Engineered Drug-Amphiphile Conjugate Nanoparticles for Targeted Inhibition of AQP4-Mediated NLRP3 Inflammasome Signaling in Collagen-Induced Rheumatoid Arthritis
Bibliographic record
Abstract
Aquaporins (AQPs) are transmembrane proteins that transport water, small solutes, and molecules across cell membranes. Studies have reported the role of AQPs in the activation, migration, and proliferation of immune cells, thus modulating the pathogenesis of autoimmune disease. In joints, the enhanced AQP4 expression exaggerates pathological changes like hydrarthrosis, acidosis, and hyperosmotic stress-inducing dysfunction of the articular chondrocytes, leading to articular cartilage destruction in collagen-induced arthritis (CIA). Acetazolamide (AZM), a sulfonamide carbonic anhydrase inhibitor of AQP4, reversibly decreases water permeability through AQP4 and is a potential molecule for targeting AQP4 in the CIA. However, its low solubility and low bioavailability limit its therapeutic effectiveness. Therefore, in this study, we have synthesized a polyphenol drug (gallic acid) (GA) and an amphiphile (glycerol monostearate) (GMS) conjugate to self-assemble into nanoparticles and encapsulated with AZM. Apart from AZM, GA is known for its antioxidant and anti-inflammatory properties. Therefore, intra-articular injection of AZM@GA-GMS NPs efficiently downregulates the expression of AQP4 and associated NLRP3 inflammasome activation. Moreover, the NPs are cytocompatible and showed enzyme-responsive drug release and thus offer a promising therapeutic strategy for RA by inhibiting AQP4-mediated inflammatory pathways. This opens up an avenue for treatment for RA.
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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".