Bibliographic record
Abstract
Infectious disease remains an ever-growing health concern worldwide due to increasing antibiotic-resistant microbial strains, immune-compromised populations, international traffic and globalisation, and bioterrorism. There exists an urgent need to develop novel prophylactic and therapeutic strategies. In addition to classic antibiotic therapeutics, immune-modulatory molecules such as cytokines or their inhibitors represent a promising form of antimicrobial therapeutics or immune adjuvant used for the purpose of vaccination. These molecules, in the form of either recombinant protein or transgene, exert their antimicrobial effect by enhancing infectious agent-specific immune activation or memory development, or by dampening undesired inflammatory and immune responses resulting from infection and host defence mechanisms. In the last two decades, a number of cytokine therapy-based experimental and clinical trials have been conducted, and some of these efforts have led to the routine clinical use of cytokines. For instance, although IFNs have been used to treat hepatitis C with great success, many other cytokines are yet to be fully evaluated for their antimicrobial potential. This review discusses the biology and therapeutic potential of selected immune modulatory cytokines and their inhibitors, including granulocyte colony-stimulating factor, granulocyte-macrophage colony-stimulating factor, IFN-gamma, IL-12 and TNF.
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 | 0.000 |
| 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 | 0.002 |
| 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".