Renal Autologous Cell Therapy (REACT) for Type 2 Diabetic Kidney Disease: Preliminary Results with Renal Cortex Implantation
Bibliographic record
Abstract
Background: Diabetic Kidney Disease (DKD) is the leading cause of kidney failure in the United States. REACT in preclinical trials demonstrated stability and improved kidney function without adverse effects. We present the 12 month findings of an ongoing Phase II multicenter randomized clinical trial (RCT) evaluating autologous homologous cell therapy on DKD progression in patients with stages 3a-4 DKD. Methods: In this open label 1:1 RCT, 83 participants, 30-80 yrs, eGFR 20-50 ml/min/1.73m2 were randomized to either REACT or a control group of standard of care. All patients had a kidney biopsy with renal progenitor cell isolation and expansion by cGMP. The treated group received two cell implants into the kidney cortex at six-month intervals with CT guidance. The control received standard of care treatment (SoC) including maximized hypertension, diabetes and comorbidity management. The primary endpoint is change in eGFR. The current analysis compares the mean eGFR and UACR of completers in each group at 12 months. Results: No differences in Hgb or HbA1c were present between groups at baseline or 12 months. Annualized mean eGFR increased and UACR decreased in the treatment group from time of first injection to 12 months (Table). Major bleeding complications occurred in 1% of each group following biopsy or cell injections. There were no cell-related adverse events. Conclusions: Preliminary findings indicate implantation of progenitor REACT into the renal cortex in DKD is safe and improved annualized eGFR and UACR. Further data will follow completion of the study. Funding: Commercial Support - ProKidney
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".