LC-MS metabolomic analysis of environmental stressor impacts on the metabolite diversity in Nephthea spp.
Bibliographic record
Abstract
Context: The soft coral Nephthea spp. is a source of terpenoid class that potentially has pharmaceutical properties. However, metabolite diversity and cytotoxic activity of this species are varied among coral reefs from various sites. Aim: To analyze the water quality in Nephthea spp. environment as a possible factor causing a difference in its metabolite diversity. Settings and Design: Nephthea spp. from seven sites were taken in October 2010 at the Alor District of Marine Protected Area, Indonesia. Materials and Methods: Water quality assessment was analyzed in situ and indexed by Canadian Council of Ministry Environment-Water Quality Index (CCME-WQI) method. Meanwhile, metabolite diversity was analyzed by a LC-MS metabolomic method, using C18 reversed phase and gradient water-acetonitrile system. Statistical Analysis Used: Spearman's rho and regression analysis were applied to correlate the water quality index to ecological index (richness, diversity, and evenness) from LC-MS results. Results: The water quality index had a significant positive correlation and strong linear regression determinant to the total metabolite (R 2 = 0.704), particularly to semipolar metabolite richness (R 2 = 0.809), the area of terpenoid class in the organism. Conclusion: It can be concluded that water quality may serve as a major factor that affects the amount of richness in Nephthea spp. metabolites. When the water quality is lower, as environment stresses increases, it may affect the metabolite richness within direct disrupt of metabolite biosynthesis or indirect ecological means. Terpenoids are known as a soft coral antipredator (coral fishes), the amount of which depends on the water quality.
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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 | 0.001 | 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".