The psychological impact of the COVID-19 pandemic in Portugal: The role of personality traits and emotion regulation strategies.

Recent evidence suggests that both personality traits (PT) and emotion regulation (ER) strategies play an important role in the way people cope with the COVID-19 pandemic. The aim of this study was two folded. First, to longitudinally investigate the psychological distress (depression, anxiety, and stress levels) taking in consideration PT and ER strategies in 3 different moments: during the first lockdown period (April/20), at the first deconfinement (May/20) and 1-month after the first deconfinement (Jun/20)-Experiment I. Second, to cross-sectionally evaluate the impact of the pandemic in psychological distress and the correlates with PT and ER 6-months after the first deconfinement November/20 to February/21 -Experiment II. A total of 722 volunteers (Experiment I = 180; Experiment II = 542) aged 18 years or older participated in this online survey. The findings from Experiment I show that psychological distress decreased after the lockdown period, however, neuroticism traits predicted higher levels of depression, anxiety and stress symptoms, while difficulties in ER strategies were identified as a risk factor for depression and stress. For experiment II, neuroticism traits and being infected with COVID-19 were associated to higher levels of symptomatology, while unemployment and the use of emotional suppression strategies to cope with emotional situations were associated to depressive and anxiety symptoms. Although the psychological impact of the COVID-19 outbreak decreased over time in our sample, the current findings suggest that difficulties in emotional regulation and high levels of neuroticism traits might be potential risk factors for psychiatric symptomatology during the COVID-19 pandemic. Thus, people with difficulties in ER and neuroticism traits would benefit from psychological interventions that provide personality-appropriate support and promote emotion regulation skills during stressful events, such as the case of the global pandemic.

Physical Vapor Deposition Technology in Personal Protective Equipment Production: Improved Antibacterial and Hydrophobic Character of Textiles

Personal protective equipment (PPE) has been adapted as biological threats have emerged, such as increasingly drug-resistant bacteria and the emergence of new viruses such as Covid-19. PPE must be increasingly resilient to prevent the proliferation of pathogens, but using sustainable raw materials and environmentally friendly technologies. The aim of this study is to show a new way of modifying the surface of various types of fabrics to enable their efficient use as PPE. The Ag/DLC coating was successfully deposited by sputtering onto several types of textiles using different chemical compositions of Ag/DLC (0, 8, 10, and 12Ag). As a crucial parameter, wettability was evaluated, showing that silver addition increases the hydrophobicity character of the coated fabrics, namely in cotton, changing from hydrophilic to hydrophobic. Antibacterial activity and cytotoxicity were evaluated on all coatings, revealing that they are efficient in eliminating the spread of bacteria (Staphylococcus aureus and Klebsiella pneumoniae) and pose no risk to the human body. The results presented here are promising in protecting healthcare workers, with the next steps being to study the efficiency of these coatings against viruses. In addition, this study reveals an opportunity to use sustainable fabrics, such as cotton, with high efficiency in protection against pathogens, instead of synthetic fiber textiles.

The Acute Impact of the Early Stages of COVID-19 Pandemic in People with Pre-Existing Psychiatric Disorders: A Systematic Review.

People with pre-pandemic health conditions are more vulnerable and more likely to suffer greater psychosocial impact due to the current COVID-19 pandemic and the lockdown measures. Thus, the objective of this work was to systematically review the impact of the early stages COVID-19 pandemic on people with pre-existing psychiatric disorders. The search was performed between 23 January and 2 September 2021 in PubMed, PsycINFO, and EMBASE. A total of 4167 published results were identified; however, only 49 were included in this review. Results show that there was considerable heterogeneity among studies, which resulted in a low consensus. However, it seems that the impact of the first stage of the COVID-19 pandemic on psychiatric disorders was two-fold: (1) an overall effect, in which people suffering from psychiatric disorders in general experienced more psychological distress and anxiety when compared to people who had no psychiatric diagnosis, and (2) a condition-specific effect, namely in people suffering from eating disorders and obsessive compulsive disorders. Moreover, the current work highlights that there were also some external factors that were related to worsening symptoms. For instance, unemployment or experiencing work and financial difficulties can be a trigger for greater distress during the pandemic for people with mood disorders, and being alone and in social isolation during the COVID-19 pandemic may actually increase substance use and relapse rates. Further studies are needed to prospectively investigate the long-term effects of the current COVID-19 pandemic on people with (pre)-existing psychiatric conditions and on the onset or deterioration of psychiatric-related symptoms in a larger number of participants, as well as exploring the long-term effects of the current pandemic on mental health.

Viability Study of Machine Learning-Based Prediction of COVID-19 Pandemic Impact in Obsessive-Compulsive Disorder Patients.

BACKGROUND: Machine learning modeling can provide valuable support in different areas of mental health, because it enables to make rapid predictions and therefore support the decision making, based on valuable data. However, few studies have applied this method to predict symptoms' worsening, based on sociodemographic, contextual, and clinical data. Thus, we applied machine learning techniques to identify predictors of symptomatologic changes in a Spanish cohort of OCD patients during the initial phase of the COVID-19 pandemic. METHODS: 127 OCD patients were assessed using the Yale-Brown Obsessive-Compulsive Scale (Y-BOCS) and a structured clinical interview during the COVID-19 pandemic. Machine learning models for classification (LDA and SVM) and regression (linear regression and SVR) were constructed to predict each symptom based on patient's sociodemographic, clinical and contextual information. RESULTS: A Y-BOCS score prediction model was generated with 100% reliability at a score threshold of ± 6. Reliability of 100% was reached for obsessions and/or compulsions related to COVID-19. Symptoms of anxiety and depression were predicted with less reliability (correlation R of 0.58 and 0.68, respectively). The suicidal thoughts are predicted with a sensitivity of 79% and specificity of 88%. The best results are achieved by SVM and SVR. CONCLUSION: Our findings reveal that sociodemographic and clinical data can be used to predict changes in OCD symptomatology. Machine learning may be valuable tool for helping clinicians to rapidly identify patients at higher risk and therefore provide optimized care, especially in future pandemics. However, further validation of these models is required to ensure greater reliability of the algorithms for clinical implementation to specific objectives of interest.

Carbon-Based Coatings in Medical Textiles Surface Functionalisation: An Overview

The COVID-19 pandemic has further highlighted the need for antimicrobial surfaces, especially those used in a healthcare environment. Textiles are the most difficult surfaces to modify since their typical use is in direct human body contact and, consequently, some aspects need to be improved, such as wear time and filtration efficiency, antibacterial and anti-viral capacity, or hydrophobicity. To this end, several techniques can be used for the surface modification of tissues, being magnetron sputtering (MS) one of [hose that have been growing in the last years to meet the antimicrobial objective. The current state of the art available on textile functionalisation techniques, the improvements obtained by using MS, and the potential of diamond-like-carbon (DLC) coatings on fabrics for medical applications will be discussed in this review in order to contribute to a higher knowledge of functionalized textiles themes.