Post-COVID multisystem inflammatory syndrome in adults: a study from a tertiary care hospital in south India.

Background & objectives: There are limited data from India on the post-COVID multisystem inflammatory syndrome in adults (MIS-A). The objective of the present study was to evaluate the clinical profile of patients with MIS-A admitted to a tertiary care centre in southern India. Methods: This single-centre retrospective study was conducted from November 2020 to July 2021, and included patients aged >18 yr admitted to the hospital as per the inclusion and exclusion criteria. Results: Nine patients (5 male, mean age 40±13 yr) met the criteria for MIS-A. Five patients had proven COVID-19 infection or contact history 36.8±11.8 days back. All patients were positive for SARS-CoV-2 IgG antibody, negative for COVID-19 PCR, and had negative blood, urine and sputum cultures. All patients had fever and gastrointestinal (GI) symptoms, and five patients had left ventricular dysfunction. All patients had neutrophilic leucocytosis at presentation and elevated biomarkers such as C-reactive protein serum procalcitonin, D-dimer and ferritin. The majority of the patients (7/9 i.e. 77.78%) were treated with intravenous hydrocortisone (50-100 mg q6h-q8h). Six patients recovered completely whereas three patients expired. Interpretation & conclusions: Fever and GI symptoms were the most common presentation of MIS-A. Elevated serum procalcitonin may not be useful in differentiating bacterial sepsis from MIS-A. Most patients responded to corticosteroids.

Fabrication of Low Cost Food Delivery Robot

In today's world, technology has drastically increased in all sectors of industries and businesses. Automated machines demands have increased rapidly. Most small and medium scale businesses are trying to use this technology to increase the speed and reliability. One of the booming technologies is robotics, which helps businesses in so many ways. This paper discusses the approach and experience in the design, simulation, modelling, testing, and deployment of a Low-Cost food delivery robot in hotels, restaurants etc. These food delivery robots give an enhanced experience for the customers and benefits the restaurant business financially by bringing attention to visitors and act as a publicity. The restaurant industry is also experiencing a downturn because of the COVID-19 breakout. With this method, food can be delivered directly from the kitchen to the customer's table while maintaining all norms and sanitary guidelines. The robot uses microcontroller mounted with DC motors. Ultrasonic sensors and IR sensors are used for mapping and localization of destination tables, motor drivers, obstacle detection, collision avoidance, path detection. The robot performed as per the test and achieved the desired result. © 2022 IEEE.

Nipah Virus Outbreak in Kerala State, India Amidst of COVID-19 Pandemic.

We report here a Nipah virus (NiV) outbreak in Kozhikode district of Kerala state, India, which had caused fatal encephalitis in a 12-year-old boy and the outbreak response, which led to the successful containment of the disease and the related investigations. Quantitative real-time reverse transcription (RT)-PCR, ELISA-based antibody detection, and whole genome sequencing (WGS) were performed to confirm the NiV infection. Contacts of the index case were traced and isolated based on risk categorization. Bats from the areas near the epicenter of the outbreak were sampled for throat swabs, rectal swabs, and blood samples for NiV screening by real-time RT-PCR and anti-NiV bat immunoglobulin G (IgG) ELISA. A plaque reduction neutralization test was performed for the detection of neutralizing antibodies. Nipah viral RNA could be detected from blood, bronchial wash, endotracheal (ET) secretion, and cerebrospinal fluid (CSF) and anti-NiV immunoglobulin M (IgM) antibodies from the serum sample of the index case. Rapid establishment of an onsite NiV diagnostic facility and contact tracing helped in quick containment of the outbreak. NiV sequences retrieved from the clinical specimen of the index case formed a sub-cluster with the earlier reported Nipah I genotype sequences from India with more than 95% similarity. Anti-NiV IgG positivity could be detected in 21% of Pteropus medius (P. medius) and 37.73% of Rousettus leschenaultia (R. leschenaultia). Neutralizing antibodies against NiV could be detected in P. medius. Stringent surveillance and awareness campaigns need to be implemented in the area to reduce human-bat interactions and minimize spillover events, which can lead to sporadic outbreaks of NiV.

Nipah virus outbreak in Kerala state, India amidst of COVID-19 pandemic (preprint)

Background: We report here a Nipah virus (NiV) outbreak in Kozhikode district of Kerala state, India which had caused fatal encephalitis in an adolescent male and the outbreak response which led to the successful containment of the disease and the related investigations. Methods: Quantitative real-time RT-PCR, ELISA based antibody detection and whole genome sequencing were performed to confirm the Nipah virus infection. Contacts of the index case were traced and isolated based on risk categorization. Bats from the areas near the epicentre of the outbreak were sampled for throat swabs, rectal swabs and blood samples for Nipah virus screening by real time RT-PCR and anti-Nipah virus bat IgG ELISA. Plaque reduction neutralization test was performed for the detection of neutralizing antibodies. Results: Nipah viral RNA and anti-NiV IgG antibodies were detected in the serum of the index case. Rapid establishment of an onsite NiV diagnostic facility and contact tracing helped in quick containment of the outbreak. NiV sequences retrieved from the clinical specimen of the index case formed a sub-cluster with the earlier reported Nipah I genotype sequences from India with more than 95% similarity. Anti-NiV IgG positivity could be detected in 21% of Pteropus medius and 37.73% of Rousettus leschenaultia. Neutralizing antibodies against NiV could be detected in P.medius. Conclusions: Stringent surveillance and awareness campaigns needs to be implemented in the area to reduce human-bat interactions and minimize spill over events which can lead to sporadic outbreaks of NiV.

Self-administration of vecuronium by a medical student: An autopsy case report

During COVID-19 pandemic the frontline healthcare workers have been at risk the most. Long working hours, emotional stress from not seeing families for days together takes a toll on the psychological well-being of a person. This leads to increase incidence of self-harm amongst the individuals. Vecuronium bromide is a muscle relaxant, easy availability of such drugs amongst the medical professionals is a matter of great concern. Monitoring and cautious dispensing of them should be done. Here we report a case of self-administration of vecuronium by a medical student .The purpose of documenting this case is to create awareness among healthcare workers to keep a watch on mental health of fellow colleagues. Psychological support groups in medical college hospitals are the need of hour.

Deep learning techniques for detection of covid-19 using chest x-rays

The COVID-19 pandemic situation keeps on ruining and affecting the wellbeing and prosperity of the worldwide population and due to this situation, the doctors around the world are working restlessly, as the coronavirus is increasing exponentially and the situation for testing has become quite a problematic and with restricted testing units, it’s impossible for every patient to be tested with available facilities. Effective screening of infected patients through chest X-ray images is a critical step in combating COVID-19. With the help of deep learning techniques, it is possible to train various radiology images and detect COVID-19. The dataset used in our research work is gathered from different sources and a specific new dataset is generated. The proposed methodology implemented is beneficial to the medical practitioner for the diagnosis of coronavirus infected patients where predictions can be done automated using deep learning. The deep learning algorithms that are used to predict the COVID with the help of chest X-ray images are evaluated for their prediction based on performance metrics such as accuracy, precision, Recall, and F1-score. In this work, the proposed model has used deep learning techniques for COVID-19 prediction and the results have shown superior performance in prediction of COVID-19. © 2021, International Institute for General Systems Studies. All rights reserved.