Cell‐Electronic Sensing of Cellular Responses to Micro‐ and Nanoparticles for Environmental Applications
Bibliographic record
Abstract
Cell‐based biosensors (CBBs) utilizing impedance measurement have become a powerful tool for cytotoxicity analysis of (i) engineered micro‐ and nanoparticles (NPs) and (ii) complex mixtures of environmental particulate matter (PM). With the recent increase in the development and application of NPs, bio‐analytical techniques capable of fast, reliable, and accurate cytotoxicity analysis are needed to prioritize these materials for further toxicological testing to ensure their safe use, both for human health and environmental safety. This article focuses on the development and application of impedance‐basedCBBsfor rapid, sensitive, efficient, and label‐free analysis of micro‐ and nanoparticle‐induced cytotoxicity through the monitoring of several cellular responses, including cell adhesion, spreading, proliferation, transepithelial or trans endothelial electrical resistance (TER), and micromotion. In addition, these techniques are potentially useful for air quality monitoring, as demonstrated through the cytotoxicity analysis of complex mixtures of PM. Instruments based on electric cell–substrate impedance sensing (ECIS) and real‐time cell analysis (RTCA) are the two most widely used impedance‐based devices commercially available. Thus, detailed descriptions of the principles directing the impedance‐based measurements and data analysis employed in these systems are presented.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 0.007 |
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".