Longitudinal analyses using 18F-Fluorodeoxyglucose positron emission tomography with computed tomography as a measure of COVID-19 severity in the aged, young, and humanized ACE2 SARS-CoV-2 hamster models.

This study compared disease progression of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) in three different models of golden hamsters: aged (≈60 weeks old) wild-type (WT), young (6 weeks old) WT, and adult (14-22 weeks old) hamsters expressing the human-angiotensin-converting enzyme 2 (hACE2) receptor. After intranasal (IN) exposure to the SARS-CoV-2 Washington isolate (WA01/2020), 2-deoxy-2-[fluorine-18]fluoro-D-glucose positron emission tomography with computed tomography (18F-FDG PET/CT) was used to monitor disease progression in near real time and animals were euthanized at pre-determined time points to directly compare imaging findings with other disease parameters associated with coronavirus disease 2019 (COVID-19). Consistent with histopathology, 18F-FDG-PET/CT demonstrated that aged WT hamsters exposed to 105 plaque forming units (PFU) developed more severe and protracted pneumonia than young WT hamsters exposed to the same (or lower) dose or hACE2 hamsters exposed to a uniformly lethal dose of virus. Specifically, aged WT hamsters presented with a severe interstitial pneumonia through 8 d post-exposure (PE), while pulmonary regeneration was observed in young WT hamsters at that time. hACE2 hamsters exposed to 100 or 10 PFU virus presented with a minimal to mild hemorrhagic pneumonia but succumbed to SARS-CoV-2-related meningoencephalitis by 6 d PE, suggesting that this model might allow assessment of SARS-CoV-2 infection on the central nervous system (CNS). Our group is the first to use (18F-FDG) PET/CT to differentiate respiratory disease severity ranging from mild to severe in three COVID-19 hamster models. The non-invasive, serial measure of disease progression provided by PET/CT makes it a valuable tool for animal model characterization.

Physical activity and sedentary behavior during COVID-19: Trajectory and moderation by personality

The present study examined the relation between personality and changes in physical activity and sedentary behaviors during the acute phase of the 2019 coronavirus disease (COVID-19) pandemic. Personality was assessed prior to the coronavirus crisis in the Understanding America Study (UAS, N = 6,702) and the Psychological, Behavioral, and Social Response (PBSR) to the coronavirus pandemic (N = 3,992) study. Physical activity was assessed 4 times from March 2020 to May 2020 in the UAS, and sedentary behavior was assessed 3 times from January 2020 to April 2020 in the PBSR. Higher neuroticism was associated with less increase in physical activity, whereas higher conscientiousness and agreeableness were related to a steeper increase in physical activity over time in the UAS. In the PBSR, higher neuroticism and lower extraversion, agreeableness, and conscientiousness were related to higher average sedentary behavior. This study provides new evidence that personality predicts behavioral responses to the COVID-19 crisis. (PsycInfo Database Record (c) 2023 APA, all rights reserved)

Deep-Learning-Based Whole-Lung and Lung-Lesion Quantification Despite Inconsistent Ground Truth: Application to Computerized Tomography in SARS-CoV-2 Nonhuman Primate Models

