Effects of extracellular matrix proteins on the growth of haematopoietic progenitor cells
Bibliographic record
Abstract
Umbilical cord blood (UCB) transplantation and haematological recovery are currently limited by the amount of haematopoietic progenitor cells (HPCs) present in each unit. HPCs and haematopoietic stem cells (HSCs) normally interact with cells and extracellular matrix (ECM) proteins present within the endosteal and vascular niches. Hence, we investigated whether coating of culture surfaces with ECM proteins normally present in the marrow microenvironment could benefit the ex vivo expansion of HPCs. Towards this, collagen types I and IV (COL I and IV), laminin (LN) and fibronectin (FN) were tested individually or as component of two ECM-mix complexes. Individually, ECM proteins had both common and unique properties on the growth and differentiation of UCB CD34+ cells; some ECM proteins favoured the differentiation of some lineages over that of others (e.g. FN for erythroids), some the expansion of HPCs (e.g. LN and megakaryocyte (MK) progenitor) while others had less effects. Next, two ECM-mix complexes were tested; the first one contained all four ECM proteins (4ECMp), while the second 'basement membrane-like structure' was without COL I (3ECMp). Removal of COL I led to strong reductions in cell growth and HPCs expansion. Interestingly, the 4ECMp-mix complex reproducibly increased CD34+ (1.3-fold) and CD41+ (1.2-fold) cell expansions at day 6 (P < 0.05) versus control, and induced greater myeloid progenitor expansion (P < 0.05) than 3ECMp. In conclusion, these results suggest that optimization of BM ECM protein complexes could provide a better environment for the ex vivo expansion of haematopoietic progenitors than individual ECM protein.
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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) | 0.001 | 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".