivo, flow-based, HIV entry assay to assess genital HIV susceptibility in endocervical CD4+ T cells.

Methods: Here we assessed whether this tool could be used to predict HIV risk using blood-derived CD4+ T cells in a rigorously-blinded, nested case-control study using blood samples collected from high-risk, HIV-uninfected South African women enrolled in the CAPRISA 004 clinical trial.