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Microfluidic Devices for Circulating Tumor Cells Isolation and Subsequent Analysis

2016· review· en· W2292571563 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

VenueCurrent Pharmaceutical Biotechnology · 2016
Typereview
Languageen
FieldEngineering
TopicMicrofluidic and Bio-sensing Technologies
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsCirculating tumor cellMicrofluidicsIsolation (microbiology)Computational biologyImmunomagnetic separationLiquid biopsyCancerPersonalized medicineTumor cellsCancer researchComputer scienceBiologyNanotechnologyBioinformaticsMedicineMolecular biologyMetastasisInternal medicineMaterials science

Abstract

fetched live from OpenAlex

The research of circulating tumor cells (CTCs) has drawn much attention in recent years. It is because of the potential values of CTCs in early diagnosis of cancer, management of clinical treatment, exploration of metastatic mechanism, and development of personalized medicine. However, isolating CTCs has been technically challenging due to their rare numbers in blood. Recently, a variety of microfluidic devices have been developed for CTC isolation, and these devices can realize high capture efficiency and high purity. While enumeration of CTCs has been achieved, further cellular and DNA analysis on the captured CTCs are less reported. In this article, we review recent reports in microfluidic methods for isolation of CTCs and subsequent cellular analysis on them.

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 score0.978
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
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 head0.072
GPT teacher head0.356
Teacher spread0.284 · 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