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Record W2968677929 · doi:10.1002/ppsc.201900195

Multidisciplinary Role of Mesoporous Silica Nanoparticles in Brain Regeneration and Cancers: From Crossing the Blood–Brain Barrier to Treatment

2019· article· en· W2968677929 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueParticle & Particle Systems Characterization · 2019
Typearticle
Languageen
FieldMaterials Science
TopicNanoparticle-Based Drug Delivery
Canadian institutionsUniversity of Calgary
FundersTaipei Medical University
KeywordsBlood–brain barrierDrug deliveryMesoporous silicaDrug delivery to the brainNanotechnologyDrugNeuroscienceMedicineMaterials scienceMesoporous materialPharmacologyChemistryPsychologyCentral nervous system

Abstract

fetched live from OpenAlex

Abstract Mesoporous silica nanoparticles (MSNs) have gained wide attention for their role in biomedicine and as drug delivery vehicles. Their structural tunability, high surface area, and easy functionalization impart significant advantages over conventional materials. In this Review, recent advances in the synthesis, drug delivery, and therapeutic roles of MSNs in the treatment of various neurodegenerative and neuroinflammatory diseases are presented. The intention is to understand how MSN formulations that are capable of encapsulating drug molecules can enhance drug delivery by overcoming the blood–brain barrier (BBB) mediated by specific transport processes. The composition and characteristics of the BBB, and how alterations are observed in neurodegenerative diseases including Alzheimer's, epilepsy, and intracerebral hemorrhage are reviewed. Finally, the factors affecting efficient delivery of MSNs into the brain are summarized, and their most promising functional outcomes are discussed.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.072
Threshold uncertainty score0.718

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.011
GPT teacher head0.240
Teacher spread0.229 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it