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Drug Delivery Systems Using Surface Markers for Targeting Cancer Stem Cells

2020· article· en· W3014581793 on OpenAlexaff
James Trevor Oswald, Haritosh Patel, Daid A. Khan, Ninweh N. Jeorje, H. Golzar, Erin Oswald, Xiaowu Tang

Bibliographic record

VenueCurrent Pharmaceutical Design · 2020
Typearticle
Languageen
FieldMedicine
TopicCancer Cells and Metastasis
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsCancer stem cellCancerPopulationCancer researchNanomedicineMetastasisStem cellDrug deliveryMicrovesiclesCancer cellDrug resistanceBiologyDrugMedicineChemistrymicroRNAPharmacologyNanotechnologyCell biologyGeneMaterials science

Abstract

fetched live from OpenAlex

The innate abilities of cancer stem cells (CSCs), such as multi-drug resistance, drug efflux, quiescence and ionizing radiation tolerance, protect them from most traditional chemotherapeutics. As a result, this small subpopulation of persistent cells leads to more aggressive and chemoresistant cancers, causing tumour relapse and metastasis. This subpopulation is differentiated from the bulk tumour population through a wide variety of surface markers expressed on the cell surface. Recent developments in nanomedicine and targeting delivery methods have given rise to new possibilities for specifically targeting these markers and preferentially eliminating CSCs. Herein, we first summarize the range of surface markers identifying CSC populations in a variety of cancers; then, we discuss recent attempts to actively target CSCs and their niches using liposomal, nanoparticle, carbon nanotube and viral formulations.

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 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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.617
Threshold uncertainty score0.973

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 communication0.0000.000
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 head0.231
GPT teacher head0.394
Teacher spread0.163 · 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 teacher head, 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

Citations10
Published2020
Admission routes1
Has abstractyes

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