Virtuality in human supervisory control: Assessing the effects of psychological and social remoteness

Virtuality would seem to offer certain advantages for human supervisory control. First, it could provide a physical analogue of the 'real world' environment. Second, it does not require control room engineers to be in the same place as each other. In order to investigate these issues, a low-fidelity simulation of an energy distribution network was developed. The main aims of the research were to assess some of the psychological concerns associated with virtual environments. First, it may result in the social isolation of the people, and it may have dramatic effects upon the nature of the work. Second, a direct physical correspondence with the 'real world' may not best support human supervisory control activities. Experimental teams were asked to control an energy distribution network. Measures of team performance, group identity and core job characteristics were taken. In general terms, the results showed that teams working in the same location performed better than team who were remote from one another

The endothelial glycocalyx prefers albumin for evoking shear stress-induced, nitric oxide-mediated coronary dilatation

Background: Shear stress induces coronary dilatation via production of nitric oxide ( NO). This should involve the endothelial glycocalyx ( EG). A greater effect was expected of albumin versus hydroxyethyl starch ( HES) perfusion, because albumin seals coronary leaks more effectively than HES in an EG-dependent way. Methods: Isolated hearts ( guinea pigs) were perfused at constant pressure with Krebs-Henseleit buffer augmented with 1/3 volume 5% human albumin or 6% HES ( 200/0.5 or 450/0.7). Coronary flow was also determined after EG digestion ( heparinase) and with nitro-L-arginine ( NO-L-Ag). Results: Coronary flow ( 9.50 +/- 1.09, 5.10 +/- 0.49, 4.87 +/- 1.19 and 4.15 +/- 0.09 ml/ min/ g for `albumin', `HES 200', `HES 450' and `control', respectively, n = 5-6) did not correlate with perfusate viscosity ( 0.83, 1.02, 1.24 and 0.77 cP, respectively). NO-L-Ag and heparinase diminished dilatation by albumin, but not additively. Alone NO-L-Ag suppressed coronary flow during infusion of HES 450. Electron microscopy revealed a coronary EG of 300 nm, reduced to 20 nm after heparinase. Cultured endothelial cells possessed an EG of 20 nm to begin with. Conclusions: Albumin induces greater endothelial shear stress than HES, despite lower viscosity, provided the EG contains negative groups. HES 450 causes some NO-mediated dilatation via even a rudimentary EG. Cultured endothelial cells express only a rudimentary glycocalyx, limiting their usefulness as a model system. Copyright (c) 2007 S. Karger AG, Basel

A physics-based machine learning technique rapidly reconstructs the wall-shear stress and pressure fields in coronary arteries

With the global rise of cardiovascular disease including atherosclerosis, there is a high demand or accurate diagnostic tools that can be used during a short consultation. In view of pathology, abnormal blood flow patterns have been demonstrated to be strong predictors of atherosclerotic lesion incidence, location, progression, and rupture. Prediction of patient-specific blood flow patterns can hence enable fast clinical diagnosis. However, the current state of art for the technique is by employing 3D-imaging-based Computational Fluid Dynamics (CFD). The high computational cost renders these methods impractical. In this work, we present a novel method to expedite the reconstruction of 3D pressure and shear stress fields using a combination of a reduced-order CFD modelling technique together with non-linear regression tools from the Machine Learning (ML) paradigm. Specifically, we develop a proof-of-concept automated pipeline that uses randomised perturbations of an atherosclerotic pig coronary artery to produce a large dataset of unique mesh geometries with variable blood flow. A total of 1407 geometries were generated from seven reference arteries and were used to simulate blood flow using the CFD solver Abaqus. This CFD dataset was then post-processed using the mesh-domain common-base Proper Orthogonal Decomposition (cPOD) method to obtain Eigen functions and principal coefficients, the latter of which is a product of the individual mesh flow solutions with the POD Eigenvectors. Being a data-reduction method, the POD enables the data to be represented using only the ten most significant modes, which captures cumulatively greater than 95% of variance of flow features due to mesh variations. Next, the node coordinate data of the meshes were embedded in a two-dimensional coordinate system using the t-distributed Stochastic Neighbor Embedding (t-SNE) algorithm. The reduced dataset for t-SNE coordinates and corresponding vector of POD coefficients were then used to train a Random Forest Regressor (RFR) model. The same methodology was applied to both the volumetric pressure solution and the wall shear stress. The predicted pattern of blood pressure, and shear stress in unseen arterial geometries were compared with the ground truth CFD solutions on 'unseen' meshes. The new method was able to reliably reproduce the 3D coronary artery haemodynamics in less than 10 seconds

