Fractalkine (CX3CL1) and Its Receptor CX3CR1: A Promising Therapeutic Target in Chronic Kidney Disease?
Bibliographic record
Abstract
Innate immune cells are key contributors to kidney inflammation and fibrosis. Infiltration of the renal parenchyma by innate immune cells is governed by multiple signalling pathways. Since the discovery of the chemokine fractalkine (CX3CL1) and its receptor, CX3CR1 over twenty years ago, a wealth of evidence has emerged linking CX3CL1-CX3CR1 signalling to renal pathologies in both acute and chronic kidney diseases (CKD). However, despite the extent of data indicating a pathogenic role for this pathway in kidney disease and its complications, no human trials of targeted therapeutic agents have been reported. Although acute autoimmune kidney disease is often successfully treated with immunomodulatory medications, there is a notable lack of treatment options for patients with progressive fibrotic CKD. In this article we revisit the CX3CL1-CX3CR1 axis and its functional roles. Furthermore we review the accumulating evidence that CX3CL1-CX3CR1 interactions mediate important events in the intra-renal pathophysiology of CKD progression, particularly via recruitment of innate immune cells into the kidney. We also consider the role that systemic activation of the CX3CL1-CX3CR1 axis in renal disease contributes to CKD-associated cardiovascular disease. Based on this evidence, we highlight the potential for therapies targeting CX3CL1 or CX3CR1 to benefit people living with CKD.
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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 | 0.001 |
| 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 | 0.000 |
| 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 | 0.002 |
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".