Bibliographic record
Abstract
There has been an explosion of our knowledge in cytokine biology in the last decade. Such knowledge is being quickly translated into the identification of etiologies and improved prophylaxis and therapy of disease. While cytokines have the potential to be used as therapeutics or immune adjuvants for certain diseases, they may also be culprits as therapeutic targets in other diseases. This review article serves as an introduction to the other five articles in this thematic issue each of which has a specific focus on the frontier of cytokine therapeutic biology. This review contains sections dealing with general cytokine properties, cytokine classifications, human conditions caused by cytokine under-expression and over-expression, Th1 and Th2 paradigm, cytokine therapy for acute/chronic inflammatory conditions, cytokine therapy for infectious diseases, and cytokine therapy for cancer. Keywords: Cytokines, Therapeutics Agnets, Prophylaxis, Th1, Th2, paradigm, therapy, Inflammatory, Infectious diseases, Recombinant, Immune, prophylactic vaccines, autoimmune, cancer, transgene, receptors, Chronic, Wound Healing, GM CSF, Fibrotic, Bronchial Asthma, Rheumatoid Arthritis
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".