A rare manifestation of COVID-19 in the abdomen: Acute pancreatitis

Introduction: Coronavirus, a novel menace, is exacting terrible human toll and mass death till date. It was thought that COVID-19 primarily affects the respiratory system, but recent studies have reported gastrointestinal manifestations as well. However, the incidence of acute pancreatitis in the setting of COVID-19 is a rising debate due to unknown mechanisms, especially in the absence of any precipitating factors for pancreatitis Case Description/Methods: An otherwise healthy 30-year-old female presented to the emergency department with pain in the lower abdomen radiating to the back, fever, cough, vomiting, diarrhea, and sore throat for the past seven to ten days. Her initial examination revealed a temperature of 100°F, a pulse rate of 85 beats /minute, blood pressure of 110/70 mmHg, respiratory rate of 20 breaths/minute, and oxygen saturation of 94% on room air. Her swab result for SARS-CoV-2 reverse transcriptase-polymerase chain reaction (RT-PCR) came positive. Laboratory testing revealed a rise in serum lipase and serum lipase level. Her ultrasonography (USG) of the abdomen was suggestive of fatty liver changes (hyperechoic parenchymal echogenicity) and no gallstones. She was managed conservatively with intravenous fluid, analgesics and antibiotics for bacterial infection. Absence of any predisposing factor for pancreatitis in the setting of COVID led to the diagnosis of acute pancreatitis induced by COVID-19. Her contrast-enhanced computed tomography (CECT) of the abdomen also confirmed our suspicion. Discussion: The rationale behind the development of acute pancreatitis in the presence of COVID- 19 infection, with no past medical history and precipitating risk factor for pancreatitis, is suggestive of COVID-19 as a triggering factor. This case highlights the importance of pancreatic enzyme monitoring if patients have abdominal complications and without further delay, proper management can be set up if diagnosed early. Future complications such as chronic pancreatitis and pseudocyst formation can also be prevented if timely diagnosed. (Figure Presented) .

Evaluation of Time Series Forecasting Models for Estimation of PM2.5 Levels in Air

Air contamination in urban areas has risen consistently over the past few years. Due to expanding industrialization and increasing concentration of toxic gases in the climate, the air is getting more poisonous step by step at an alarming rate. Since the arrival of the Coronavirus pandemic, it is getting more critical to lessen air contamination to reduce its impact. The specialists and environmentalists are making a valiant effort to gauge air contamination levels. However, it's genuinely unpredictable to mimic sub-atomic communication in the air, which brings about off-base outcomes. There has been an ascent in using machine learning and deep learning models to foresee the results on time series data. This study adopts ARIMA, FBProphet, and deep learning models such as LSTM, 1D-CNN, to estimate the concentration of PM2.5 in the environment. Our predicted results convey that all adopted methods give comparative outcomes in terms of average root mean squared error. However, the LSTM outperforms all other models with reference to mean absolute percentage error.