Publications

The term 'mechanosensation' describes the capacity of cells to translate mechanical stimuli into the coordinated regulation of intracellular signals, cellular function, gene expression and epigenetic programming. This capacity is related not only to the sensitivity of the cells to tissue motion, but also to the decryption of tissue geometric arrangement and mechanical properties. The cardiac stroma, composed of fibroblasts, has been historically considered a mechanically passive component of the heart. However, the latest research suggests that the mechanical functions of these cells are an active and necessary component of the developmental biology programme of the heart that is involved in myocardial growth and homeostasis, and a crucial determinant of cardiac repair and disease. In this Review, we discuss the general concept of cell mechanosensation and force generation as potent regulators in heart development and pathology, and describe the integration of mechanical and biohumoral pathways predisposing the heart to fibrosis and failure. Next, we address the use of 3D culture systems to integrate tissue mechanics to mimic cardiac remodelling. Finally, we highlight the potential of mechanotherapeutic strategies, including pharmacological treatment and device-mediated left ventricular unloading, to reverse remodelling in the failing heart.© 2022. Springer Nature Limited.

JTD Keywords: cardiomyocyte proliferation, cross-linking, extracellular-matrix, focal adhesions, gene-expression, mechanical regulation, myocardial-infarction, substrate stiffness affects, t-cells, Ventricular assist device

Objective: Starting from the controversial results showed by empirical research on Linehan’s Biosocial model of Borderline Personality Disorder (BPD), this study aims to empirically evaluate Linehan’s conceptualization of emotional hypersensitivity and hyperreactivity, as well as to investigate the role of pre-existing emotional states in BPD altered physiological responsivity. Methods: We asked 24 participants (BPD = 12; Healthy Controls = 12) to complete a self-reported questionnaire (Positive and Negative Affect Schedule) in order to assess their pre-task affective state. Subsequently, 36 emotional pictures from four valence categories (i.e. erotic, negative, positive, neutral) were administered while assessing participants self-reported and electrodermal responses. Results: BPD patients showed higher levels of pre-task negative affectivity as well as an enhanced physiological response to neutral stimuli. No main BPD group effect was found for the physiological data. Moreover, pre-task negative affectivity levels were exclusively related to physiological responses among BPD subjects. Discussion: Our findings supported the hypersensitivity hypothesis operationalized as an enhanced responsiveness to non-emotional cues. Hyperreactivity assumption was not supported. Conversely, our study revealed heightened physiological responses in relation to pre-existent negative emotional states in BPD. We discussed our results in the context of the putative pathological processes underlying BPD.

JTD Keywords: Borderline Personality Disorder, Biosocial model, Hyperreactivity, Hypersensitivity, Negative affectivity, Physiology.

Estimating the affective state of a user during a computer task traditionally relies on either subjective reports or analysis of physiological signals, facial expressions, and other measures. These methods have known limitations, can be intrusive and may require specialized equipment. An alternative would be employing a ubiquitous device of everyday use such as a standard keyboard. Here we investigate if we can infer the emotional state of a user by analyzing their typing patterns. To test this hypothesis, we asked 400 participants to caption a set of emotionally charged images taken from a standard database with known ratings of arousal and valence. We computed different keystroke pattern dynamics, including keystroke duration (dwell time) and latency (flight time). By computing the mean value of all of these features for each image, we found a statistically significant negative correlation between dwell times and valence, and between flight times and arousal. These results highlight the potential of using keystroke dynamics to estimate the affective state of a user in a non-obtrusive way and without the need for specialized devices.

JTD Keywords: Feature extraction, Correlation, Keyboards, Task analysis, Statistical analysis, Affective computing, Standards, Keystroke, Keyboard, Typing, Arousal, Valence, Affect