Super resolution measurement of collagen fibers in biological samples: Validation of a commercial solution for multiphoton microscopy
Bibliographic record
Abstract
Multiphoton microscopy is a powerful, non-invasive technique to image biological specimens. One current limitation of multiphoton microscopy is resolution as many of the biological molecules and structures investigated by research groups are similar in size or smaller than the diffraction limit. To date, the combination of multiphoton and super-resolution imaging has proved technically challenging for biology focused laboratories to implement. Here we validate that the commercial super-resolution Airyscan detector from ZEISS, which is based on image scanning microscopy, can be integrated under warranty with a pulsed multi-photon laser to enable multiphoton microscopy with super-resolution. We demonstrate its biological application in two different imaging modalities, second harmonic generation (SHG) and two-photon excited fluorescence (TPEF), to measure the fibre thicknesses of collagen and elastin molecules surpassing the diffraction limit by a factor of 1.7±0.3x and 1.4±0.3x respectively, in human heart and lung tissues, and 3-dimensional in vitro models. We show that enhanced resolution and signal-to-noise of SHG using the Airyscan compared to traditional GaAs detectors allows for automated and precise measurement of collagen fibres using texture analysis in biological tissues.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".