Targeted drug delivery into glial scar using CAQK peptide in a mouse model of multiple sclerosis
Bibliographic record
Abstract
In multiple sclerosis, lesions are formed in various areas of the CNS, which are characterized by reactive gliosis, immune cell infiltration, extracellular matrix changes and demyelination. CAQK peptide (peptide sequence: cysteine-alanine-glutamine-lysine) was previously introduced as a targeting peptide for the injured site of the brain. In the present study, we aimed to develop a multifunctional system using nanoparticles coated by CAQK peptide, to target the demyelinated lesions in animal model of multiple sclerosis. We investigated the binding of fluorescein amidite-labelled CAQK and fluorescein amidite-labelled CGGK (as control) on mouse brain sections. Then, the porous silicon nanoparticles were synthesized and coupled with fluorescein amidite-labelled CAQK. Five days after lysolecithin-induced demyelination, male mice were intravenously injected with methylprednisolone-loaded porous silicon nanoparticles conjugated to CAQK or the same amount of free methylprednisolone. Our results showed that fluorescein amidite-labelled CAQK recognizes demyelinated lesions in brain sections of animal brains injected with lysolecithin. In addition, intravenous application of methylprednisolone-loaded nanoparticle porous silicon conjugated to CAQK at a single dose of 0.24 mg reduced the levels of microglial activation and astrocyte reactivation in the lesions of mouse corpus callosum after 24 and 48 h. No significant effect was observed following the injection of the same dose of free methylprednisolone. CAQK seems a potential targeting peptide for delivering drugs or other biologically active chemicals/reagents to the CNS of patients with multiple sclerosis. Low-dose methylprednisolone in this targeted drug delivery system showed significant beneficial effect.
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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) | 0.001 | 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 | 0.001 | 0.001 |
| 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".