Effects of a Bioengineered Allogeneic Cellularized Construct (BACC) on Primary Human Macrophage Phenotype
Bibliographic record
Abstract
The mechanisms behind the pro-healing effects of multicellular, bioengineered allogeneic cellularized constructs (BACC) are not known. Macrophages are key regulators of every phase of the wound healing process and the primary cells that mediate the response to biomaterials. It is hypothesized that cells within the BACC modulate macrophage behavior, which may contribute to the mechanism by which BACC promotes healing. To probe the influence of cells within the BACC compared to effects of the underlying collagen substrate, primary human macrophages are cultured in direct or indirect contact with BACC or with the same collagen substrate used in the BACC manufacturing. Macrophage phenotype is characterized over time via multiplex gene expression, protein secretion, multidimensional flow cytometry, and functional assays with fibroblasts and endothelial cells. The BACC causes macrophages to exhibit a predominately reparative phenotype over time compared to relevant collagen substrate controls, with multiple subpopulations expressing both pro-inflammatory and reparative markers. Conditioned media from macrophage-BACC co-cultures causes distinct effects on fibroblast and endothelial cell proliferation, migration, and network formation. Given the critical role of the reparative macrophage phenotype in wound healing, these results suggest that modulation of macrophage phenotype may be a critical part of the mechanisms behind BACC's pro-healing effects.
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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".