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Record W2519528100 · doi:10.1007/s40204-016-0054-6

Recent progress on magnetic nanoparticles for magnetic hyperthermia

2016· review· en· W2519528100 on OpenAlexafffund
Lina Kafrouni, O. Savadogo

Bibliographic record

VenueProgress in Biomaterials · 2016
Typereview
Languageen
FieldMaterials Science
TopicNanoparticle-Based Drug Delivery
Canadian institutionsPolytechnique Montréal
FundersNatural Sciences and Engineering Research Council of CanadaFonds Québécois de la Recherche sur la Nature et les TechnologiesUniversitaire Stichting
KeywordsNanotechnologyMagnetic nanoparticlesNanomaterialsBiocompatible materialSuperparamagnetismMaterials scienceCancer therapyNanoparticleMagnetic hyperthermiaHyperthermiaHyperthermia therapyCancerMagnetic fieldBiomedical engineeringMedicineMagnetizationPhysics

Abstract

fetched live from OpenAlex

Recent advances in nanomaterials science contributed to develop new micro- and nano-devices as potential diagnostic and therapeutic tools in the field of oncology. The synthesis of superparamagnetic nanoparticles (SPMNPs) has been intensively studied, and the use of these particles in magnetic hyperthermia therapy has demonstrated successes in treatment of cancer. However, some physical limitations have been found to impact the heating efficiency required to kill cancer cells. Moreover, the bio-safety of NPs remains largely unexplored. The primary goals of this review are to summarize the recent progress in the development of magnetic nanoparticles (MNPs) for hyperthermia, and discuss the limitations and advances in the synthesis of these particles. Based on this knowledge, new perspectives on development of new biocompatible and biofunctional nanomaterials for magnetic hyperthermia are discussed.

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)0.001
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 score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.002

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 head0.046
GPT teacher head0.331
Teacher spread0.285 · 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

Citations164
Published2016
Admission routes2
Has abstractyes

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