Emergence of Novel Coronavirus and COVID-19: whether to stay or die out?

Abstract

The last century has witnessed several assaults from RNA viruses, resulting in millions of death throughout the world. The 21st century appears no longer an exception, with the trend continued with escalated fear of SARS coronavirus in 2002 and further concern of influenza H5N1 in 2003. A novel influenza virus created the first pandemic of the 21st century, the pandemic flu in 2009 preceded with the emergence of another deadly virus, MERS-CoV in 2012. A novel coronavirus “SARS-CoV-2” (and the disease COVID-19) emerged suddenly, causing a rapid outbreak with a moderate case fatality rate. This virus is continuing to cause health care providers grave concern due to the lack of any existing immunity in the human population, indicating their novelty and lack of previous exposure. The big question is whether this novel virus will be establishing itself in an endemic form or will it eventually die out? Endemic viruses during circulation may acquire mutations to infect naïve, as well as individual with pre-existing immunity. Continuous monitoring is strongly advisable, not only to the newly infected individuals, but also to those recovered individuals who were infected by SARS-CoV-2 as re-infection may lead to the selection of escape mutants and subsequent dissemination to the population.     

Basosquamous carcinoma and melanoma collision tumor in a child with xeroderma pigmentosum

Xeroderma pigmentosum (XP) is a rare autosomal recessive genodermatosis associated with hypersensitivity to ultraviolet radiation (UVR), being due to defects involving the nucleotide excision repair pathway. Patients with XP are prone to develop multiple cutaneous neoplasms including non-melanoma skin cancers and melanoma. Collision tumors in patients with XP have been reported in the literature including the following lesions, actinic keratosis, basal cell carcinoma, squamous cell carcinoma, and in situ melanoma. Herein, we present a rare collision tumor composed of melanoma and basosquamous carcinoma in a 13-year-old XP patient and describe the dermoscopic features.     

Neuroimaging in Metabolic Disorders

Although genetic and chemical studies are most helpful for the diagnosis of metabolic disorders of the brain, there is a role for neuroimaging to quantify the degree of brain involvement and the response to therapy. Also, in some instances the imaging pattern can guide in the selection of genes or gene products to be studied.     

Prediction of cardiac arrest in critically ill patients presenting to the emergency department using a machine learning score incorporating heart rate variability compared with the modified early warning score

ABSTRACT: INTRODUCTION: A key aim of triage is to identify those with high risk of cardiac arrest, as they require intensive monitoring, resuscitation facilities, and early intervention. We aim to validate a novel machine learning (ML) score incorporating heart rate variability (HRV) for triage of critically ill patients presenting to the emergency department by comparing the area under the curve, sensitivity and specificity with the modified early warning score (MEWS). METHODS: We conducted a prospective observational study of critically ill patients (Patient Acuity Category Scale 1 and 2) in an emergency department of a tertiary hospital. At presentation, HRV parameters generated from a 5-minute electrocardiogram recording are incorporated with age and vital signs to generate the ML score for each patient. The patients are then followed up for outcomes of cardiac arrest or death. RESULTS: From June 2006 to June 2008 we enrolled 925 patients. The area under the receiver operating characteristic curve (AUROC) for ML scores in predicting cardiac arrest within 72 hours is 0.781, compared with 0.680 for MEWS (difference in AUROC: 0.101, 95% confidence interval: 0.006 to 0.197). As for in-hospital death, the area under the curve for ML score is 0.741, compared with 0.693 for MEWS (difference in AUROC: 0.048, 95% confidence interval: -0.023 to 0.119). A cutoff ML score ≥ 60 predicted cardiac arrest with a sensitivity of 84.1%, specificity of 72.3% and negative predictive value of 98.8%. A cutoff MEWS ≥ 3 predicted cardiac arrest with a sensitivity of 74.4%, specificity of 54.2% and negative predictive value of 97.8%. CONCLUSION: We found ML scores to be more accurate than the MEWS in predicting cardiac arrest within 72 hours. There is potential to develop bedside devices for risk stratification based on cardiac arrest prediction.