Microbial translocation and microbiome dysbiosis in HIV-associated immune activation
Bibliographic record
Abstract
PURPOSE OF REVIEW: This article describes the mechanisms and consequences of both microbial translocation and microbial dysbiosis in HIV infection. RECENT FINDINGS: Microbes in HIV are likely playing a large role in contributing to HIV pathogenesis, morbidities and mortality. Two major disruptions to microbial systems in HIV infection include microbial translocation and microbiome dysbiosis. Microbial translocation occurs when the bacteria (or bacterial products) that should be in the lumen of the intestine translocate across the tight epithelial barrier into systemic circulation, where they contribute to inflammation and pathogenesis. This is associated with poorer health outcomes in HIV-infected individuals. In addition, microbial populations in the gastrointestinal tract are also altered after HIV infection, resulting in microbiome dysbiosis, which further exacerbates microbial translocation, epithelial barrier disruption, inflammation and mucosal immune functioning. SUMMARY: Altered microbial regulation in HIV infection can lead to poor health outcomes, and understanding the mechanisms underlying microbial dysbiosis and translocation may result in novel pathways for therapeutic interventions.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".