Gut lactate-producing bacteria promote CD4 T cell recovery on Anti-retroviral therapy in HIV-infected patients
Bibliographic record
Abstract
Anti-retroviral therapy (ART) effectively suppresses viral replication in HIV-infected patients, however CD4 + cell restoration to normal value is not achieved by 15–20% of patients who are called immune non-responders. Gut microbiota composition has been shown to influence host immunity. Herein, to identify intestinal microbial agents that may influence the CD4 recovery in HIV-infected patients, we utilized a “Quasi-paired cohort” method to analyze intestinal metagenome data from immunological responders (IRs) and immunological non-responders (INRs). This method identified significant enrichment for Streptococcus sp. and related lactate-producing bacteria (LAB) in IRs. In a validation cohort, positive correlations between the abundance of these LAB and the post-ART CD4 + recovery was observed, and a prediction model based on these LAB performed well in predicting immune recovery. Finally, experiments using a germ-free mouse model of antibody-induced CD4 + cell depletion showed that supplementation with a lactate-producing commensal Streptococcus thermophilus strongly promoted CD4 recovery. In conclusion, our study identified a group of LAB that was associated with enhanced immune recovery in post-ART HIV-infected patients and promotes CD4 + cell restoration in a mouse model. These findings favour supplementation of LAB commensal as a therapeutic strategy for CD4 + cell count improvement in HIV-infected patients.
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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 | 0.001 |
| 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) | 0.001 | 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".