Sternal Bone Marrow Harvesting and Culturing Techniques from Patients Undergoing Cardiac Surgery
Bibliographic record
Abstract
BACKGROUND: Mesenchymal stromal cells (MSCs) are the most prominent cell type used in clinical regenerative medicine and stem cell research. MSCs are commonly harvested from bone marrow that has been aspirated from patients' iliac crest. However, the ethical challenges of finding consenting patients and obtaining fresh autologous cells via invasive extraction methods remain to be barriers to MSC research. METHODS: Techniques of harvesting sternal bone marrow, isolating and culturing MSCs, MSC surface phenotyping, and MSC differentiation are described. Samples from 50 patients undergoing a sternotomy were collected, and the time taken to reach 80% confluency and cell count at the second splitting of MSC were measured. RESULTS: MSC isolated from the sternal bone marrow of patients undergoing cardiac surgery demonstrated successful MSC surface phenotyping and MSC differentiation. The mean cell count at the time of the second split was 1,628,025, and the mean time taken to reach the second split was 24.8 days. CONCLUSION: Herein, we describe the first reported technique of harvesting sternal bone marrow from patients already undergoing open-chest cardiac surgery to reduce the invasiveness of bone marrow harvesting, as well as the methods of isolating, culturing, and identifying MSCs for the clinical application of constructing autologous MSC-derived biomaterials.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.003 | 0.001 |
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".