A CCL2-Based Fusokine as a Novel Biopharmaceutical for the Treatment of CCR2-Driven Autoimmune Diseases
Bibliographic record
Abstract
Autoimmune diseases represent one of the most challenging clinical entities with unmet medical needs, so the continued development of novel therapeutics is well justified. Most autoimmune diseases are marked by the infiltration of lymphomyeloid cells in target tissues, leading to inflammation and tissue damage. This process is guided by chemokines that act as signaling bridges amidst a complex network of immune cells. For example, monocytes are believed to be the primary cell type responsible for pathology initiation and tissue damage, while T lymphocytes are thought to orchestrate the process by secreting more cytokines/chemokines to amplify leukocyte homing. Many studies have addressed the molecular basis of monocyte recruitment in different autoimmune diseases, and the conclusions pointed to a major role played by monocyte chemoattractant protein 1 (MCP-1), also known as CC chemokine ligand 2 (CCL2), and its cell-surface receptor, CC chemokine receptor (CCR) 2. These findings suggest that by interfering with CCL2 or its receptor, it is possible to inhibit the progression of CCR2-dependent diseases. Therefore, future therapy design targeting a maladapted immune response could target chemokine receptors starting with the CCL2-CCR2 axis.
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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".