Bath Concentration of Anionic Contrast Agents Does Not Affect Their Diffusion and Distribution in Articular Cartilage <i>In Vitro</i>
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: Differences in contrast agent diffusion reflect changes in composition and structure of articular cartilage. However, in clinical application the contrast agent concentration in the joint capsule varies, which may affect the reliability of contrast enhanced cartilage tomography (CECT). In the present study, effects of concentration of x-ray contrast agents on their diffusion and equilibrium distribution in cartilage were investigated. DESIGN: Full-thickness cartilage discs (d = 4.0 mm, n = 120) were detached from bovine patellae (n = 24). The diffusion of various concentrations of ioxaglate (5, 10, 21, 50 mM) and iodide (30, 60, 126, 300 mM) was allowed only through the articular surface. Samples were imaged with a clinical peripheral quantitative computed tomography scanner before immersion in contrast agent, and after 1, 5, 9, 16, 25, and 29 hours in the bath. RESULTS: Diffusion and partition coefficients were similar between different contrast agent concentrations. The diffusion coefficient of iodide (473 ± 133 µm(2)/s) was greater (P ≤ 0.001) than that of ioxaglate (92 ± 46 µm(2)/s). In full-thickness cartilage, the partition coefficient (at 29 h) of iodide (71 ± 5%) was greater (P ≤ 0.02 with most concentrations) than that of ioxaglate (62 ± 6%). CONCLUSIONS: Significant differences in partition and diffusion coefficient of two similarly charged (-1) contrast agents were detected, which shows the effect of steric interactions. However, the increase in solute concentration did not increase its partition coefficient. In clinical application, it is important that contrast agent concentration does not affect the interpretation of CECT imaging.
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 | 0.000 | 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 it