Regulation of antiviral and antimicrobial innate immunity and immune evasion
Bibliographic record
Abstract
The interplay between host innate immunity and pathogen evasion is a dynamic battle shaping infection outcomes. The Topical Collection "Regulation of Antiviral and Antimicrobial Innate Immunity and Immune Evasion" synthesizes findings from thirteen recent studies to elucidate the molecular mechanisms of innate immune signaling and pathogen countermeasures. Host pattern-recognition receptors (PRRs), including Toll-like receptors (TLRs), RIG-I-like receptors (RLRs), and DNA sensor cyclic GMP-AMP synthase (cGAS), drive type I interferon (IFN-I) and interferon-stimulated genes (ISGs) responses, alongside processes like autophagy and inflammasome activation, to combat viral and bacterial infections. Pathogens, such as severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), cytomegalovirus, and porcine reproductive and respiratory syndrome virus, deploy sophisticated strategies to target immune sensors and adaptors, enabling replication and persistence. Novel insights, including the roles of ISG15, autophagy protein ATG7, and host factors such as THAP11 and PSMB4, highlight complex interactions influencing viral replication and host defense. These studies propose targeted therapeutic strategies, such as inflammasome modulation for human immunodeficiency viruses (HIV), and prostaglandin E2 regulation for foot-and-mouth disease virus vaccine production, offering promising avenues to enhance host immunity and counter pathogen evasion.
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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 | 0.002 | 0.002 |
| 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) | 0.003 | 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".