Hyo Jin Gwon

Bio: Hyo Jin Gwon is an academic researcher from Yonsei University. The author has contributed to research in topics: Scanning electrochemical microscopy. The author has an hindex of 1, co-authored 1 publications receiving 2 citations.

Monitoring an oxidative stress mechanism at a single human fibroblast

Abstract: Easily oxidizable substances inside human diploid fibroblast cell strains were monitored amperometrically with a platinized carbon-fiber microelectrode The experiment involved positioning a microelectrode over a single biological cell, forcing the electrode tip into the cell via micromanipulator control, and measuring the transient current corresponding to the complete electrolysis of electroactive species released by the cell A second series of experiments involved puncturing a hole into the cell with a micropipet and measuring the transient current corresponding to the complete electrolysis of electroactive species emitted by the cell with an electrode positioned above the cell The selectivity of both amperometric measurements was demonstrated through the use of known hydrogen peroxide scavengers (added catalase or intracellular peroxidase + added o-dianisidine) to the media bathing the cells The abolition of the amperometric signal under these conditions suggested that hydrogen peroxide was the primary substance detected The magnitude and the time course of the transient current measured implied that the hydrogen peroxide detected was not only that initially present in the cell before its membrane was pierced but represented mostly an oxidative stress response of the cell to its injury

Scanning Electrochemical Microscope Based on Visual Recognition and Machine Learning

Abstract: Scanning electrochemical microscopy is an advanced tool for studying electrochemically active surfaces, including biological ones. Experiments with biological systems must be performed fast since their reactions and states change very fast. SECM can be easily equipped with a top-mounted light microscope with a known distance between the probe and the camera. This hardware solution, in combination with machine learning algorithms, would allow for the automatic finding of target locations, selecting exact positions for measurements, and compensating for positioning inaccuracies. This article demonstrates a newly constructed SECM setup. In addition, it allows faster user adaptation to unknown topography and shortened scanning times.