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Record W4281720743 · doi:10.1016/j.xpro.2022.101433

Characterizing circulating tumor cells using affinity-based microfluidic capture and AFM-based biomechanics

2022· article· en· W4281720743 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

fundA Canadian funder is recorded on the work.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueSTAR Protocols · 2022
Typearticle
Languageen
FieldEngineering
TopicMicrofluidic and Bio-sensing Technologies
Canadian institutionsnot available
FundersTerry Fox FoundationNew York University Abu Dhabi
KeywordsCirculating tumor cellAtomic force microscopyMicrofluidicsNanotechnologyCharacterization (materials science)Microfluidic chipChipMaterials scienceElasticity (physics)Force spectroscopyBiomedical engineeringComputer scienceCancerEngineeringBiologyComposite material

Abstract

fetched live from OpenAlex

Elasticity and bio-adhesiveness of circulating tumor cells (CTCs) are important biomarkers of cancer. CTCs are rare in blood, thus their capture and atomic force microscopy (AFM)-based biomechanical characterization require use of multifunctional microfluidic device. Here, we describe procedures for fabrication of such device, AFM-Chip, and give details on its use in affinity-based CTC capture, and integration with AFM via reversable physical assembly. In the AFM-Chip, CTC capture is efficient, and transition to AFM characterization is seamless with minimal cell loss. For complete details on the use and execution of this protocol, please refer to Deliorman et al. (2020).

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.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.066
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.026
GPT teacher head0.240
Teacher spread0.214 · 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