Mass spectrometry imaging under ambient conditions
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Mass spectrometry imaging (MSI) has emerged as an important tool in the last decade and it is beginning to show potential to provide new information in many fields owing to its unique ability to acquire molecularly specific images and to provide multiplexed information, without the need for labeling or staining. In MSI, the chemical identity of molecules present on a surface is investigated as a function of spatial distribution. In addition to now standard methods involving MSI in vacuum, recently developed ambient ionization techniques allow MSI to be performed under atmospheric pressure on untreated samples outside the mass spectrometer. Here we review recent developments and applications of MSI emphasizing the ambient ionization techniques of desorption electrospray ionization (DESI), laser ablation electrospray ionization (LAESI), probe electrospray ionization (PESI), desorption atmospheric pressure photoionization (DAPPI), femtosecond laser desorption ionization (fs-LDI), laser electrospray mass spectrometry (LEMS), infrared laser ablation metastable-induced chemical ionization (IR-LAMICI), liquid microjunction surface sampling probe mass spectrometry (LMJ-SSP MS), nanospray desorption electrospray ionization (nano-DESI), and plasma sources such as the low temperature plasma (LTP) probe and laser ablation coupled to flowing atmospheric-pressure afterglow (LA-FAPA). Included are discussions of some of the features of ambient MSI for example the ability to implement chemical reactions with the goal of providing high abundance ions characteristic of specific compounds of interest and the use of tandem mass spectrometry to either map the distribution of targeted molecules with high specificity or to provide additional MS information on the structural identification of compounds. We also describe the role of bioinformatics in acquiring and interpreting the chemical and spatial information obtained through MSI, especially in biological applications for tissue diagnostic purposes. Finally, we discuss the challenges in ambient MSI and include perspectives on the future of the field.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.004 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.087 | 0.005 |
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 it