Typhoid perforation in Maiduguri, Nigeria

Background: Typhoid fever is still a serious health burden in our environment. Though it is primarily a medical problem, its complications such as perforation require the attention of the surgeon. The disease spears no age or sex; including pregnant women. Four patients with pregnancy and pregnancy related conditions were managed during this period of review and this actually stimulated this study. Method: This is a retrospective study of all patients managed for typhoid perforation over a five-year period in University of Maiduguri Teaching Hospital. Results: Four hundred and sixty-seven patients were managed for typhoid fever in University of Maiduguri Teaching Hospital during the 5-year study period. Forty-three (9.2%) of these patients had typhoid perforations. There were six (14%) deaths among those patients with perforations. High rate of mortality was noted among those with pregnancy and pregnancy related perforations (50%) and multiple perforations requiring resection and anastomosis (100%). Conclusion: The morbidity and mortality associated with typhoid fever in our environment can only be reduced significantly when the nation and public health officials begin to pursue the principles of primary health care with all seriousness it deserves i.e. emphasis on provision of potable water supply and sanitation. Key Words: Typhoid perforation, pregnancy, public health Annals of African Medicine Vol.3(2) 2004: 69-7

Oral gram-negative rods and yeasts in hospitalized patients

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Online optimization of visual stimuli for reducing fatigue in SSVEP-based BCIs

INTRODUCTION: Visual fatigue induced by flickering stimuli has always been a problem to steady state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs). Some previous studies revealed that different stimulation properties such as frequencies, duty cycles and colors have impact on user’s fatigue and performance. Importantly, the stimulation inducing less fatigue usually causes a reduction of system performance [1], and thus to design an optimal visual stimulator for SSVEP-based BCIs, there is a tradeoff between the user’s fatigue and performance. Unfortunately, so far most of the visual fatigue evaluation methods relied on subjective ...published_or_final_versio

Fatigue evaluation through EEG analysis using multi-scale entropy in SSVEP-based BCIs

INTRODUCTION: Fatigue is a big challenge when moving a steady state visual evoked potential (SSVEP) based brain-computer interfaces (BCIs) from laboratory into real-life applications [1], as it not only harms the system performance, but also causes users’ discomfort. Towards eventually fatigue reduction, an accurate and objective evaluation of fatigue level is the first and also a crucial step. On the other hand, multi-scale entropy (MSE) can ...published_or_final_versio

Tolerance of banana for fusarium wilt is associated with early H2O2 accumulation in the roots

Banana plants derived from a tissue culture process possess a high rate of random variations that were widely used as popular cultivars due to the new desired traits. In this study, two near-isogenic lines, one susceptible (parental Williams-8818) and the other resistant (somaclonal variation progeny Williams-8818-1 from Williams-8818) to Fusarium oxysporum f. sp. Cubense (Foc4), were inoculated with race 4 of F. oxysporum (Fox). Production of O2•− , H2O2 and MDA, as well as changes in enzyme activities, and transcript levels of SOD and CAT in root extracts were monitored every 24 h over 4 days. The histochemical location of H2O2 was also detected. In the resistant iso-line, the accumulation of O2•− and H2O2, and the activation of SOD occurred in the first 24 h, but activation of CAT reached its maximum only after 48 h. All changes were generally lower in the susceptible iso-line when compared to the resistant iso-line. SOD transcripts were further up-regulated until 72 h in the resistant iso-line, but not in the susceptible iso-line. CAT expression was not affected in any of the two iso-lines. This suggests that expressions of the two key genes in the antioxidant system are less suitable indicators for Foc resistance in banana. In contrast, the first “oxidative burst” is a better indicator for different susceptibility of banana to Foc.Key words: Banana, Fusarium oxysporum, catalase, reactive oxygen species, somaclonal variation, disease resistance

