Granulocyte macrophage colony stimulating factor in virus-host interactions and its implication for immunotherapy
Bibliographic record
Abstract
Viruses have evolved to strategically exploit cellular signaling pathways to evade host immune defenses. GM-CSF signaling plays a pivotal role in regulating inflammation, activating myeloid cells, and enhancing the immune response to infections. Due to its central role in the immune system, viruses may target this pathway to further establish infection. This review focuses on key studies elucidating virus interactions with GM-CSF signaling proteins and summarizes findings on the impact of viral infections on GM-CSF production. Additionally, therapeutic strategies centered around GM-CSF are investigated, such as the potential benefits of administering GM-CSF versus inhibiting GM-CSF signaling to mitigate viral-induced aberrant inflammation. Understanding these virus-host interactions provides valuable insights that help further our understanding to develop future therapeutic approaches in modulating the immune response during viral infections. General schematic of viral interactions and GM-CSF signaling. A) Illustration of a virus interfering with the GM-CSF signaling pathway. B) Virally infected cells will release cytokines which may activate a cell such as Th17 and lead to the production of GM-CSF after infection further recruiting more inflammatory cells that may result in worse disease outcomes. C) Representation of therapeutic interventions of GM-CSF including administering GM-CSF to virally infected patients to improve their immune response. On the other hand, use of monoclonal antibodies against GM-CSF or its receptor has shown clinical improvements in reducing viral-induced hyperinflammation and better recovery. Created with BioRender.com • GM-CSF is a cytokine crucial for inflammation and immunity to infections. • Viruses can interfere directly with GM-CSF signaling proteins. • Viruses alter GM-CSF induction & secretion by modulating NF-κB activity. • Therapeutic strategies involving GM-CSF have potential against viral infections.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".