Analysis of Metabolic Changes in Plant Pathosystems by Imprint Imaging DESI-MS
Bibliographic record
Abstract
The response of plants to microbial pathogens is based on the production of secondary metabolites. The complexity of plant-pathogen interactions makes their understanding a challenging task for metabolomic studies requiring powerful analytical approaches. In this paper, the ability of ambient mass spectrometry to provide a snapshot of plant metabolic response to pathogen invasion was tested. The fluctuations of glycoalkaloids present in sprouted potatoes infected by the phytopathogen Pythium ultimum were monitored by imprint imaging desorption electrospray ionization mass spectrometry (DESI-MS). After 8 d from the inoculation, a decrease of the relative abundance of potato glycoalkaloids α-solanine (m/z 706) and α-chaconine (m/z 722) was observed, whereas the relative intensity of solanidine (m/z 398), solasodenone (m/z 412), solanaviol (m/z 430), solasodiene (m/z 396), solaspiralidine (m/z 428), γ-solanine/γ-chaconine (m/z 560) , β-solanine (m/z 706), and β-chaconine (m/z 722) increased. The progression of the disease, expressed by the development of brown necrotic lesions on the potato, led to the further decrease of all the glycoalkaloid metabolites. Therefore, the applicability of imprint imaging DESI-MS in studying the plant metabolic changes in a simple pathosystem was demonstrated with minimal sample preparation.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".