Application of Modular Therapy for Renoprotection in Experimental Chronic Kidney Disease
Bibliographic record
Abstract
Cell-based regenerative therapies offer a new alternative approach to the treatment of chronic disease. Specifically, studies by our laboratory and others have shown that a subpopulation of cells derived from the bone marrow, known as early outgrowth cells (EOCs), are able to attenuate the progression of chronic kidney disease (CKD). In this study we examined the efficacy of a tissue engineering system, in which EOCs were embedded into submillimeter-sized collagen cylinders. These small individual units are referred to as modules and together form a functional microtissue. Due to their resemblance to endothelial cells, late outgrowth cells (LOCs) were used to coat the module surface, hypothesizing that as such they would promote vascularization and enhance engraftment of the encapsulated EOCs. These coated modules were transplanted subcutaneously into the subtotally nephrectomized rat model of CKD. While coated module therapy significantly improved both renal structure and function, noncoated modules with embedded EOCs were unable to reproduce these salutary effects on the kidney. Nevertheless, in both treatments, the embedded EOCs quickly degraded the modular environment and were seen to migrate to the liver, spleen, and bone marrow as early as 6 days after transplantation. With the efflux of EOCs, and unexpectedly no evidence of vascularization, we hypothesized that the LOCs did not enhance EOC engraftment, but rather augmented the renoprotection provided by EOCs by secretion of their own soluble and potent antifibrotic factors. To the best of our knowledge, this is the first study to document an effective subcutaneous approach for renoprotection.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".