Mass Spectrometric Lipid Profiles of Picosecond Infrared Laser‐Generated Tissue Aerosols Discriminate Different Brain Tissues
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: A picosecond infrared laser (PIRL) has recently been demonstrated to cut biological tissue without scar formation based on the minimal destructive action on the surrounding cells. During cutting with PIRL, the irradiated tissue is ablated by a cold vaporization process termed desorption by impulsive vibrational excitation. In the resulting aerosol, all molecules are dissolved in small droplets and even labile biomolecules like proteins remain intact after ablation. It is hypothesized that these properties enable the PIRL in combination with mass spectrometry as an intelligent laser scalpel for guided surgery. In this study, it was tested if PIRL-generated tissue aerosols are applicable for direct analysis with mass spectrometry, and if the acquired mass spectra can be used to discriminate different brain areas. MATERIALS AND METHODS: Brain tissues were irradiated with PIRL. The aerosols were collected and directly infused into a mass spectrometer via electrospray ionization without any sample preparation or lipid extraction. RESULTS: The laser produced clear cuts with no marks of burning. Lipids from five different classes were identified in the mass spectra of all samples. By principal component analysis the different brain areas were clearly distinguishable from each other. CONCLUSIONS: The results demonstrate the potential for real-time analysis of lipids with a PIRL-based laser scalpel, coupled to a mass spectrometer, for the discrimination of tissues during surgeries. Lasers Surg. Med. © 2019 Wiley Periodicals, Inc.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.008 | 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".