Reviewing photon absorption remote sensing (PARS): an emerging approach for label-free absorption microscopy across biological scales
Bibliographic record
Abstract
Label-free optical absorption microscopy techniques have evolved as effective tools for non-invasive chemical-specific structural and functional imaging. Yet most label-free microscopy modalities target only a fraction of the contrast afforded by optical absorption interactions. This work reviews an emerging optical absorption microscopy technique, photon absorption remote sensing (PARS), which simultaneously captures the dominant light–matter interactions occurring as pulsed light is absorbed by a specimen. In PARS, scattering, attenuation, and radiative and non-radiative relaxation processes are collected during each optical absorption event. This provides a comprehensive representation of the absorption interaction, enabling unique measurements presented as the total absorption and the quantum efficiency ratio. Through these measurements, PARS bridges many specific challenges associated with label-free imaging, recovering a wider range of biomolecules than independent radiative or non-radiative modalities. To show the versatility of PARS, a range of biological specimens is imaged, from single cells to in vivo living subjects. These examples of label-free histopathological imaging and vascular imaging illustrate fields where PARS may have profound impacts. Overall, PARS may provide comprehensive and otherwise inaccessible, label-free visualizations in biological specimens, representing a new source of data to develop AI and machine learning methods for diagnostics and visualization.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".