Immune Suppression by Myeloid Cells in HIV Infection: New Targets for Immunotherapy
Bibliographic record
Abstract
Over thirty years of extensive research has not yet solved the complexity of HIV pathogenesis leading to a continued need for a successful cure. Recent immunotherapy-based approaches are aimed at controlling the infection by reverting immune dysfunction. Comparatively less appreciated than the role of T cells in the context of HIV infection, the myeloid cells including macrophages monocytes, dendritic cells (DCs) and neutrophils contribute significantly to immune dysfunction. Host restriction factors are cellular proteins expressed in these cells which are circumvented by HIV. Guided by the recent literature, the role of myeloid cells in HIV infection will be discussed highlighting potential targets for immunotherapy. HIV infection, which is mainly characterized by CD4 T cell dysfunction, also manifests in a vicious cycle of events comprising of inflammation and immune activation. Targeting the interaction of programmed death-1 (PD-1), an important regulator of T cell function; with PD-L1 expressed mainly on myeloid cells could bring promising results. Macrophage functional polarization from pro-inflammatory M1 to anti-inflammatory M2 and vice versa has significant implications in viral pathogenesis. Neutrophils, recently discovered low density granular cells, myeloid derived suppressor cells (MDSCs) and yolk sac macrophages provide new avenues of research on HIV pathogenesis and persistence. Recent evidence has also shown significant implications of neutrophil extracellular traps (NETs), antimicrobial peptides and opsonizing antibodies. Further studies aimed to understand and modify myeloid cell restriction mechanisms have the potential to contribute in the future development of more effective anti-HIV interventions that may pave the way to viral eradication.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| 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".