Subject(s)
Acidosis/immunology , Angiotensin Receptor Antagonists/adverse effects , Angiotensin-Converting Enzyme Inhibitors/adverse effects , COVID-19/immunology , Immunity, Cellular/immunology , Pulmonary Ventilation , Renin-Angiotensin System/immunology , Acid-Base Equilibrium , Acidosis/chemically induced , Acidosis/metabolism , Angiotensin Receptor Antagonists/therapeutic use , Angiotensin-Converting Enzyme 2/metabolism , Angiotensin-Converting Enzyme Inhibitors/therapeutic use , CD4-Positive T-Lymphocytes/immunology , COVID-19/metabolism , Cytokine Release Syndrome/immunology , Glucocorticoids/immunology , Glucocorticoids/metabolism , Histocompatibility Antigens Class I/immunology , Humans , Hypertension/drug therapy , Killer Cells, Natural/immunology , Natural Killer T-Cells/immunology , Renal Elimination , Renin-Angiotensin System/physiology , SARS-CoV-2/metabolism , T-Lymphocytes, Cytotoxic/immunologyABSTRACT
BACKGROUND: Child growth in populations is commonly characterised by cross-sectional surveys. These require data collection from large samples of individuals across age ranges spanning 1-20 years. Such surveys are expensive and impossible in restrictive situations, such as, e.g. the COVID pandemic or limited size of isolated communities. A method allowing description of child growth based on small samples is needed. METHODS: Small samples of data (N~50) for boys and girls 6-20 years old from different socio-economic situations in Africa and Europe were randomly extracted from surveys of thousands of children. Data included arm circumference, hip width, grip strength, height and weight. Polynomial regressions of these measurements on age were explored. FINDINGS: Polynomial curves based on small samples correlated well (r = 0.97 to 1.00) with results of surveys of thousands of children from same communities and correctly reflected sexual dimorphism and socio-economic differences. CONCLUSIONS: Fitting of curvilinear regressions to small data samples allows expeditious assessment of child growth in a number of characteristics when situations change rapidly, resources are limited and access to children is restricted.
Subject(s)
COVID-19 , Child Development , Adolescent , COVID-19/epidemiology , Child , Cross-Sectional Studies , Female , Humans , Male , Sample Size , Surveys and Questionnaires , Young AdultABSTRACT
The novel pathogen Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), the cause of coronavirus disease (COVID-19), has created a global crisis. Currently, the limits of public health systems and medical knowhow have been exposed. COVID-19 has challenged our best minds, forcing them to return to the drawing board. Fear of infection leading to possible life-long morbidity or death has embedded itself in the collective imagination leading to both altruistic and maladaptive behaviours. Although, COVID-19 has been a global concern, its advent is a defining moment for artificial intelligence. Medicorobots have been increasingly used in hospitals during the last twenty years. Their various applications have included logistic support, feeding, nursing support and surface disinfection. In this article we examine how COVID-19 is reframing technology/human interactions via medicorobots, and the future implications of this relationship. In the last section we predict possible developments in artificial intelligence and how they may benefit future humanity in medicine.