Borgduggan2391

Altogether, our results may help develop method(s) for measuring COVID-19 antibody response, selectivity of methods detecting such SARS-CoV-2 antibodies and vaccine development.

Exposure to adversity is a risk factor for many mental and somatic health problems. Hypothalamic-pituitary-adrenal (HPA) axis dysregulation is a potential mechanism linking adversity exposure and negative health outcomes. However, associations between adversity exposure and HPA-axis activity have been inconsistent. To understand the impact of adversity on the HPA-axis, we examined associations between early-life and recent adversity with hair cortisol concentration, an indicator of long-term systemic cortisol levels.

We included 1166 adult participants of the Netherlands Study of Depression and Anxiety (NESDA). Hair cortisol was measured in 3cm of proximal hair, representing cortisol exposure during the previous 3 months. Childhood maltreatment, childhood negative life events, and recent negative life events were retrospectively assessed using interview and self-report questionnaires. Linear regression analyses were performed to assess the associations between childhood maltreatment, childhood life eventss measured in hair.

There were no significant associations between childhood and recent adversity with systemic cortisol levels in adults. Effects of early-life and adult adversity are complex and may not directly impact on long-term systemic cortisol levels as measured in hair.Black gay men (MSM) in the rural United States South are inequitably burdened by stigmatization and the HIV epidemic. Drawing from twelve oral history interviews with middle-aged and older Black gay narrators from rural North Carolina, this research explores the impact of sexual marginalization and the HIV epidemic on lived experiences of the rural South. Despite describing increasingly empowered views of HIV and sexual health, narrators expressed persistent difficulty managing social determinants of HIV vulnerability-sexual stigma and disconnection from LGBTQ collectivity. Narrators reported better managing sexual marginalization over their lifetimes in urban settings and places outside of the South such as New York (NY). This research suggests stressful structural and interpersonal experiences of stigma may define lived experiences of particular settings.The production of pro-inflammatory cytokines during inflammatory processes has been associated with preterm birth (PTB) and fetal injury in humans and mice. We previously demonstrated that exposition to an enriched environment (EE), defined as a noninvasive and biological significant stimulus of the sensory pathway combined with voluntary physical activity, prevented PTB and perinatal death induced by the systemic administration of bacterial lipopolysaccharide (LPS) in mice. This work aimed to analyze whether EE modulates the immune response to the inflammatory process induced by LPS in peripheral blood and the amniotic fluid (AF). We observed that EE modulated maternal white blood cell count and its response to LPS. Furthermore, we found higher levels of IL-10 and a higher percentage of B cells in AF from EE exposed mothers compared to controls. Albeit LPS significantly increased IL-6 levels in AF from both groups, it was 3.6 times higher in control environment (CE) exposed group when compared to EE. Similarly, levels of IL-22 were significantly increased by LPS in both groups, but it was 6.7 times higher in EE group. Interestingly, levels of PGE2 in AF were only increased in the EE-LPS treated group, and a positive correlation between IL-22 and PGE2 levels was observed. During lactation, EE prevented LPS-induced delay in physical landmarks analyzed to assess offspring development. Our results suggest that EE modulates the immune response to systemic LPS-administration protecting the offspring. We propose that an EE-like protocol could be designed for pregnant women aiming at preventing the sequelae present in premature children.

The yield of epileptiform EEG abnormalities is lower in unselected Paediatric populations than in prospective studies of incident seizures or prevalent epilepsy studies. At a time of limited capacity, it is important to match available EEG resources to children who are most likely to benefit. In this study we evaluated a prospective triage tool for estimating the likelihood of epileptiform abnormality in children's first out-patient EEG.

We prospectively triaged 1865 out-patient referrals to the largest Paediatric EEG laboratory in Ireland. Based on a structured algorithm, we dichotomized first EEG referrals into priority and non-priority groups and assigned one of 5 sub-levels based on anticipated EEG yield. EEGs were reported by a single Consultant in Clinical Neurophysiology.

Triage designated 757 (41 %) EEG referrals as non-priority. Priority exceeded non-priority referrals for all age groups except children between 18 months and 3.5 years. find more EEGs showed a two-fold higher incidence of interictal epilepileptiform abnormality. In a mixed population of Paediatric referrals, the epileptiform yield of first time EEG is 49 % for children over 5 years who are referred with an appropriate EEG indication. This is subject to much variability with epileptiform yields as low as 13 % in younger children with non-priority referrals. The use of a structured triage algorithm can help to optimise utility of EEG in situations of limited laboratory capacity.

The aim of the current study was to investigate whether seizure is among the presenting manifestations of COVID-19.

All patients referred to emergency rooms anywhere in Iran between 12 and 25 April 2020 and who were sufficiently ill to require hospital admission with COVID-19, confirmed by a positive COVID-19 test, were studied. Data on the presenting manifestations were collected.

