Characterizing diffusion‐controlled release of small‐molecules using quantitative MRI in view of applications to orthopedic infection
Bibliographic record
Abstract
Abstract Calcium sulfate is an established carrier for localized drug delivery, but a means to non‐invasively measure drug release, which would improve our understanding of localized delivery, remains an unmet need. We aim to quantitatively estimate the diffusion‐controlled release of small molecules loaded into a calcium sulfate carrier through a gadobutrol‐based contrast agent, which acts as a surrogate small molecule. A central cylindrical core made of calcium sulfate, either alone or within a metal scaffold, is loaded with contrast agents that release into agar. Multi‐echo scans are acquired at multiple time points over 4 weeks and processed into R2* and quantitative susceptibility mapping (QSM) maps. Mean R2* values are fit to a known drug delivery model, which are then compared with the decrease in core QSM. Fitting R2* measurements of calcium sulfate core while constraining constants to a drug release model results in an R2‐value of 0.991, yielding a diffusion constant of 4.59 × 10−11 m2 s−1. Incorporating the carrier within a metal scaffold results in a slower release. QSM shows the resulting loss of susceptibility in the non‐metal core but is unreliable around metal. R2* characterizes the released gadobutrol, and QSM detects the resulting decrease in core susceptibility. The addition of a porous metal scaffold slows the release of gadobutrol, as expected.
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 | 0.001 |
| 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".