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Record W2950087269 · doi:10.1039/c9bm00552h

Efficient targeted cancer cell detection, isolation and enumeration using immuno-nano/hybrid magnetic microgels

2019· article· en· W2950087269 on OpenAlexafffund
Amir Seyfoori, S.A. Seyyed Ebrahimi, Arman Yousefi, Mohsen Akbari

Bibliographic record

VenueBiomaterials Science · 2019
Typearticle
Languageen
FieldEngineering
TopicNanopore and Nanochannel Transport Studies
Canadian institutionsUniversity of Victoria
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of TehranCanada Foundation for InnovationNational Institute for Medical Research DevelopmentUniversity of Victoria
KeywordsEnumerationIsolation (microbiology)Nano-NanotechnologyMagnetic nanoparticlesCancer cellChemistryBiological systemCancerBiologyMaterials scienceNanoparticleMathematicsChemical engineeringMicrobiologyEngineeringGeneticsCombinatorics

Abstract

fetched live from OpenAlex

) nanorods with controlled morphology are synthesized using hydrolysis method. The effects of the different synthesis conditions on morphology, phase composition, and magnetic properties of lepidocrocite are studied to determine the best synthesis conditions. We coat the nanorods with chitosan and attach them to the poly(N-isopropylacrylamide-co-acrylic acid) (PNIPAM-AA) microgel through chemical bonding to form a nano/hybrid microstructure. Our results suggest that the hybrid magnetic microgels have more antibody binding capacity and higher cancer cell capturing rate compared to pristine maghemite nanorods. In addition, new cell magnetometery method was applied for cancer cell quantification after capturing step in which different magnetized labelled cells were correlated to the saturation magnetization. In this method, higher concentrations of the primary cell suspension resulted in more binding of the magnetic immuno-microgels to the cells which was shown as saturation magnetization drop in the microgel-cell complex.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.033
Threshold uncertainty score0.486

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.006
GPT teacher head0.198
Teacher spread0.193 · 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

Citations8
Published2019
Admission routes2
Has abstractyes

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