Basic Mechanisms and Clinical Implications of Peritoneal Fibrosis
Bibliographic record
Abstract
We have stressed the role of certain growth factors and cytokines in peritoneal fibrosis, including TGFbeta, TIMP-1, and inflammatory cytokines, especially IL-1beta. Recent research highlights the myofibroblast-like transformation of mesothelial cells as a central initiating event in peritoneal fibrosis. The induction, survival, and apoptosis of the myofibroblast cell population likely dictate the nature of the fibrogenic response. The accumulation of collagen occurs in a nondegradative environment, and collagenases and their inhibitors have a role in the maintenance of fibrosis. Fibrosis appears to be a ubiquitous response of peritoneal tissues to the damaging effects of uremia, bioincompatible dialysate, recurrent infection, and inflammation. Recent research has focused on the induction of angiogenesis, as this appears to correlate with increased solute transport and peritoneal membrane ultrafiltration failure. Fibrosis may play an integral part in peritoneal membrane dysfunction in several aspects. Angiogenesis may be induced as part of the fibrotic response, as many key fibrogenic cytokines are also strongly angiogenic. Fibrotic tissue may support and preserve angiogenesis. Changes in the interstitium may have a direct effect on the hydrodynamic properties of the peritoneum and may directly influence fluid movement. In its most extreme form, fibrosis manifests as the rare but devastating EPS. Peritoneal biopsy studies have identified a high prevalence of peritoneal fibrosis in PD patients. Research into peritoneal fibrosis will be enhanced by new animal models where the role of various cytokines and growth factors, cellular processes, and matrix interactions can be studied. With these models, the role of fibrosis in alteration of peritoneal membrane function can be better assessed. Clinical trials to assess the role of prevention of peritoneal injury using biocompatible solutions and treatments targeted directly at peritoneal fibrosis will be important, but challenging to design and carry out.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".