IL-17A as a Potential Therapeutic Target for Patients on Peritoneal Dialysis
Bibliographic record
Abstract
Chronic kidney disease (CKD) is a health problem reaching epidemic proportions. There is no cure for CKD, and patients may progress to end-stage renal disease (ESRD). Peritoneal dialysis (PD) is a current replacement therapy option for ESRD patients until renal transplantation can be achieved. One important problem in long-term PD patients is peritoneal membrane failure. The mechanisms involved in peritoneal damage include activation of the inflammatory and immune responses, associated with submesothelial immune infiltrates, angiogenesis, loss of the mesothelial layer due to cell death and mesothelial to mesenchymal transition, and collagen accumulation in the submesothelial compact zone. These processes lead to fibrosis and loss of peritoneal membrane function. Peritoneal inflammation and membrane failure are strongly associated with additional problems in PD patients, mainly with a very high risk of cardiovascular disease. Among the inflammatory mediators involved in peritoneal damage, cytokine IL-17A has recently been proposed as a potential therapeutic target for chronic inflammatory diseases, including CKD. Although IL-17A is the hallmark cytokine of Th17 immune cells, many other cells can also produce or secrete IL-17A. In the peritoneum of PD patients, IL-17A-secreting cells comprise Th17 cells, γδ T cells, mast cells, and neutrophils. Experimental studies demonstrated that IL-17A blockade ameliorated peritoneal damage caused by exposure to PD fluids. This article provides a comprehensive review of recent advances on the role of IL-17A in peritoneal membrane injury during PD and other PD-associated complications.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".