Rationale and Objectives Animal modeling of infectious diseases such as coronavirus disease 2019 (COVID-19) is important for exploration of natural history, understanding of pathogenesis, and evaluation of countermeasures. Preclinical studies enable rigorous control of experimental conditions as well as pre-exposure baseline and longitudinal measurements, including medical imaging, that are often unavailable in the clinical research setting. Computerized tomography (CT) imaging provides important diagnostic, prognostic, and disease characterization to clinicians and clinical researchers. In that context, automated deep-learning systems for the analysis of CT imaging have been broadly proposed, but their practical utility has been limited. Manual outlining of the ground truth (i.e., lung-lesions) requires accurate distinctions between abnormal and normal tissues that often have vague boundaries and is subject to reader heterogeneity in interpretation. Indeed, this subjectivity is demonstrated as wide inconsistency in manual outlines among experts and from the same expert [1]. The application of deep-learning data-science tools has been less well-evaluated in the preclinical setting, including in nonhuman primate (NHP) models of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection/COVID-19, in which the translation of human-derived deep-learning tools is challenging. The automated segmentation of the whole lung and lung lesions provides a potentially standardized and automated method to detect and quantify disease. Materials and Methods We used deep-learning-based quantification of the whole lung and lung lesions on CT scans of NHPs exposed to SARS-CoV-2. We proposed a novel multi-model ensemble technique to address the inconsistency in the ground truths for deep-learning-based automated segmentation of the whole lung and lung lesions. Multiple models were obtained by training the convolutional neural network (CNN) on different subsets of the training data instead of having a single model using the entire training dataset. Moreover, we employed a feature pyramid network (FPN), a CNN that provides predictions at different resolution levels, enabling the network to predict objects with wide size variations. Results We achieved an average of 99.4 and 60.2% Dice coefficients for whole-lung and lung-lesion segmentation, respectively. The proposed multi-model FPN outperformed well-accepted methods U-Net (50.5%), V-Net (54.5%), and Inception (53.4%) for the challenging lesion-segmentation task. We show the application of segmentation outputs for longitudinal quantification of lung disease in SARS-CoV-2-exposed and mock-exposed NHPs. Conclusion Deep-learning methods should be optimally characterized for and targeted specifically to preclinical research needs in terms of impact, automation, and dynamic quantification independently from purely clinical applications.

Deep-Learning-Based Whole-Lung and Lung-Lesion Quantification Despite Inconsistent Ground Truth: Application to Computerized Tomography in SARS-CoV-2 Nonhuman Primate Models.

RATIONALE AND OBJECTIVES: Animal modeling of infectious diseases such as coronavirus disease 2019 (COVID-19) is important for exploration of natural history, understanding of pathogenesis, and evaluation of countermeasures. Preclinical studies enable rigorous control of experimental conditions as well as pre-exposure baseline and longitudinal measurements, including medical imaging, that are often unavailable in the clinical research setting. Computerized tomography (CT) imaging provides important diagnostic, prognostic, and disease characterization to clinicians and clinical researchers. In that context, automated deep-learning systems for the analysis of CT imaging have been broadly proposed, but their practical utility has been limited. Manual outlining of the ground truth (i.e., lung-lesions) requires accurate distinctions between abnormal and normal tissues that often have vague boundaries and is subject to reader heterogeneity in interpretation. Indeed, this subjectivity is demonstrated as wide inconsistency in manual outlines among experts and from the same expert. The application of deep-learning data-science tools has been less well-evaluated in the preclinical setting, including in nonhuman primate (NHP) models of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection/COVID-19, in which the translation of human-derived deep-learning tools is challenging. The automated segmentation of the whole lung and lung lesions provides a potentially standardized and automated method to detect and quantify disease. MATERIALS AND METHODS: We used deep-learning-based quantification of the whole lung and lung lesions on CT scans of NHPs exposed to SARS-CoV-2. We proposed a novel multi-model ensemble technique to address the inconsistency in the ground truths for deep-learning-based automated segmentation of the whole lung and lung lesions. Multiple models were obtained by training the convolutional neural network (CNN) on different subsets of the training data instead of having a single model using the entire training dataset. Moreover, we employed a feature pyramid network (FPN), a CNN that provides predictions at different resolution levels, enabling the network to predict objects with wide size variations. RESULTS: We achieved an average of 99.4 and 60.2% Dice coefficients for whole-lung and lung-lesion segmentation, respectively. The proposed multi-model FPN outperformed well-accepted methods U-Net (50.5%), V-Net (54.5%), and Inception (53.4%) for the challenging lesion-segmentation task. We show the application of segmentation outputs for longitudinal quantification of lung disease in SARS-CoV-2-exposed and mock-exposed NHPs. CONCLUSION: Deep-learning methods should be optimally characterized for and targeted specifically to preclinical research needs in terms of impact, automation, and dynamic quantification independently from purely clinical applications.

