ABSTRACT
Many complications and symptoms were documented on COVID-19 patients in the second-phase of the COVID-19 outbreak in India. Patients with COVID-19 are already of increased risk of pulmonary embolism (PE), acute cardiac injury (ACI), arrhythmias, and a variety of additional consequences such as altered mental status and proptosis. Mucormycosis, a fungal infection produced by a type of moulds known as mucormycetes, was discovered in a COVID-19 patient. It is a very rare and serious fungal infection (Black Fungus). Mucormycosis, one of the most rapidly spreading infections in COVID-19 patients, has been recorded in 11,717 cases in India. Molds dwell in the environment and primarily affect people who have a weak immune system. Inhaling pathogenic organisms from the air usually causes it to harm the sinuses and lungs. COVID-19 individuals have immunosuppressive with significant drop in CD4+T and CD8+T cells, in addition to alveoli damage and severe pulmonary inflammation. As a result, severely ill-patients, particularly those sent in the intensive-care-unit (ICU) and requiring mechanical ventilation, or those with extended hospital stays 40 to 50 days were more susceptible to mucormycosis. Further, it is crucial to find out that COVID-19 patients especially the ones who are severely ill resulting in weaker immune system can further develop a fungal infection during the middle and or latter stages of COVID-19.
ABSTRACT
On 30 January 2020, the World Health Organization announced a new coronavirus, which later turned out to be very dangerous. Since that date, COVID-19 has spread to become a pandemic that has now affected practically all regions in the world. Since then, many researchers in medicine have contributed to fighting COVID-19. In this context and given the great growth of scientific publications related to this global pandemic, manual text and data retrieval has become a challenging task. To remedy this challenge, we are proposing CovBERT, a pre-trained language model based on the BERT model to automate the literature review process. CovBERT relies on prior training on a large corpus of scientific publications in the biomedical domain and related to COVID-19 to increase its performance on the literature review task. We evaluate CovBERT on the classification of short text based on our scientific dataset of biomedical articles on COVID-19 entitled COV-Dat-20. We demonstrate statistically significant improvements by using BERT.
ABSTRACT
Recent evidence suggests that proinflammatory cytokines, such as tumor necrosis factor α (TNF-α), play a pivotal role in the development of inflammatory-related pathologies (covid-19, depressive disorders, sepsis, cancer, etc.,). More importantly, the development of TNF-α biosensors applied to biological fluids (e.g. sweat) could offer non-invasive solutions for the continuous monitoring of these disorders, in particular, polydimethylsiloxane (PDMS)-based biosensors. We have therefore investigated the biofunctionalization of PDMS surfaces using a silanization reaction with 3-aminopropyltriethoxysilane, for the development of a human TNF-α biosensor. The silanization conditions for 50 µm PDMS surfaces were extensively studied by using water contact angle measurements, electron dispersive X-ray and Fourier transform infrared spectroscopies, and fluorescamine detection. Evaluation of the wettability of the silanized surfaces and the Si/C ratio pointed out to the optimal silanization conditions supporting the formation of a stable and reproducible aminosilane layer, necessary for further bioconjugation. An ELISA-type immunoassay was then successfully performed for the detection and quantification of human TNF-α through fluorescent microscopy, reaching a limit of detection of 0.55 µg/mL (31.6 nM). Finally, this study reports for the first time a promising method for the development of PDMS-based biosensors for the detection of TNF-α, using a quick, stable, and simple biofunctionalization process.