Gut microbial diversity in HIV infection post combined antiretroviral therapy
Bibliographic record
Abstract
PURPOSE OF REVIEW: Although the HIV-infected population is living longer and getting older under current treatment regimens, significant challenges arise for health management as the infection is associated with various premature aging phenotypes, particularly increased incidence of cardiovascular diseases (CVDs). Here we review the current understanding of HIV-related gut dysbiosis in association with CVD and advances in clinical trials aiming to restore gut microbial diversity. RECENT FINDING: Identification of a unique signature for gut dysbiosis in HIV infection between different cohorts remains challenging. However, low diversity of microbiota combined with the outgrowth of pathogenic bacterial species together with dysregulated metabolic pathways have been linked to compromised gut immunity, bacterial translocation and systemic inflammation, hence higher CVD risk among different cohorts. Data from recent clinical trials aiming to evaluate the tolerability and efficacy of probiotics in treated HIV+ patients are promising and support a significant increase in microbiota diversity and reduction of systemic inflammation. However, the impact of these microbial and immunological corrections on the prevalence of CVD in HIV+ patients remains unclear. SUMMARY: Positive immunological outcomes following enrichment of the gut microbial diversity have been documented, and further trials are in progress to evaluate the range of patients, with different immunological backgrounds, who might benefit from these treatments.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".