The Covid-19 pandemic had polarizing effects on trait scores depending on a person's resilience and predispositions: A longitudinal prospective study

While Covid-19 is, first and foremost, a pernicious physical illness, its highly contagious nature has led to significant disruption in social life and psychological stress, occasionally resulting in dire mental health consequences that are still not fully understood. To address this issue, a prospective longitudinal design study was conducted by administering standard self-reporting questionnaires covering the NEO-five factor inventory (NEO-FFI), shyness, alexithymia, autism quotient, anxiety, depression, and sensory processing sensitivity (SPS). A total of 114 participants (of which 71.93% were females) with an average age of 30.29 (standard deviation = 11.04) completed the survey before and a few months after the pandemic. Results revealed the distribution of population scores to become more extreme in either positive or negative trait directions despite the stability of average trait scores across the population. Higher resilience was found to be positively correlated with improved trait scores post-pandemic but corona anxiety score was not correlated with trait score changes. In addition, in the subjects with moderate negative trait scores, agreeableness and autism scores and in subjects with high negative trait scores, openness, SPS and shyness scores were significantly correlated with trait scores changes post-pandemic. These results reveal the nuanced effects of the pandemic on the people's psychological well-being and highlight vulnerabilities for certain groups despite the overall stability of population that needs to be taken into account for mental health policies going forward.

Predicting one-year left ventricular mass index regression following transcatheter aortic valve replacement in patients with severe aortic stenosis: A new era is coming

Aortic stenosis (AS) is the most common valvular heart disease in the western world, particularly worrisome with an ever-aging population wherein postoperative outcome for aortic valve replacement is strongly related to the timing of surgery in the natural course of disease. Yet, guidelines for therapy planning overlook insightful, quantified measures from medical imaging to educate clinical decisions. Herein, we leverage statistical shape analysis (SSA) techniques combined with customized machine learning methods to extract latent information from segmented left ventricle (LV) shapes. This enabled us to predict left ventricular mass index (LVMI) regression a year after transcatheter aortic valve replacement (TAVR). LVMI regression is an expected phenomena in patients undergone aortic valve replacement reported to be tightly correlated with survival one and five year after the intervention. In brief, LV geometries were extracted from medical images of a cohort of AS patients using deep learning tools, and then analyzed to create a set of statistical shape models (SSMs). Then, the supervised shape features were extracted to feed a support vector regression (SVR) model to predict the LVMI regression. The average accuracy of the predictions was validated against clinical measurements calculating root mean square error and R 2 score which yielded the satisfactory values of 0.28 and 0.67, respectively, on test data. Our work reveals the promising capability of advanced mathematical and bioinformatics approaches such as SSA and machine learning to improve medical output prediction and treatment planning.

Localizing sensory processing sensitivity and its subdomains within its relevant trait space: a data-driven approach

Sensitivity arising from enhanced processing of external and internal stimuli or sensory processing sensitivity (SPS) is known to be present in a sizable portion of the population. Yet a clear localization of SPS and its subdomains with respect to other relevant traits is currently lacking. Here, we used a data-driven approach including hierarchical clustering, t-distributed stochastic neighbor embedding (t-SNE) and graph learning to portrait SPS as measured by Highly Sensitive Person Scale (HSPS) in relation to the Big-Five Inventory (neuroticism, extraversion, openness, agreeableness, and conscientiousness) as well as to shyness, alexithymia, autism quotient, anxiety, and depression (11 total traits) using data from more than 800 participants. Analysis revealed SPS subdomains to be divided between two trait clusters with questions related to aesthetic sensitivity (AES) falling within a cluster of mainly positive traits and neighbored by openness while questions addressing ease of excitation (EOE) and low sensory threshold (LST) to be mostly contained within a cluster of negative traits and neighbored by neuroticism. A similar spread across clusters was seen for questions addressing autism consistent with it being a spectrum disorder, in contrast, alexithymia subdomains were closely fit within the negative cluster. Together, our results support the view of SPS as a distinct yet non-unitary trait and provide insights for further refinements of the current SPS concept and scales.