<p>This study investigates cattle futures response to the equities crash in March of 2020 and the subsequent COVID-19 linked production delays at beef packing plants. I observe that the initial declines in cattle futures began prior to the onset of beef packing plant shutdowns. Fitting a Vector Error Correction Model on live cattle futures, feeder cattle futures, and corn futures to the E-Mini S&P 500 futures contract finds evidencethat the S&P 500 had a significant impact on cattle prices during  March  of  2020.  These  results  are  an  example  of  increased  cross-asset  correlation  during periods of financial distress.</p>

The idiom by Thomas Fuller fantasizes the fact that seeing is believing, but the feeling is the truth. This ideology has fired the vision and innovation of the Mulsemedia, multiple-sensorial media, and Internet of Skills (IoS) which enable the exchange of control, skills, and expertise anytime/everywhere across the Internet. With the emergence of the new generation of mobile network (5G), Tactile Internet, as well as the deployment of Industry 4.0 and Health 4.0, multimedia systems are moving towards immersed haptic enabled human-machine interaction systems such as the Digital Twin (DT). Specifically, Industry 4.0 will be using DT and robots on a large scale. This will increase human-machine and interaction to a great extent. There will be multimodal communications used to interact with digital twins and robots, specially haptics. Hence, tactile internet will replace the conventional internet today. In fact, a DT system can also be extended in Health 4.0 domain to act as a COVID-19 early warning system. Tracking a person’s temperature and other symptom data in real-time can signal if as well as when it’s time to see a doctor or take a COVID test. Link to a COVID tracing app, the digital twin might help get more information about the virus relative to the person itself. Since there are currently no well-recognized models to evaluate the performance of these systems, to address this research lacuna, we proposed a Quality-of-Experience (QoE) model for DT systems containing multi-levels of subjective, objective, and physiopsychological influencing factors. The model is itemized through a fully detailed taxonomy that deduces the perceived user’s emotional and physical states during and after consuming spatial, temporal, proximal, and abstracted multi-modality media between humans and machines. Further, the taxonomy was modelled using the best practice of machine learning methods to show how QoE for digital twin applications can be inferred and predicted from interactions and biosignals in this class of applications. Furthermore, the taxonomy was applied to two use cases. The first one addresses the objective quality optimization for transmission in a large scale immersed haptic virtual reality over the Internet while the second one aims to objectively infer an important DT QoE physiological aspect i.e, fatigue.

Introduction: The high transmissibility and lethality of the novel coronavirus SARS-CoV-2 (COVID-19) have been catastrophic. Acute kidney injury (AKI) is one of the frequent complications in patients with respiratory insufficiency caused by the virus. The pathogenic mechanism is based on the binding of its S-proteins to the angiotensin-converting enzyme (ACE) receptors, which will trigger a cellular damage. A podocyte and tubular compromise are found in the kidneys which can lead to tubular necrosis and the consequent AKI. Objectives: The objective of this report is to identify the main risk factor to develop AKI in patients infected with SARS-CoV-2 with critical acute respiratory distress. Patients and Methods: We performed this report study, collecting data from 48 ICU patients. Data from 13 of them who developed AKI and needed renal replacement therapy (RRT)were analyzed. Clinical characteristics and laboratory findings were reported using STATA 10.0. Results: AKI was present in 27.08% of patients, mostly male (92.3%) with a mean age of 63.8 years old. Hypertension, diabetes and obesity were the main comorbidities in those patients. Additionally, the meantime between admission and AKI diagnosis was 2.69 days. All patients showed fibrinogen, D-dimer, ALT and values above normal range. Mortality was seen in 61.5% of patients. Conclusion: This report tries to show AKI as an important clinical manifestation in critically ill patients infected with SARS-CoV-2, with high mortality. Further studies are needed to demonstrate if there are independent risk factors. / Revisión por pares

