Tackling the cytotoxicity and genotoxicity of cellulose nanofibers from the banana rachis: A new food packaging alternative
Bibliographic record
Abstract
Cellulose nanofibrils from the banana rachis are a good alternative as packaging materials, food packaging, stabilizing agents, and functional food ingredients. To address the potential effects of ingested banana rachis cellulose nanofibrils (BR-CNFs), their toxicity in vitro and in vivo was evaluated using Caco-2 intestinal cells and mice, respectively. The results showed that BR-CNFs did not cause cytotoxic effects at the concentrations evaluated on Caco-2 cells. In addition to cytotoxicity tests, genotoxicity assays using comet assay indicated that Caco-2 cells showed no DNA damage at the concentrations of CNFs tested. Finally, acute in vivo cytotoxicity assays indicated that mice showed no sign of pathogenesis or lesions in the liver, kidney, or small intestine when treated with a single dose of BR-CNFs. Moreover, when the mice were treated daily for a month with BR-CNFs no hyperplasia or hypertrophy was observed in any of the organs evaluated. Additionally, biochemical parameters such as blood chemistry, creatinine, liver enzymes, and renal function showed that the BR-CNFs do not cause organ damage. Overall, this study shows that BR-CNFs are neither cytotoxic nor genotoxic. In conclusion, these studies are essential to guarantee the safety of this high value-added product in the food industry.
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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".