Of 5872 people, who were admitted to hospitals in Iran with COVID-19 during the study period, 45 came to the emergency room with seizures. This makes seizure as the presenting manifestation of COVID-19 in 0.8 % of all patients with a severe illness. 93 % of the patients were 15 years of age and older. Four of the individuals presenting with seizures (9%) had a past history of epilepsy. Fifteen of these individuals (33 %) had other chronic medical conditions (e.g., cancer, diabetes mellitus, heart disease, etc.).

This case series provides evidence that seizures are among the presenting manifestations of COVID-19 in 0.8 % of the patients who are admitted to hospital due to a severe illness.

This case series provides evidence that seizures are among the presenting manifestations of COVID-19 in 0.8 % of the patients who are admitted to hospital due to a severe illness.Biological transport processes near the aortic valve play a crucial role in calcific aortic valve disease initiation and bioprosthetic aortic valve thrombosis. Hemodynamics coupled with the dynamics of the leaflets regulate these transport patterns. Herein, two-way coupled fluid-structure interaction (FSI) simulations of a 2D bicuspid aortic valve and a 3D mechanical heart valve were performed and coupled with various convective mass transport models that represent some of the transport processes in calcification and thrombosis. link2 Namely, five different continuum transport models were developed to study biochemicals that originate from the blood and the leaflets, as well as residence-time and flow stagnation. Low-density lipoprotein (LDL) and platelet activation were studied for their role in calcification and thrombosis, respectively. Coherent structures were identified using vorticity and Lagrangian coherent structures (LCS) for the 2D and 3D models, respectively. A very close connection between vortex structures and biochemical concentration patterns was shown where different vortices controlled the concentration patterns depending on the transport mechanism. Additionally, the relationship between leaflet concentration and wall shear stress was revealed. Our work shows that blood flow physics and coherent structures regulate the flow-mediated biological processes that are involved in aortic valve calcification and thrombosis, and therefore could be used in the design process to optimize heart valve replacement durability.Traumatic brain injury (TBI) is a leading cause of death in the United States. Depending on the severity of injury, complications such as memory loss and emotional changes are common. While the exact mechanisms are still unclear, these cognitive deficiencies are thought to arise from microstructural damages to the brain tissue, such as in diffuse-axonal injury where neuron fibers are sheared. Constitutive models can predict such damage at a microstructural level and allow for insight into the mechanisms of injury initiating at lower length scales. In this study, we developed a continuum damage model of brain tissue that is validated by experimental quasi-static stress-strain tests in tension, compression, and shear. The present work shows that damage is most present in the shear stress state, making the tissue suitable for damage modeling via shear interaction terms. Using this model, new insights into microstructural breakdown due to shear stresses and strains can be gained by application to simulations.Data-driven modeling directly utilizes experimental data with machine learning techniques to predict a material's response without the necessity of using phenomenological constitutive models. Although data-driven modeling presents a promising new approach, it has yet to be extended to the modeling of large-deformation biological tissues. Herein, we extend our recent local convexity data-driven (LCDD) framework (He and Chen, 2020) to model the mechanical response of a porcine heart mitral valve posterior leaflet. link3 The predictability of the LCDD framework by using various combinations of biaxial and pure shear training protocols are investigated, and its effectiveness is compared with a full structural, phenomenological model modified from Zhang et al. (2016) and a continuum phenomenological Fung-type model (Tong and Fung, 1976). We show that the predictivity of the proposed LCDD nonlinear solver is generally less sensitive to the type of loading protocols (biaxial and pure shear) used in the data set, while more sensitive to the insufficient coverage of the experimental data when compared to the predictivity of the two selected phenomenological models. While no pre-defined functional form in the material model is necessary in LCDD, this study reinstates the importance of having sufficiently rich data coverage in the date-driven and machine learning type of approaches. It is also shown that the proposed LCDD method is an enhancement over the earlier distance-minimization data-driven (DMDD) against noisy data. This study demonstrates that when sufficient data is available, data-driven computing can be an alternative method for modeling complex biological materials.Springy poles are a unique load-carrying tool and inspire novel designs of other load-carrying systems. Previous experiments have shown that highly compliant poles with a natural frequency lower than step frequency are more economical than rigid poles during load carriage in walking and this was successfully explained in later modeling studies. However, an energetic benefit was not observed during running with highly compliant poles. We speculate that gait type (running versus walking) may be a factor accounting for the different observations. An optimization-based biped model is adopted to predict the energy cost of load carriage with poles during running, with the parameters from previous experimental studies. The predicted load motion and load-body interaction force agree well with experimental measurements. Compared to running with rigid poles, the highly compliant pole results in reduced peak ground reaction force, longer stance phase duration, and higher energy cost. The changes in running energetics are further found to depend on the natural frequency of the load-pole system relative to the step frequency, but with an opposite trend compared to the changes in walking energetics during pole carriage.