Programmed death 1: a critical regulator of T-cell function and a strong target for immunotherapies for chronic viral infections
Bibliographic record
Abstract
PURPOSE OF REVIEW: The intricate balance between positive and negative signals delivered by accessory molecules is crucial to generate efficient immune responses while maintaining tolerance and preventing autoimmunity. Of these molecules, programmed death 1 has been described as a negative regulator of T-cell activation. This review will focus on current knowledge about PD-1 regulation in different diseases and discuss its potential benefits for the development of novel immune therapies. RECENT FINDINGS: PD-1 has recently been shown to be upregulated on HIV-specific CD8 T cells, whereas the PD-1 expression level was significantly correlated with viral load. Blockade of the PD-1/PD-L1 interaction enhanced the capacity of HIV-specific CD8 and CD4 T cells to proliferate or secrete cytokines and cytotoxic molecules. Future manipulations of this pathway could rescue the function of exhausted CD8 and CD4 T cells. SUMMARY: The engagement of PD-1 with its ligands induces inhibitory signals as it blocks T-cell receptor-induced T-cell proliferation and cytokine production. The PD-1 pathway plays a crucial role in the maintenance of peripheral tolerance and the pathogenesis of cancer and chronic viral infections. Understanding the mechanisms by which PD-1 interferes with T-cell functions will pave the way for novel therapeutic immune interventions to treat these diseases.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".