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Record W3185914791 · doi:10.3390/vaccines9080830

Frontiers in Bioengineering and Biotechnology: Plant Nanoparticles for Anti-Cancer Therapy

2021· review· en· W3185914791 on OpenAlexaff
Erum Shoeb, Uzma Badar, Srividhya Venkataraman, Kathleen Hefferon

Bibliographic record

VenueVaccines · 2021
Typereview
Languageen
FieldAgricultural and Biological Sciences
TopicPlant Virus Research Studies
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsNanocarriersCancer therapyDrug deliveryBiotechnologyCancerNanotechnologyBiologyComputational biologyMedicineMaterials science

Abstract

fetched live from OpenAlex

Naturally occurring viral nanomaterials have gained popularity owing to their biocompatible and biodegradable nature. Plant virus nanoparticles (VNPs) can be used as nanocarriers for a number of biomedical applications. Plant VNPs are inexpensive to produce, safe to administer and efficacious as treatments. The following review describes how plant virus architecture facilitates the use of VNPs for imaging and a variety of therapeutic applications, with particular emphasis on cancer. Examples of plant viruses which have been engineered to carry drugs and diagnostic agents for specific types of cancer are provided. The drug delivery system in response to the internal conditions is known as stimuli response, recently becoming more applicable using plant viruses based VNPs. The review concludes with a perspective of the future of plant VNPs and plant virus-like particles (VLPs) in cancer research and therapy.

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 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 score0.994
Threshold uncertainty score0.271

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 head0.080
GPT teacher head0.313
Teacher spread0.233 · 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 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

Citations19
Published2021
Admission routes1
Has abstractyes

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