21 research outputs found
Listeria monocytogenes Infection in Israel and Review of Cases Worldwide
Listeria monocytogenes, an uncommon foodborne pathogen, is increasingly recognized as a cause of life-threatening disease. A marked increase in reported cases of listeriosis during 1998 motivated a retrospective nationwide survey of the infection in Israel. From 1995 to 1999, 161 cases were identified; 70 (43%) were perinatal infections, with a fetal mortality rate of 45%. Most (74%) of the 91 nonperinatal infections involved immunocompromised patients with malignancies, chronic liver disease, chronic renal failure, or diabetes mellitus. The common clinical syndromes in these patients were primary bacteremia (47%) and meningitis (28%). The crude case-fatality rate in this group was 38%, with a higher death rate in immunocompromised patients
DeepCADe: A Deep Learning Architecture for the Detection of Lung Nodules in CT Scans
Early detection of lung nodules in thoracic Computed Tomography (CT) scans is of great importance for the successful diagnosis and treatment of lung cancer. Due to improvements in screening technologies, and an increased demand for their use, radiologists are required to analyze an ever increasing amount of image data, which can affect the quality of their diagnoses. Computer-Aided Detection (CADe) systems are designed to assist radiologists in this endeavor.
In this thesis, we present DeepCADe, a novel CADe system for the detection of lung nodules in thoracic CT scans which produces improved results compared to the state-of-the-art in this field of research. CT scans are grayscale images, so the terms scans and images are used interchangeably in this work. DeepCADe was trained with the publicly available Lung Image Database Consortium and Image Database Resource Initiative (LIDC-IDRI) database, which contains 1018 thoracic CT scans with nodules of different shape and size, and is built on a Deep Convolutional Neural Network (DCNN), which is trained using the backpropagation algorithm to extract volumetric features from the input data and detect lung nodules in sub-volumes of CT images.
Considering only lung nodules that have been annotated by at least three radiologists, DeepCADe achieves a 2.1% improvement in sensitivity (true positive rate) over the best result in the current published scientific literature, assuming an equal number of false positives (FPs) per scan. More specifically, it achieves a sensitivity of 89.6% with 4 FPs per scan, or a sensitivity of 92.8% with 10 FPs per scan. Furthermore, DeepCADe is validated on a larger number of lung nodules compared to other studies (Table 5.2). This increases the variation in the appearance of nodules and therefore makes their detection by a CADe system more challenging.
We study the application of Deep Convolutional Neural Networks (DCNNs) for the detection of lung nodules in thoracic CT scans. We explore some of the meta parameters that affect the performance of such models, which include:
1. the network architecture, i.e. its structure in terms of convolution layers, fully-connected layers, pooling layers, and activation functions,
2. the receptive field of the network, which defines the dimensions of its input, i.e. how much of the CT scan is processed by the network in a single forward pass,
3. a threshold value, which affects the sliding window algorithm with which the network is used to detect nodules in complete CT scans, and
4. the agreement level, which is used to interpret the independent nodule annotations of four experienced radiologists.