Toward the determination of sensitive and reliable whole-lung computed tomography features for robust standard radiomics and delta-radiomics analysis in a nonhuman primate model of coronavirus disease 2019.

Purpose: We propose a method to identify sensitive and reliable whole-lung radiomic features from computed tomography (CT) images in a nonhuman primate model of coronavirus disease 2019 (COVID-19). Criteria used for feature selection in this method may improve the performance and robustness of predictive models. Approach: Fourteen crab-eating macaques were assigned to two experimental groups and exposed to either severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) or a mock inoculum. High-resolution CT scans were acquired before exposure and on several post-exposure days. Lung volumes were segmented using a deep-learning methodology, and radiomic features were extracted from the original image. The reliability of each feature was assessed by the intraclass correlation coefficient (ICC) using the mock-exposed group data. The sensitivity of each feature was assessed using the virus-exposed group data by defining a factor R that estimates the excess of variation above the maximum normal variation computed in the mock-exposed group. R and ICC were used to rank features and identify non-sensitive and unstable features. Results: Out of 111 radiomic features, 43% had excellent reliability ( ICC > 0.90 ), and 55% had either good ( ICC > 0.75 ) or moderate ( ICC > 0.50 ) reliability. Nineteen features were not sensitive to the radiological manifestations of SARS-CoV-2 exposure. The sensitivity of features showed patterns that suggested a correlation with the radiological manifestations. Conclusions: Features were quantified and ranked based on their sensitivity and reliability. Features to be excluded to create more robust models were identified. Applicability to similar viral pneumonia studies is also possible.

Novel bispecific human antibody platform specifically targeting a fully open spike conformation potently neutralizes multiple SARS-CoV-2 variants.

Rapid emergence of new variants of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has prompted an urgent need for the development of broadly applicable and potently neutralizing antibody platform against the SARS-CoV-2, which can be used for combatting the coronavirus disease 2019 (COVID-19). In this study, based on a noncompeting pair of phage display-derived human monoclonal antibodies (mAbs) specific to the receptor-binding domain (RBD) of SARS-CoV-2 isolated from human synthetic antibody library, we generated K202.B, a novel engineered bispecific antibody with an immunoglobulin G4-single-chain variable fragment design, with sub- or low nanomolar antigen-binding avidity. Compared with the parental mAbs or mAb cocktail, the K202.B antibody showed superior neutralizing potential against a variety of SARS-CoV-2 variants in vitro. Furthermore, structural analysis of bispecific antibody-antigen complexes using cryo-electron microscopy revealed the mode of action of K202.B complexed with a fully open three-RBD-up conformation of SARS-CoV-2 trimeric spike proteins by simultaneously interconnecting two independent epitopes of the SARS-CoV-2 RBD via inter-protomer interactions. Intravenous monotherapy using K202.B exhibited potent neutralizing activity in SARS-CoV-2 wild-type- and B.1.617.2 variant-infected mouse models, without significant toxicity in vivo. The results indicate that this novel approach of development of immunoglobulin G4-based bispecific antibody from an established human recombinant antibody library is likely to be an effective strategy for the rapid development of bispecific antibodies, and timely management against fast-evolving SARS-CoV-2 variants.

Face painting as an anatomical learning tool based on individual ultrasonographic examination.

Considering the shift to online education during the COVID-19 pandemic, new and easily accessible educational videos and content on clinical anatomy are necessary. This study utilized numerous references and data on the anatomy of Asian facial muscles and blood vessels to accurately depict human anatomy through face painting. It aimed to provide clinicians accurate educational video content on anatomy to help prevent possible complications during noninvasive facial and surgical procedures. A 26-year-old Korean-Chinese male volunteer was used as a face painting model. The location of the blood vessels of the face was confirmed through ultrasonography images using a real-time two-dimensional B-mode. The model's face was painted by an artist majoring in anatomy. To reveal most anatomical structures on both sides of the face, the left side showed the structures observed when the skin and superficial fat layer are removed, and the right side revealed the deeper layer structures that can be seen when some muscles are cut. Fifteen superficial and deep muscles important in esthetic procedures were meticulously painted on the face. The face painting took a total of 6 h, and the video was edited to 5 min. This study merged the advantages of 2D and 3D by painting directly on the skin surface of a living model. Thus, it can provide more dynamic surface anatomy data. These contents inform clinicians about 3D anatomic location, which can help avoid complications when performing clinical procedures on the face.

