Fabrication & Characterization of Hyaluronic Acid/Eucalyptus Hydrogels Loaded with PLGA Nanoparticles with Methotrexate as an Injectable Therapy for Rheumatoid Arthritis
Bibliographic record
Abstract
Abstract Rheumatoid arthritis is an autoimmune disease that affects about 250,000 Colombians, 82% of whom are women. Current treatments include disease-modifying antirheumatic drugs (DMARDs), such as methotrexate (MTX), analgesics and physiotherapy. The most common DMARD is MTX, which binds to plasma proteins with low efficiency (50%) and has a half-life of 6 hours. Due to its limitations when administered orally, nanoparticles (NPs) have been proposed to overcome these limitations. NPs support the release of therapeutic molecules, minimizing side effects and increasing the bioavailability of the drug in a controlled administration. NPs synthesized from biodegradable polymers, such as polyglycolic lactic acid (PLGA), are convenient for drug delivery due to their high biocompatibility and ability to bind DMARDs such as MTX. PLGA NPs loaded with MTX (MTX-PLGA-NPs) have reduced the presence of proinflammatory factors such as IL-10 and TGF-β, suggesting their potential as anti-inflammatory therapies for arthritis. Therefore, this study aims to develop MTX-PLGA-NPs in bioactive and biocompatible hyaluronic acid-eucalyptus (GelHA-E) hydrogels to preserve their stability and delay their degradation. PLGA-NPs were synthesized with an average hydrodynamic diameter of 200 nm, the 1237 cm-1 band found in FITR indicated the successful covalent conjugation with MTX; the mass loss of only 1% in GelHA-E indicated the thermogravimetric stability of the biomaterial and the low hemolytic and platelet aggregation percentage confirmed the biocompatibility of the biomaterial as a potential localized, anti-inflammatory, and injectable treatment for rheumatoid arthritis.
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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".