How iMALDI can improve clinical diagnostics
Bibliographic record
Abstract
Protein mass spectrometry (MS) is an indispensable tool to detect molecular signatures that can be associated with cellular dysregulation and disease. Despite its huge success in the life sciences, where it has led to novel insights into disease mechanisms and the identification of potential protein biomarkers, protein MS is rarely used for clinical protein assays. While conventional matrix-assisted laser desorption/ionization (MALDI) MS is not compatible with complex samples, liquid chromatography-MS (LC-MS)-based assays may be too complex and may lack the robustness and ease of automation required for routine use in the clinic. Therefore, clinical protein assays are dominated by immunohistochemistry and immunoassays which, however, often lack standardization and fully depend on antibody specificity. Immuno-MALDI (iMALDI) MS may overcome these hurdles by utilizing anti-peptide antibodies for the specific enrichment of targeted analytes and on-target detection of the captured analytes, thus combining the unique properties of MS for the unambiguous detection and quantitation of analytes with a workflow that can be fully automated. Here we discuss the requirements for clinical protein assays, the pitfalls of existing methods, how iMALDI has been successfully used to quantify endogenous peptides and proteins from clinical samples, as well as its potential as a powerful tool for companion diagnostics in the light of precision medicine.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".