Effect of cells on spatial quantification of proteoglycans in articular cartilage of small animals
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Objective Histochemical characterization of proteoglycan content in articular cartilage is important for the understanding of osteoarthritis pathogenesis. However, cartilage cells may interfere with the measurement of matrix proteoglycan content in small animal models (e.g. mice and rats) due to the high cell volume fraction (38%) in mice compared to human tissue (~1%). We investigated whether excluding the cells from image analysis affects the histochemically measured proteoglycan content of rat knee joint cartilage and assessed the effectiveness of a deep learning algorithm-based tool named U-Net in cell segmentation.Design Histological sections were stained with Safranin-O, after which optical densities were measured using digital densitometry to estimate proteoglycan content. U-Net was trained with 600 annotated Safranin-O cartilage images for exclusion of cells from the cartilage extracellular matrix. Optical densities of the ECM with and without cells were compared as a function of normalized tissue depth.Results U-Net cell segmentation was accurate, with the measured cell area fraction following largely that of ground-truth images (average difference: 4.3%). Cell area fraction varied as a function of tissue depth and took up 8–21% of the tissue area. The exclusion of cells from the analysis led to an increase in the analyzed depth-dependent optical density of cartilage by approximately 0.6–1.8% (p < 0.01).Conclusions Although the effect of cells on the analyzed proteoglycan content is small, it should be considered for improved sensitivity, especially at the onset of the disease during which cells may proliferate in small animals.
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.
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 | 0.002 | 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 it