Polarization-Sensitive Second Harmonic Generation Microscopy for Investigations of Diseased Collagenous Tissues
Bibliographic record
Abstract
The advancement of non-invasive quantitative optical diagnosis techniques such as polarization-sensitive second harmonic generation microscopy (PSHG) for diseases such as cancer presents opportunities for improving disease understanding and survival rates. Here, novel and developing techniques in PSHG microscopy applied for the differentiation of cancerous or diseased tissues are presented, including circular dichroism, modulation of laser linear polarization, detection of outgoing linear laser polarization, and double-Stokes Mueller. Typically, initial cancer diagnosis is performed by visual inspection of stained biopsy or surgical resection tissue sections under bright-field microscopy, however, early diagnosis is challenging due to variability in morphological interpretation of the tissues, and because cancer initiation regions can be small and easy to miss. Therefore, pathologists could benefit in identifying cancer on biopsy or surgical resection sections by using unbiased quantitative automated technologies with high spatial resolution and improved disease specificity that can check the entire slide pixel-by-pixel. Second harmonic generation microscopy offers the opportunity to measure ultrastructural alterations in collagenous scaffolds of organ tissues virtually background free on submicron-sized tissue regions. The approach is particularly interesting for cancer diagnosis applications, because during cancer initiation and progression, the collagen in the affected tissue extracellular matrix is often deregulated and becomes disorganized. This mini-review contains a thorough summary of PSHG techniques that have interrogated diseased tissues, and discusses their technical variations and successes in disease discrimination.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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".