RESUMO
PURPOSE: Applications of diffusion-weighted magnetic resonance imaging outside the brain have gained increasing importance in recent years, and recent studies have shown the usage of diffusion-weighted (DW) imaging in diagnosing pyelonephritis based on renal cortical and medullary apparent diffusion coefficient (ADC) values. The aim of this study was to assess the validity of DW magnetic resonance (MR) imaging in comparison with contrast-enhanced computed tomography (CECT) in diagnosing pyelonephritis. MATERIAL AND METHODS: A cross-sectional observational study was conducted for a period of six months in a tertiary hospital in Coimbatore. All patients with clinical and laboratory diagnosis of acute pyelonephritis, who were referred for radiological imaging (CECT), were taken into the study. Out of 112 patients with a clinical and laboratorial diagnosis of acute pyelonephritis (APN), who underwent both DW MR and CECT, diagnosis of APN was made in 100 patients based on CECT, while in 12 cases the investigation (CECT) was negative. Finally, these 100 patients were included in the study. The validity of DW MR imaging in diagnosing APN was assessed by deriving sensitivity, specificity, and positive and negative predictive value in comparison with CECT findings. RESULTS: The validity report of DW MR imaging in the detection of APN showed a very high sensitivity (96-100%) and specificity (86-90%) and very low false positives (6-10%) and negatives (< 5%), and it also showed that in the areas of affected renal parenchyma ADC values were consistently lower compared to unaffected renal parenchyma. CONCLUSION: Based on the generated hypothesis, DW MR imaging of the kidneys seems to be highly sensitive and specific for the detection of focal or diffuse infections within the kidney in comparison with CECT.
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We show that the COVID-19 pandemic under social distancing exhibits universal dynamics. The cumulative numbers of both infections and deaths quickly cross over from exponential growth at early times to a longer period of power law growth, before eventually slowing. In agreement with a recent statistical forecasting model by the IHME, we show that this dynamics is well described by the erf function. Using this functional form, we perform a data collapse across countries and US states with very different population characteristics and social distancing policies, confirming the universal behavior of the COVID-19 outbreak. We show that the predictive power of statistical models is limited until a few days before curves flatten, forecast deaths and infections assuming current policies continue and compare our predictions to the IHME models. We present simulations showing this universal dynamics is consistent with disease transmission on scale-free networks and random networks with non-Markovian transmission dynamics.
RESUMO
The present study was carried out to find out the incidence of measles in under five children and its complications in four selected urban slums of inter-state border districts of West Bengal. 20 cluster sampling technique was followed and in each cluster 250 under five children were covered. Paramedical workers found out the children of the target age group who had the history of measles in past one year and Medical Officer confirmed the case following standard case definition. Incidence of measles was round to be 3.3% in Purulia, 5.5% in Bankura. 4.6% in Midnapur, 5.7% in Haldia-Tamluk and with an overall rate of 4.8%. Incidence was higher in 0-11 and 12-23 months age group and decreased with increasing age, but no sex difference in incidence of Measles was observed. Only 20% of cases were vaccinated with measles vaccine and 26% received Vitamin A in oil. Diarrhoea was the commonest complications followed by cough and cold, pneumonia and weight loss. The study highlighted the necessity of timely measles vaccine coverage, additional dose at a higher age and Vitamin A oil supplementation through IEC activities.
