Cold Atmospheric Plasma Induces a Predominantly Necrotic Cell Death via the Microenvironment.

Cold plasma is a partially ionized gas generated by an electric field at atmospheric pressure that was initially used in medicine for decontamination and sterilization of inert surfaces. There is currently growing interest in using cold plasma for more direct medical applications, mainly due to the possibility of tuning it to obtain selective biological effects in absence of toxicity for surrounding normal tissues,. While the therapeutic potential of cold plasma in chronic wound, blood coagulation, and cancer treatment is beginning to be documented, information on plasma/cell interaction is so far limited and controversial.Using normal primary human fibroblast cultures isolated from oral tissue, we sought to decipher the effects on cell behavior of a proprietary cold plasma device generating guided ionization waves carried by helium. In this model, cold plasma treatment induces a predominantly necrotic cell death. Interestingly, death is not triggered by a direct interaction of the cold plasma with cells, but rather via a transient modification in the microenvironment. We show that modification of the microenvironment redox status suppresses treatment toxicity and protects cells from death. Moreover, necrosis is not accidental and seems to be an active response to an environmental cue, as its execution can be inhibited to rescue cells.These observations will need to be taken into account when studying in vitro plasma/cell interaction and may have implications for the design and future evaluation of the efficacy and safety of this new treatment strategy

Improvement of Mucosal Lesion Diagnosis with Machine Learning Based on Medical and Semiological Data: An Observational Study

Despite artificial intelligence used in skin dermatology diagnosis is booming, application in oral pathology remains to be developed. Early diagnosis and therefore early management, remain key points in the successful management of oral mucosa cancers. The objective was to develop and evaluate a machine learning algorithm that allows the prediction of oral mucosa lesions diagnosis. This cohort study included patients followed between January 2015 and December 2020 in the oral mucosal pathology consultation of the Toulouse University Hospital. Photographs and demographic and medical data were collected from each patient to constitute clinical cases. A machine learning model was then developed and optimized and compared to 5 models classically used in the field. A total of 299 patients representing 1242 records of oral mucosa lesions were used to train and evaluate machine learning models. Our model reached a mean accuracy of 0.84 for diagnostic prediction. The specificity and sensitivity range from 0.89 to 1.00 and 0.72 to 0.92, respectively. The other models were proven to be less efficient in performing this task. These results suggest the utility of machine learning-based tools in diagnosing oral mucosal lesions with high accuracy. Moreover, the results of this study confirm that the consideration of clinical data and medical history, in addition to the lesion itself, appears to play an important role

Theoretical inflammation model using ALife technics

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Relative concentration of the main emissive species in the guided ionization wave (conditions: He gas flow = 2slm; voltage frequency = 10kH; voltage amplitude = 5kV; voltage duty cycle = 1%.

<p>Optical fiber placed 5mm after the exit ceramic tube).</p

He-GIW induces a necrotic cell death.

<p><b>(a-c)</b> Presence of apoptosis was assayed by FACS analysis of Annexin V/PI dual staining. <b>(a)</b> An example of flow cytometry profile 6 h after treatment with He-GIW or with staurosporine, a positive control for apoptosis. X axis represents annexin-V-APC staining and Y axis PI staining. <b>(b)</b> Percentage of cells positive for Annexin V only (black), PI only (white) or both (grey) was measured 6 h and 24 h after treatment with He-GIW or staurosporine in standard cell culture conditions (c) or with 1% or 5% FCS. <b>(a-c)</b> are representative of 3 independent experiments. <b>(d)</b> Caspase 3 cleavage (red) was assayed by immunofluorescence 6h after He-GIW or staurosporine treatment. Cell nuclei are labeled with Hoechst and appear blue. <b>(e)</b> Caspase 3 cleavage was monitored by Western Blot for 2 h after He-GIW treatment. <b>(f)</b> Adherent cell percentage 6 h after He-GIW treatment in presence or absence of caspase inhibitor Z-VAD. Error bars represent S.E.M. of three independent experiments. p<0.05 (*), p<0.01 (**) and p<0.001 (***)</p

Main emissive species produced with Helium as plasma carrier gas.

<p>Main emissive species produced with Helium as plasma carrier gas.</p

Mitochondrial membrane depolarization precedes cell membrane alteration.

<p><b>(a)</b> Adherent cell percentage was monitored for 6 h after He-GIW treatment. <b>(b)</b> Cell Δψm was monitored by FACS analysis for 6h using DiOC6 staining. Results of one representative experiment of at least three. <b>(c)</b> Geometric means of DiOC6 staining relative to control 1, 3 or 6 h after He-GIW treatment. <b>(d)</b> Δψm (DiOC6) and plasma cell membrane permeability (7AAD) were monitored by FACS analysis for 6 h after He-GIW treatment. When present, error bars represent S.E.M. of three independent experiments p<0.05 (*), p<0.01 (**) and p<0.001 (***)</p

Experimental set-up.

<p>Experimental set-up.</p

Device settings and cell culture parameters influence cell fate.

<p><b>(a)</b> hPDL were treated with He-GIW using different voltages and <b>(b)</b> exposure lengths. Adherent cell number was evaluated 24h after treatment and expressed as a percentage of control. <b>(c)</b> Cell morphology was monitored by phase contrast microscopy 6h and 24h after treatment. <b>(d)</b> Cell culture incidence on He-GIW treatment was evaluated using different FCS concentrations and <b>(e)</b> cell confluence. Adherent cell number was assayed 24h after treatment and expressed as percentage of control. Error bars represent S.E.M. of three independent experiments p<0.05 (*), p<0.01 (**) and p<0.001 (***).</p

Electrical high voltage and current signals analyzed during one impulse (2a) and during one period (2b) (He gas flow = 2slm, voltage frequency = 10kHz, voltage amplitude = 5kV, voltage duty cycle = 1).

<p>Electrical high voltage and current signals analyzed during one impulse (2a) and during one period (2b) (He gas flow = 2slm, voltage frequency = 10kHz, voltage amplitude = 5kV, voltage duty cycle = 1).</p