Tissue storage affects lipidome profiling in comparison to in vivo microsampling approach
Bibliographic record
Abstract
Low-invasive in vivo solid-phase microextraction (SPME) was used to investigate the lipid profiles of muscle tissue of living fish. Briefly, mixed mode SPME fibers were inserted into the muscle for 20 min extraction, and then the fibers were desorbed in an optimal mixture of solvents. The obtained lipid profile was then compared and contrasted to that obtained with employment of ex vivo SPME and solid-liquid extraction (SLE) from fish muscle tissue belonging to the same group of fish, following a one-year storage period. Ex vivo SPME analysis of stored muscle samples revealed 10-fold decrease in the number of detected molecular features in comparison to in vivo study. Moreover, in vivo microsampling enabled the identification of different classes of bioactive lipids, including fatty acyls, not present in the lipid profile obtained through ex vivo SPME and SLE, suggesting the alterations occurring in the unbound lipid fraction of the system under study during the storage and also indicating the advantage of the in vivo extraction approach.
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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".