Personality predicts pre-COVID-19 to COVID-19 trajectories of transdiagnostic anxiety and depression symptoms.

This study aimed to characterize within-person pre-COVID-19 and coronavirus pandemic (COVID-19) transdiagnostic anxiety and depression symptom trajectories in emerging adults and determine the roles of neuroticism and behavioral activation in predicting these COVID-19-related changes. We recruited a sample of 342 emerging adults (aged 18-19 at baseline) who were screened on neuroticism and behavioral activation and completed symptom questionnaires on multiple occasions before and after the start of the pandemic. We examined estimates of the symptom factors of General Distress, Anhedonia-Apprehension, and Fears at each wave. The stress amplification model predicts a multiplicative neuroticism-adversity interaction with those high on neuroticism showing the greatest symptom increases to the pandemic. The stably elevated negative affect model is an additive model and predicts that persons high on neuroticism will display elevated symptoms at every wave. General Distress and Anhedonia-Apprehension showed large increases from the pre-COVID-19 to COVID-19 transition then decreased thereafter. The increase brought the average General Distress score to clinical levels at the first COVID-19 wave. There was a small decrease in Fears from the pre-COVID-19 to COVID-19 transition followed by a large increase. Thus, COVID-19 was associated with both increases in psychological symptoms and some resilience. Neuroticism positively predicted the pre-COVID-19 to COVID-19 transition change in Fears but was associated with a dampening of increases in General Distress and Anhedonia-Apprehension. The results disconfirmed the stress amplification model of neuroticism but partially supported the stably elevated negative affect model. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Fast Prediction of Binding Affinities of SARS-CoV-2 Spike Protein and Its Mutants with Antibodies through Intermolecular Interaction Modeling-Based Machine Learning.

Since the introduction of the novel SARS-CoV-2 virus (COVID-19) in late 2019, various new variants have appeared with mutations that confer resistance to the vaccines and monoclonal antibodies that were developed in response to the wild-type virus. As we continue through the pandemic, an accurate and efficient methodology is needed to help predict the effects certain mutations will have on both our currently produced therapeutics and those that are in development. Using published cryo-electron microscopy and X-ray crystallography structures of the spike receptor binding domain region with currently known antibodies, in the present study, we created and cross-validated an intermolecular interaction modeling-based multi-layer perceptron machine learning approach that can accurately predict the mutation-caused shifts in the binding affinity between the spike protein (wild-type or mutant) and various antibodies. This validated artificial intelligence (AI) model was used to predict the binding affinity (Kd) of reported SARS-CoV-2 antibodies with various variants of concern, including the most recently identified "Deltamicron" (or "Deltacron") variant. This AI model may be employed in the future to predict the Kd of developed novel antibody therapeutics to overcome the challenging antibody resistance issue and develop structural bases for the effects of both current and new mutants of the spike protein. In addition, the similar AI strategy and approach based on modeling of the intermolecular interactions may be useful in development of machine learning models predicting binding affinities for other protein-protein binding systems, including other antibodies binding with their antigens.

Implications of microvascular dysfunction and nitric oxide mediated inflammation in severe COVID-19 infection.

Infection with COVID-19 has resulted in over 276,000 deaths in the United States and over 1.5 million deaths globally, with upwards of 15% of patients requiring hospitalization. Severe COVID-19 infection is, in essence, a microvascular disease. This contention has been emphasized throughout the course of the pandemic, particularly due to the clinical manifestation of severe infection. In fact, it has been hypothesized and shown in particular instances that microvascular function is a significant prognosticator for morbidity and mortality. Initially thought to be isolated to the pulmonary system and resulting in ARDS, patients with COVID-19 have been observed to have acute cardiac, renal, and thrombolytic complications. Therefore, severe COVID-19 is a vascular disease that has systemic implications. The objective of this review is to provide a mechanistic background for the microvascular nature of severe COVID-19 infection, with a particular emphasis on dysfunction of the endothelial glycocalyx and nitric oxide mediated pathogenesis.

Generalized Methodology for the Quick Prediction of Variant SARS-CoV-2 Spike Protein Binding Affinities with Human Angiotensin-Converting Enzyme II.

Variants of the SARS-CoV-2 virus continue to remain a threat 2 years from the beginning of the pandemic. As more variants arise, and the B.1.1.529 (Omicron) variant threatens to create another wave of infections, a method is needed to predict the binding affinity of the spike protein quickly and accurately with human angiotensin-converting enzyme II (ACE2). We present an accurate and convenient energy minimization/molecular mechanics Poisson-Boltzmann surface area methodology previously used with engineered ACE2 therapeutics to predict the binding affinity of the Omicron variant. Without any additional data from the variants discovered after the publication of our first model, the methodology can accurately predict the binding of the spike/ACE2 variant complexes. From this methodology, we predicted that the Omicron variant spike has a Kd of ∼22.69 nM (which is very close to the experimental Kd of 20.63 nM published during the review process of the current report) and that spike protein of the new "Stealth" Omicron variant (BA.2) will display a Kd of ∼12.9 nM with the wild-type ACE2 protein. This methodology can be used with as-yet discovered variants, allowing for quick determinations regarding the variant's infectivity versus either the wild-type virus or its variants.

