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Record W4379185278 · doi:10.3390/s23115300

Recent Developments in Inertial and Centrifugal Microfluidic Systems along with the Involved Forces for Cancer Cell Separation: A Review

2023· review· en· W4379185278 on OpenAlexafffund
A. Farahinia, Wenjun Zhang, Ildikó Badea

Bibliographic record

VenueSensors · 2023
Typereview
Languageen
FieldEngineering
TopicMicrofluidic and Bio-sensing Technologies
Canadian institutionsUniversity of Saskatchewan
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsCirculating tumor cellContext (archaeology)MicrofluidicsMetastasisCancerComputer scienceCancer cellProcess (computing)Blood circulationNanotechnologyBiologyMedicineMaterials scienceInternal medicine

Abstract

fetched live from OpenAlex

The treatment of cancers is a significant challenge in the healthcare context today. Spreading circulating tumor cells (CTCs) throughout the body will eventually lead to cancer metastasis and produce new tumors near the healthy tissues. Therefore, separating these invading cells and extracting cues from them is extremely important for determining the rate of cancer progression inside the body and for the development of individualized treatments, especially at the beginning of the metastasis process. The continuous and fast separation of CTCs has recently been achieved using numerous separation techniques, some of which involve multiple high-level operational protocols. Although a simple blood test can detect the presence of CTCs in the blood circulation system, the detection is still restricted due to the scarcity and heterogeneity of CTCs. The development of more reliable and effective techniques is thus highly desired. The technology of microfluidic devices is promising among many other bio-chemical and bio-physical technologies. This paper reviews recent developments in the two types of microfluidic devices, which are based on the size and/or density of cells, for separating cancer cells. The goal of this review is to identify knowledge or technology gaps and to suggest future works.

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 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 score0.813
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 head0.049
GPT teacher head0.300
Teacher spread0.251 · 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.

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

Citations40
Published2023
Admission routes2
Has abstractyes

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