Enhancement of postharvest longan fruit quality through chitosan (CTS)-induced modulation of energy and proline metabolism
Bibliographic record
Abstract
Abstract Chitosan (CTS), a biologically safe preservative, is crucial for post-harvest treatment of fruit and vegetables, yet its regulatory mechanisms remain poorly understood. Our research on various CTS concentrations in longan fruit post-harvest showed that 1.5% CTS curtailed Weight loss and malondialdehyde content while enhancing soluble solid content, carotenoids, ascorbic acid, total phenols, and flavonoids. Transcript-level analysis revealed that 1.5% CTS maintains longan fruit quality by modulating energy and amino acid metabolism pathways. Further evaluations demonstrated increased energy charge, adenosine triphosphate (ATP), and adenosine diphosphate (ADP) levels, alongside elevated activities of H+-ATPase, Ca2+-ATPase, cytochrome C oxidase, and succinate dehydrogenase in CTS-treated fruit. CTS also upregulated genes like H + -ATPase1, SDH1, SDH6, CCO5b-2, and CCO6b-2, boosted proline content, and enhanced activities of proline-metabolizing enzymes Δ1-pyrroline-5-carboxylic acid synthase (P5CS) and ornithine-ծ-aminotransferase (OAT), while reducing pyruvate dehydrogenase (PDH) activity. These results suggest that CTS effectively modulates gene expression and enzymatic functions related to energy and proline metabolism, enhancing the preservation of longan fruit freshness during storage.
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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".