Fused deposition modeling three-dimensional printing of flexible polyurethane intravaginal rings with controlled tunable release profiles for multiple active drugs
Bibliographic record
Abstract
We designed and engineered novel intravaginal ring (IVR) medical devices via fused deposition modeling (FDM) three-dimensional (3D) printing for controlled delivery of hydroxychloroquine, IgG, gp120 fragment (encompassing the CD4 binding site), and coumarin 6 PLGA-PEG nanoparticles (C6NP). The hydrophilic polyurethanes were utilized to 3D-print reservoir-type IVRs containing a tunable release controlling membrane (RCM) with varying thickness and adaptable micro porous structures (by altering the printing patterns and interior fill densities) for controlled sustained drug delivery over 14 days. FDM 3D printing of IVRs were optimized and implemented using a lab-developed Cartesian 3D printer. The structures were investigated by scanning electron microscopy (SEM) imaging and in vitro release was performed using 5 mL of daily-replenished vaginal fluid simulant (pH 4.2). The release kinetics of the IVR segments were tunable with various RCM (outer diameter to inner diameter ratio ranging from 1.12 to 2.61) produced from FDM 3D printing by controlling the printing perimeter to provide daily zero-order release of HCQ ranging from 23.54 ± 3.54 to 261.09 ± 32.49 µg/mL/day. IgG, gp120 fragment, and C6NP release rates demonstrated pattern and in-fill density-dependent characteristics. The current study demonstrated the utility of FDM 3D printing to rapidly fabricate complex micro-structures for tunable and sustained delivery of a variety of compounds including HCQ, IgG, gp120 fragment, and C6NP from IVRs in a controlled manner.
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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 | 0.000 | 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 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".