Conventional MRI Criteria to Differentiate Progressive Disease from Treatment-Induced Effects in High-Grade (WHO Grade 3-4) Gliomas

BACKGROUND AND OBJECTIVES: Posttreatment radiologic deterioration of an irradiated high-grade (WHO grade 3-4) glioma (HGG) may be the result of true progressive disease or treatment-induced effects (TIE). Differentiation between these entities is of great importance but remains a diagnostic challenge. This study assesses the diagnostic value of conventional MRI characteristics to differentiate progressive disease from TIE in HGGs. METHODS: In this single-center, retrospective, consecutive cohort study, we included adults with a HGG who were treated with (chemo-)radiotherapy and subsequently developed a new or increasing contrast-enhancing lesion on conventional follow-up MRI. TIE and progressive disease were defined radiologically as stable/decreased for ≥6 weeks or Response Assessment in Neuro-Oncology progression and histologically as TIE without viable tumor or progressive disease. Two neuroradiologists assessed 21 preselected MRI characteristics of the progressive lesions. The statistical analysis included logistic regression to develop a full multivariable model, a diagnostic model with model reduction, and a Cohen kappa interrater reliability (IRR) coefficient. RESULTS: A total of 210 patients (median age 61 years, interquartile range 54-68, 189 male) with 284 lesions were included, of whom 141 (50%) had progressive disease. Median time to progressive disease was 2 (0.7-6.1) and to TIE 0.9 (0.7-3.5) months after radiotherapy. After multivariable modeling and model reduction, the following determinants prevailed: radiation dose (odds ratio [OR] 0.68, 95% CI 0.49-0.93), longer time to progression (TTP; OR 3.56, 95% CI 1.84-6.88), marginal enhancement (OR 2.04, 95% CI 1.09-3.83), soap bubble enhancement (OR 2.63, 95% CI 1.39-4.98), and isointense apparent diffusion coefficient (ADC) signal (OR 2.11, 95% CI 1.05-4.24). ORs >1 indicate higher odds of progressive disease. The Hosmer & Lemeshow test showed good calibration ( p = 0.947) and the area under the receiver operating characteristic curve was 0.722 (95% CI 0.66-0.78). In the glioblastoma subgroup, TTP, marginal enhancement, and ADC signal were significant. IRR analysis between neuroradiologists revealed moderate to near perfect agreement for the predictive items but poor agreement for others. DISCUSSION: Several characteristics from conventional MRI are significant predictors for the discrimination between progressive disease and TIE. However, IRR was variable. Conventional MRI characteristics from this study should be incorporated into a multimodal diagnostic model with advanced imaging techniques. CLASSIFICATION OF EVIDENCE: This study provides Class II evidence that in patients with irradiated HGGs, radiation dose, longer TTP, marginal enhancement, soap bubble enhancement, and isointense ADC signal distinguish progressive disease from TIE

Wind-Mediated Spread of Low-Pathogenic Avian Influenza Virus into the Environment during Outbreaks at Commercial Poultry Farms

Avian influenza virus-infected poultry can release a large amount of virus-contaminated droppings that serve as sources of infection for susceptible birds. Much research so far has focused on virus spread within flocks. However, as fecal material or manure is a major constituent of airborne poultry dust, virus-contaminated particulate matter from infected flocks may be dispersed into the environment. We collected samples of suspended particulate matter, or the inhalable dust fraction, inside, upwind and downwind of buildings holding poultry infected with low-pathogenic avian influenza virus, and tested them for the presence of endotoxins and influenza virus to characterize the potential impact of airborne influenza virus transmission during outbreaks at commercial poultry farms. Influenza viruses were detected by RT-PCR in filter-rinse fluids collected up to 60 meters downwind from the barns, but virus isolation did not yield any isolates. Viral loads in the air samples were low and beyond the limit of RT-PCR quantification except for one in-barn measurement showing a virus concentration of 8.48 x 10(4) genome copies/m(3). Air samples taken outside poultry barns had endotoxin concentrations of ~50 EU/m(3) that declined with increasing distance from the barn. Atmospheric dispersion modeling of particulate matter, using location-specific meteorological data for the sampling days, demonstrated a positive correlation between endotoxin measurements and modeled particulate matter concentrations, with an R(2) varying from 0.59 to 0.88. Our data suggest that areas at high risk for human or animal exposure to airborne influenza viruses can be modeled during an outbreak to allow directed interventions following targeted surveillance

Wind-mediated spread of low-pathogenic avian influenza virus into the environment during outabreaks at commercial poultry farms

