Solute adsorption to surfaces of articular cartilage explants: apparent versus actual partition coefficients
Bibliographic record
Abstract
Solute transport is important to biological activities and contrast agent-based imaging of articular cartilage. Solute partitioning, which characterizes equilibrium concentration distributions within tissues, is typically measured assuming that solute is distributed within the tissue volume. However, solutes also adsorb to cartilage surfaces. Ignoring this adsorption results in measurements of apparent partition coefficients (Ka) significantly greater than actual ones (K). A theoretical model was developed to predict the resulting errors in partition coefficient measurements and to estimate K based on Ka data. These errors increase as explant surface/volume ratio (S/V) increases. A range of solutes including 3 or 4 kDa and 40 kDa dextrans, chondroitin sulfate, insulin and myoglobin conjugated to three different fluorophores (fluorescein isothiocyanate (FITC), tetramethylrhodamine isothiocyanate (TRITC) and carboxytetramethylrhodamine (TAMRA)) and fluorophores alone were studied. Experiments showed significant increases in Ka with S/V for FITC and TRITC and their conjugates. For TAMRA, while increased S/V increased Ka of dextrans, it did not affect Ka of chondroitin sulfate, insulin, myoglobin or TAMRA alone. S/V had the most significant effect on Ka for TRITC and its conjugates and smallest effects for TAMRA and its conjugates. Fluorescence microscopy confirmed solute accumulation at cartilage surfaces for all solutes, with strongest adsorption for TAMRA and weakest for FITC. Consistent with theory, under these extreme conditions Ka was less affected by adsorption; otherwise differences between K and Ka were significant. Differences in adsorption and transport properties of solutes labeled with different fluorophores highlighted the importance of molecular-level solute–matrix interactions on solute transport in cartilage.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".