Micro-CT imaging technique to characterize diffusion of small-molecules
Bibliographic record
Abstract
Optimization and characterization of small-molecule diffusion are important in the development of drug-delivery systems. For example, the delivery of local antibiotics is an important component of therapy for orthopedic devicerelated infections (ODRI). However, despite its wide use, the exact elution mechanism is not yet fully understood. In this study, we developed a quantitative, non-destructive, micro-CT technique to characterize 2D diffusion of small-molecules in a tissue-equivalent phantom. Our objective is to use a radio-opaque molecule (Iohexol; molecular weight (MW) 821 Da) as a surrogate for small-molecule antibiotics (e.g., Vancomycin; MW 1449 Da) to characterize diffusion from a finite, cylindrical-core carrier into an agar, tissue-equivalent, sink. A single-phase diffusion experiment was performed to validate our micro-CT imaging method. A two-part phantom consisted of an inner, cylindrical, agar core loaded with Iohexol as a drug-surrogate, directly communicating with an outer annulus of pure agar. The estimate of a single-phase diffusion coefficient for agar was derived from the analysis of 2D radial diffusion distance. We then applied the validated method to evaluate diffusion in two-phases, using calcium-sulphate matrices loaded with Iohexol eluting into an agar tissue-equivalent sink. Image acquisition was performed at regular intervals up to 25 days. Cumulative release amount was used to calculate diffusion coefficients in two-phase phantoms. Iohexol diffusion coefficient was 2.6 ×10-10 m2 s-1, 0.46 ×10-10 m2 s-1, 0.85 ×10-10 m2 s-1 through agar, Stimulan, and Plaster of Paris, respectively. This approach could be used to validate drug delivery in the development of new carrier structures and materials.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".