Effects of a bioengineered allogeneic cellular construct on burn‐related macrophage phenotype
Bibliographic record
Abstract
Bioengineered allogeneic cellularised constructs (BACC) exert pro-healing effects in burn wounds and skew macrophage phenotype towards a predominately reparative phenotype. However, whether BACC can modulate the phenotype of dysregulated macrophages, like those present in burn wounds, is not known. To better understand the macrophage modulatory characteristics of the BACC, primary human macrophages were polarised to the M2b phenotype, an immunosuppressive phenotype relevant to burn wounds, by simultaneously exposing macrophages to polystyrene plate-coated immunoglobulin G and the endotoxin lipopolysaccharide (LPS). The resulting macrophage phenotype upregulated both inflammatory and reparative genes, and increased secretion of the M2b marker CCL1 compared to five different in vitro macrophage phenotypes. M2b macrophages were cultured with the BACC in the presence or absence of LPS to mimic infection, which is a common occurrence in burn wounds. The BACC caused up-regulation of reparative gene sets and down-regulation of pro-inflammatory gene sets, even when LPS was present in the cell culture media. Co-cultures were maintained for 1, 3, or 5 days in the presence of LPS, and by day 1 both non-activated macrophages and M2b macrophages exhibited signs of endotoxin tolerance, as demonstrated by a reduced secretion of tumour necrosis factor α (TNFα) in response to fresh LPS stimulus. The BACC was not able to prevent endotoxin tolerance, but reparative genes were upregulated in macrophages chronically exposed to LPS. These results suggest that the BACC can promote a reparative phenotype in dysregulated macrophages relevant to the pathophysiology of burns.
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) | 0.000 | 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) | 0.000 | 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".