MétaCan
Menu
Back to cohort

Microfluidic Devices for Circulating Tumor Cells Isolation and Subsequent Analysis

2016· review· en· W2292571563 on OpenAlexaff
Avid Khamenehfar, Paul C. H. Li

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.

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)0.001
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 score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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

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

Citations30
Published2016
Admission routes1
Has abstractyes

Explore more

Same venueCurrent Pharmaceutical BiotechnologySame topicMicrofluidic and Bio-sensing TechnologiesFrench-language works237,207