Toward the determination of sensitive and reliable whole-lung computed tomography features for robust standard radiomics and delta-radiomics analysis in a nonhuman primate model of coronavirus disease 2019

. Purpose We propose a method to identify sensitive and reliable whole-lung radiomic features from computed tomography (CT) images in a nonhuman primate model of coronavirus disease 2019 (COVID-19). Criteria used for feature selection in this method may improve the performance and robustness of predictive models. Approach Fourteen crab-eating macaques were assigned to two experimental groups and exposed to either severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) or a mock inoculum. High-resolution CT scans were acquired before exposure and on several post-exposure days. Lung volumes were segmented using a deep-learning methodology, and radiomic features were extracted from the original image. The reliability of each feature was assessed by the intraclass correlation coefficient (ICC) using the mock-exposed group data. The sensitivity of each feature was assessed using the virus-exposed group data by defining a factor R that estimates the excess of variation above the maximum normal variation computed in the mock-exposed group. R and ICC were used to rank features and identify non-sensitive and unstable features. Results Out of 111 radiomic features, 43% had excellent reliability ( Conclusions Features were quantified and ranked based on their sensitivity and reliability. Features to be excluded to create more robust models were identified. Applicability to similar viral pneumonia studies is also possible.

Development and Characterization of Phage-Display-Derived Novel Human Monoclonal Antibodies against the Receptor Binding Domain of SARS-CoV-2.

Severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) has resulted in an ongoing global pandemic crisis, caused by the life-threatening illness coronavirus disease 2019 (COVID-19). Thus, the rapid development of monoclonal antibodies (mAbs) to cope with COVID-19 is urgently necessary. In this study, we used phage display to develop four human mAbs specific to the receptor-binding domain (RBD) of SARS-CoV-2. Our intensive in vitro functional analyses demonstrated that K102.1, an anti-SARS-CoV-2 RBD-specific mAb, exerted potent neutralizing activity against pseudoviral and live viral infection and the interaction between SARS-CoV-2 RBD and human angiotensin-converting enzyme 2. Monotherapy with K102.1 also revealed the therapeutic potential against SARS-CoV-2 infection in vivo. Further, this study developed a sandwich enzyme-linked immunosorbent assay with a non-competing mAb pair, K102.1 and K102.2, that accurately detected the RBDs of SARS-CoV-2 wild-type and variants with high sensitivity in the picomolar range. These findings suggest that the phage-display-based mAb selection from an established antibody library may be an effective strategy for the rapid development of mAbs against the constantly evolving SARS-CoV-2.

Differential personality change earlier and later in the coronavirus pandemic in a longitudinal sample of adults in the United States.

Five-factor model personality traits (neuroticism, extraversion, openness, agreeableness, conscientiousness) are thought to be relatively impervious to environmental demands in adulthood. The coronavirus pandemic is an unprecedented opportunity to examine whether personality changed during a stressful global event. Surprisingly, two previous studies found that neuroticism decreased early in the pandemic, whereas there was less evidence for change in the other four traits during this period. The present research used longitudinal assessments of personality from the Understanding America Study (N = 7,109; 18,623 assessments) to examine personality changes relatively earlier (2020) and later (2021-2022) in the pandemic compared to pre-pandemic levels. Replicating the two previous studies, neuroticism declined very slightly in 2020 compared to pre-pandemic levels; there were no changes in the other four traits. When personality was measured in 2021-2022, however, there was no significant change in neuroticism compared to pre-pandemic levels, but there were significant small declines in extraversion, openness, agreeableness, and conscientiousness. The changes were about one-tenth of a standard deviation, which is equivalent to about one decade of normative personality change. These changes were moderated by age and Hispanic/Latino ethnicity, but not race or education. Strikingly, younger adults showed disrupted maturity in that they increased in neuroticism and declined in agreeableness and conscientiousness. Current evidence suggests the slight decrease in neuroticism early in the pandemic was short-lived and detrimental changes in the other traits emerged over time. If these changes are enduring, this evidence suggests population-wide stressful events can slightly bend the trajectory of personality, especially in younger adults.

