Multimodal imaging with optical coherence tomography and multiphoton microscopy of human hip joint osteoarthritis
Bibliographic record
Abstract
Osteoarthritis (OA) is the most common form of arthritis, where the protective cartilage on the ends of bones wears down over time, causing pain, tenderness, stiffness, loss of flexibility and bone spurs. Degenerative alterations start before cartilage loss happens, which include surface swelling, cartilage fibrillation, and calcification. Detecting the early degenerative alterations can assist the diagnosis of early-stage OA. In this study, two imaging modalities are applied on human hip-joint specimens in ex vivo imaging, including polarization-sensitive optical coherence tomography (PS-OCT) and multiphoton microscopy (MPM). OCT detects the layered tissue structure of cartilage and bone using backscattered light and PS-OCT is a functional extension of OCT. PS-OCT measures tissue birefringence which is sensitive to the orderly organization of collagen in cartilage. MPM can visualize collagen fibers with sub-cellular resolution. Complementary information about cartilage on the cellular and tissue level can be obtained by the multimodal imaging. Using the multimodal system, the variation of the thickness of the cartilage structural zones, abnormal birefringence caused by collagen alterations and fibrillation, and uneven structure resulted from calcification are imaged and quantified. The imaging results show distinctive features of degenerative alterations in the OA specimen, such as uneven tissue surface, fibrillation, and reduced birefringence. It is shown that PS-OCT has great potential in detecting early stage OA.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".