Avian influenza virus-infected poultry can release a large amount of virus-contaminated droppings that serve as sources of infection for susceptible birds. Much research so far has focused on virus spread within flocks. However, as fecal material or manure is a major constituent of airborne poultry dust, virus-contaminated particulate matter from infected flocks may be dispersed into the environment. We collected samples of suspended particulate matter, or the inhalable dust fraction, inside, upwind and downwind of buildings holding poultry infected with low-pathogenic avian influenza virus, and tested them for the presence of endotoxins and influenza virus to characterize the potential impact of airborne influenza virus transmission during outbreaks at commercial poultry farms. Influenza viruses were detected by RT-PCR in filter-rinse fluids collected up to 60 meters downwind from the barns, but virus isolation did not yield any isolates. Viral loads in the air samples were low and beyond the limit of RT-PCR quantification except for one in-barn measurement showing a virus concentration of 8.48x104 genome copies/m3. Air samples taken outside poultry barns had endotoxin concentrations of ∼50 EU/m3 that declined with increasing distance from the barn. Atmospheric dispersion modeling of particulate matter, using location-specific meteorological data for the sampling days, demonstrated a positive correlation between endotoxin measurements and modeled particulate matter concentrations, with an R2 varying from 0.59 to 0.88. Our data suggest that areas at high risk for human or animal exposure to airborne influenza viruses can be modeled during an outbreak to allow directed interventions following targeted surveillance.</p

Spatial transmission risk during the 2007-2010 Q fever epidemic in The Netherlands: Analysis of the farm-to-farm and farm-to-resident transmission.

Between 2007 and 2010 a Q fever epidemic in Dutch dairy goat farms caused a large Q fever outbreak in human residents in the southern part of the Netherlands. Here we characterize the transmission of Coxiella burnetii, the aetiological agent of Q fever, between infected and susceptible dairy goat farms by estimating a spatial transmission kernel. In addition, we characterize the zoonotic transmission of C. burnetii by estimating the spatial kernel for transmission from infected farms to neighbouring residents. Whereas the range of between-farm transmission is comparable to the scale of the Netherlands, likely due to long-range between-farm contacts such as animal transport, the transmission risk from farms to humans is more localized, although still extending to 10 km and beyond. Within a range of about 10 km, the transmission risk from an infected goat farm to a single resident is of the same order of magnitude as the farm-to-farm transmission risk per animal in a receiving farm. We illustrate how, based on the estimated kernels, spatial patterns of transmission risks between farms and from farms to residents can be calculated and visualized by means of risk maps, offering further insight relevant to policy making in a one-health context.</p

Combined dataset depicting the farms, air-sample type, and location relative to the barn, and corresponding laboratory results including influenza virus RT-PCR detection, turkey COI RT-PCR detection, endotoxin quantification, and modeled relative particulate matter concentrations of all GSP and MD8-AirPort filters assayed.

<p>nd) not determined</p><p>*) Below detection limit</p><p>#) outside plume</p><p>Combined dataset depicting the farms, air-sample type, and location relative to the barn, and corresponding laboratory results including influenza virus RT-PCR detection, turkey COI RT-PCR detection, endotoxin quantification, and modeled relative particulate matter concentrations of all GSP and MD8-AirPort filters assayed.</p

Improved correlation of human Q fever incidence to modelled C. burnetii concentrations by means of an atmospheric dispersion model

BACKGROUND: Atmospheric dispersion models (ADMs) may help to assess human exposure to airborne pathogens. However, there is as yet limited quantified evidence that modelled concentrations are indeed associated to observed human incidence. METHODS: We correlated human Q fever (caused by the bacterium Coxiella burnetii) incidence data in the Netherlands to modelled concentrations from three spatial exposure models: 1) a NULL model with a uniform concentration distribution, 2) a DISTANCE model with concentrations proportional to the distance between the source and residential addresses of patients, and 3) concentrations modelled by an ADM using three simple emission profiles. We used a generalized linear model to correlate the observed incidences to modelled concentrations and validated it using cross-validation. RESULTS: ADM concentrations generally correlated the best to the incidence data. The DISTANCE model always performed significantly better than the NULL model. ADM concentrations based on wind speeds exceeding threshold values of 0 and 2 m/s performed better than those based on 4 or 6 m/s. This might indicate additional exposure to bacteria originating from a contaminated environment. CONCLUSIONS: By adding meteorological information the correlation between modelled concentration and observed incidence improved, despite using three simple emission profiles. Although additional information is needed - especially regarding emission data - these results provide a basis for the use of ADMs to predict and to visualize the spread of airborne pathogens during livestock, industry and even bio-terroristic related outbreaks or releases to a surrounding human population

