Label-free lipid contrast imaging using non-contact near-infrared photoacoustic remote sensing microscopy
Bibliographic record
Abstract
Histopathology of lipid-rich tissues is often a difficult endeavor, owing to the limited tissue processing workflows that can appropriately preserve tissue while keeping fatty deposits intact. Here, we present the first usage of near-infrared (NIR) photoacoustic remote sensing (PARS) to achieve imaging contrast from lipids without the need for exogenous stains or labels. In our system, the facile production of 1225 nm excitation pulses is achieved by the stimulated Raman scattering of a 1064 nm source propagating through an optical fiber. PARS-based detection is achieved by monitoring the change in the scattering profile of a co-aligned 1550 nm continuous-wave interrogation beam in response to absorption of the 1225 nm light by lipids. Our non-contact, reflection-mode approach can achieve a FWHM resolution of up to 0.96 µm and signal-to-noise ratios as high as 45 dB from carbon fibers and 9.7 dB from a lipid phantom. NIR-PARS offers a promising approach to image lipid-rich samples with a simplified workflow.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".