Personality and change in perceived control during the acute stage of the coronavirus pandemic.

Lower perceived control (PC) is related to maladaptive psychological responses to stressful events, yet it is unclear whether longer-term situations are associated with PC change over time. This study examined PC change during the beginning of the coronavirus pandemic and whether trajectories varied by age and personality. Personality was assessed in 2455 U.S. adults (18-100 years) from an online study conducted January-February 2020. PC was assessed across three follow-ups (March-July 2020). Latent growth curves modeled PC change. In controlled models, PC decreased (ß = -0.107, p = .005). Older adults had higher PC than younger adults (ß = 0.012, p = .001), and experienced less PC decline (ß = 0.012, p < .001). All personality traits but Openness were related to PC at baseline (ßs ranged from -0.912 to 0.543, ps < .001). Conscientiousness (ß = 0.155, p = .002), Extraversion (ß = 0.128, p = .008), and Agreeableness (ß = 0.099, p = .044) were associated with less PC decline. Employment (ß = 0.160, p = .022), health (ß = 0.133, p = .002), and disease burden (ß = -0.056, p = .014) were also associated with PC change. These results were largely driven by the financial dimension of PC. This study provides evidence for PC change during the COVID-19 pandemic and identifies sociodemographic, personality, and health moderators of PC trajectory.

Rationale design and efficacy of a smartphone application for improving self-awareness of adherence to edoxaban treatment: study protocol for a randomised controlled trial (adhere app).

INTRODUCTION: High adherence to oral anticoagulants is essential for stroke prevention in patients with atrial fibrillation (AF). We developed a smartphone application (app) that pushes alarms for taking medication and measuring blood pressure (BP) and heart rate (HR) at certain times of the day. In addition to drug alarms, the habit of measuring one's BP and HR may reinforce drug adherence by improving self-awareness of the disease. This pilot study aims to test the feasibility and efficacy of the smartphone app-based intervention for improving drug adherence in patients with AF. METHODS AND ANALYSIS: A total of 10 university hospitals in Korea will participate in this randomised control trial. Patients with AF, being treated with edoxaban for stroke prevention will be included in this study. Total of 500 patients will be included and the patients will be randomised to the conventional treatment group (250 patients) and the app conditional feedback group (250 patients). Patients in the app conditional feedback group will use the medication reminder app for medication and BP check alarms. The automatic BP machine will be linked to the smartphone via Bluetooth. The measured BP and HR will be updated automatically on the smartphone app. The primary endpoint is edoxaban adherence by pill count measurement at 3 and 6 months of follow-up. Secondary endpoints are clinical composite endpoints including stroke, systemic embolic event, major bleeding requiring hospitalisation or transfusion, or death during the 6 months. As of 24t November 2021, 80 patients were enrolled. ETHICS AND DISSEMINATION: This study was approved by the Seoul National University Bundang Hospital Institutional Review Board and will be conducted according to the principles of the Declaration of Helsinki. The study results will be published in a reputable journal. TRIAL REGISTRATION NUMBER: KCT0004754.

Comparison of Six Serological Immunoassays for the Detection of SARS-CoV-2 Neutralizing Antibody Levels in the Vaccinated Population.

