A hybrid core-shell rhOP-1 nanoparticulate delivery system for enhanced new bone regeneration in distraction osteogenesis
Bibliographic record
Abstract
Distraction osteogenesis (DO) is a prevalent surgical technique for the correction of congenital orthopaedic deformities and craniofacial developmental conditions. Yet, clinical benefits continue to be limited by a number of complications mainly as a result of the protracted treatment time during which the fixator has to be kept in situ until the newly-formed bone in the distracted zone consolidates (or hardens), thus exacerbating significant medical, psychological and socio-economical problems on patients, their families and caregivers. On the other hand, protein therapy particularly with the use of potent osteoinductive cytokines from the TGF-B superfamily has been hailed as the most promising alternative to conventional bone grafts. Currently, rhBMP-2 and rhBMP-7/OP-1 have been approved for their "restricted" clinical use in long bone healing and spinal fusion. Prospective clinical trials have reported variability in results ranging from full bone bridging to no bone union and to optimize the therapeutical outcome, the incorporated high and unsafe dosages of the growth factors, timing of release and their application systems necessitate further development. Thus far, loading the protein solution into collagen sponges prior to surgical implantation has shown poor retention and rapid clearance of BMPs within a much shorter period than bone healing requires, especially in humans. Also, such carriers do not provide controlled or customizable release and can comprise outcome by foreign body reactions due to their nature, composition and incomplete degradation. Hence, biocompatible delivery systems that release the bioactive load locally and continuously over proper periods of time for the regeneration of native bone using lower and safer drug concentrations are needed. This doctoral dissertation describes the development and evaluation of a novel hybrid nanoparticulate rhOP-1 delivery system demonstrating characteristics suitable for enhancing de novo bone regenera
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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) | 0.001 | 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".