Stress is known as a silent killer that contributes to several life-threatening health conditions such as high blood pressure, heart disease, and diabetes. The current standard for stress evaluation is based on self-reported questionnaires and standardized stress scores. There is no gold standard to independently evaluate stress levels despite the availability of numerous biophysiological stress indicators. With an increasing interest in wearable health monitoring in recent years, several studies have explored the potential of various biophysiological indicators of stress for this purpose. However, there is no clear understanding of the relative sensitivity and specificity of these stress-related biophysiological indicators of stress in the literature. Hence this study aims to perform statistical analysis and classification modelling of biophysiological data gathered from healthy individuals, undergoing various induced emotional states, and to assess the relative sensitivity and specificity of common biophysiological indicators of stress. In this paper, several frequently used key indicators of stress, such as heart rate, respiratory rate, skin conductance, RR interval, heart rate variability in the electrocardiogram, and muscle activation measured by electromyography, are evaluated based on a detailed statistical analysis of the data gathered from an already existing, publicly available WESAD (Wearable Stress and Affect Detection) dataset. Respiratory rate and heart rate were the two best features for distinguishing between stressed and unstressed states.

Cancer-therapy related cardiotoxicity (CTRCT) is a significant and frequent complication of monoclonal antibody directed therapy, especially Trastuzumab, for human epidermal growth factor receptor 2 (HER2) overexpressing breast cancers. Reliable, clinically available molecular predictive markers of CTRCT have not yet been developed. Identifying specific genetic variants and their molecular markers, which make the host susceptible to this complication is key to personalised risk stratification. A systematic review was conducted until April 2021, using the Medline, Embase databases and Google Scholar, to identify studies genetic and RNA-related markers associated with CTRCT in HER2 positive breast cancer patients. So far, researchers have mainly focused on HER2 related polymorphisms, revealing codons 655 and 1170 variants as the most likely SNPs associated with cardiotoxicity, despite some contradictory results. More recently, new potential genetic markers unrelated to the HER2 gene, and linked to known cardiomyopathy genes or to genes regulating cardiomyocytes apoptosis and metabolism, have been detected. Moreover, microRNAs are gaining increasing recognition as additional potential molecular markers in the cardio-oncology field, supported by encouraging preliminary data about their relationship with cardiotoxicity in breast cancers. In this review, we sought to synthesize evidence for genetic variants and RNA-related molecular markers associated with cardiotoxicity in HER2-positive breast cancer.

Purpose Respiratory rate can provide auxiliary information on the physiological changes within the human body, such as physical and emotional stress. In a clinical setup, the abnormal respiratory rate can be indicative of the deterioration of the patient's condition. Most of the existing algorithms for the estimation of respiratory rate using photoplethysmography (PPG) are sensitive to external noise and may require the selection of certain algorithm-specific parameters, through the trial-and-error method. Methods This paper proposes a new algorithm to estimate the respiratory rate using a photoplethysmography sensor signal for health monitoring. The algorithm is resistant to signal loss and can handle low-quality signals from the sensor. It combines selective windowing, preprocessing and signal conditioning, modified Welch filtering and postprocessing to achieve high accuracy and robustness to noise. Results The Mean Absolute Error and the Root Mean Square Error of the proposed algorithm, with the optimal signal window size, are determined to be 2.05 breaths count per minute and 2.47 breaths count per minute, respectively, when tested on a publicly available dataset. These results present a significant improvement in accuracy over previously reported methods. The proposed algorithm achieved comparable results to the existing algorithms in the literature on the BIDMC dataset (containing data of 53 subjects, each recorded for 8 min) for other signal window sizes. Conclusion The results endorse that integration of the proposed algorithm to a commercially available pulse oximetry device would expand its functionality from the measurement of oxygen saturation level and heart rate to the continuous measurement of the respiratory rate with good efficiency at home and in a clinical setting.