Rhamnolipids from non-pathogenic Acinetobacter calcoaceticus: Bioreactor-scale production, characterization and wound healing potency
Bibliographic record
Abstract
Rhamnolipids are predominantly produced from the opportunistic pathogen Pseudomonas aeruginosa, which restricts their scaled-up production and biomedical applications. Moreover, the wound healing property of rhamnolipids is mainly focused on either mono- or di-rhamnolipid congeners, which are obtained after extensive and costly purification procedures. Here, crude rhamnolipids from non-pathogenic Acinetobacter calcoaceticus BU-03 have been prepared and characterized and their wound healing potency evaluated in vitro and in vivo. Rhamnolipid extract was produced in a bioreactor by batch fermentation at a concentration of 12.7 ± 1.4 g/L. Characterization of the extract by Fourier Transform Infrared spectroscopy and mass spectrometry revealed characteristic rhamnolipid peaks. Rha-C10-C10 and Rha-Rha-C10-C10 appeared as the predominant congeners along with minor quantities of six more congeners. The rhamnolipid extract obtained from A. calcoaceticus had no toxicity against mouse fibroblast L929 cells and accelerated their migration. Transforming growth factor beta 1 (TGF-β1) has been shown to promote fibroblast migration by activating Smad3. It was found that the rhamnolipid extract enhanced Smad3 phosphorylation in L929 cells. In vivo studies showed that it promoted wound healing in mice with excisional wounds. The protein levels of TGF-β1 and alpha smooth muscle actin (α-SMA), a highly contractile protein, were significantly increased by 2.56- and 1.51-fold, respectively, in extract-treated compared with vehicle control-treated wounds, indicating that the activation of TGF-β1 signaling is possibly involved in the wound healing effect. These results suggest that a rhamnolipid extract obtained from A. calcoaceticus has potential as a wound healing material for topical application in cutaneous wound treatment.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 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".