The use of nanoparticles in the diagnosis and treatment of multiple sclerosis: A scoping review
Bibliographic record
Abstract
Multiple sclerosis (MS) is an autoimmune disease for which there is no existing cure. Diagnosis of the disease occurs primarily by analysis of demyelinated lesions, and their dissemination in space and time. Nanoparticles (NPs) are currently being investigated for diagnostic and therapeutic applications for MS due to their unique physical and chemical properties. This review aims to investigate the use of NPs for the diagnosis and treatment of CNS disorders, to investigate the applicability of NPs to assist in the diagnosis and treatment of MS. In this scoping review, 24 studies on different applications of NPs for diagnosis and treatment of MS as well as studies on their safety both in vivo and vitro were analyzed. The results indicate that the majority of studies on the different applications of NPs opted for intravenous and intraperitoneal administration routes with NP size varying from 5.6-500 nm. NPs were used for better enhancement and identification of demyelinating lesions in the central nervous system (CNS) by labelling immune cells. As for drug delivery applications, NPs were shown to increase cargo half-life, and enable the controllable release of drugs. Studies on their safety indicates that while particle size, concentration, and the target tissue greatly influence a NP’s biocompatibility, they are relatively safe for short-term use. These results indicate that NPs’ success in experimental models of demyelinating diseases should be further studied for its future application to assist in the diagnosis and treatment of patients with MS. Further analysis of long-term adverse effects, experimental models employed by different studies, use of various compounds to enhance NPs’ effect in the CNS, and the study of future use of NPs in theranostic applications are needed before clinical application can be considered.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".