Review and perspectives on the applications of mass spectrometry imaging under ambient conditions
Bibliographic record
Abstract
Ambient mass spectrometry (AMS)-based techniques are performed under ambient conditions in which the ionization and desorption occur in the open environment allowing the direct analysis of molecules with minimal or no sample preparation. A selected group of AMS techniques demonstrate imaging capabilities that can provide information about the localization of molecules on complex sample surfaces such as biological tissues. 2D, 3D, and multimodal imaging have unlocked an array of applications to systematically address complex problems in many areas of research such as drug monitoring, natural products, forensics, and cancer diagnostics. In the present review, we summarize recent advances in the field with respect to the implementation of new ambient ionization techniques and current applications in the last 5 years. In more detail, we mainly focus on imaging applications in topics related to animal whole bodies and tissues, single cells, cancer diagnostics and biomarkers, microbial cultures and co-cultures, plant and natural product metabolomics, and forensic applications. Finally, we discuss new areas of research, future perspectives, and the overall direction that the field may take in the years to come.
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 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".