Abstract 355: Heart Failure Impairs Bone Marrow Mesenchymal Stem Cell Renewing Capacity and Migration
Bibliographic record
Abstract
Background: Clinical trials have shown a sustained beneficial effect of bone marrow mesenchymal stem cells (MSCs) as a therapy for acute and chronic heart failure (HF). Clinical grade cell manufacturing of autologous and allogeneic MSCs is becoming a standard procedure. This study investigates the differences of bone marrow MSC isolation and expansion in favorable in vitro conditions. We compared MSC production from both healthy young donors and chronic HF patients. Methods: We analyzed MSC manufacturing records from five clinical trials: TAC-HFT (Ischemic cardiomyopathy (CMP), patient and donors), POSEIDON (Ischemic CMP, donors), POSEIDON DCM (Dilated CMP, donors), TRIDENT (Ischemic CMP, donors), and CRATUS (Frailty, donors). Results: All cells expressed CD105, CD90 and lacked hematopoietic marker CD45. The age of healthy donors (25.5 ± 0.7 y.o., N=24) and HF patients (56.1 ± 2.5, y.o., N=23), was significantly different (P<0.0001). Collectively, the number of mononuclear cells (MNCs) isolated from bone marrow (volume range 58-125ml) didn’t differ between the groups. However, plated MNCs from healthy adults generated more MSCs than MNCs from HF patients (p0: 5.9x10^6±0.5x10^6, N=23 vs 3.8x10^6±0.4x10^6, N=24, respectively, P=0.003 and p1: 15.4x10^6±2.5x10^6, N=23 vs 10.8x10^6±1.1x10^6, N=24, respectively, P=0.036). No correlation was found between stem cell growth and age (35 to 78y.o.). Left ventricular ejection fraction (LVEF) in patients with HF had a direct correlation with MSC growth rate (P=0.03), particularly, in patients with severely depressed LVEF (<30%), which had a tendency to generate less MSCs overall. Moreover, MSCs from HF patients demonstrated less migration compared to MSCs from healthy donors at 6, 10 and 24 hours (h) relative to baseline (6 h: 9.5±3.0% vs 30.7±2.1%; 10 h: 19.9±13.7% vs 53.1±2.5% and 24 h: 41.5±15.8% vs 88.9±1.6%, N=4, respectively. P=0.03). Conclusions: Despite equivalent numbers of MNCs, healthy young donors generate significantly more MSCs than HF patients, due to increased growth rate and higher number of resident stromal bone marrow stem cells. MSC migration was impaired in HF patients compared to healthy donors. HF appears to be an independent factor for MSC renewal capacity and culture phenotype.
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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) | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".