ABSTRACT
Background: Important healthcare differences exist between the US and Canada. The goal of this investigation is to compare clinical characteristics, treatment strategies and clinical outcomes of STEMI patients with COVID-19 infection treated in the US versus Canada. Methods and Results: The North American COVID-19 Myocardial Infarction (NACMI) registry is a prospective, investigator-initiated study enrolling STEMI patients with documented COVID infection in the US and Canada. The primary end-point is in-hospital mortality. The secondary end-points include stroke, reinfarction and a composite of death, stroke or reinfarction. Of the 767 STEMI-COVID patients, 67 (9%) were from Canada and 669 (91%) from the US. Patients enrolled in Canada were more likely to present with chest pain (79% vs. 54%, p< 0.001), otherwise patients across both countries had comparable presenting demographics (Table 1). The proportion of patients not undergoing coronary angiography was significantly lower in Canada compared with the US (9% vs. 19%, p=0.039);of those who underwent angiography, no significant differences in reperfusion modalities were noted. Compared with the US, patients in Canada had a significantly lower unadjusted risk for in-hospital mortality (15% vs. 29%, p=0.016) and the risk for the composite of death, stroke or re-infarction (15% vs. 31%, p=0.006). Vaccination status was available in Canada 26 / 67 patients (unvaccinated 13, vaccinated 13) and US 328/ 669 patients (unvaccinated 282, vaccinated 46);a strong association between vaccination and adverse clinical composite is noted in both countries (Canada: 3/13, 23% (unvaccinated) vs. 0/13, 0% (vaccinated), p=0.22;and, US: 75/282, 27% (unvaccinated) vs. 6/46, 13% (vaccinated), p=0.048). Conclusion(s): Among patients with STEMI and COVID-19 infection those treated in Canada had higher proportions undergoing angiography and a lower risk of death, stroke or reinfarction. Regardless of geography, vaccination was associated with significantly lower risk of mortality in both countries. [Formula presented] Copyright © 2022
ABSTRACT
Background: The CVnCoV (CureVac) mRNA vaccine for SARS-CoV-2 has recently been evaluated in a Phase IIb/III efficacy trial in humans. CV2CoV is a second-generation mRNA vaccine with optimized non-coding regions and enhanced antigen expression. Methods: Here we report a head-to-head study of the immunogenicity and protective efficacy of CVnCoV and CV2CoV in nonhuman primates. We immunized 18 cynomolgus macaques with two doses of 12 ug of lipid nanoparticle formulated CVnCoV, CV2CoV, or sham (N=6/group). Results: CV2CoV induced substantially higher binding and neutralizing antibodies, memory B cell responses, and T cell responses as compared with CVnCoV. CV2CoV also induced more potent neutralizing antibody responses against SARS-CoV-2 variants, including B.1.351 (beta), B.1.617.2 (delta), and C.37 (lambda). While CVnCoV provided partial protection against SARS-CoV-2 challenge, CV2CoV afforded robust protection with markedly lower viral loads in the upper and lower respiratory tract. Antibody responses correlated with protective efficacy. Conclusion: These data demonstrate that optimization of non-coding regions can greatly improve the immunogenicity and protective efficacy of an mRNA SARS-CoV-2 vaccine in nonhuman primates.
ABSTRACT
We designed and developed a low-cost mechanical ventilator prototype that meets the government's minimum viable standards. We substituted alternative off-the-shelf food-grade for the medical-grade parts and improvised some components for our prototype. We cleaned the air from the oxygen tanks and compressors before going to the lung test bag. We designed a solar-powered battery system that can run electronic components for a fail-safe operation. We demonstrated how the AIC Near Cloud system can store air flow rate and air pressure data which were generated during the prototype's operation. We used Embedded Machine Learning in sensors and data processing by using flow and pressure sensors to provide accumulated data that can be utilized in training the machine learning software. The patient-ventilator asynchrony detection model was tested using data generated from the emulated ventilator waveform events that mimic the patient-ventilator asynchrony. A different compression pattern was applied to the test lung and results showed the training, validation, and model testing that yielded 98.7%, 99.1%, and 97.18 percent accuracy, respectively. Having demonstrated that the Tiny ML can be trained to detect anomalies from several data points, we realized the feasibility of detecting ventilator patient vibration anomaly, and unusual acoustic signatures, among others, for future works.
ABSTRACT
The study aims to provide a software and hardware platform for asynchronous content distribution and content management with the goal of augmenting and supporting remote learning workflows for both instructors and students in low-bandwidth situations, which is the prevailing condition in the country. The intent of the project is to enable individual school units the ability to prepare, store, and act upon educational materials for students where bandwidth is limited or non-existent without the need to install and maintain on-site traditional ICT infrastructure or rely on cloud-enabled services that require always-on connectivity. This comes at a time when the need to have a risk resilience and disaster mitigation plan for education amidst the COVID-19 pandemic-induced restrictions to human mobility leading to social isolation of educators and students alike. © 2021 ACM.