A biosensor capable of identifying low quantities of breast cancer cells by electrical impedance spectroscopy
Bibliographic record
Abstract
Breast cancer (BC) is a malignant disease with a high prevalence worldwide. The main cause of death is not the primary tumor, but instead the spread of tumor cells to distant sites. The aim of the present study was to examine a new method for the detection of cancer cells in aqueous medium using bioimpedance spectroscopy assisted with magnetic nanoparticles (MNP's) exposure to a constant magnetic field. The spectroscopic patterns were identified for three breast cancer cell lines. Each BC cell line represents a different pathologic stage: the early stage (MCF-7), invasive phase (MDA-MB-231) and metastasis (SK-BR-3). For this purpose, bioimpedance measurements were carried out at a certain frequency range with the aid of nanoprobes, consisting of magnetic nanoparticles (MNPs) coupled to a monoclonal antibody. The antibody was specific for the predominant cell surface protein for each cell line, which was identified by using RT-qPCR and flow cytometry. Accordingly, EpCAM corresponds to MCF-7, MUC-1 to MDA-MB-231, and HER-2 to SK-BR-3. Despite their low concentrations, BC cells could be detected by impedance spectroscopy. Hence, this methodology should permit the monitoring of circulating tumor cells (CTC) and therefore help to prevent recurrences and metastatic processes during BC treatment.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".