Matrix‐assisted laser desorption/ionization imaging mass spectrometry of intraperitoneally injected danegaptide (ZP1609) for treatment of stroke‐reperfusion injury in mice
Bibliographic record
Abstract
RATIONALE: This work focuses on direct matrix-assisted laser desorption/ionization imaging mass spectrometry (MALDI-IMS) detection of intraperitoneally (IP)-injected dipeptide ZP1609 in mouse brain tissue. Direct analysis of drug detection in intact tissue sections provides distribution information that can impact drug development. MALDI-IMS capabilities of uncovering drug transport across the blood-brain barrier are demonstrated. METHODS: Successful peptide detection using MALDI-IMS was achieved using a MALDI TOF/TOF system. Upon optimization of sample preparation procedures for dipeptide ZP1609, an additional tissue acidification procedure was found to greatly enhance signal detection. The imaging data acquired was able to determine successful transport of ZP1609 across the blood-brain barrier. Data obtained from MALDI-IMS can help shape our understanding of biological functions, disease progression, and effects of drug delivery. RESULTS: Direct detection of ZP1609 throughout the brain tissue sections was observed from MALDI-MS images. However, in cases where there was induction of stroke, a peak of lower signal intensity was also detected in the target m/z region. Although distinct differences in signal intensity can be seen between control and experimental groups, fragments and adducts of ZP1609 were investigated using MALDI-IMS to verify detection of the target analyte. CONCLUSIONS: Overall, the data reveals successful penetration of ZP1609 across the blood-brain barrier. The benefits of tissue acidification in the enhancement of detection sensitivity for low-abundance peptides were demonstrated. MALDI-IMS has been shown to be a useful technique in the direct detection of drugs within intact brain tissue sections.
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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 | 0.001 | 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) | 0.001 | 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".