Improved correlation of human Q fever incidence to modelled C. burnetii concentrations by means of an atmospheric dispersion model

BACKGROUND: Atmospheric dispersion models (ADMs) may help to assess human exposure to airborne pathogens. However, there is as yet limited quantified evidence that modelled concentrations are indeed associated to observed human incidence. METHODS: We correlated human Q fever (caused by the bacterium Coxiella burnetii) incidence data in the Netherlands to modelled concentrations from three spatial exposure models: 1) a NULL model with a uniform concentration distribution, 2) a DISTANCE model with concentrations proportional to the distance between the source and residential addresses of patients, and 3) concentrations modelled by an ADM using three simple emission profiles. We used a generalized linear model to correlate the observed incidences to modelled concentrations and validated it using cross-validation. RESULTS: ADM concentrations generally correlated the best to the incidence data. The DISTANCE model always performed significantly better than the NULL model. ADM concentrations based on wind speeds exceeding threshold values of 0 and 2 m/s performed better than those based on 4 or 6 m/s. This might indicate additional exposure to bacteria originating from a contaminated environment. CONCLUSIONS: By adding meteorological information the correlation between modelled concentration and observed incidence improved, despite using three simple emission profiles. Although additional information is needed - especially regarding emission data - these results provide a basis for the use of ADMs to predict and to visualize the spread of airborne pathogens during livestock, industry and even bio-terroristic related outbreaks or releases to a surrounding human population

Dispersion of particulate matter around poultry farms, based on field measurements of endotoxin concentrations in air samples and OPS-ST particulate matter modeling.

<p>A) Maps illustrating the air sampling locations together with the atmospheric dispersion of particulate matter (relative to the source) that was modeled using meteorological data corresponding with the day and timeframe (10:00AM—16:00PM) of air sampling. B) Scatterplot of modeled dispersion and measured endotoxin concentration. Qualitative results of influenza virus RNA and turkey cell DNA detection are depicted as well.</p

Endotoxin concentrations in air samples outside poultry barns are depicted in relation to the distance from the poultry barn, illustrating a reduction of airborne endotoxin with increasing distance from the source.

<p>Endotoxin concentrations in air samples outside poultry barns are depicted in relation to the distance from the poultry barn, illustrating a reduction of airborne endotoxin with increasing distance from the source.</p

Nationwide surveillance reveals frequent detection of carbapenemase-producing Enterobacterales in Dutch municipal wastewater

Carbapenemase-producing Enterobacterales (CPE) pose a threat to public health necessitating restriction of further spread. Tools to efficiently monitor the prevalence of these still relatively rare resistant bacteria and insight in routes of dissemination are pivotal for the development of prevention strategies. By analysis of untreated and treated wastewater from 100 municipal wastewater treatment plants (WWTPs) together serving over 40% of the Dutch population, this study investigated both the distribution of CPE in the Dutch population, and WWTPs as a source of CPE in the aquatic environment. CPE were detected at 89% of the WWTPs, in 87 influents and 53 effluents. Overall, 15 different CPE-types were detected based on species and carbapenemase gene. The most widely distributed were E. coli carrying blaOXA-48-like genes, which were detected at 88 WWTPs including small WWTPs without connected health care institutions (HCI). BlaOXA-48-like-positive-K. pneumoniae, blaNDM- or blaKPC-positive E. coli, and blaNDM- or blaKPC-positive K. pneumoniae were detected at 33, 20, and 14 WWTPs, respectively. Mean influent and effluent CPE concentrations were 7.9×102 cfu/l and 11 cfu/l. The total daily number of CPE discharged by the 100 WWTPs was estimated to be 2.2×1011 cfu. In multivariate analysis, CPE concentrations in untreated wastewater were positively associated with WWTP size and E. coli concentrations, but not with the presence of HCI. Based on the total number of CPE (9.7×1012 cfu) and ESBL-E. coli (2.41015 cfu) in influents, and a prevalence of approximately 5% in the Dutch population for ESBL-E. coli, the prevalence of CPE in the Dutch population was roughly estimated to be 0.02%. Wastewater surveillance is an efficient tool to monitor the distribution of CPE in the population at a national level and may supplement human surveillance data. CPE are emitted to the aquatic environment with treated wastewater and associated public health risks need to be determined