Beyond conventional biomass valorisation: pyrolysis-derived products for biomedical applications
Bibliographic record
Abstract
Biomass valorisation is conventionally associated with the production of green biofuels. However, this could extend beyond the conventional perception of biomass application into other domains such as medical sciences. Acid condensate (AC) obtained from pyrolysis promises a good potential for biomedical applications, notably for its antimicrobial, antioxidant, and anti-inflammatory properties. In this study, concentrated AC extract (CACE) obtained from microwave-assisted pyrolysis of palm kernel shells was fractionated, and the resulting fractions were pooled according to similar thin layer chromatography profiles into combined fractions (CFACs). CFACs were evaluated for total phenolic content, antioxidant level, cytotoxicity, and wound healing activities toward human skin fibroblast cells (HSF 1184). CFAC-3 showed the highest total phenolic content (624.98 ± 8.70 µg GAE/mg of sample) and antioxidant activities (DPPH IC50 of 29.47 ± 0.74 µg/mL, ABTS of 1247.13 ± 27.89 μg TE/mg sample, FRAP of 24.26 ± 0.71 mmol Fe(II)/mg sample, HFRS of 257.74 ± 1.74 µg/mL) compared to CACE (DPPH IC50 of 81.76 ± 2.81 µg/mL, ABTS of 816.95 ± 30.49 μg TE/mg sample, FRAP of 9.22 ± 0.66 mmol Fe(II)/mg sample, HFRS of 689.30 ± 36.00 µg/mL), no cytotoxic properties at ≤50 µg/mL, and significantly faster wound closure (at 1.25 µg/mL) compared to the control 12 h after treatment. The phosphorylation of the phosphatidylinositol 3-kinase (PI3K) and protein kinase B (AKT) were upregulated, thus indicating that wound healing of CFAC-3 followed through this signalling pathway. To conclude, phenolic-rich CFAC-3 obtained from the pyrolysis of palm kernel shells demonstrated potential biomedical application as an alternative wound healing agent with high antioxidant and wound-healing activity. To the best of our knowledge, this was the first study to report on the wound healing activity of AC and its wound healing mechanism.
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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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".