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Record W2022102744 · doi:10.1039/c3lc50355k

Recent advances in microfluidic techniques for single-cell biophysical characterization

2013· review· en· W2022102744 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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueLab on a Chip · 2013
Typereview
Languageen
FieldEngineering
TopicMicrofluidic and Bio-sensing Technologies
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsMicrofluidicsNanotechnologyCharacterization (materials science)Biochemical engineeringComputer scienceLiving cellEngineeringBiologyMaterials scienceBiological system

Abstract

fetched live from OpenAlex

Biophysical (mechanical and electrical) properties of living cells have been proven to play important roles in the regulation of various biological activities at the molecular and cellular level, and can serve as promising label-free markers of cells' physiological states. In the past two decades, a number of research tools have been developed for understanding the association between the biophysical property changes of biological cells and human diseases; however, technical challenges of realizing high-throughput, robust and easy-to-perform measurements on single-cell biophysical properties have yet to be solved. In this paper, we review emerging tools enabled by microfluidic technologies for single-cell biophysical characterization. Different techniques are compared. The technical details, advantages, and limitations of various microfluidic devices 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.

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 score0.914
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.032
GPT teacher head0.263
Teacher spread0.232 · 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