The Activated Macrophage – A Tough Fortress for Virus Invasion: How Viruses Strike Back
Bibliographic record
Abstract
Macrophages (Mφ) are innate immune cells with a variety of functional phenotypes depending on the cytokine microenvironment they reside in. Mφ exhibit distinct activation patterns that are found within a wide array of activation states ranging from the originally discovered classical pro-inflammatory (M1) to the anti-inflammatory (M2) with their multi-facades. M1 cells are induced by IFNγ + LPS, while M2 are further subdivided into M2a (IL-4), M2b (Immune Complex) and M2c (IL-10) based on their inducing stimuli. Not surprisingly, Mφ activation influences the outcome of viral infections as they produce cytokines that in turn activate cells of the adaptive immune system. Generally, activated M1 cells tend to restrict viral replication, however, influenza and HIV exploit inflammation to support their replication. Moreover, M2a polarization inhibits HIV replication at the post-integration level, while HCMV encoded hrIL-10 suppresses inflammatory reactions by facilitating M2c formation. Additionally, viruses such as LCMV and Lassa Virus directly suppress Mφ activation leading to viral chronicity. Here we review how Mφ activation affects viral infection and the strategies by which viruses manipulate Mφ polarization to benefit their own fitness. An understanding of these mechanisms is important for the development of novel immunotherapies that can sway Mφ phenotype to inhibit viral replication.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.003 |
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".