Association Between Gut Microbiota and CD4 Recovery in HIV-1 Infected Patients
Bibliographic record
Abstract
Composition of the gut microbiota has been linked with human immunedeficiency virus (HIV)-infected patients on antiretroviral therapy (ART). Evidence suggests that ART-treated patients with poor CD4+ T-cell recovery have higher levels of microbial translocation and immune activation. However, the association of the gut microbiota and immune recovery remains unclear. We performed a cross-sectional study on 30 healthy controls (HC) and 61 HIV-infected individuals, including 15 immunological ART responders (IRs), 20 immunological ART non-responders (INRs) and 26 untreated individuals (VU). IR and INR groups were classified by CD4+ T-cell counts of ≥350 cells/mm3 and <350 cells/mm3 after two years of ART, respectively. Each subject’s gut microbiota composition was analyzed by metagenomics sequencing. Levels of CD4+ T cells, CD8+HLA-DR+ T cells and CD8+CD38+ T cells were measured by flow cytometry. We identified more Prevotella and fewer Bacteroides in HIV-infected individuals than in HC. Patients in INR group were enriched with Faecalibacterium prausnitzii, unclassified Subdoligranulum sp. and Coprococcus comes when compared with those in IR group. F. prausnitzii and unclassified Subdoligranulum sp. were overrepresented in individuals in VU group with CD4+ T-cell counts < 350 cells/mm3. Moreover, we found that the relative abundance of unclassified Subdoligranulum sp. and C. comes were positively correlated with CD8+HLA-DR+ T-cell count and CD8+HLA-DR+/CD8+ percentage. Our study has shown that gut microbiota changes were associated with CD4+ T-cell counts and immune activation in HIV-infected subjects. Interventions to reverse gut dysbiosis and inhibit immune activation could be a new strategy for improving immune reconstitution of HIV-1-infected individuals.
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 | 0.000 | 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 | 0.001 | 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".