Bibliographic record
Abstract
Histology has long recognized the intimate link between structure and function. Over centuries histologists have utilized an assortment of optical microscopy techniques to elucidate functional attributes of tissues through investigating tissue architecture. This thesis includes developments in the field of nonlinear optical microscopy for use in histology\nand pathology. The main contributions focused on the study of fibrillar collagen in the extracellular matrix (ECM) with polarization-dependent second harmonic generation (P-SHG) microscopy and the study of harmonophore-stained cellular nuclei with third harmonic generation (THG) microscopy. The P-SHG microscopy technique, âpolarization-in, polarization-outâ (PIPO), was developed to accurately determine the second-order polarization properties of thin tissue sections. The polarization instrumentation was implemented into a nonlinear optical microscope and a custom fitting algorithm extracted ratios of the second-order nonlinear susceptibility elements at every pixel of an obtained image. Hierarchical organization, at every level of structure, can contribute significantly to the macroscopic second-order polarization properties of fibrillar collagen in the ECM and quantifiable differences between the various tissue architectures were observed. A framework was developed, based on the collagen hierarchical organization, to interpret the submicron polarization properties of various tissues. Complimentary to the P-SHG study of connective tissue, the structure of hematoxylin and eosin (H&E) stained nuclei was revealed by THG microscopy. Imaging the 3D organization of nuclei was possible using the inherent optical sectioning provided by nonlinear microscopy. The origin of THG was investigated with spectrally- and temporally-resolved measurements, as well as the THG ratio method. A rather complex situation involving multiple dye complexes was revealed. The structure of dye aggregates was investigated with THG PIPO microscopy.\nThe techniques of PIPO and harmonophore-stained harmonic generation microscopy show great potential for ultimately furthering understanding of tissue structure and function. H&E stained tissue investigations with THG microscopy has applications as a tool for cancer diagnostics. PIPO can elucidate the symmetry and organization of materials beyond tissues, including starch, nanowires, and protein crystals. In pathology, the developed collagen framework has strong implications, as collagen is recognized as playing a more active role in a number of diseases including idiopathic pulmonary fibrosis, wound repair, and tumour development and progression.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".