Can interruption/withdrawl of anti‐retroviral therapy provide personalized immunotherapy against HIV‐1?
Bibliographic record
Abstract
Abstract We propose a treatment of HIV‐1+ individuals designed to harness protective immunity, lead to viral containment, and so render the individual minimally infectious. A few HIV‐infected individuals, ‘elite controllers’, generate a stable Th1, cytotoxic T lymphocyte response that contains the virus. Most infected individuals, in the absence of therapy, first generate a similarly protective response that evolves with time a Th2 component, associated with antibody production and loss of viral control. Cessation of anti‐retroviral treatment after three years results in viral rebound in most, but about one in seven individuals contains the virus, so‐called post‐treatment controllers. We suggest an understanding, of how the Th1/Th2 phenotype of immune responses is controlled, can explain these different outcomes and leads us to propose a non‐invasive, personalized strategy of immunotherapy. We propose that monitoring the relative prevalence of HIV‐1 specific IgG1 and IgG2 antibodies can provide a biomarker for deciding when to interrupt/withdraw anti‐retroviral therapy to optimally harness protective immunity.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".