A physics-based machine learning technique rapidly reconstructs the wall-shear stress and pressure fields in coronary arteries

With the global rise of cardiovascular disease including atherosclerosis, there is a high demand for accurate diagnostic tools that can be used during a short consultation. In view of pathology, abnormal blood flow patterns have been demonstrated to be strong predictors of atherosclerotic lesion incidence, location, progression, and rupture. Prediction of patient-specific blood flow patterns can hence enable fast clinical diagnosis. However, the current state of art for the technique is by employing 3D-imaging-based Computational Fluid Dynamics (CFD). The high computational cost renders these methods impractical. In this work, we present a novel method to expedite the reconstruction of 3D pressure and shear stress fields using a combination of a reduced-order CFD modelling technique together with non-linear regression tools from the Machine Learning (ML) paradigm. Specifically, we develop a proof-of-concept automated pipeline that uses randomised perturbations of an atherosclerotic pig coronary artery to produce a large dataset of unique mesh geometries with variable blood flow. A total of 1,407 geometries were generated from seven reference arteries and were used to simulate blood flow using the CFD solver Abaqus. This CFD dataset was then post-processed using the mesh-domain common-base Proper Orthogonal Decomposition (cPOD) method to obtain Eigen functions and principal coefficients, the latter of which is a product of the individual mesh flow solutions with the POD Eigenvectors. Being a data-reduction method, the POD enables the data to be represented using only the ten most significant modes, which captures cumulatively greater than 95% of variance of flow features due to mesh variations. Next, the node coordinate data of the meshes were embedded in a two-dimensional coordinate system using the t-distributed Stochastic Neighbor Embedding ((Formula presented.) -SNE) algorithm. The reduced dataset for (Formula presented.) -SNE coordinates and corresponding vector of POD coefficients were then used to train a Random Forest Regressor (RFR) model. The same methodology was applied to both the volumetric pressure solution and the wall shear stress. The predicted pattern of blood pressure, and shear stress in unseen arterial geometries were compared with the ground truth CFD solutions on “unseen” meshes. The new method was able to reliably reproduce the 3D coronary artery haemodynamics in less than 10 s

Benzene with Alkyl Chains Is a Universal Scaffold for Multivalent Virucidal Antivirals

Most viruses start their invasion by binding to glycoproteins' moieties on the cell surface (heparan sulfate proteoglycans [HSPG] or sialic acid [SA]). Antivirals mimicking these moieties multivalently are known as broad-spectrum multivalent entry inhibitors (MEI). Due to their reversible mechanism, efficacy is lost when concentrations fall below an inhibitory threshold. To overcome this limitation, we modify MEIs with hydrophobic arms rendering the inhibitory mechanism irreversible, i.e., preventing the efficacy loss upon dilution. However, all our HSPG-mimicking MEIs only showed reversible inhibition against HSPG-binding SARS-CoV-2. Here, we present a systematic investigation of a series of small molecules, all containing a core and multiple hydrophobic arms terminated with HSPG-mimicking moieties. We identify the ones that have irreversible inhibition against all viruses including SARS-CoV-2 and discuss their design principles. We show efficacy in vivo against SARS-CoV-2 in a Syrian hamster model through both intranasal instillation and aerosol inhalation in a therapeutic setting (12 h postinfection). We also show the utility of the presented design rules in producing SA-mimicking MEIs with irreversible inhibition against SA-binding influenza viruses

Gender-Related Differences in the Prevalence of Cardiovascular Disease Risk Factors and their Correlates in Urban Tanzania.

\ud Urban areas in Africa suffer a serious problem with dual burden of infectious diseases and emerging chronic diseases such as cardiovascular diseases (CVD) and diabetes which pose a serious threat to population health and health care resources. However in East Africa, there is limited literature in this research area. The objective of this study was to examine the prevalence of cardiovascular disease risk factors and their correlates among adults in Temeke, Dar es Salaam, Tanzania. Results of this study will help inform future research and potential preventive and therapeutic interventions against such chronic diseases. The study design was a cross sectional epidemiological study. A total of 209 participants aged between 44 and 66 years were included in the study. A structured questionnaire was used to evaluate socioeconomic and lifestyle characteristics. Blood samples were collected and analyzed to measure lipid profile and fasting glucose levels. Cardiovascular risk factors were defined using World Health Organization criteria. The age-adjusted prevalence of obesity (BMI > or = 30) was 13% and 35%, among men and women (p = 0.0003), respectively. The prevalence of abdominal obesity was 11% and 58% (p < 0.0001), and high WHR (men: >0.9, women: >0.85) was 51% and 73% (p = 0.002) for men and women respectively. Women had 4.3 times greater odds of obesity (95% CI: 1.9-10.1), 14.2-fold increased odds for abdominal adiposity (95% CI: 5.8-34.6), and 2.8 times greater odds of high waist-hip-ratio (95% CI: 1.4-5.7), compared to men. Women had more than three-fold greater odds of having metabolic syndrome (p = 0.001) compared to male counterparts, including abdominal obesity, low HDL-cholesterol, and high fasting blood glucose components. In contrast, female participants had 50% lower odds of having hypertension, compared to men (95%CI: 0.3-1.0). Among men, BMI and waist circumference were significantly correlated with blood pressure, triglycerides, total, LDL-, and HDL-cholesterol (BMI only), and fasting glucose; in contrast, only blood pressure was positively associated with BMI and waist circumference in women. The prevalence of CVD risk factors was high in this population, particularly among women. Health promotion, primary prevention, and health screening strategies are needed to reduce the burden of cardiovascular disease in Tanzania.\u

