Polydopamine nanoparticles for the treatment of Autosomal Recessive Spastic Ataxia of Charlevoix-Saguenay: a study about blood-brain barrier crossing
Bibliographic record
Abstract
This work is focused on investigating the therapeutic effect of antioxidant polydopamine nanoparticles (PDNPs) for the treatment of autosomal recessive spastic ataxia of Charlevoix-Saguenay (ARSACS), a neurodegenerative disease caused by a mutation in the SACS gene which codes for sacsin, a protein involved in mitochondrial dynamics. The first step was the synthesis of PDNPs and their characterization in terms of morphology, hydrodynamic diameter and z-potential, then they have been functionalized with a peptide belonging to Angiopep-2 (Ang2), known for its ability to cross blood-brain barrier (BBB) with a receptor-mediated transport mechanism. PDNPs have been tested on three different cell types that are part of the BBB, that is endothelial cells (bEnd.3), astrocytes (C8D1A) and microglial cells. A series of viability and internalization assays (live/dead, Quant-iT™ PicoGreen® dsDNA Assay Kit, staining, cytofluorometry, confocal microscopy) were made. At the same time, a series of assays to evaluate antioxidant activity (CellROX® assay), apoptosis/necrosis (annexin/propidium iodide assay) and mitochondrial effects (rhodamine staining to evaluate mitochondrial morphology and membrane potential) were carried out. Two different static and dynamic BBB in vitro models were used to test the capacity of PDNPs to cross the BBB: the static model was based on a transwell with endothelial cells and astrocytes put on opposite sides of the artificial membrane, while the dynamic one involved the same cell types but adding a flow to simulate the shear stress from the blood.
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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".