Predicting Bacterial Vaginosis Development using Artificial Neural Networks.

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Tác giả: Kristal J Aaron, Caleb M Ardizzone, Nuno Cerca, Jacob H Elnaggar, Sheridan D George, Keonte J Graves, Melissa M Herbst-Kralovetz, Clayton Jacobs, John W Lammons, Paweł Łaniewski, Meng Luo, Christina A Muzny, Alison J Quayle, Ashutosh Tamhane, Christopher M Taylor

Ngôn ngữ: eng

Ký hiệu phân loại: 004.66 Data transmission modes and data switching methods

Thông tin xuất bản: United States : medRxiv : the preprint server for health sciences , 2025

Mô tả vật lý:

Bộ sưu tập: NCBI

ID: 748412

UNLABELLED: Bacterial vaginosis (BV) is a dysbiosis of the vaginal microbiome, characterized by the depletion of protective IMPORTANCE: Bacterial vaginosis (BV) is the most common vaginal infection and is associated with numerous comorbidities. BV is associated with infertility, preterm birth, pelvic inflammatory disease, and increased risk of HIV/STI acquisition. BV is difficult to detect prior to onset, and infection commonly recurs after treatment. Our model allows for the accurate early detection of iBV by surveying the vaginal microbiome, potentially serving as a valuable tool to determine which patients are at risk of developing iBV. Early detection of iBV could lead to wider adoption of clinical interventions useful in the prevention of iBV such as live biotherapeutics, prophylactic antibiotics, and/or behavioral modifications. Our findings indicate that few microbial targets are required for accurate predictions, facilitating cost and time effective clinical testing. Similarly, our study highlights the value of developing models personalized to specific patient populations, improving accuracy while reducing the number of taxa required for accurate predictions.
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