Revealing chirality of biological structures using different polarimetric SHG microscopy techniques
Bibliographic record
Abstract
Polarimetric second harmonic generation (SHG) microscopy techniques are powerful tools to reveal sub-molecular information from biological specimens. Among biological samples collagen with a noncentrosymmetric structure and efficient SHG conversion has been the focus of many studies. Since collagen remodeling takes place due to cancer progression, it is important to develop tools to detect and understand the ultrastructural changes in collagen assembly using polarimetric nonlinear microscopy. Several polarimetric techniques have been developed to probe susceptibility ratios, in-plane orientation, and out of the image plane orientation of collagen. Polarization-In Polarization-Out (PIPO) and SHG circular dichroism (SHG-CD) techniques have been used to calculate the out of the image plane orientation and chirality of collagen. In this work, we study the correlation between SHG-CD and the chiral susceptibility ratio (C) in order to reveal the collagen chirality, and the collagen fiber tilt out of image plane. A numerical modeling is used to understand the relation between aforementioned parameters and the chirality and out of the image plane orientation of collagen. The results of numerical modeling show similar behaviors for SHG-CD and the chiral susceptibility ratio (C) calculated from PIPO measurements. The results obtained from rat tail tendon collagen confirms that the sign of both SHG-CD and C ratio changes by flipping the sample as it is predicted by the numerical modeling. The results also show that both SHG-CD and C ratio may become miscalculated when antiparallel chiral fibers are present in the focal volume of the microscope. The results of this study confirm that polarimetric SHG microscopy techniques are able to reveal 3D structure of biological samples and therefore they are beneficial to the diagnosis of collagen related diseases.
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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) | 0.000 | 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".