Nonattendance in pediatric pulmonary clinics: an ambulatory survey

<p>Abstract</p> <p>Background</p> <p>Nonattendance for scheduled appointments disturbs the effective management of pediatric pulmonary clinics. We hypothesized that the reasons for non-attendance and the necessary solutions might be different in pediatric pulmonary medicine than in other pediatric fields. We therefore investigated the factors associated with nonattendance this field in order to devise a corrective strategy.</p> <p>Methods</p> <p>The effect of age, gender, ethnic origin, waiting time for an appointment and the timing of appointments during the day on nonattendance proportion were assessed. Chi-square tests were used to analyze statistically significant differences of categorical variables. Logistic regression models were used for multivariate analysis.</p> <p>Results</p> <p>A total of 1190 pediatric pulmonology clinic visits in a 21 month period were included in the study. The overall proportion of nonattendance was 30.6%. Nonattendance was 23.8% when there was a short waiting time for an appointment (1–7 days) and 36.3% when there was a long waiting time (8 days and above) (p-value < 0.001). Nonattendance was 28.7% between 8 a.m. to 3 p.m. and 37.5% after 3 p.m. (p = 0.007). Jewish rural patients had 15.4% nonattendance, Jewish urban patients had 31.2% nonattendance and Bedouin patients had 32.9% nonattendance (p < 0.004). Age and gender were not significantly associated with nonattendance proportions. A multivariate logistic regression model demonstrated that the waiting time for an appointment, time of the day, and the patients' origin was significantly associated with nonattendance.</p> <p>Conclusion</p> <p>The factors associated with nonattendance in pediatric pulmonary clinics include the length of waiting time for an appointment, the hour of the appointment within the day and the origin of the patient.</p

Withdrawal-induced delirium associated with a benzodiazepine switch: a case report

<p>Abstract</p> <p>Introduction</p> <p>Introduced in the early 1960s, diazepam remains among the most frequently prescribed benzodiazepine-type sedatives and hypnotics. Patients with chronic use of short-acting benzodiazepines are frequently switched to diazepam because the accumulating, long-acting metabolite, N-desmethyl-diazepam, prevents benzodiazepine-associated withdrawal symptoms, which can occur during trough plasma levels of short-acting benzodiazepines. Although mild to moderate withdrawal symptoms are frequently observed during benzodiazepine switching to diazepam, severe medical complications associated with this treatment approach have thus far not been reported.</p> <p>Case presentation</p> <p>A 64-year-old female Caucasian with major depression, alcohol dependence and benzodiazepine dependence was successfully treated for depression and, after lorazepam-assisted alcohol detoxification, was switched from lorazepam to diazepam to facilitate benzodiazepine discontinuation. Subsequent to the benzodiazepine switch, our patient unexpectedly developed an acute delirious state, which quickly remitted after re-administration of lorazepam. A newly diagnosed early form of mixed dementia, combining both vascular and Alzheimer-type lesions, was found as a likely contributing factor for the observed vulnerability to benzodiazepine-induced withdrawal symptoms.</p> <p>Conclusion</p> <p>Chronic use of benzodiazepines is common in the elderly and a switch to diazepam often precedes benzodiazepine discontinuation trials. However, contrary to common clinical practice, benzodiazepine switching to diazepam may require cross-titration with slow tapering of the first benzodiazepine to allow for the build-up of N-desmethyl-diazepam, in order to safely prevent severe withdrawal symptoms. Alternatively, long-term treatment with low doses of benzodiazepines may be considered, especially in elderly patients with chronic use of benzodiazepines and proven vulnerability to benzodiazepine-associated withdrawal symptoms.</p