Multimodal Imaging Mass Spectrometry to Identify Markers of Pulmonary Arterial Hypertension in Human Lung Tissue Using MALDI-ToF, ToF-SIMS, and Hybrid SIMS
Bibliographic record
Abstract
Abstract Pulmonary arterial hypertension (PAH) is a rare and deadly disease affecting roughly 15–60 people per million in Europe with a poorly understood pathology. There are currently no diagnostic tools for early detection nor does a curative treatment exist. The lipid composition of arteries in lung tissue samples from human PAH and control patients were investigated using matrix-assisted laser desorption ionization (MALDI) imaging mass spectrometry (IMS) combined with time-of-flight secondary ion mass spectrometry (TOF-SIMS) imaging. Using random forests as an IMS data analysis technique, it was possible to identify the ion at m/z 885.6 as a marker of PAH in human lung tissue. The m/z 885.6 ion intensity was shown to be significantly higher around diseased arteries and was confirmed to be a diacylglycerophosphoinositol PI(C18:0/C20:4) via MS/MS using a novel hybrid SIMS instrument. The discovery of a potential biomarker opens up new research avenues which may finally lead to a better understanding of the PAH pathology and highlights the vital role IMS can play in modern biomedical research.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".