ABSTRACT
Objectives: First, to determine prevalence of vaccinated COVID-19 patients among hospitalized patients;second, to determine the epidemiological, clinical, and laboratory characteristics of vaccinated and unvaccinated COVID-19 patients.Methods: The study was carried out on 300 adult COVID-19 hospitalized patients at Duhok COVID-19 health facilities. A prospective cross-sectional study was used as the study design. Between October 1, 2021, and March 31, 2022, all patients with PCR-confirmed COVID-19 were enrolled.Results: The majority of people in this study were unvaccinated. Pfizer was most popular among people who had received vaccination. The majority of hospitalized patients were old ages, the mean age was 60.73 +/- 15.83 yr. In our study, the unvaccinated females had higher infection rates while vaccinated males had higher hospital admission rates. In our study, vaccinated patients had shorter hospital duration stays. In both vaccinated and unvaccinated patients, predominated cases were severe cases. D dimer was significantly higher among vaccinated patients. The mortality rate was relatively high among both groups. Patients who had received vaccinations tended to experience vomiting and flu-like symptoms more frequently than those who had not. In terms of comorbidities, smoking and malignancy were significant risk factors for COVID-19 infection in unvaccinated patients.Conclusion: We looked at 300 COVID-19 hospitalized patients. In this study, the majority of people were unvaccinated. Pfizer, had higher prevalence among vaccinated individuals. Majority were elderly. The unvaccinated cases had a higher rate of female hospital admissions than male. The D.Dimer level was significantly different between the two groups. Vomiting and flu-like illness showed higher prevalence in vaccinated cases with significant difference. Smoking and malignancy were significant risk factors for COVID-19 infection in unvaccinated patients. In the fight against a public health disaster like a COVID-19 pandemic, the availability of a COVID-19 vaccines campaign are crucial.
ABSTRACT
The ongoing COVID-19 pandemic due to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection has resulted in a significant public health care system crisis. This disease has resulted in devastating damage to human lives and significant disruption in economies. Use of "machine-learning " algorithms as tools of artificial intelligence may help identify a suspected or infected individual with an estimation of chances of survival. These algorithms make use of recorded observational data including medical histories, patient demographics as well as any related data on COVID-19.
ABSTRACT
Spelling is an essential anchor for literacy skills. In the Caribbean, there are limited resources to support struggling readers with their spelling practice. This paper describes an exploratory study of an online Spelling tutor, Ozzypi which was built in response to Covid-19 related school closures across the region and the subsequent need for novel approaches to facilitate spelling practice. It has since been transformed to feature an intelligent tutoring system core that supports children in spelling practice exercises using speech-enabled technologies. Twenty-eight users (14 learners and 14 parents/guardians) from Trinidad and Jamaica used a basic version of the tutor over several weeks. Analysis of interview responses and logged usage data revealed broad learner and parent engagement, positive shifts in on-task behaviour. Importantly, the need for a culturally-situated design emerged as students interacted with the speech-enabled features. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
ABSTRACT
Exhaled breath analysis is a promising noninvasive method for rapid diagnosis of diseases by detecting different types of volatile organic compounds (VOCs) that are used as biomarkers for early detection of various diseases such as lung cancer, diabetes, anemias, etc... and more recently COVID-19. Infrared spectroscopy seems to be a promising method for VOCs detection due to its ease of use, selectivity, and existence of compact low-cost devices. In this work, the use of Fourier transforms infrared (FTIR) spectrometer to analyze breath samples contained in a gas cell is investigated using deep learning and taking into account the practical performance limits of the spectrometer. Synthetic spectra are generated using infrared gas spectra databases to emulate real spectra resulted from a breath sample and train the neural network model (NNM). The dataset is generated in the spectral range of 2000 cm-1 to 6500 cm-1 and assuming a light-gas interaction length of 5 meters. The FTIR device performance is assumed with a signal-to-noise ratio (SNR) of 20,000:1 and a spectral resolution of 40 cm-1. The proposed NNM contains a locally connected and 4 fully connected layers. The concentrations of 9 biomarker gases in the exhaled breath are predicted with r2 score higher than 0.93, including carbon dioxide, water vapor, acetone, ethene, ammonia, methane, carbonyl sulfide, carbon monoxide and acetaldehyde demonstrating the possibility of detection. © 2021 IEEE.