Myeloid Cells during Viral Infections and Inflammation
Bibliographic record
Abstract
Myeloid cells represent a diverse range of innate leukocytes that are crucial for mounting successful immune responses against viruses. These cells are responsible for detecting pathogen-associated molecular patterns, thereby initiating a signaling cascade that results in the production of cytokines such as interferons to mitigate infections. The aim of this review is to outline recent advances in our knowledge of the roles that neutrophils and inflammatory monocytes play in initiating and coordinating host responses against viral infections. A focus is placed on myeloid cell development, trafficking and antiviral mechanisms. Although known for promoting inflammation, there is a growing body of literature which demonstrates that myeloid cells can also play critical regulatory or immunosuppressive roles, especially following the elimination of viruses. Additionally, the ability of myeloid cells to control other innate and adaptive leukocytes during viral infections situates these cells as key, yet under-appreciated mediators of pathogenic inflammation that can sometimes trigger cytokine storms. The information presented here should assist researchers in integrating myeloid cell biology into the design of novel and more effective virus-targeted therapies.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".