Improvement in cardiac function after bone marrow cell thearpy is associated with an increase in myocardial inflammation
Bibliographic record
Abstract
The mechanisms for the beneficial impact of bone marrow cell (BMC) therapy after myocardial infarction (MI) are ill defined. We hypothesized that the implanted cells improve function by attenuating post-MI inflammation and repair. In mice, 3 x 10(5) fresh BMCs were implanted immediately after coronary ligation. Cardiac function was evaluated over time. Inflammatory cytokines and cells were measured, and their impacts on the (myo)fibroblastic repair response, angiogenesis, and scar formation were determined. All differences below had P values of <0.05. BMC implantation reduced the decline in fractional shortening and ventricular dilation. Invasive hemodynamics confirmed a difference in systolic function at day 7 and diastolic function at day 28 favoring the BMC group. Interestingly, BMC implantation caused a 1.6-fold increase in the number of macrophages infiltrating the infarct but did not affect neutrophils. This increase was associated with a 1.9-fold higher myocardial TNF-alpha level. The heightened inflammatory response was associated with a 1.4-fold induction of transforming growth factor-beta and a 1.3-fold induction of basic fibroblast growth factor. These changes resulted in a 1.6-fold increase in alpha-smooth muscle actin and a 1.9-fold increase in total discoidin domain receptor 2-expressing cells in the BMC group. These two markers are expressed by cardiac (myo)fibroblasts. Capillary density in the border zone increased 2.0-fold. Consistent with a more robust repair-mediated scar "contracture," the final scar size was 0.7-fold smaller in the BMC group. In conclusion, after MI, BMC therapy induced a more robust inflammatory response that improved the "priming" of the (myo)fibroblast repair phase. Enhancing this response may further improve the beneficial impact of cellular therapy.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".