Solving the Jigsaw Puzzle of Wound-Healing Potato Cultivars: Metabolite Profiling and Antioxidant Activity of Polar Extracts
Bibliographic record
Abstract
Potato (Solanum tuberosum L.) is a worldwide food staple, but substantial waste accompanies the cultivation of this crop due to wounding of the outer skin and subsequent unfavorable healing conditions. Motivated by both economic and nutritional considerations, this metabolite profiling study aims to improve understanding of closing layer and wound periderm formation and guide the development of new methods to ensure faster and more complete healing after skin breakage. The polar metabolites of wound-healing tissues from four potato cultivars with differing patterns of tuber skin russeting (Norkotah Russet, Atlantic, Chipeta, and Yukon Gold) were analyzed at three and seven days after wounding, during suberized closing layer formation and nascent wound periderm development, respectively. The polar extracts were assessed using LC-MS and NMR spectroscopic methods, including multivariate analysis and tentative identification of 22 of the 24 biomarkers that discriminate among the cultivars at a given wound-healing time point or between developmental stages. Differences among the metabolites that could be identified from NMR- and MS-derived biomarkers highlight the strengths and limitations of each method, also demonstrating the complementarity of these approaches in terms of assembling a complete molecular picture of the tissue extracts. Both methods revealed that differences among the cultivar metabolite profiles diminish as healing proceeds during the period following wounding. The biomarkers included polyphenolic amines, flavonoid glycosides, phenolic acids and glycoalkaloids. Because wound healing is associated with oxidative stress, the free radical scavenging activities of the extracts from different cultivars were measured at each wounding time point, revealing significantly higher scavenging activity of the Yukon Gold periderm especially after 7 days of wounding.
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 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".