MétaCan
Menu
Back to cohort
Record W3118454190 · doi:10.3390/molecules26020272

Chitosan Nanoparticles-Insight into Properties, Functionalization and Applications in Drug Delivery and Theranostics

2021· review· en· W3118454190 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

VenueMolecules · 2021
Typereview
Languageen
FieldPharmacology, Toxicology and Pharmaceutics
TopicAdvanced Drug Delivery Systems
Canadian institutionsLaurentian University
Fundersnot available
KeywordsSurface modificationDrug deliveryChitosanNanotechnologyNanoparticleMaterials scienceDrugChemistryMedicinePharmacologyOrganic chemistry

Abstract

fetched live from OpenAlex

Nanotechnology-based development of drug delivery systems is an attractive area of research in formulation driven R&D laboratories that makes administration of new and complex drugs feasible. It plays a significant role in the design of novel dosage forms by attributing target specific drug delivery, controlled drug release, improved, patient friendly drug regimen and lower side effects. Polysaccharides, especially chitosan, occupy an important place and are widely used in nano drug delivery systems owing to their biocompatibility and biodegradability. This review focuses on chitosan nanoparticles and envisages to provide an insight into the chemistry, properties, drug release mechanisms, preparation techniques and the vast evolving landscape of diverse applications across disease categories leading to development of better therapeutics and superior clinical outcomes. It summarizes recent advancement in the development and utility of functionalized chitosan in anticancer therapeutics, cancer immunotherapy, theranostics and multistage delivery systems.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.997
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.092
GPT teacher head0.387
Teacher spread0.294 · 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