Enhanced Visualization of Small Peptides Absorbed in Rat Small Intestine by Phytic-Acid-Aided Matrix-Assisted Laser Desorption/Ionization-Imaging Mass Spectrometry
Bibliographic record
Abstract
Enhanced visualization of small peptides absorbed through a rat intestinal membrane was achieved by matrix-assisted laser desorption/ionization time-of-flight imaging mass spectrometry (MALDI-IMS) with the aid of phytic acid as a matrix additive. Penetrants through intestinal peptide transporter 1, i.e., glycyl-sarcosine (Gly-Sar, 147.1 m/z) and antihypertensive dipeptide, Val-Tyr (281.2 m/z), were chosen for MALDI-IMS. The signal-to-noise (S/N) ratios of dipeptides Gly-Sar and Val-Tyr were seen to increase by 2.4- and 8.0-fold, respectively, when using a 2',4',6'-trihydroxyacetophenone (THAP) matrix containing 5.0 mM phytic acid, instead of the THAP matrix alone. Owing to the phytic-acid-aided MALDI-IMS method, Gly-Sar and Val-Tyr absorbed in the rat intestinal membrane were successfully visualized. The proposed imaging method also provided useful information on intestinal peptide absorption; to some extent, Val-Tyr was rapidly hydrolyzed to Tyr by peptidases located at the intestinal microvillus during the absorption process. In conclusion, the strongly acidic additive, phytic acid, is beneficial for enhancing the visualization of small peptides using MALDI-IMS, owing to the suppression of ionization-interfering salts in the tissue.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".