ABSTRACT
After facing the horrible COVID-19 pandemic, steadily life is getting back to normal again. This pandemic came with opportunities as well, especially for researchers to come out with novel ideas and handle the situation. Many researchers have contributed with their dedicated research work with the help of recent technology to overcome similar circumstances. This paper presents a novel idea for proper monitoring and detecting normal/abnormal health using AI-based models. Proper monitoring and detection of symptoms are essential to ensure the health of members. This model is devised using several IoTs components and various ML (Machine Learning) techniques have been used to get comparative enhanced results. The hardware used Raspberry Pi 4 model B, which is the main hardware connected to several sensors like MLX906014 non-contact thermal sensor and MAX30100 pulse oximeter and heart rate sensor to measure body temperature without contact and to calculate the level of oxygen in the blood and measuring pulse rate respectively. Additionally, a Camera module for facilitating face recognition features for devices has been used. An Alert will be sent to Admin if someone has an abnormal temperature and oxygen level. The Firebase database is used to store information and it gets updated in real-time. People's health history can be further analyzed through graphs for visualization and monitored by the administrator. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
ABSTRACT
Introduction: The avian influenza virus (IAV) commonly cause acute respiratory distress syndrome (ARDS), shock and death. Unlike the current coronavirus outbreak, IAV does not grab the same level of public attention, although its clinical implications remain dire. As of 2019, a recorded 10,000 patients have died and 180,000 were hospitalized according to CDC. ARDS leads to a surge in the use of mechanical ventilators for life support. Host-derived cellular microRNAs (miRs) play a critical role in ARDS dependent lung injury, alveolar-capillary membrane integrity, and host responses to viral infections. We hypothesize that increased miR193b-5p upon IAV infection, downregulates occludin expression, and is associated with increased inflammation and injury in experimental models of pneumonia. Methods: Wild type mice (C57Bl/6J, 10 -14 weeks) were randomized to infection with H1N1 virus (A/PR/8/34) treated a miR193b-5p inhibitor (INH) versus placebo delivered on day 4 post-infection. On day 8, lung injury was assessed using histology, bronchoalveolar lavage fluid cell counts and differential, membrane permeability, and viral load. In vitro, Beas2b cells were infected with H1N1 and treated with or without miR- 193b-5p inhibitor (INH) or mimic, and occludin was knocked down. Transcript expression levels were determined by qRT-PCR. Beas2b cells were treated with IFNb and qRT-PCR and digital droplet PCR was used to assess targets. Results: Intranasal infection of IAV increased pulmonary inflammation, lung edema, increased levels of miR193b-5p (20- fold) and decreased expression of occludin (>50%) that peak at day 5 days post-infection. Reporter construct demonstrates miR-193b-5p binds specifically to the 3'UTR of occludin. Inhibition of miR193b- 5p mitigates H1N1-induced lung injury, edema formation, viral load, and anti-viral Interferon b (IFNb) and Interferon Regulated Genes expression. In vitro, silencing of occludin results in increased viral load and dysregulation of the host antiviral response. miR193b-5p is IFNb responsive confirmed by qRT-PCR and ddPCR. Conclusions: We demonstrate the role of miR193b-5p in barrier function, viral infection and host anti-viral responses in a murine model of H1N1. IFNb, a natural host antiviral response, promotes miR193b upregulation. The upregulation of miR-193b-5p downregulates occludin. And the inhibition of miR-193b-5p results in decreased lung injury, inflammation, and viral load.