Analysis of the Ribonuclease A Superfamily of Antimicrobial Peptides in Patients Undergoing Chronic Peritoneal Dialysis
Bibliographic record
Abstract
Infectious peritonitis is a common complication in patients undergoing chronic peritoneal dialysis (PD), limiting the duration of PD as a modality for renal replacement therapy and increasing patient morbidity and mortality. Antimicrobial peptides (AMPs) serve critical roles in mucosal defense, but their expression and activity during peritonitis are poorly understood. We hypothesized that AMPs belonging to the Ribonuclease (RNase) A Superfamily are present in peritoneal fluid and increase during peritonitis in patients undergoing chronic PD. In the absence of peritonitis, we detected RNase 3, RNase 6, and RNase 7 in cell-free supernatants and viable cells obtained from peritoneal fluid of chronic PD patients. The cellular sources of these RNases were eosinophils (RNase 3), macrophages (RNase 6), and mesothelial cells (RNase 7). During peritonitis, RNase 3 increased 55-fold and RNase 7 levels increased 3-fold on average, whereas RNase 6 levels were unchanged. The areas under the receiver-operating characteristic curves for RNase 3 and RNase 7 were 0.99 (95% confidence interval (CI): 0.96-1.0) and 0.79 (95% CI: 0.64-0.93), respectively, indicating their potential as biomarkers of peritonitis. Discrete omental reservoirs of these RNases were evident in patients with end stage kidney disease prior to PD initiation, and omental RNase 3 reactive cells increased in patients undergoing PD with a history of peritonitis. We propose that constitutive and inducible pools of antimicrobial RNases form a network to shield the peritoneal cavity from microbial invasion in patients undergoing chronic PD.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".