Diagnostic value of non-enhanced computed tomography in identifying location of ruptured cerebral aneurysm in patients with aneurysmal subarachnoid haemorrhage

Background. In patients with SAH and multiple aneurysms, the ruptured lesion must be identified to prevent recurrent bleeding.Aim of the study. To assess the diagnostic value of non-enhanced computed tomography (NECT) in identifying the rupture site in patients with subarachnoid haemorrhage (SAH) and multiple aneurysms.Material and methods. We included patients with SAH revealed by NECT and multiple aneurysms detected on computed tomography angiography (CTA) in whom a ruptured aneurysm was identified during neurosurgery. Two radiologists predicted the location of the ruptured aneurysm based on the distribution of the SAH and location of intracerebral haematoma (ICH) by NECT.Results. Eighty-three patients with a mean age of 55.7 ± 14.4 years were included. Ruptured aneurysms were significantly larger (mean size 7.7 ± 4.7 mm) than unruptured aneurysms (mean size 5.9 ± 4.5 mm; p = 0.014). Interobserver agreement was 0.86 (p < 0.001). Overall sensitivity and specificity of radiological prediction were 78.3% (95% CI, 68.6%-87.1%) and 96.4% (95% CI, 94.3%-97.8%) respectively. Overall PPV and NPV were 78.3% (95% CI, 67.6%-86.3%) and 96.8% (95% CI, 94.8%-98.1%) respectively. The sensitivity and PPV for aneurysms in the anterior communicating, anterior, and middle cerebral arteries appeared to be significantly higher than in other locations (p = 0.015 and 0.019 respectively). Analysis of independent predictive factors of correct radiological location revealed that ICH predisposes to a correct radiological diagnosis with an odds ratio of 8.57 (95% CI, 1.07-68.99; p = 0.03).Conclusions. NECT has a high diagnostic value in identifying the source of bleeding in patients with multiple aneurysms for anterior circulation aneurysms, especially with coexisting ICH. For other locations, NECT is not reliable enough to base treatment decisions upon

Release of Lungworm Larvae from Snails in the Environment: Potential for Alternative Transmission Pathways

Background: Gastropod-borne parasites may cause debilitating clinical conditions in animals and humans following the consumption of infected intermediate or paratenic hosts. However, the ingestion of fresh vegetables contaminated by snail mucus and/or water has also been proposed as a source of the infection for some zoonotic metastrongyloids (e.g., Angiostrongylus cantonensis). In the meantime, the feline lungworms Aelurostrongylus abstrusus and Troglostrongylus brevior are increasingly spreading among cat populations, along with their gastropod intermediate hosts. The aim of this study was to assess the potential of alternative transmission pathways for A. abstrusus and T. brevior L3 via the mucus of infected Helix aspersa snails and the water where gastropods died. In addition, the histological examination of snail specimens provided information on the larval localization and inflammatory reactions in the intermediate host. Methodology/Principal Findings: Twenty-four specimens of H. aspersa received ~500 L1 of A. abstrusus and T. brevior, and were assigned to six study groups. Snails were subjected to different mechanical and chemical stimuli throughout 20 days in order to elicit the production of mucus. At the end of the study, gastropods were submerged in tap water and the sediment was observed for lungworm larvae for three consecutive days. Finally, snails were artificially digested and recovered larvae were counted and morphologically and molecularly identified. The anatomical localization of A. abstrusus and T. brevior larvae within snail tissues was investigated by histology. L3 were detected in the snail mucus (i.e., 37 A. abstrusus and 19 T. brevior) and in the sediment of submerged specimens (172 A. abstrusus and 39 T. brevior). Following the artificial digestion of H. aspersa snails, a mean number of 127.8 A. abstrusus and 60.3 T. brevior larvae were recovered. The number of snail sections positive for A. abstrusus was higher than those for T. brevior. Conclusions: Results of this study indicate that A. abstrusus and T. brevior infective L3 are shed in the mucus of H. aspersa or in water where infected gastropods had died submerged. Both elimination pathways may represent alternative route(s) of environmental contamination and source of the infection for these nematodes under field conditions and may significantly affect the epidemiology of feline lungworms. Considering that snails may act as intermediate hosts for other metastrongyloid species, the environmental contamination by mucus-released larvae is discussed in a broader context

