Imaging Method by Matrix-Assisted Laser Desorption/Ionization Mass Spectrometry (MALDI-MS) for Tissue or Tumor: A Mini Review
Bibliographic record
Abstract
Matrix-assisted laser desorption/ionization mass spectrometry (MALDI-MS) is an advanced technique that uses minimum fragmented ions from complex molecules for mass spectrometry (MS) analysis (tissue profiling by mass spectrometry). It is able to analyze spatially resolved tissue or tumor sections at the molecular level. It has become a valuable tool for tumor and tissue imaging, due to its ease of operation and high mass resolution, but it still has vast room for development in the instrumentation of larger proteins in some tissues. In this review, we focus on the main components of MALDI-MS instrumentation, sample handling and processing, the working principle of MALDI-MS, and its applications in diagnostic and prognostic assessments, tumor removal and drug development. Although it is less effective at detecting larger proteins in some tissues, it still shows huge potential because of its advancements in instrumentation and processing protocols. This article may benefit those who have interests in MALDI-MS for tissue or tumor imaging.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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".