The Bacterium Akkermansia muciniphila: A Sentinel for Gut Permeability and Its Relevance to HIV-Related Inflammation
Bibliographic record
Abstract
Gut dysbiosis, namely dysregulation of the intestinal microbiota, and increased gut permeability lead to enhanced inflammation and are commonly seen in chronic conditions such as obesity and aging. In people living with HIV (PLWH), several lines of evidence suggest a depletion of gut CD4 T-cells is associated with gut dysbiosis, microbial translocation and systemic inflammation. Antiretroviral therapy (ART) rapidly controls viral replication, which leads to CD4 T-cells recovery and control of the disease. However, gut dysbiosis, epithelial damage and microbial translocation persist despite ART, increasing risk of developing inflammatory non-AIDS comorbidities such as cardiovascular disease, diabetes mellitus (DM), liver steatosis and cancer. In addition to ART, an emerging research priority is to discover strategies to improve the gut microbial composition and intestinal barrier function. Probiotic interventions have been extensively used with controversial benefits in humans. Encouragingly, within the last decade, the intestinal symbiotic bacterium Akkermansia (A.) muciniphila has emerged as the “sentinel of the gut”. A lower abundance of A. muciniphila has been shown in diabetic and obese people as well as in PLWH. Interventions with high levels of polyphenols such as tea or diets rich in fruit, the antibiotic vancomycin and the antidiabetic drug metformin have been shown to increase A. muciniphila abundance, contributing to improved metabolic function in diabetic and obese individuals. We hypothesize that gut microbiota rich in A. muciniphila can reduce microbial translocation and inflammation, preventing occurrences of non-AIDS comorbidities in PLWH. To this aim, we will discuss the protective effect of A. muciniphila and its potential applications, paving the way towards novel therapeutic strategies to improve gut health in PLWH.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".