Novel Coronavirus (COVID-19) outbreak that emerged originally in Wuhan, the Hubei province of China has put the entire human race at risk. This virus was declared as Pandemic on 11 th March 2020. Considering the massive growth rate in the number of cases and highly contagious nature of the virus, machine learning prediction models and algorithms are essential to predict the number of cases in the coming days. This could help in reducing the stress on health care systems and administrations by helping them plan better. In this paper the datasets used are obtained from the John Hopkins University's publicly available datasets to develop a state-of-the-art forecasting model of COVID-19 outbreak. We have incorporated data-driven estimations and time series analysis to predict the trends in coming days such as the number of cases confirmed positive, number of deaths caused by the virus and number of people recovered from the novel coronavirus. To achieve the estimations, we have used the Deep learning model long-shortterm memory network (LSTM).

To describe the training of healthcare workers (HCW) in nasopharyngeal swabbing during the COVID 19 pandemic. Study design: Retrospective study. Setting: Tertiary care teaching hospital. Subjects and methods: One hundred and seventy eight health care workers were trained from May 2020 to January 2021. Three modules were designed to train the health care workers in the technique of obtaining a nasopharyngeal swab specimen.Training consisted of an instructional video on how to perform nasopharyngeal swabs and live demonstration followed by hands-on supervised training. The trainees included 30 doctors, 101 nurses, 31 respiratory therapists, 3 physiotherapists, 9 interns and 4 lab technicians. There were 39 male and 139 female trainees. After attending all 3 modules of training, they were confident and efficient in taking a non-traumatic nasopharyngeal swab. Good knowledge and adequate training is key to a good nasopharyngeal sampling for SARS CoV-2 testing.

A young girl presented with an insidious onset, gradually increasing midline submental swelling of 1-year duration and pain on swallowing for 6 months. Ultrasonography of the neck suggested a hypoechoic cystic swelling of the submental region between the muscles of the floor of the mouth, with no increased vascularity. An extraoral surgical enucleation was done and a postoperative biopsy suggested an epidermoid cyst. Epidermoid cysts of the submental region are extremely rare and any midline head and neck lesion in children requires critical examination and evaluation to avoid complications. Here, we present a rare case of a paediatric submental epidermoid cyst and its clinical features and management.

Objectives: This study was conducted with the aim of clinicopathological evaluation of thyroid swellings.Setting: Tertiary referral centre, Davangere, Karnataka, India.Design: Retrospective study. Materials and methods:Clinical details, sonological reports, laboratory reports were retrieved from the records for the 110 patients with thyroid swellings who were included in our study, between May 2009 and April 2013 and the data was analyzed. Cytological smears in all patients and histopathology slides in operated patients were retrieved and studied. Results:The highest incidence (37.4%) of thyroid swellings were found in age group of 21 to 30 years. The youngest patient being 10 years. Females (90%) predominated in this study, male to female ratio being 1:9. Majority of patients (35%) came with complaints of swelling of duration less than 6 months. Among 110 patients, 36.36% of them were treated conservatively, out of which 7 cases (17.5%) were hyperthyroid, 10 cases (25%) were hypothyroid and 23 cases (57.5%) were euthyroid and the remaining 63.63% of them underwent surgery. Of the 110 patients subjected to FNAC, 16 patients (14.54%) were neoplastic and 94 patients (85.45%) were non-neoplastic. Upon correlation with the histopathology report, the sensitivity of FNAC was 78.57%, specificity was 91.07%, with a positive pre dictive value of 68.75% and negative predictive value of 94.44%. Diagnostic accuracy of FNAC is 88.50%.Conclusion: FNAC and USG are valuable tools in assessing the need for surgical intervention in thyroid swellings. USG guided aspiration will further enhance the cytological yield and diagnostic accuracy.

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