The Role of Type III Interferons in Human Disease
Bibliographic record
Abstract
PURPOSE: This literature review summarizes the main immunological characteristics of type III interferons (IFN) and highlights the clinically relevant aspects and future therapeutic perspectives for these inflammatory molecules. SOURCE: Relevant articles in PubMed MEDLINE from the first publication (2003) until 2020. N=101 articles were included in this review. PRINCIPAL FINDINGS: Type III IFNs represent a relatively newly described inflammatory cytokine family. Although they induce substantially similar signalling to the well-known type I IFNs, significant functional differences make these molecules remarkable. Type III IFNs have extensive biological effects, contributing to the pathogenesis of several diseases and also offering new diagnostic and therapeutic approaches: 1) their potent anti-viral properties make them promising therapeutics against viral hepatitis and even against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which is causing the current coronavirus disease 2019 (COVID-19) pandemic; 2) imbalances in the IFN-λs contribute to several forms of chronic inflammation (e.g., systemic and organ-specific autoimmune diseases) and potentially predict disease progression and therapeutic response to biologic therapies; and 3) the antitumor properties of the type III IFNs open up new therapeutic perspectives against malignant diseases. CONCLUSION: Over the last 18 years, researchers have gathered extensive information about the presence and role of these versatile inflammatory cytokines in human diseases, but further research is needed to clarify the mechanistic background of those observations. Better understanding of their biological activities will permit us to use type III IFNs more efficiently in new diagnostic approaches and individualized therapies, consequently improving patient care.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".