Imagerie MALDI
Bibliographic record
Abstract
Within the growing field of proteomics, mass spectrometry is now established as a powerful tool for peptide and protein identification and discovery from purified samples. A new era is now beginning, with the development of MALDI imaging, maintaining the sensitivity and efficacy of both discovery and identification while additionally preserving the anatomical integrity of biomolecules like peptides, proteins, oligonucleotides and lipids within tissues. Crucial developments for sample preparations have made leaps and bounds, as it is now possible to work with freezed conserved biopsies (- 80 degrees c) of more than 6 months or even conserved after paraformaldehyde fixation and paraffin embedding. The latter development has opened the door to archived tissues in hospital libraries and biomarkers hunting from tissues derived from these libraries are now a key objective. The relationship between MALDI imaging and immunocytochemistry used by the pathologist is important. The development of specific MALDI imaging using probes with a tag (peptide or organic) called << Tag-Mass >> adds a whole new perspective. It is possible henceforth to localize a protein with its specific mRNA and more specifically, with its signalling pathway on the same sections or within a pathology expression phenotype from a biopsy. Development of such a technology is similar to the one that occurred several years ago for nuclear magnetic resonance (NMR) that leads the development of imaging technologies called MRI in hospital which is intensively used for pathology diagnostics.
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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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.053 | 0.096 |
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".