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Record W2337799606 · doi:10.4236/jbnb.2016.72011

Effects of Size and Surface Charge of Polymeric Nanoparticles on <i>in Vitro</i> and <i>in Vivo</i> Applications

2016· article· en· W2337799606 on OpenAlexafffund
Sams M. A. Sadat, Sheikh Tasnim Jahan, Azita Haddadi

Bibliographic record

VenueJournal of Biomaterials and Nanobiotechnology · 2016
Typearticle
Languageen
FieldMaterials Science
TopicNanoparticle-Based Drug Delivery
Canadian institutionsUniversity of Saskatchewan
FundersNatural Sciences and Engineering Research Council of CanadaCure Brain Cancer Foundation
KeywordsIn vivoDrug deliveryNanotechnologyIn vitroSurface chargeNanoparticleMaterials scienceBiophysicsChemistryBiochemistryBiologyBiotechnologyPhysical chemistry

Abstract

fetched live from OpenAlex

Biodegradable polymeric materials are the most common carriers for use in drug delivery systems. With this trend, newer drug delivery systems using targeted and controlled release polymeric nanoparticles (NPs) are being developed to manipulate their navigation in complex in vivo environment. However, a clear understanding of the interactions between biological systems and these nanoparticulates is still unexplored. Different studies have been performed to correlate the physicochemical properties of polymeric NPs with the biological responses. Size and surface charge are the two fundamental physicochemical properties that provide a key direction to design an effective NP formulation. In this critical review, our goal is to provide a brief overview on the influences of size and surface charge of different polymeric NPs in vitro and to highlight the challenges involved with in vivo trials.

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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)0.0010.000

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 head0.010
GPT teacher head0.237
Teacher spread0.227 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations160
Published2016
Admission routes2
Has abstractyes

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