Telehealth for Endocrinology: Experience and Effect on Utilization in the Prepandemic Era.

OBJECTIVE: Telehealth (TH) use in endocrinology was limited before the COVID-19 pandemic but will remain a major modality of care postpandemic. Reimbursement policies have been limited historically due to concerns of overutilization of visits and testing. Additionally, there is limited literature on endocrinology care delivered via TH for conditions other than diabetes. We assess real-world TH use for endocrinology in a prepandemic environment with the hypothesis that TH would not increase the utilization of total visits or related ancillary testing services compared with conventional (CVL) face-to-face office visits. METHODS: A single-institution retrospective cohort study assessing the prepandemic use of TH in endocrinology, consisting of 75 patients seen via TH and 225 patients seen in CVL visits. For most patients, TH was conducted via a clinic-to-clinic model. Outcomes measured were total endocrine visit frequency and frequency of related laboratory and radiology testing per patient, hemoglobin A1C, microalbumin, low-density lipoprotein, thyroid-stimulating hormone, thyroglobulin, and thyroid ultrasounds. RESULTS: For all endocrine visits, TH patients had a median of 0.24 (interquartile range, 0.015-0.36) visits per month. CVL patients had a median of 0.20 visits per month (interquartile range, 0.11-0.37). Total visits per month did not vary significantly between groups (P = .051). Hemoglobin A1C outcomes were equivalent and there was no increase in ancillary laboratory testing for the TH group. CONCLUSION: Our observations demonstrate that, in a prepandemic health care setting, TH visits can provide equivalent care for endocrinology patients, without increasing utilization of total visits or ancillary services.

Fast Prediction of Binding Affinities of the SARS-CoV-2 Spike Protein Mutant N501Y (UK Variant) with ACE2 and Miniprotein Drug Candidates.

A recently identified variant of SARS-CoV-2 virus, known as the United Kingdom (UK) variant (lineage B.1.1.7), has an N501Y mutation on its spike protein. SARS-CoV-2 spike protein binds with angiotensin-converting enzyme 2 (ACE2), a key protein for the viral entry into the host cells. Here, we report an efficient computational approach, including the simple energy minimizations and binding free energy calculations, starting from an experimental structure of the binding complex along with experimental calibration of the calculated binding free energies, to rapidly and reliably predict the binding affinities of the N501Y mutant with human ACE2 (hACE2) and recently reported miniprotein and hACE2 decoy (CTC-445.2) drug candidates. It has been demonstrated that the N501Y mutation markedly increases the ACE2-spike protein binding affinity (Kd) from 22 to 0.44 nM, which could partially explain why the UK variant is more infectious. The miniproteins are predicted to have ∼10,000- to 100,000-fold diminished binding affinities with the N501Y mutant, creating a need for design of novel therapeutic candidates to overcome the N501Y mutation-induced drug resistance. The N501Y mutation is also predicted to decrease the binding affinity of a hACE2 decoy (CTC-445.2) binding with the spike protein by ∼200-fold. This convenient computational approach along with experimental calibration may be similarly used in the future to predict the binding affinities of potential new variants of the spike protein.

Control of systemic inflammation through early nitric oxide supplementation with nitric oxide releasing nanoparticles.

Amelioration of immune overactivity during sepsis is key to restoring hemodynamics, microvascular blood flow, and tissue oxygenation, and in preventing multi-organ dysfunction syndrome. The systemic inflammatory response syndrome that results from sepsis ultimately leads to degradation of the endothelial glycocalyx and subsequently increased vascular leakage. Current fluid resuscitation techniques only transiently improve outcomes in sepsis, and can cause edema. Nitric oxide (NO) treatment for sepsis has shown promise in the past, but implementation is difficult due to the challenges associated with delivery and the transient nature of NO. To address this, we tested the anti-inflammatory efficacy of sustained delivery of exogenous NO using i.v. infused NO releasing nanoparticles (NO-np). The impact of NO-np on microhemodynamics and immune response in a lipopolysaccharide (LPS) induced endotoxemia mouse model was evaluated. NO-np treatment significantly attenuated the pro-inflammatory response by promoting M2 macrophage repolarization, which reduced the presence of pro-inflammatory cytokines in the serum and slowed vascular extravasation. Combined, this resulted in significantly improved microvascular blood flow and 72-h survival of animals treated with NO-np. The results from this study suggest that sustained supplementation of endogenous NO ameliorates and may prevent the morbidities of acute systemic inflammatory conditions. Given that endothelial dysfunction is a common denominator in many acute inflammatory conditions, it is likely that NO enhancement strategies may be useful for the treatment of sepsis and other acute inflammatory insults that trigger severe systemic pro-inflammatory responses and often result in a cytokine storm, as seen in COVID-19.