Tracking Live‐Cell Response to Hexavalent Chromium Toxicity by using Scanning Electrochemical Microscopy
Bibliographic record
Abstract
Abstract The effects of exposure to toxic heavy metals, such as chromium, are of interest in scientific research, owing to its association with oxidative stress, cytotoxicity, and carcinogenicity. This study aims to explore the effects of Cr (VI) on live cell responses. Herein, scanning electrochemical microscopy (SECM) is employed by using depth scan imaging and feedback mode to monitor the membrane permeability of single live human bladder cancer (T24) cells following 1 h incubations with Cr (VI) stimuli. By using membrane‐permeable and impermeable redox mediators, ferrocenemethanol and ferrocenecarboxylic acid, respectively, SECM depth scans yield both electrochemical and topographic information. This provides insights into the relative changes in membrane homeostasis with increased exposure to Cr (VI). Here, SECM has shown great power in determining membrane response to Cr (VI) exposure. Dependent on the level of exposure, transition between three distinct trends was observed. At low incubation concentrations of Cr (VI), the cell membrane permeability coefficients were relatively unaffected. With moderate increases in Cr (VI) concentrations, membrane permeability coefficients of the incubated cells were observed to decrease. Finally, with the higher incubation concentrations, membrane permeability coefficients were found to increase toward values similar to control cells. The Cr (VI) toxicity was further investigated by means of a MTT cell viability study, which exhibited a similar decreasing trend to the cell membrane permeability. These findings further demonstrate the strength of SECM as a bioanalytical technique for monitoring cellular homeostasis.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".