Cytokine immunomodulation for the treatment of infectious diseases: lessons from primary immunodeficiencies
Bibliographic record
Abstract
Traditionally, management of infectious diseases focuses on identification of the causative microbe and the use of pathogen-targeted therapy. With increasing antimicrobial resistance, novel approaches are required. One strategy is to modulate those natural host immune responses that critically mediate resistance to specific microbes. Clinically, this host-directed tactic could be used either alone or in combination with antimicrobial therapy. While conceptually attractive, there is potential concern that the pathways governing host resistance to pathogens in animal models may not extrapolate linearly to humans. Targeting these immune processes clinically may precipitate damaging, epiphenomenal responses. The field of Primary Immunodeficiencies focuses on the characterization of humans with inborn errors of immunity. These rare conditions permit the identification of those molecular and cellular processes that are central to human susceptibility to microbes. In efforts to compensate for defective host responses, this field has also provided a wealth of clinical experience in the effective use of cytokines to treat various active infections, while demonstrating their safety. In this review, we provide a historical perspective of the treatment of infectious diseases, evolving from a focus on the microbe, to an understanding of human immunity; we then outline the growing contribution of Primary Immunodeficiencies to the rational use of adjunctive cytokine immunotherapy in the management of 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| 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".