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Record W2044379816 · doi:10.1109/nebec.2014.6972890

See-through cartridge for real time monitoring of tumor cells capturing on microfilters

2014· article· en· W2044379816 on OpenAlexaff
Amir Sanati‐Nezhad, Kate Tuner, Javier Alejandro Hernández-Castro, David Juncker

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMicrofluidic and Bio-sensing Technologies
Canadian institutionsMcGill University
Fundersnot available
KeywordsCirculating tumor cellFilter (signal processing)CartridgeMaterials scienceComputer scienceBiomedical engineeringVolumetric flow rateMicrofluidicsMetastasisNanotechnologyCancerMedicineInternal medicine

Abstract

fetched live from OpenAlex

In metastasis process, it is hypothesized that tumor cells detach from the primary tumor and are released into the bloodstream and to distant sites, forming secondary tumors. To use these Circulating Tumor Cells (CTC) as a cancer biomarker, significant research has been directed to the development of devices capable of detecting and enumerating CTCs. Size-based isolation approach using microfilters has been effectively developed to capture CTCs. However due to the lack of capability for live monitoring the tumor cell capturing on the filter, the optimization of the current filter-based devices for capturing heterogeneous CTCs is very challenging. We report a low-cost cartridge with a see-through site enabling the enumeration of tumor cells and imaging cells entrapment on the filter which help to characterize the parameters involved in the capturing process. Real time imaging along with the pressure and flow control assist to simply correlate the pressure on the filter, the cell velocity and flow rate, and the entrapment of cells from the pores. Based on the velocity of cells over pores, an appropriate flow rate was selected and the capture efficiency of breast cancer cells was then measured for different filter pore sizes. The device will be a breakthrough platform for the current filter-based detection devices with high potential for optimization of the isolation parameters, and to find out how these parameters may affect the squeezing of tumor cells through the pores and, as a result, the reduction of the capture efficiency.

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 categoriesnone
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 score0.017
Threshold uncertainty score0.552

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 head0.012
GPT teacher head0.208
Teacher spread0.196 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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

Citations1
Published2014
Admission routes1
Has abstractyes

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