Think Big, Start Small: How Nanomedicine Could Alleviate the Burden of Rare CNS Diseases
Bibliographic record
Abstract
The complexity and organization of the central nervous system (CNS) is widely modulated by the presence of the blood-brain barrier (BBB) and the blood-cerebrospinal fluid barrier (BCSFB), which both act as biochemical, dynamic obstacles impeding any type of undesirable exogenous exchanges. The disruption of these barriers is usually associated with the development of neuropathologies which can be the consequence of genetic disorders, local antigenic invasions, or autoimmune diseases. These disorders can take the shape of rare CNS-related diseases (other than Alzheimer's and Parkinson's) which a exhibit relatively low or moderate prevalence and could be part of a potential line of treatments from current nanotargeted therapies. Indeed, one of the most promising therapeutical alternatives in that field comes from the development of nanotechnologies which can be divided between drug delivery systems and diagnostic tools. Unfortunately, the number of studies dedicated to treating these rare diseases using nanotherapeutics is limited, which is mostly due to a lack of interest from industrial pharmaceutical companies. In the present review, we will provide an overview of some of these rare CNS diseases, discuss the physiopathology of these disorders, shed light on how nanotherapies could be of interest as a credible line of treatment, and finally address the major issues which can hinder the development of efficient therapies in that area.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".