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Record W2946277620 · doi:10.18433/jpps30318

Drug Targeting Strategies Based on Charge Dependent Uptake of Nanoparticles into Cancer Cells

2019· review· en· W2946277620 on OpenAlexvenueno aff
Maryam Saadat, Fahimeh Zahednezhad, Parvin Zakeri‐Milani, Hamid Reza Heidari, Javid Shahbazi-Mojarrad, Hadi Valizadeh

Bibliographic record

VenueJournal of Pharmacy & Pharmaceutical Sciences · 2019
Typereview
Languageen
FieldMaterials Science
TopicNanoparticle-Based Drug Delivery
Canadian institutionsnot available
FundersStudent Research Committee, Tabriz University of Medical SciencesUniversity of TabrizChina Scholarship CouncilTabriz University of Medical Sciences
KeywordsEndocytosisCancer cellBiophysicsChemistryNanoparticleSurface chargeZeta potentialMultiple drug resistanceCellNanotechnologyDrug deliveryCancerCell biologyBiochemistryBiologyMaterials science

Abstract

fetched live from OpenAlex

The aim of this review was to describe the preferred charged nano-particles (CNPs) for targeted delivery in tumor cells. Zeta Potential (ZP), which represents the surface charge of NPs was highlighted in cell entrance and interactions. In this regard, various types of endocytosis pathways which are involved in NPs' uptake were first introduced. Then, significance of positively charged NPs (PCNPs) in proton sponge effect corresponding to lysosomal escape was discussed. Cells prefer to endocyte the NPs with positive charge in passive targeting and gene delivery, while in active targeting; the charge of receptors' ligand binding site determines the NPs cellular uptake. Moreover, pH-sensitive NPs represent charge reversible behavior depending on pH changes which leads to longer blood circulation residence and higher uptake at acidic microenvironment of the cancer media. Role of the CNPs in overcoming multidrug resistance (MDR) and bypassing p-glycoprotein was further investigated.

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)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.099
GPT teacher head0.418
Teacher spread0.319 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations86
Published2019
Admission routes1
Has abstractyes

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