Finally, we visualize the shape and location of annotated lung nodules and compare them to the output of DeepCADe. This demonstrates the compactness and flexibility in shape of the nodule predictions made by our proposed CADe system. In addition to the 5-fold cross validation results presented in this thesis, these visual results support the applicability of our proposed CADe system in real-world medical practice
Few ns Pulse Duration of Gain-Switched Ho:YAG Laser Pumped by an Active Q-Switched Tm:YLF Laser
This paper describes a gain-switched Ho:YAG laser that emitted at 2089 nm, driven by an actively Q-switched Tm:YLF laser as the pumping source. The laser attained few ns short pulse durations with high energies at controlled repetition rates due to the active Q-switch pump source. Using the gain-switch method, stable short pulse durations ranging from 3.5 to 7.2 ns, with an energy per pulse of 0.4 to 0.52 mJ, were achieved at repetition rates of up to 2.5 kHz. This design can have significant advantages in various fields, where accuracy in the repetition rate is essential and a passive Q-switch cannot be implemented due to its accuracy limitations, including sensing, LIDAR, medical procedures, and material processing
Precise determination of dolomite content in marine sediments
Dolomite (CaMg[CO3](2)) is a common rock-forming mineral. Nevertheless, its mechanisms of formation and the factors that cause dolomite concentration variations within the sedimentary records constitute long-standing geochemical questions. In addition, the flux of Mg2+ leaving the ocean by the formation of dolomite is a controversial question, with some studies arguing that dolomite formation is a negligible Mg2+ sink in the modern ocean, while others show that it constitutes more than 50% of the total Mg2+ removal rate. An important factor that impedes the resolution of the dolomite Mg2+ flux is the lack of analytical methods with adequate precision and detection limit to directly measure minute quantities of authigenic dolomite in marine sediments. Here, we present a new analytical method for direct, precise measurement of dolomite content in marine sediments. The method is based on sequential leaching of carbonate minerals in acid and tracks the CO2 emitted by the dissolution. Based on the measurement of gravimetric standards of calcite and dolomite, the method's detection limit and precision were determined as better than 0.2 and +/- 0.2 dry wt% of dolomite, respectively. The method out-performed dolomite quantification made by x-ray diffraction and by inductive coupled plasma mass-spectrometry, which provided precision of +/- 2 and +/- 1 dry wt%, respectively. Measurements of the dolomite content in modern sediments from the seafloor below the oligotrophic Eastern Mediterranean and the eutrophic Mississippi plume, and in clayey-silty alluvial soil from south-eastern Israel, demonstrated that the aforementioned precisions are also valid for natural samples.ISSN:1541-585
Structural analysis of an Escherichia coli endonuclease VIII covalent reaction intermediate
Endonuclease VIII (Nei) of Escherichia coli is a DNA repair enzyme that excises oxidized pyrimidines from DNA. Nei shares with formamidopyrimidine-DNA glycosylase (Fpg) sequence homology and a similar mechanism of action: the latter involves removal of the damaged base followed by two sequential β-elimination steps. However, Nei differs significantly from Fpg in substrate specificity. We determined the structure of Nei covalently crosslinked to a 13mer oligodeoxynucleotide duplex at 1.25 Å resolution. The crosslink is derived from a Schiff base intermediate that precedes β-elimination and is stabilized by reduction with NaBH(4). Nei consists of two domains connected by a hinge region, creating a DNA binding cleft between domains. DNA in the complex is sharply kinked, the deoxyribitol moiety is bound covalently to Pro1 and everted from the duplex into the active site. Amino acids involved in substrate binding and catalysis are identified. Molecular modeling and analysis of amino acid conservation suggest a site for recognition of the damaged base. Based on structural features of the complex and site-directed mutagenesis studies, we propose a catalytic mechanism for Nei
The association between ADHD and the severity of COVID-19 infection
Objective: Patients with ADHD are at increased risk of acquiring COVID-19. The present study assessed the possibility that ADHD also increases the risk of severe COVID-19 infection. Method: We assessed 1,870 COVID-19 positive patients, aged 5 to 60 years, registered in the database of Leumit Health Services (LHS, Israel), February to -June 2020, of whom 231 with ADHD. Logistic regression analysis models evaluated the association between ADHD and the dependent variables of being symptomatic/referral to hospitalization, controlling for demographic and medical variables. Results: Age, male sex, and BMI were confirmed to be significant risk factors for increased COVID-19 severity. ADHD was found to be associated with increased severity of COVID-19 symptoms (OR = 1.81, 95% CI [1.29, 2.52], p <.05) and referral to hospitalization (OR =1.93, 95% CI [1.06, 3.51], p =.03). Conclusion: ADHD is associated with poorer outcomes in COVID-19 infection.</p