Novel Approaches to Managing and Contrasting Complex Ion Mobility MALDI Imaging Datasets.
Bibliographic record
Abstract
Mass spectrometry imaging (MSI) has proved to be a powerful analytical tool for the detection, localization and identification of many analytes, including metabolites, lipids and proteins, originating from complex, biological sample surfaces. MSI experiments can generate vast amount of data, depending on image size and acquisition mass range, which will both directly relate to the number of ions detected, the number of pixels and possibly the addition of ion mobility to improve the specificity of the analysis. Tissue sections used were xenograph tissue, where the rat animals were administrated an anti-cancer drug called Dasanitib at a concentration of 30 mg/kg, and scarified at different time points (1 and 3-hours). In situ digestion was performed with a trypsin solution being sprayed directly on the tissue samples and an overnight incubation. Several layers of matrix, a-cyano-4-hydroxicinnamic acid (CHCA) containing aniline in acentonitrile: water:TFA (1:1:0,1), was also sprayed directly onto the tissue samples. We are presenting a new approach where HDMS Compare software is used in combination with High Definition Imaging (HDI) software. HDMS compare is a powerful analytical tool that investigates the data by comparing two datasets based on multi-dimensional differences in the m/z and drift time domains. It measure differences between samples that are believed to be very similar and were the difference cannot by detected by MS only. After Comparing, Inspecting and Detecting the 2-D plot images, a peak list was generated where the tryptic peptides were more abundant in the 3-hours vs. the 1-hour post dose tissue sections. The peak list was used to generate ion images of the contrasted tissue sections in the HDI software to confirm the highest intensities and identity of the tryptic peptides in the 3-hour tissue section.
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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 | 0.009 | 0.027 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".