Evolution of macrolide resistance in Streptococcus pneumoniae clinical isolates in the prevaccine era

Six hundred twelve invasive and noninvasive Streptococcus pneumoniae isolates were examined. Serogrouping was performed by the latex agglutination test and serotyping by the quellung reaction. Susceptibilities to macrolides were determined by Etest. The presence of mef(A), mef(E), and erm(B) genes were detected by polymerase chain reaction. Outpatient macrolide and lincosamide consumption was expressed in defined daily doses per 1000 inhabitants daily (DID). A significant increase in macrolide resistance rate was noted from 7.4% (14/190) in the period 1985 to 1996 to 53.7% (144/268) in 2001 to 2004 (P = 0.003). An increase in macrolide and lincosamide consumption was also observed from 4.31 ± 0.72 in 1990 to 1996 to 6.97 ± 1.02 DID in 2001 to 2004 (P = 0.002). Macrolide resistance was mediated by mef(E) gene in 44.5% of isolates, mef(A) in 25.6%, erm(B) in 19.8%, both erm(B) and mef(E) genes in 4.8%, and none of the examined genes in 5.3%. In the setting of increasing macrolide use, there has been a parallel increase in macrolide resistance among pneumococci in our region. The predominant resistance determinants were the mef(A) and mef(E) genes. © 2008 Elsevier Inc. All rights reserved

Preparing dental schools to refunction safely during the COVID-19 pandemic: An infection prevention and control perspective

In late 2019 a novel coronavirus named severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) emerged in China and spread throughout the world over a short period of time causing a pandemic of a respiratory disease named coronavirus disease 2019 (COVID-19). SARS-CoV-2 is easily transmitted from person to person through respiratory droplets and direct contact. The scarce available data indicate that dental healthcare personnel are at increased risk for acquisition of infection. Following the lockdown lifting, dental schools should be prepared to refunction safely and provide essential educational and healthcare services while protecting their students, patients, and personnel. The generation of aerosols in dental practice, in association with the high-transmissibility of SARS-CoV-2 through aerosol-generation procedures, the simultaneous provision of dental services to patients in the same areas, and the fact that asymptomatic and pre-symptomatic infected persons may transmit the virus, render the implementation of specific infection prevention and control measures imperative for dental schools. Herein we review the few evidence-based data available to guide infection prevention and control measures for COVID-19 in dental schools. © 2021 Journal of Infection in Developing Countries. All rights reserved

Wastewater monitoring as a supplementary surveillance tool for capturing SARS-COV-2 community spread. A case study in two Greek municipalities

A pilot study was conducted from late October 2020 until mid-April 2021, aiming to examine the association between SARS-CoV-2 RNA concentrations in untreated wastewater and recorded COVID-19 cases in two Greek municipalities. A population of Random Forest and Linear Regression Machine Learning models was trained and evaluated incorporating the concentrations of SARS-CoV-2 RNA in 111 wastewater samples collected from the inlets of two Wastewater Treatment Plants, along with physicochemical parameters of the wastewater influent. The model's predictions were adequately associated with the 7-day cumulative cases with the correlation coefficients (after 5-fold cross validation) ranging from 0.754 to 0.960 while the mean relative errors ranged from 30.42% to 59.46%. Our results provide indications that wastewater-based predictions can be applied in diverse settings and in prolonged time periods, although the accuracy of these predictions may be mitigated. Wastewater-based epidemiology can support and strengthen epidemiological surveillance. © 202

Relating SARS-CoV-2 shedding rate in wastewater to daily positive tests data: A consistent model based approach

During the COVID-19 pandemic, wastewater-based epidemiology (WBE) has been engaged to complement medical surveillance and in some cases to also act as an early diagnosis indicator of viral spreading in the community. Most efforts worldwide by the scientific community and commercial companies focus on the formulation of protocols for SARS-CoV-2 analysis in wastewater and approaches addressing the quantitative relationship between WBE and medical surveillance are lacking. In the present study, a mathematical model is developed which uses as input the number of daily positive medical tests together with the highly non-linear shedding rate curve of individuals to estimate the evolution of global virus shedding rate in wastewater along calendar days. A comprehensive parametric study by the model using as input actual medical surveillance and WBE data for the city of Thessaloniki (similar to 700,000 inhabitants, North Greece) during the outbreak of November 2020 reveals the conditions under which WBE can be used as an early warning tool for predicting pandemic outbreaks. It is shown that early warning capacity is different along the days of an outbreak and depends strongly on the number of days apart between the day of maximum shedding rate of infected individuals in their disease cycle and the day of their medical testing. The present data indicate for Thessaloniki an average early warning capacity of around 2 days. Moreover, the data imply that there exists a proportion between unreported cases (asymptomatic persons with mild symptoms that do not seek medical advice) and reported cases. The proportion increases with the number of reported cases. The early detection capacity of WBE improves substantially in the presence of an increasing number of unreported cases. For Thessaloniki at the peak of the pandemic in mid-November 2020, the number of unreported cases reached a maximum around 4 times the number of reported cases. (C) 2021 The Authors. Published by Elsevier B.V

Wastewater monitoring as a supplementary surveillance tool for capturing SARS-COV-2 community spread. A case study in two Greek municipalities

A pilot study was conducted from late October 2020 until mid-April 2021, aiming to examine the association between SARS-CoV-2 RNA concentrations in untreated wastewater and recorded COVID-19 cases in two Greek municipalities. A population of Random Forest and Linear Regression Machine Learning models was trained and evaluated incorporating the concentrations of SARS-CoV-2 RNA in 111 wastewater samples collected from the inlets of two Wastewater Treatment Plants, along with physicochemical parameters of the wastewater influent. The model’s predictions were adequately associated with the 7-day cumulative cases with the correlation coefficients (after 5-fold cross validation) ranging from 0.754 to 0.960 while the mean relative errors ranged from 30.42% to 59.46%. Our results provide indications that wastewater-based predictions can be applied in diverse settings and in prolonged time periods, although the accuracy of these predictions may be mitigated. Wastewater-based epidemiology can support and strengthen epidemiological surveillance