Erythropoietin Delivery by Genetically Engineered Bone Marrow Stromal Cells for Correction of Anemia in Mice with Chronic Renal Failure
Bibliographic record
Abstract
The goal of this research was to develop a strategy to couple stem cell and gene therapy for in vivo delivery of erythropoietin (Epo) for treatment of anemia of ESRD. It was shown previously that autologous bone marrow stromal cells (MSCs) can be genetically engineered to secrete pharmacologic amounts of Epo in normal mice. Therefore, whether anemia in mice with mild to moderate chronic renal failure (CRF) can be improved with Epo gene-modified MSCs (Epo+MSCs) within a subcutaneous implant was examined. A cohort of C57BL/6 mice were rendered anemic by right kidney electrocoagulation and left nephrectomy. In these CRF mice, the hematocrit (Hct) dropped from a prenephrectomy baseline of approximately 55% to 40% after induction of renal failure. MSCs from C57BL/6 donor mice were genetically engineered to secrete murine Epo at a rate of 3 to 4 units of Epo/10(6) cells per 24 h, embedded in a collagen-based matrix, and implanted subcutaneously in anemic CRF mice. It was observed that Hct increased after administration of Epo+MSCs, according to cell dose. Implants of 3 million Epo+MSCs per mouse had no effect on Hct, whereas 10 million led to a supraphysiologic effect. The Hct of CRF mice that received 4.5 or 7.5 million Epo+MSCs rose to a peak 54+/-4.0 or 63+/-5.5%, respectively, at 3 wk after implantation and remained above 48 or 54% for >19 wk. Moreover, mice that had CRF and received Epo+MSCs showed significantly greater swimming exercise capacity. In conclusion, these results demonstrate that subcutaneous implantation of Epo-secreting genetically engineered MSCs can correct anemia that occurs in a murine model of CRF.
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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 | 0.000 |
| 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".