Quantifying the lagged Poincaré plot geometry of ultrashort heart rate variability series: automatic recognition of odor hedonic tone

Automatic COVID-19 severity assessment from HRV

COVID-19 is known to be a cause of microvascular disease imputable to, for instance, the cytokine storm inflammatory response and the consequent blood coagulation. In this study, we propose a methodological approach for assessing the COVID-19 presence and severity based on Random Forest (RF) and Support Vector Machine (SVM) classifiers. Classifiers were applied to Heart Rate Variability (HRV) parameters extracted from photoplethysmographic (PPG) signals collected from healthy and COVID-19 affected subjects. The supervised classifiers were trained and tested on HRV parameters obtained from the PPG signals in a cohort of 50 healthy subjects and 93 COVID-19 affected subjects, divided into two groups, mild and moderate, based on the support of oxygen therapy and/or ventilation. The most informative feature set for every group's comparison was determined with the Least Absolute Shrinkage and Selection Operator (LASSO) technique. Both RF and SVM classifiers showed a high accuracy percentage during groups' comparisons. In particular, the RF classifier reached 94% of accuracy during the comparison between the healthy and minor severity COVID-19 group. Obtained results showed a strong capability of RF and SVM to discriminate between healthy subjects and COVID-19 patients and to differentiate the two different COVID-19 severity. The proposed method might be helpful for detecting, in a low-cost and fast fashion, the presence and severity of COVID-19 disease; moreover, these reasons make this method interesting as a starting point for future studies that aim to investigate its effectiveness as a possible screening method.

Cardiovascular Dynamics in COVID-19: A Heart Rate Variability Investigation

COVID-19 is known to be a cause of microvascular disease due, for example, to the cytokine storm inflammatory response and the result of blood coagulation. This study reports an investigation on Heart Rate Variability (HRV) extracted from photoplethysmography (PPG) signals measured from healthy subjects and COVID-19 affected patients. We aimed to determine a statistical difference between HRV parameters among subjects' groups. Specifically, statistical analysis through Mann-Whitney U Test (MWUT) was applied to compare 42 dif-ferent parameters extracted from PPG signals of 143 subjects: 50 healthy subjects (i.e. group 0) and 93 affected from COVID-19 patients stratified through increasing COVID severity index (i.e. groups 1 and 2). Results showed significant statistical differences between groups in several HRV parameters. In particular, Multiscale Entropy (MSE) analysis provided the master key in patient stratification assessment. In fact, MSE11, MSE12, MSE15, MSE16, MSE17, MSE18, MSE19 and MSE20 keep statistical significant difference during all the comparisons between healthy subjects and patients from all the pathological groups. Our preliminary results suggest that it could be possible to distinguish between healthy and COVID-19 affected subjects based on cardiovascular dynamics. This study opens to future evaluations in using machine learning models for automatic decision-makers to distinguish between healthy and COVID-19 subjects, as well as within COVID-19 severity levels. Clinical Relevance - This establishes the possibility to distin-guish healthy subjects from COVID-19 affected patients basing on HRV parameters monitored non invasively by PPG.

QuinoMit Q10-Fluid attenuates hydrogen peroxide-induced irregular beating in mouse pluripotent stem cell-derived cardiomyocytes

BACKGROUND

Coenzyme Q10 (CoQ10) is a crucial component of the mitochondrial structure which is involved in producing more than 90% of cellular ATP. This study aimed to investigate the protective effects and underlying mechanisms of QuinoMit Q10-Fluid against hydrogen peroxide (H₂O₂)-induced arrhythmias on cardiomyocytes (CMs).

METHODS

Undifferentiated stem cell-derived CMs were cultured in the presence of different concentrations of QuinoMit Q10-Fluid. To investigate if CoQ10 has anti-apoptotic activity, CMs were exposed to H₂O₂ for up to 100 h with or without CoQ10. The expression levels of cardiac reference genes were determined by RT-PCR. The structural and functional properties of CMs were examined by immunofluorescence and the xCELLigence system. Caspase 3/7 assay was also performed for cell apoptosis study.

RESULTS

The study showed that QuinoMit Q10-Fluid inhibits the proliferation of pluripotent stem cells at high concentrations and had less effect on cardiomyogenesis. However, the beating rate of clusters containing CMs generated under QuinoMit Q10-Fluid (1:100) was significantly increased. This increase was accompanied by the up-regulated expression level of some important cardiac markers during differentiation. Treatment of CMs with H₂O₂ notably induced irregular beating and decreased the amplitude of the beating signal of CMs, concomitantly with increased caspase-3/7 activity. However, CMs pretreated with QuinoMit exhibited a protective effect against H₂O₂-induced arrhythmia.

CONCLUSION

Our results reveal that QuinoMit Q10-Fluid attenuates H₂O₂-induced irregular beating in mouse pluripotent stem cell-derived CMs, at least partly by reducing the generation of ROS, suggesting a protective effect against CM dysfunctions.