Ultra-performance liquid chromatography-tandem mass spectrometry revealed the significantly different metabolic profiles of <i>Auricularia cornea</i> growing on weakly acidic and weakly alkaline substrates
Bibliographic record
Abstract
Auricularia cornea is a widely prized basidiomycetous mushroom with culinary and medicine value, which is cultivated artificially on a large scale in China. However, little attention has been paid to the differences in metabolic profiles under different pH growth conditions. In the present study, weakly acidic and weakly alkaline artificial substrates were developed and used for the cultivation of A. cornea, and the metabolic profiles of its fruiting bodies were determined using ultra performance liquid chromatography-tandem mass spectrometry. The results show that the weakly alkaline substrate environment promoted mycelial growth, increased body surface area, and improved yield and transformation efficiency, but attenuated metabolite accumulation by A. cornea. A total of 412 different metabolites were identified in negative and positive ion mode, of which 99 had significantly different amounts and covered 51 metabolic pathways. Principal component analysis and orthogonal patrial least squares discriminant analysis showed clear separation between two treatments, indicating different metabolic profiles. The different metabolites mainly included seven chemical categories, including amino acids and derivatives, nucleotides and derivatives, phenolic acids, organic acids, lipids, flavonoids and alkaloids. This study revealed the biological significance of these metabolites, which could be useful for further unexplored compounds and possible biological functions of A. cornea.
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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) | 0.001 | 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".