Digital polarimetric second harmonic generation microscopy of partially oriented fiber structures
Bibliographic record
Abstract
Abstract Second harmonic generation (SHG) in biological tissue originates predominantly from noncentrosymmetric fibrillar structures partially oriented within the focal volume (voxel) of a multiphoton excitation microscope. The study is aimed to elucidate fibrillar organization factors influencing SHG intensity, as well as achiral, R, and chiral, C, nonlinear susceptibility tensor component ratios. SHG response is calculated for various configurations of fibrils in a voxel using digital nonlinear microscope. The R and C ratios are calculated using linear incident and outgoing polarization states that simulate polarization-in polarization-out (PIPO) polarimetric measurements. The investigation shows strong SHG intensity dependence on parallel/antiparallel fiber organization. The R and C ratio is strongly influenced by the fiber chirality, tilting of the fibers out of image plane and crossing of the fibers. The study facilitates interpretation of polarimetric SHG microscopy images in terms of ultrastructural organization of fibers in the imaged structures. Statement of Significance Second harmonic generation microscopy is widely used for imaging non-centrosymmetric biological structures such as collagen. The ultrastructure of collagen can be determined with polarimetric SHG microscopy. The coherent nonlinear response of biological structures depends on the 3D orientations and positions of the collagen fibers in the focal volume of the microscope. Here, we show how different fiber organizations and 3D orientations in the focal volume can affect the polarimetric SHG responses. The results are important for understanding and interpreting images obtained with polarimetric SHG microscopy.
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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) | 0.001 | 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".