Molecular Identification through Membrane Engineering as a Revolutionary Concept for the Construction of Cell Sensors with Customized Target Recognition Properties: the Example of Superoxide Detection
Bibliographic record
Abstract
Membrane-engineering is a generic methodology for increasing the selectivity of a cell biosensor against a target molecule, by electroinserting target-specific receptor molecules on the cell surface. We have previously reported the construction of an ultra-sensitive superoxide anion (O 2 •− ) sensor based on immobilized cells, which have been membrane-engineered with superoxide dismutase (SOD). In the present study, we provide evidence that superoxide dismutation triggered changes to the membrane potential of membrane-engineered fibroblast cells, as confirmed by electrophysiological and fluorescence assays. In addition, by conducting selective inhibition assays, we show that electroinserted SOD molecules retained their characteristic catalytic properties. We also investigated the effect of the concentration of electroinserted SOD molecules. Finally, we increased the sensitivity of the sensor by hundredfold to a detection limit of 1 pM O 2 •− by changing the intensity of the electrical field during electroinsertion and the concentration of immobilized cells on the performance of the biosensor.
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 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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".