Engineered Commensals as Next-Generation Drug Delivery Agents for Nose-to-Brain Therapeutics in Neurological Disorders
Bibliographic record
Abstract
Central nervous system (CNS) disorders such as Alzheimer's diseases, Parkinson's diseases, stroke, and glioma remain among the most challenging to treat, largely due to the restrictive nature of the blood-brain barrier (BBB). In recent years, intranasal administration has emerged as a noninvasive route for CNS drug delivery. Due to its anatomical advantage over the traditional route, the nose-to-brain route can easily bypass the BBB and deliver drugs directly to the brain. Parallel advances in the interface of synthetic biology and materials engineering have led to the development of engineered living materials (ELMs) dynamic structures that embed mammalian cells, bacteria, or viruses within self-renewing or engineered matrices. These bioengineered systems have been developed as next-generation therapeutic platforms for various biomedical applications, utilizing intrinsic or engineered capabilities such as disease-targeted migration, localized therapeutic production, adaptive delivery, immune activation, and metabolic regulation. Therefore, developing a bioengineered commensal based delivery system that uses the intranasal route to effectively deliver drug across the BBB could represent a transformative strategy for treating CNS disorder and advancing neurotherapeutic research.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".