Toxicity evaluation and nasal mucosal tissue deposition of dexamethasone-infused mucoadhesive in situ nasal gelling systems
Bibliographic record
Abstract
To demonstrate safety of a developed intranasal dexamethasone-infused in situ gelling formulation, quantification of a validated clinical biomarker indicative of cytotoxic potential using a human sinonasal explant model was first confirmed. Systematic cytotoxicity studies using the lactate dehydrogenase (LDH) detection assay revealed no elevation from baseline, in LDH levels, with tissue integrity of explanted human nasal mucosa also maintained; this was further corroborated using tissue histopathological examination. Next, with safety confirmed ex vivo, freshly excised human nasal tissue was utilised to quantify dexamethasone release from the lead sol–gel systems; this being achieved through development and validation of a HPLC-UV analytical method, which reliably quantified controlled therapeutic release and deposition into mucosal tissue. Collectively, these findings indicate promise in the safety of each excipient within the concentrations employed in the functional sol–gel system, complemented by successful and reliable drug release and deposition into human nasal mucosal tissue. These findings pave the way for application of the dexamethasone-based sol–gel system to the extended delivery of corticosteroids to nasal mucosa in the management of localised inflammatory conditions of an acute and chronic nature, such as chronic rhinosinusitis, which can be expected to benefit from controlled and extended drug delivery characteristics imparted by appropriately engineered in situ gelling systems.
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 | 0.000 | 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".