Neutralizing antibody (NAb) detection is critical for evaluating herd immunity and monitoring the efficacy of vaccines against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). In this study, quantitative SARS-CoV-2 antibody levels after vaccination were measured by chemiluminescent immunoassays, enzyme immunoassays, and surrogate virus neutralization tests (sVNTs), as well as plaque reduction neutralization tests (PRNT). Sequential blood samples were collected before and 1 and 3 months after vaccination in 30 healthy participants (two doses of Oxford-AstraZeneca [AZ] or Pfizer-BioNTech [BNT]). After vaccination, all sera tested positive for PRNT, with NAb titers ranging from 1:10 to 1:723. Median NAb titers were higher in the BNT vaccine group than in the AZ vaccine group at both one and three months post-vaccination. Excellent overall concordance rates were observed between serological assays and PRNT. In a quantitative correlation analysis, the results of sVNTs showed a strong correlation with those of PRNT. Results of the four binding antibody assays showed a significant correlation with those of PRNT. The serologic assays evaluated in this study could be used as sVNTs to evaluate the efficacy of SARS-CoV-2 vaccines.

Intramuscular Neural Distribution of the Serratus Anterior Muscle: Regarding Botulinum Neurotoxin Injection for Treating Myofascial Pain Syndrome.

The serratus anterior muscle is commonly involved in myofascial pain syndrome and is treated with many different injective methods. Currently, there is no definite injection point for the muscle. This study provides a suggestion for injection points for the serratus anterior muscle considering the intramuscular neural distribution using the whole-mount staining method. A modified Sihler method was applied to the serratus anterior muscles (15 specimens). The intramuscular arborization areas were identified in terms of the anterior (100%), middle (50%), and posterior axillary line (0%), and from the first to the ninth ribs. The intramuscular neural distribution for the serratus anterior muscle had the largest arborization patterns in the fifth to the ninth rib portion of between 50% and 70%, and the first to the fourth rib portion had between 20% and 40%. These intramuscular neural distribution-based injection sites are in relation to the external anatomical line for the frequently injected muscles to facilitate the efficiency of botulinum neurotoxin injections. Lastly, the intramuscular neural distribution of serratus anterior muscle should be considered in order to practice more accurately without the harmful side effects of trigger-point injections and botulinum neurotoxin injections.

Anatomical Proposal for Botulinum Neurotoxin Injection for Glabellar Frown Lines.

Botulinum neurotoxin injection for treating glabellar frown lines is a commonly used method; however, side effects, such as ptosis and samurai eyebrow, have been reported due to a lack of comprehensive anatomical knowledge. The anatomical factors important for the injection of the botulinum neurotoxin into the corrugator supercilii muscle has been reviewed in this study. Current understanding on the localization of the botulinum neurotoxin injection point from newer anatomy examination was evaluated. We observed that for the glabellar-frown-line-related muscles, the injection point could be more accurately demarcated. We propose the injection method and the best possible injection sites for the corrugator supercilii muscle. We propose the optimal injection sites using external anatomical landmarks for the frequently injected muscles of the face to accelerate effective glabellar frown line removal. Moreover, these instructions would support a more accurate procedure without adverse events.

Personality and change in perceived control during the acute stage of the coronavirus pandemic

Lower perceived control (PC) is related to maladaptive psychological responses to stressful events, yet it is unclear whether longer-term situations are associated with PC change over time. This study examined PC change during the beginning of the coronavirus pandemic and whether trajectories varied by age and personality. Personality was assessed in 2455 U.S. adults (18–100 years) from an online study conducted January–February 2020. PC was assessed across three follow-ups (March–July 2020). Latent growth curves modeled PC change. In controlled models, PC decreased (β = −0.107, p = .005). Older adults had higher PC than younger adults (β = 0.012, p = .001), and experienced less PC decline (β = 0.012, p < .001). All personality traits but Openness were related to PC at baseline (βs ranged from −0.912 to 0.543, ps < .001). Conscientiousness (β = 0.155, p = .002), Extraversion (β = 0.128, p = .008), and Agreeableness (β = 0.099, p = .044) were associated with less PC decline. Employment (β = 0.160, p = .022), health (β = 0.133, p = .002), and disease burden (β = −0.056, p = .014) were also associated with PC change. These results were largely driven by the financial dimension of PC. This study provides evidence for PC change during the COVID-19 pandemic and identifies sociodemographic, personality, and health moderators of PC trajectory.

