Functional T cell subsets contribute differentially to HIV peptide-specific responses within infected individuals: Correlation of these functional T cell subsets with markers of disease progression
Bibliographic record
Abstract
Using a dual color ELISPOT assay able to detect HIV-specific IFN-gamma, IL-2 and dual IFN-gamma/IL-2 secreting lymphocytes we screened for HIV peptide-specific responses directed against the entire HIV proteome in two groups of untreated HIV-infected individuals, slow progressors (SP) and progressors. We found that the three functional lymphocyte subsets contributed differentially to individual HIV peptide-specific responses within a study subject. Among the identified stimulatory peptides, a higher proportion induced dual IFN-gamma/IL-2 secretion in SP than progressors. While the magnitude of single IFN-gamma secreting lymphocytes is similar between groups, the magnitude of peptide-specific dual IFN-gamma/IL-2 secreting lymphocytes is significantly more intense in SP. Neither single nor total IFN-gamma secreting cell magnitude and breadth measurements correlated with CD4 cell count or viral load whereas both parameters of dual IFN-gamma/IL-2 secreting responses correlated positively with CD4 counts and negatively with viremia.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".