The use of matrix coating assisted by an electric field (MCAEF) to enhance mass spectrometric imaging of human prostate cancer biomarkers
Bibliographic record
Abstract
In this work, we combined a newly developed matrix coating technique - matrix coating assisted by an electric field (MCAEF) and matrix-assisted laser desorption/ionization mass spectrometry (MALDI-MS) to enhance the imaging of peptides and proteins in tissue specimens of human prostate cancer. MCAEF increased the signal-to-noise ratios of the detected proteins by a factor of 2 to 5, and 232 signals were detected within the m/z 3500-37500 mass range on a time-of-flight mass spectrometer and with the sinapinic acid MALDI matrix. Among these species, three proteins (S100-A9, S100-A10, and S100-A12) were only observed in the cancerous cell region and 14 proteins, including a fragment of mitogen-activated protein kinase/extracellular signal-regulated kinase kinase kinase 2, a fragment of cAMP-regulated phosphoprotein 19, 3 apolipoproteins (C-I, A-I, and A-II), 2 S100 proteins (A6 and A8), β-microseminoprotein, tumor protein D52, α-1-acid glycoprotein 1, heat shock protein β-1, prostate-specific antigen, and 2 unidentified large peptides at m/z 5002.2 and 6704.2, showed significantly differential distributions at the p < 0.05 (t-test) level between the cancerous and the noncancerous regions of the tissue. Among these 17 species, the distributions of apolipoprotein C-I, S100-A6, and S100-A8 were verified by immunohistological staining. In summary, this study resulted in the imaging of the largest group of proteins in prostate cancer tissues by MALDI-MS reported thus far, and is the first to show a correlation between S100 proteins and prostate cancer in a MS imaging study. The successful imaging of the three proteins only found in the cancerous tissues, as well as those showing differential expressions demonstrated the potential of MCAEF-MALDI/MS for the in situ detection of potential cancer biomarkers. Copyright © 2015 John Wiley & Sons, Ltd.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".