Objective: The objectives of this work is to develop and test the ability of a wearable physiological sensors system, based on ECG, EDA and EEG, to capture human stress and to assess whether the detected changes in physiological signals correlate with changes in salivary cortisol level, which is a reliable, objective biomarker of stress. Methods: 15 healthy participants, seven males and six females, mean age 40.8 $\pm$ 9.5 years, wore a set of three commercial sensors to record physiological signals during the Maastricht Acute Stress Test, an experimental protocol known to elicit robust physical and mental stress in humans. Salivary samples were collected throughout the different phases of the test. Statistical analysis was performed using a Support Vector Machine (SVM) classification algorithm. A correlation analysis between extracted physiological features and salivary cortisol levels was also performed. Results: 15 features extracted from heart rate variability, electrodermal and electroencephalography signals showed a high degree of significance in disentangling stress from a relaxed state. The classification algorithm, based on significant features, provided satisfactory outcomes with 86% accuracy. Furthermore, correlation analysis showed that the observed changes in physiological features were consistent with the trend of salivary cortisol levels (R2 = 0.714). Conclusion: The tested set of wearable sensors was able to successfully capture human stress and quantify stress level. Significance: The results of this pilot study may be useful in designing portable and remote control systems, such as medical devices used to turn on interventions and prevent stress consequences.

Obesity is a complex pathology with interacting and confounding causes due to the environment, hormonal signaling patterns, and genetic predisposition. At present, the Zucker rat is an eligible genetic model for research on obesity and metabolic syndrome, allowing scrutiny of gene expression profiles. Real-time PCR is the benchmark method for measuring mRNA expressions, but the accuracy and reproducibility of its data greatly depend on appropriate normalization strategies. In the Zucker rat model, no specific reference genes have been identified in myocardium, kidney, and lung, the main organs involved in this syndrome. The aim of this study was to select among ten candidates (Actb, Gapdh, Polr2a, Ywhag, Rpl13a, Sdha, Ppia, Tbp, Hprt1andTfrc) a set of reference genes that can be used for the normalization of mRNA expression data obtained by real-time PCR in obese and lean Zucker rats both at fasting and during acute hyperglycemia. The most stable genes in the heart wereSdha, Tbp, andHprt1; in kidney,Tbp,Actb, andGapdhwere chosen, whileActb,Ywhag, andSdhawere selected as the most stably expressed set for pulmonary tissue. The normalization strategy was used to analyze mRNA expression of tumor necrosis factor α, the main inflammatory mediator in obesity, whose variations were more significant when normalized with the appropriately selected reference genes. The findings obtained in this study underline the importance of having three stably expressed reference gene sets for use in the cardiac, renal, and pulmonary tissues of an experimental model of obese and hyperglycemic Zucker rats.