Human mesenchymal stromal cells improve scar thickness without enhancing cardiac function in a chronic ischaemic heart failure model
Bibliographic record
Abstract
Few data address the role of human mesenchymal stromal cells (MSCs) in the management of chronic ischaemic heart failure. We assessed their effect in immune-deficient animals. MSCs were cultured from bone marrow of human volunteers. Non-obese diabetes severe combined immunodeficiency (NOD/SCID) gamma null mice were randomly assigned to intramyocardial injection of human MSCs or phosphate-buffered saline 4 weeks after induction of acute myocardial infarction (MI). Echocardiography was performed 4 weeks after MI and 1 and 4 weeks after injection. Donor cell chimerism was assessed by DNA for human Alu sequences 2 and 4 weeks after injection. Histological assessment and quantification of neovascularization were determined 4 weeks after treatment. Donor MSCs at frequencies of 0.006 and 0.001% were present 2 and 4 weeks after cell injection, respectively. The infarcted ventricular wall was significantly thicker in the cohort receiving MSCs compared with control mice. There was no difference in fractional shortening, left ventricular dimensions or scar area between the groups. Small vessel density was also similar between the groups. Human MSCs increased the thickness of the infarcted ventricular wall without improving cardiac function in this chronic ischaemic heart failure model. Further studies are required to assess the benefit of MSCs in this setting.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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".