The botulinum neurotoxin for pain control after breast reconstruction: neural distribution of the pectoralis major muscle.

INTRODUCTION: The use of the botulinum neurotoxin injection is a growing area of research and clinical activity, with a focus on its role in facilitating postoperative pain management after reconstructive breast surgery. The study aimed to find out the standard injection points for botulinum neurotoxin injection by revealing the intramuscular nerve arborization of the pectoralis major. METHODS: Sihler's technique was conducted on the pectoralis major muscles (16 cadaveric specimens). The intramuscular nerve arborization was documented relative to the inferior border of the clavicle bone and lateral border of the sternum. RESULTS: After the staining, the pectoralis major was divided into fifths transversely from the inferior border of the clavicle and vertically into fifths from the lateral border of the sternum. Intramuscular nerve arborization of the pectoralis major muscle was the largest in the middle sections of the muscle belly. DISCUSSION: The results indicate that botulinum neurotoxin should be applied to the pectoralis major in certain regions. The regions of major arborization are optimal as the most effective and most reliable points for injecting botulinum neurotoxin.

Does Personality Shape COVID-19 Responses in Older Adults?

Knowing how personality plays out in a pandemic can provide guidance to improve public health messaging. In a sample of 2066 participants (Mage = 51.42;48.5% female), we examined whether personality is associated with concerns, precautions, preparations, and duration estimates of the COVID-19 pandemic. Personality traits were measured before the pandemic;responses were assessed in late March 2020. We investigated whether age moderates the trait-response associations because older adults are at higher risk for severe complications of COVID-19. Among the 65-96-year-olds, higher conscientiousness was associated with more preparations, higher openness was associated with greater concerns, and both higher openness and agreeableness were related to more preparations and longer duration estimates. This pattern has implications: If all older adults took COVID-19 seriously, individual differences in personality should not matter;however, our findings indicate that they do matter and could be considered in the development of personality-tailored communication to older adults.

Personality and change in perceived control across the first four months of the coronavirus pandemic

Objective: This study examined change in perceived control (PC) across the first four months of the global coronavirus pandemic and whether change varied significantly by age and personality traits during the first four months of the pandemic. Methods: Personality was assessed prior to the pandemic in a large national sample of 2,455 American adults (18-100 years) from a preregistered online survey (https://osf.io/q8cpd), first conducted between January 31, 2020 and February 10, 2020. Three additional follow-up waves were conducted: mid-March 2020 (following the World Health Organization declaration of the coronavirus a pandemic), late April 2020 (toward the end of the White House’s 30 Days to Slow the Spread guidance), and late July 2020 (when patient deaths in the U.S. reached 132,918). PC was assessed in Waves 2-4. Results: There were age differences in the trajectory of PC such that PC increase for middle-aged and older adults, whereas younger adults had lower PC and did not increase over the follow-ups. All personality traits but Openness were associated with PC at the first assessment. Conscientiousness, Extraversion, and Agreeableness positively predicted PC change over time. The association between Conscientiousness and Extraversion and increased PC over time was stronger at older ages. Conclusion: Pre-pandemic personality predicted PC and PC change during the first four months of the pandemic, with middle-aged and older adults showing better adaption than younger adults. This study provides new evidence for PC change and associations between personality and PC during the COVID-19 pandemic.

Medical imaging of pulmonary disease in SARS-CoV-2-exposed non-human primates.

Chest X-ray (CXR), computed tomography (CT), and positron emission tomography-computed tomography (PET-CT) are noninvasive imaging techniques widely used in human and veterinary pulmonary research and medicine. These techniques have recently been applied in studies of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)-exposed non-human primates (NHPs) to complement virological assessments with meaningful translational readouts of lung disease. Our review of the literature indicates that medical imaging of SARS-CoV-2-exposed NHPs enables high-resolution qualitative and quantitative characterization of disease otherwise clinically invisible and potentially provides user-independent and unbiased evaluation of medical countermeasures (MCMs). However, we also found high variability in image acquisition and analysis protocols among studies. These findings uncover an urgent need to improve standardization and ensure direct comparability across studies.