Classification of Foetal Distress and Hypoxia Using Machine Learning Approaches

© 2018, Springer International Publishing AG, part of Springer Nature. Foetal distress and hypoxia (oxygen deprivation) is considered as a serious condition and one of the main factors for caesarean section in the obstetrics and Gynecology department. It is the third most common cause of death in new-born babies. Many foetuses that experienced some sort of hypoxic effects can develop series risks including damage to the cells of the central nervous system that may lead to life-long disability (cerebral palsy) or even death. Continuous labour monitoring is essential to observe the foetal well being. Foetal surveillance by monitoring the foetal heart rate with a cardiotocography is widely used. Despite the indication of normal results, these results are not reassuring, and a small proportion of these foetuses are actually hypoxic. In this paper, machine-learning algorithms are utilized to classify foetuses which are experiencing oxygen deprivation using PH value (a measure of hydrogen ion concentration of blood used to specify the acidity or alkalinity) and Base Deficit of extra cellular fluid level (a measure of the total concentration of blood buffer base that indicates the metabolic acidosis or compensated respiratory alkalosis) as indicators of respiratory and metabolic acidosis, respectively, using open source partum clinical data obtained from Physionet. Six well know machine learning classifier models are utilised in our experiments for the evaluation; each model was presented with a set of selected features derived from the clinical data. Classifier’s evaluation is performed using the receiver operating characteristic curve analysis, area under the curve plots, as well as the confusion matrix. Our simulation results indicate that machine-learning algorithms provide viable methods that could delivery improvements over conventional analysis

Application of modified fuzzy clustering to medical data classification

Classification plays very important role in medical diagnosis. This paper presents fuzzy clustering method dedicated to classification algorithms. It focuses on two additional sub-methods modifying obtained clustering prototypes and leading to final prototypes, which are used for creating the classifier fuzzy if-then rules. The main goal of that work was to examine a performance of the classifier which uses such rules. Commonly used including medical benchmark databases were applied. In order to validate the results, each database was represented by 100 pairs of learning and testing subsets. The obtained classification quality was better in relation to the one of the best classifiers - Lagrangian SVM and suggests that presented clustering with additional sub-methods are appropriate to application to classification algorithms

Lightweight ceramsite concrete used as an additional protection of fuel oil tanks on ships

Introduction of the semi-elastic barrier into ship fuel tanks requires of use of core material which will play role of carrier for elastic, fuel resistant layer. Such core material should be ligth, non reactive with water and fuel, fire resistant, corrosion neutral and finally easy applicable and relatively cheap. In paper the different problems like components, preparation, application as well as surface treatment for using of the light concrete as core material are presented

Fuzzy prediction of fetal acidemia

Cardiotocography is the primary method for biophysical assessment of a fetal state. It is based mainly on the recording and analysis of fetal heart rate signal (FHR). Computer systems for fetal monitoring provide a quantitative description of FHR signals, however the effective methods for their qualitative assessment are still needed. The measurements of hydronium ions concentration (pH) in newborn cord blood is considered as the objective indicator of the fetal state. Improper pH level is a symptom of acidemia being the result of fetal hypoxia. The paper proposes a twostep analysis of signals allowing for effective prediction of the acidemia risk. The first step consists in the fuzzy classification of FHR signals. The task of fuzzy inference is to indicate signals that according to the FIGO guidelines represent the fetal wellbeing. These recordings are eliminated from the further classification with Lagrangian Support Vector Machines. The proposed procedure was evaluated using data collected with computerized fetal surveillance system. The classification results confirmed the high quality of the proposed fuzzy method of fetal state evaluation

Fuzzy system for evaluation of fetal heart rate signals using FIGO criteria

Cardiotocography is a biophysical method of fetal monitoring during pregnancy and labour. It is mainly based on recording and analysis of fetal heart activity. The computerized fetal monitoring systems provide the quantitative description of the recorded signals but the effective methods supporting the conclusion generation are still needed. The evaluation of the signal can be made using criteria recommended by FIGO. Nevertheless, the quantitative description of the traces is inconsistent with qualitative nature of the obstetric knowledge. Therefore, we applied the fuzzy system based on Takagi-Sugeno-Kang model to evaluate and classify signals. FIGO guidelines were used for developing a set of fuzzy conditional rules defining the system performance. The proposed system was evaluated using data collected with computerized fetal surveillance system – MONAKO. The classification results confirm the improvement of the fetal state evaluation quality while using the proposed fuzzy system support

Influence of gestational age on neural networks interpretation of fetal monitoring signals

Cardiotocographic monitoring (CTG) is a primary biophysical monitoring method for assessment of the fetal state and is based on analysis of fetal heart rate, uterine contraction activity and fetal movement signals. Visual analysis of CTG traces is very difficult so computer-aided fetal monitoring systems have become a standard in clinical centres. We proposed the application of neural networks for the prediction of fetal outcome using the parameters of quantitative description of acquired signals as inputs. We focused on the influence of the gestational age (during trace recording) on the fetal outcome classification quality. We designed MLP and RBF neural networks with changing the number of neurons in the hidden layer to find the best structure. Networks were trained and tested fifty times, with random cases assignment to training, validating and testing subset. We obtained the value of sensitivity index above 0.7, what may be regarded as good result. However additional trace grouping within similar gestational age, increased classification quality in the case of MLP networks