Analysis of myelomonocytic populations during HIV infection (P6185)
Bibliographic record
Abstract
Abstract We hypothesized that alterations in the monocytic and myeloid-derived suppressor cells (MDSCs) subsets contributed to impaired T cell function in HIV infection. CD14++CD16- classical, CD14+CD16+ intermediate, CD14lowCD16+ non classical monocytes and HLA-DR-CD14+CD33+CD11b+ MDSCs surface markers (PDL-1, CD40, HLA-ABC, HLA-DR, CD86, CCR2) and intracellular molecules (IL-12, TNF-α) were analyzed by FACS. Studies were performed on freshly isolated PBMC of healthy controls (HD) and compared to HIV-infected subjects: ART Treated (ARTC), untreated progressors (CP) and elite controllers (EC). CP subjects had significantly higher MDSCs frequency than HD, ARTC and EC. CP also express higher level of all monocyte subsets than HD and ARTC. Classical monocytes of HIV+ individuals express less HLA Class I and II molecules than HD, with a trend observed for CD86. A marked decrease in IL-12 secretion in CP upon stimulation with recombinant IFN-γ and/or LPS was also present. Also, CCR2 looks like a good marker to evaluate the importance of non classical monocytes and MDSC populations as well. Our data show that compared to HD, myelomonocytic subsets in HIV+ individuals present phenotypic and functional differences that are not fully corrected by spontaneous or therapy-induced control of viral replication. Perturbations of these subsets can contribute to ongoing immune dysfunction in treated and untreated HIV-infected subjects and may represent a target 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 | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".