Tweaking Innate Immunity: The Promise of Innate Immunologicals as Anti‐Infectives
Bibliographic record
Abstract
New and exciting insights into the importance of the innate immune system are revolutionizing our understanding of immune defense against infections, pathogenesis, and the treatment and prevention of infectious diseases. The innate immune system uses multiple families of germline-encoded pattern recognition receptors (PRRs) to detect infection and trigger a variety of antimicrobial defense mechanisms. PRRs are evolutionarily highly conserved and serve to detect infection by recognizing pathogen-associated molecular patterns that are unique to microorganisms and essential for their survival. Toll-like receptors (TLRs) are transmembrane signalling receptors that activate gene expression programs that result in the production of proinflammatory cytokines and chemokines, type I interferons and antimicrobial factors. Furthermore, TLR activation facilitates and guides activation of adaptive immune responses through the activation of dendritic cells. TLRs are localized on the cell surface and in endosomal/lysosomal compartments, where they detect bacterial and viral infections. In contrast, nucleotide-binding oligomerization domain proteins and RNA helicases are located in the cell cytoplasm, where they serve as intracellular PRRs to detect cytoplasmic infections, particularly viruses. Due to their ability to enhance innate immune responses, novel strategies to use ligands, synthetic agonists or antagonists of PRRs (also known as 'innate immunologicals') can be used as stand-alone agents to provide immediate protection or treatment against bacterial, viral or parasitic infections. Furthermore, the newly appreciated importance of innate immunity in initiating and shaping adaptive immune responses is contributing to our understanding of vaccine adjuvants and promises to lead to improved next-generation vaccines.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".