152 research outputs found
Bartonella spp. DNA Associated with Biting Flies from California
Bartonella DNA was investigated in 104 horn flies (Haematobia spp.), 60 stable flies (Stomoxys spp.), 11 deer flies (Chrysops spp.), and 11 horse flies (Tabanus spp.) collected on cattle in California. Partial sequencing indicated B. bovis DNA in the horn fly pool and B. henselae type M DNA in one stable fly
A literature review of magnetic resonance imaging sequence advancements in visualizing functional neurosurgery targets
OBJECTIVE: Historically, preoperative planning for functional neurosurgery has depended on the indirect localization of target brain structures using visible anatomical landmarks. However, recent technological advances in neuroimaging have permitted marked improvements in MRI-based direct target visualization, allowing for refinement of "first-pass" targeting. The authors reviewed studies relating to direct MRI visualization of the most common functional neurosurgery targets (subthalamic nucleus, globus pallidus, and thalamus) and summarize sequence specifications for the various approaches described in this literature. METHODS: The peer-reviewed literature on MRI visualization of the subthalamic nucleus, globus pallidus, and thalamus was obtained by searching MEDLINE. Publications examining direct MRI visualization of these deep brain stimulation targets were included for review. RESULTS: A variety of specialized sequences and postprocessing methods for enhanced MRI visualization are in current use. These include susceptibility-based techniques such as quantitative susceptibility mapping, which exploit the amount of tissue iron in target structures, and white matter attenuated inversion recovery, which suppresses the signal from white matter to improve the distinction between gray matter nuclei. However, evidence confirming the superiority of these sequences over indirect targeting with respect to clinical outcome is sparse. Future targeting may utilize information about functional and structural networks, necessitating the use of resting-state functional MRI and diffusion-weighted imaging. CONCLUSIONS: Specialized MRI sequences have enabled considerable improvement in the visualization of common deep brain stimulation targets. With further validation of their ability to improve clinical outcomes and advances in imaging techniques, direct visualization of targets may play an increasingly important role in preoperative planning
Targeted exhaled breath analysis for detection of Pseudomonas aeruginosa in cystic fibrosis patients
Background Pseudomonas aeruginosa (PA) is an important respiratory pathogen for cystic fibrosis (CF) patients. Routine microbiology surveillance is time-consuming, and is best performed on expectorated sputum. As alternative, volatile organic compounds (VOCs) may be indicative of PA colonisation. In this study, we aimed to identify VOCs associated with PA in literature and perform targeted exhaled breath analysis to recognize PA positive CF patients non-invasively. Methods This study consisted of 1) a literature review to select VOCs of interest, and 2) a cross-sectional CF study. Definitions used: A) PA positive, PA culture at visit/chronically; B) PA free, no PA culture in ≥12 months. Exhaled VOCs were identified via quadrupole MS. The primary endpoint was the area under the receiver operating characteristics curve (AUROCC) of individual VOCs as well as combined VOCs against PA culture. Results 241 VOCs were identified in literature, of which 56 were further evaluated, and 13 could be detected in exhaled breath in our cohort. Exhaled breath of 25 pediatric and 28 adult CF patients, PA positive (n=16) and free (n=28) was available. 3/13 VOCs were significantly (p<0.05) different between PA groups in children; none were in adults. Notably, a composite model based on 5 or 1 VOC(s) showed an AUROCC of 0.86 (CI 0.71–1.0) and 0.87 (CI 0.72–1.0) for adults and children, respectively. Conclusions Targeted VOC analysis appears to discriminate children and adults with and without PA positive cultures with clinically acceptable sensitivity values
Sarcoma classification by DNA methylation profiling
Sarcomas are malignant soft tissue and bone tumours affecting adults, adolescents and children. They represent a morphologically heterogeneous class of tumours and some entities lack defining histopathological features. Therefore, the diagnosis of sarcomas is burdened with a high inter-observer variability and misclassification rate. Here, we demonstrate classification of soft tissue and bone tumours using a machine learning classifier algorithm based on array-generated DNA methylation data. This sarcoma classifier is trained using a dataset of 1077 methylation profiles from comprehensively pre-characterized cases comprising 62 tumour methylation classes constituting a broad range of soft tissue and bone sarcoma subtypes across the entire age spectrum. The performance is validated in a cohort of 428 sarcomatous tumours, of which 322 cases were classified by the sarcoma classifier. Our results demonstrate the potential of the DNA methylation-based sarcoma classification for research and future diagnostic applications
Effect of natural gamma background radiation on portal monitor radioisotope unmixing
National security relies on several layers of protection. One of the most
important is the traffic control at borders and ports that exploits Radiation
Portal Monitors (RPMs) to detect and deter potential smuggling attempts. Most
portal monitors rely on plastic scintillators to detect gamma rays. Despite
their poor energy resolution, their cost effectiveness and the possibility of
growing them in large sizes makes them the gamma-ray detector of choice in
RPMs. Unmixing algorithms applied to organic scintillator spectra can be used
to reliably identify the bare and unshielded radionuclides that triggered an
alarm, even with fewer than 1,000 detected counts and in the presence of two or
three nuclides at the same time. In this work, we experimentally studied the
robustness of a state-of-the-art unmixing algorithm to different radiation
background spectra, due to varying atmospheric conditions, in the 16 C
to 28 C temperature range. In the presence of background, the algorithm
is able to identify the nuclides present in unknown radionuclide mixtures of
three nuclides, when at least 1,000 counts from the sources are detected. With
fewer counts available, we found larger differences of approximately 35.9
between estimated nuclide fractions and actual ones. In these low count rate
regimes, the uncertainty associated by our algorithm with the identified
fractions could be an additional valuable tool to determine whether the
identification is reliable or a longer measurement to increase the
signal-to-noise ratio is needed. Moreover, the algorithm identification
performances are consistent throughout different data sets, with negligible
differences in the presence of background types of different intensity and
spectral shape
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