Mass Spectrometry-Based Tissue Imaging: The Next Frontier in Clinical Diagnostics?
Bibliographic record
Abstract
The diagnosis of tissue samples traditionally has been performed by anatomic pathologists using a combination of cellular staining and light microscopy. With these techniques, pathologists can characterize various tissue features including cell morphology, structure, and composition to subsequently confirm whether a disease process is present. Although the histopathologic “gold-standard” methods are invaluable for routine tissue diagnosis, the results can be subjective, owing to a combination of factors such as variability in staining quality, nature of the sample, and human interpretation, and inconclusive for diseases that present indistinguishable histologic features. There is a need for new technologies that can be used as complementary tools in pathology for objective tissue analysis and disease diagnosis. Mass spectrometry (MS)7 imaging has been heralded as an upcoming advance in tissue analysis. The ability of MS to rapidly identify a variety of biomolecules present in a sample is highly attractive to the clinical laboratory. Indeed, MS coupled to chromatographic separation techniques is currently used to detect and/or quantify small molecules such as pharmacological agents and hormones in blood and urine. The advent of MS techniques that allow direct tissue analysis, including MALDI-MS and secondary ion MS, has allowed laboratories to extend the use of MS beyond biofluids into tissue samples. Using MALDI MS, for example, thin tissue sections can be analyzed in a nontargeted manner for the abundance and spatial distribution of biomolecules. As such, MALDI-MS imaging is being increasingly applied in clinical research, especially in the context of cancer diagnostics based on tissue proteomic and lipidomic signatures. The potential of MS in revolutionizing tissue analysis and diagnosis has driven several advances to accelerate its feasibility and utility in the clinical setting. The development of ambient ionization MS (AIMS), for example, has brought MS-based tissue imaging even closer to routine clinical use. Unlike MALDI and …
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.019 | 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 teacher head, 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".