GM-CSF–Based Fusion Cytokines as Ligands for Immune Modulation
Bibliographic record
Abstract
Chromosomal translocations that combine distinct functional domains of unrelated proteins are an experiment in nature. They demonstrate how endogenous regulatory checkpoints can be overridden by altered cell biochemistry, informing a means to engineering an aberrant signal that the cell is incapable of counterregulating. Thus, our laboratory and others have synthesized fusions of GM-CSF with peptides, ILs, and chemokines, which we have termed fusokines, with the aim of inducing an enhanced immune response against cancer, aiming to overcome the maladapted biological processes causing disease. In doing so, we found that these fusokines did not behave as merely the sum of their natural unfused counterparts, but as entirely novel ligands co-opting their cognate receptor to communicate a unique message to responsive cellular targets. In this review, we discuss how fusion proteins combining different bioactive ligands can alter immune responses and briefly discuss the regulatory pathways that they circumvent.
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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 | 0.000 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".