Expression profiling of human milk derived exosomal microRNAs and their targets in HIV-1 infected mothers
Bibliographic record
Abstract
Despite the use of antiretroviral therapy (ART) in HIV-1 infected mothers approximately 5% of new HIV-1 infections still occur in breastfed infants annually, which warrants for the development of novel strategies to prevent new HIV-1 infections in infants. Human milk (HM) exosomes are highly enriched in microRNAs (miRNAs), which play an important role in neonatal immunity. Furthermore, HM exosomes from healthy donors are known to inhibit HIV-1 infection and transmission; however, the effect of HIV-1 on HM exosomal miRNA signatures remains unknown. In this study, we used nCounter NanoString technology and investigated miRNAs expression profiles in first week postpartum HM exosomes from HIV-1 infected and uninfected control mothers (n = 36). Our results indicated that HIV-1 perturbed the differential expression patterns of 19 miRNAs (13 upregulated and 6 downregulated) in HIV-1 infected women compared to healthy controls. DIANA-miR functional pathway analyses revealed that multiple biological pathways are involved including cell cycle, pathways in cancer, TGF-β signaling, FoxO signaling, fatty acid biosynthesis, p53 signaling and apoptosis. Moreover, the receiver operating characteristics (ROC) curve analyses of miR-630 and miR-378g yielded areas under the ROC curves of 0.82 (95% CI 0.67 to 0.82) and 0.83 (95% CI 0.67 to 0.83), respectively highlighting their potential to serve as biomarkers to identify HIV-1 infection in women. These data may contribute to the development of new therapeutic strategies in prevention of mother-to-child transmission (MTCT) of HIV-1.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".