Mercury‐induced necrosis of murine renal cell carcinoma

Herr, Harry W.; Kleinert, Edward L.; Whitmore, Willet F.

doi:10.1002/jso.2930180114

Cited by 2 publications

(3 citation statements)

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“…For mercury (II) chloride experiments, the right panel of Figure 2 shows that mercury (II) chloride caused a sharp initial decrease in the CI, which occurred at the fi rst hour of the treatment and was strictly dose-dependent. Mercury-induced cytotoxicity is due to apoptosis and necrosis mediated by reactive oxygen species with increasing membrane permeability (Kim and Sharma, 2003;Herr et al, 1981). The quick drop of the CI values in cultured cells treated with mercury (II) chloride appears to refl ect the cell necrosis and quick apoptosis, which is consistent with the previous reports (Kim and Sharma, 2003;Herr et al, 1981).…”

Section: Modelling and Parameter Estimation For Necrosis Dominant Mecsupporting

confidence: 87%

Dynamic Modelling and Prediction of Cytotoxicity on Microelectronic cell Sensor Array

Huang

Jin

2006

Can J Chem Eng

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A real‐time cell electronic sensing (RT‐CES) system has been used for label‐free dynamic measurements of cell responses to toxicant. Cells are grown onto the surfaces of the microelectronic sensors. Changes in cell number expressed as cell index (CI) have been recorded on‐line as time series. The CI data are used for dynamic modelling or parameter estimation for cell cytotoxicity process. We consider two dynamic modelling approaches, namely data‐based system identification and first principle modelling. It is shown that data‐based system identification can provide a quick solution for the cytotoxicity dynamic models and is effective for short‐term predictions. It, however, can be poor for long‐term predictions, particularly if there is no output correction, i.e., when the model is used for simulation. In view of this, the first principle modelling approach by considering fundamental physical principles such as toxicant transport is explored. For long‐term prediction or simulation, the prediction performance for some of cytotoxicity process is dramatically improved using the models obtained from the latter approach. This happens only if the underlying mechanism is truly understood. Through several cytotoxicity modelling and validation studies, it is shown that the black box modelling and first principle modelling both should be considered in challenging modelling problems such as the cytotoxicity. Pros and cons of the two modelling approaches are discussed.

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Section: Modelling and Parameter Estimation For Necrosis Dominant Mecsupporting

confidence: 87%

Dynamic Modelling and Prediction of Cytotoxicity on Microelectronic cell Sensor Array

Huang

Jin

2006

Can J Chem Eng

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“…Unlike As(III), Figure B shows that mercury caused a quick decrease in the CI, which occurred at the first hour of the treatment and was strictly dose-dependent. Mercury-induced cytotoxicity is due to apoptosis and necrosis mediated by reactive oxygen species with increasing membrane permeability ( , ), requiring no de novo protein synthesis (). The quick drop of the CI values in cultured cells treated with mercury appears to reflect the cell necrosis and quick apoptosis, which is consistent with the previous reports ( , ).…”

Section: Resultsmentioning

confidence: 99%

“…Mercury-induced cytotoxicity is due to apoptosis and necrosis mediated by reactive oxygen species with increasing membrane permeability ( , ), requiring no de novo protein synthesis (). The quick drop of the CI values in cultured cells treated with mercury appears to reflect the cell necrosis and quick apoptosis, which is consistent with the previous reports ( , ). Afterward, the CI values at the concentration around the IC 50 value ranging from 10.43 to 32.8 μM were either flat or increased steadily.…”

Section: Resultsmentioning

confidence: 99%

Dynamic Monitoring of Cytotoxicity on Microelectronic Sensors

Jin

Zhu

Jackson

et al. 2005

Chem. Res. Toxicol.

298

250

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A real-time cell electronic sensing (RT-CES) system was used for label-free, dynamic measurement of cell responses to cytotoxicants. Cells were grown onto the surfaces of microelectronic sensors, which are comprised of circle-on-line electrode arrays and are integrated into the bottom surfaces of the microtiter plate. Changes in cell status such as cell number, viability, morphology, and adherence were monitored and quantified by detecting sensor electrical impedance. For cell quantification and viability measurement, the data generated on the RT-CES system correlated well with those from the colorimetric (MTT) assay. For cytotoxicity assessment, cells growing on microelectronic sensors were treated with different cytotoxicants, such as arsenic, mercury, and sodium dichromate. The dynamic responses of the cells to the toxicants were continuously monitored by the RT-CES system. On the basis of the IC50 values, the RT-CES system displays an equal sensitivity to the neutral red uptake assay at specific time points. Furthermore, because the RT-CES system provides real-time information regarding the state of cell morphology and adhesion in addition to cell number, we were able to discern a previously unreported effect of arsenic on NIH 3T3 cells prior to cell death. Also, using the RT-CES system, we were able to monitor cytotoxicity effects that occur within a minute of compound addition. Taken together, the RT-CES system allows for real-time, continuous monitoring and quantitative recording of the whole assay process and provides new insight into the cell-toxicant interaction.

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Mercury‐induced necrosis of murine renal cell carcinoma

Cited by 2 publications

References 1 publication

Dynamic Modelling and Prediction of Cytotoxicity on Microelectronic cell Sensor Array

Dynamic Modelling and Prediction of Cytotoxicity on Microelectronic cell Sensor Array

Dynamic Monitoring of Cytotoxicity on Microelectronic Sensors

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