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Record W2528431678 · doi:10.1089/hum.2016.066

Endothelial Progenitor Cells as Shuttle of Anticancer Agents

2016· review· en· W2528431678 on OpenAlexaff
Anna Laurenzana, Francesca Margheri, Anastasia Chillà, Alessio Biagioni, Giancarlo Margheri, Lido Calorini, Gabriella Fibbi, Mario Del Rosso

Bibliographic record

VenueHuman Gene Therapy · 2016
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAngiogenesis and VEGF in Cancer
Canadian institutionsNovelis (Canada)
FundersFondazione Italiana per la Ricerca sul CancroIstituto Toscano TumoriAssociazione Italiana per la Ricerca sul CancroEnte Cassa di Risparmio di Firenze
KeywordsProgenitor cellHoming (biology)Genetic enhancementCancer researchCell therapyNanomedicineMedicineEndothelial progenitor cellOncolytic virusMetastasisStem cellCancerImmunologyBiologyTumor cellsInternal medicineCell biologyGeneNanotechnologyMaterials science

Abstract

fetched live from OpenAlex

Cell therapies are treatments in which stem or progenitor cells are stimulated to differentiate into specialized cells able to home to and repair damaged tissues. After their discovery, endothelial progenitor cells (EPCs) stimulated worldwide interest as possible vehicles to perform autologous cell therapy of tumors. Taking into account the tumor-homing properties of EPCs, two different approaches to control cancer progression have been pursued by combining cell-based therapy with gene therapy or with nanomedicine. The first approach is based on the possibility of engineering EPCs to express different transgenes, and the second is based on the capacity of EPCs to take up nanomaterials. Here we review the most important progress covering the following issues: the characterization of bona fide endothelial progenitor cells, their role in tumor vascularization and metastasis, and preclinical data about their use in cell-based tumor therapy, considering antiangiogenic, suicide, immune-stimulating, and oncolytic virus gene therapy. The mixed approach of EPC cell therapy and nanomedicine is discussed in terms of plasmonic-dependent thermoablation and molecular imaging.

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)0.000
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 score0.806
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
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 head0.055
GPT teacher head0.367
Teacher spread0.312 · 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.

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

Citations21
Published2016
Admission routes1
Has abstractyes

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