On the Thermoelectric Effect of Interface Imperfections

Ordinary thermocouples use the well-known Seebeck effect to measure the temperature at the junction of two different conductors. The electromotive force generated by the heat depends on the difference between the respective thermoelectric powers of the contacting metals and the junction temperature. Figure 1 shows the schematic diagram of the thermoelectric measurement as most often used in nondestructive materials characterization. One of the reference electrodes is heated by electrical means to a preset temperature of 100 – 300 °C, pretty much like the tip of a temperature-stabilized soldering iron, and connected to the inverting (−) input of the differential amplifier driving the indicator. The other electrode is left cold at essentially room temperature and connected to the non-inverting (+) input. The measurement is done quickly in a few seconds to assure (i) that the hot reference electrode is not cooled down perceivably by the specimen and (ii) that the rest of the specimen beyond the close vicinity of the contact point is not warmed up perceivably. Ideally, regardless of the temperature difference between the junctions, only thermocouples made of different materials, i.e., materials of different thermoelectric power, will generate thermoelectric signal. This unique feature makes the simple thermoelectric tester one of the most sensitive material discriminators used in nondestructive inspection

Liver grafts for transplantation from donors with diabetes: an analysis of the Scientific Registry of Transplant Recipients database

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Automatic segmentation of prostate MRI using convolutional neural networks: Investigating the impact of network architecture on the accuracy of volume measurement and MRI-ultrasound registration

Convolutional neural networks (CNNs) have recently led to significant advances in automatic segmentations of anatomical structures in medical images, and a wide variety of network architectures are now available to the research community. For applications such as segmentation of the prostate in magnetic resonance images (MRI), the results of the PROMISE12 online algorithm evaluation platform have demonstrated differences between the best-performing segmentation algorithms in terms of numerical accuracy using standard metrics such as the Dice score and boundary distance. These small differences in the segmented regions/boundaries outputted by different algorithms may potentially have an unsubstantial impact on the results of downstream image analysis tasks, such as estimating organ volume and multimodal image registration, which inform clinical decisions. This impact has not been previously investigated. In this work, we quantified the accuracy of six different CNNs in segmenting the prostate in 3D patient T2-weighted MRI scans and compared the accuracy of organ volume estimation and MRI-ultrasound (US) registration errors using the prostate segmentations produced by different networks. Networks were trained and tested using a set of 232 patient MRIs with labels provided by experienced clinicians. A statistically significant difference was found among the Dice scores and boundary distances produced by these networks in a non-parametric analysis of variance (p < 0.001 and p < 0.001, respectively), where the following multiple comparison tests revealed that the statistically significant difference in segmentation errors were caused by at least one tested network. Gland volume errors (GVEs) and target registration errors (TREs) were then estimated using the CNN-generated segmentations. Interestingly, there was no statistical difference found in either GVEs or TREs among different networks, (p = 0.34 and p = 0.26, respectively). This result provides a real-world example that these networks with different segmentation performances may potentially provide indistinguishably adequate registration accuracies to assist prostate cancer imaging applications. We conclude by recommending that the differences in the accuracy of downstream image analysis tasks that make use of data output by automatic segmentation methods, such as CNNs, within a clinical pipeline should be taken into account when selecting between different network architectures, in addition to reporting the segmentation accuracy

Bioinformatics advances in saliva diagnostics

There is a need recognized by the National Institute of Dental & Craniofacial Research and the National Cancer Institute to advance basic, translational and clinical saliva research. The goal of the Salivaomics Knowledge Base (SKB) is to create a data management system and web resource constructed to support human salivaomics research. To maximize the utility of the SKB for retrieval, integration and analysis of data, we have developed the Saliva Ontology and SDxMart. This article reviews the informatics advances in saliva diagnostics made possible by the Saliva Ontology and SDxMart

Longitudinal Image Registration with Temporal-order and Subject-specificity Discrimination

Morphological analysis of longitudinal MR images plays a key role in monitoring disease progression for prostate cancer patients, who are placed under an active surveillance program. In this paper, we describe a learning-based image registration algorithm to quantify changes on regions of interest between a pair of images from the same patient, acquired at two different time points. Combining intensity-based similarity and gland segmentation as weak supervision, the population-data-trained registration networks significantly lowered the target registration errors (TREs) on holdout patient data, compared with those before registration and those from an iterative registration algorithm. Furthermore, this work provides a quantitative analysis on several longitudinal-data-sampling strategies and, in turn, we propose a novel regularisation method based on maximum mean discrepancy, between differently-sampled training image pairs. Based on 216 3D MR images from 86 patients, we report a mean TRE of 5.6 mm and show statistically significant differences between the different training data sampling strategies.Comment: Accepted at MICCAI 202