Introduction: Only 3 types of coronavirus cause aggressive respiratory disease in humans (MERS-Cov, SARS-Cov-1, and SARS-Cov-2). It has been reported higher infection rates and severe manifestations (ICU admission, need for mechanical ventilation, and death) in patients with comorbidities such as diabetes mellitus (DM). For this reason, this study aimed to determine the prevalence of diabetes comorbidity and its associated unfavorable health outcomes in patients with acute respiratory syndromes for coronavirus disease according to virus types. Methods: Systematic review of literature in Pubmed/Medline, Scopus, Web of Science, Cochrane, and Scielo until April of 2020. We included cohort and cross-sectional studies with no restriction by language or geographical zone. The selection and extraction were undertaken by 2 reviewers, independently. The study quality was evaluated with Loney’s instrument and data were synthesized by random effects model meta-analysis. The heterogeneity was quantified using an I2 statistic. Funnel plot, Egger, and Begg tests were used to evaluate publication biases, and subgroups and sensitivity analyses were performed. Finally, we used the GRADE approach to assess the evidence certainty (PROSPERO: CRD42020178049). Results: We conducted the pooled analysis of 28 studies (n = 5960). The prevalence analysis according to virus type were 451.9 diabetes cases per 1000 infected patients (95% CI: 356.74-548.78; I2 = 89.71%) in MERS-Cov; 90.38 per 1000 (95% CI: 67.17-118.38) in SARS-Cov-1; and 100.42 per 1000 (95% CI: 77.85, 125.26 I2 = 67.94%) in SARS-Cov-2. The mortality rate were 36%, 6%, 10% and for MERS-Cov, SARS-Cov-1, and SARS-Cov-2, respectively. Due to the high risk of bias (75% of studies had very low quality), high heterogeneity (I2 higher than 60%), and publication bias (for MERS-Cov studies), we down rate the certainty to very low. Conclusion: The prevalence of DM in patients with acute respiratory syndrome due to coronaviruses is high, predominantly with MERS-Cov infection. The unfavorable health outcomes are frequent in this subset of patients. Well-powered and population-based studies are needed, including detailed DM clinical profile (such as glycemic control, DM complications, and treatment regimens), comorbidities, and SARS-Cov-2 evolution to reevaluate the worldwide prevalence of this comorbidity and to typify clinical phenotypes with differential risk within the subpopulation of DM patients. / Revisión por pares

No / Non-invasive positive pressure ventilation (NPPV) provides respiratory support to patients without the need for invasive intubation. Although it has been used for several years in critical care, NPPV has come to prominence as a management option for certain patients with respiratory complications of coronavirus disease 2019 (COVID-19). This has led to increased care provision by nurses with little or no experience and expertise in critical care and NPPV. This article provides an overview of the principles of NPPV and its use in type 1 and type 2 respiratory failure. It explains the pathophysiology of several conditions that often lead to respiratory failure and how NPPV can mitigate respiratory failure and improve gas exchange. An individualised assessment of the patient’s suitability for NPPV and an evaluation of the effectiveness of the therapy are crucial to ensure its safe and effective use. Nurses also have an important role in providing explanations and support to patients.

40% of severe acute respiratory syndrome coronavirus two (SARS-CoV-2) severe cases develop acute kidney injury (AKI). Current treatment for renal complications limits financial and material resources available. To explore alternative treatments and accelerate research in case of future coronavirus outbreaks, a mouse model of coronavirus disease 2019-associated AKI (C19-AKI) would represent a critical biomedical research tool. The surrogate model of C19-AKI (SMC) developed consisted of angiotensin-converting enzyme two (ACE2) knockout (KO) mice receiving 400 ng/kg/min of angiotensin (Ang) II by osmotic minipump for eight days with a single injection of lipopolysaccharide (LPS; 10 mg/kg) on the seventh day of Ang II and euthanasia 24 hours after LPS. Similarly, to C19-AKI, the SMC exhibited albuminuria, elevated blood urea nitrogen, electrolyte imbalance, neutrophil infiltration, and upregulation of the G-coupled protein receptor (GPR)84 and pro-inflammatory and injury markers. GPR84 was found in bronchoalveolar lavage fluid neutrophils of coronavirus disease 2019 (COVID-19) patients, suggesting a potential implication of GPR84 in the disease. We hypothesised that GPR84 deletion or antagonism with GLPG-1205 could attenuate SMC’s indices of renal injury and inflammation. GLPG-1205 and GPR84 KO had no effects in the SMC model, as suggested by unchanged albuminuria, electrolytes, and markers expression. Interestingly, neutrophil infiltration was attenuated by GLPG-1205 only. The SMC is an interesting tool for therapeutic development for infections associated with renal injury, such as SARS-CoV-2. GPR84 role in the SMC needs to be further assessed.