Chromophore selective multi-wavelength photoacoustic remote sensing of unstained human tissues
Bibliographic record
Abstract
Identifying positive surgical margins after resection of cancer often triggers re-excision and adjuvant treatments. Incomplete initial resections result in poorer patient outcomes, psychological and financial stress to the patient and increased healthcare costs. Surgical margins are typically assessed post-operatively using time consuming and expensive slide-based histopathology tissue analysis. Currently, a real-time non-contact virtual histology-like intraoperative margin assessment tool is not available. To address this need, we have developed a non-contact multi-wavelength reflection-mode, photoacoustic remote sensing (PARS) microscope demonstrating chromophore selective contrast in human tissues. We show the capabilities of multi-wavelength PARS microscopy utilizing both 266 nm and 532 nm excitation wavelengths and a 1310 nm detection wavelength. Cell nuclei and hemoglobin were visualized at the cellular scale without the addition of exogenous contrast agents. These works provide a critical step towards a virtual histology tool to provide intraoperative histology-like information in living tissue.
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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) | 0.001 | 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".