IMI are emerging as an important cause of mortality and morbidity among the growing number of immunocompromised children. A retrospective chart review was performed in all patients with a proven diagnosis of IMI over an eight-yr period (1997-2004) at The Hospital for Sick Children, Toronto, Canada to document the incidence, clinical spectrum, microbiology, treatment, and outcome of pediatric IMI. Twenty-eight patients developed IMI over the study period (10 cancer, 12 HCT, and six SOT patients). IMI occurred in 0.51%, 2.2% and 3.2% after a median time of 118, 60 and 71 days, among cancer, HCT and SOT recipients, respectively. Aspergillus spp. infection was diagnosed most commonly (23 patients) and the most common site of infection was the lung (21 patients). Patients at increased risk included those with acute myelogenous leukemia, allogeneic unrelated HCT recipients, graft-versus-host disease, and lung transplant recipients. The mortality after one yr was 60% among cancer patients, 58% among HCT patients, and 16% among SOT patients.

Torovirus remains the most commonly identified cause of NVG at The Hospital for Sick Children. Most NVG cases were epidemiologically linked, and a significant reduction in cases occurred after the institution of enhanced infection control practices following an outbreak of vancomycin-resistant Enterococcus. Improved education and surveillance for NVG should lead to further reduction in this problem.

Background The detection of coronary artery disease (CAD) from the X-ray coronary angiography is a crucial process which is hindered by various issues such as presence of noise, insufficient contrast of the input images along with the uncertainties caused by the motion due to respiration and variation of angles of vessels. Methods In this article, an Automated Segmentation and Diagnosis of Coronary Artery Disease (ASCARIS) model is proposed in order to overcome the prevailing challenges in detection of CAD from the X-ray images. Initially, the preprocessing of the input images was carried out by using the modified wiener filter for the removal of both internal and external noise pixels from the images. Then, the enhancement of contrast was carried out by utilizing the optimized maximum principal curvature to preserve the edge information thereby contributing to increasing the segmentation accuracy. Further, the binarization of enhanced images was executed by the means of OTSU thresholding. The segmentation of coronary arteries was performed by implementing the Attention-based Nested U-Net, in which the attention estimator was incorporated to overcome the difficulties caused by intersections and overlapped arteries. The increased segmentation accuracy was achieved by performing angle estimation. Finally, the VGG-16 based architecture was implemented to extract threefold features from the segmented image to perform classification of X-ray images into normal and abnormal classes. Results The experimentation of the proposed ASCARIS model was carried out in the MATLAB R2020a simulation tool and the evaluation of the proposed model was compared with several existing approaches in terms of accuracy, sensitivity, specificity, revised contrast to noise ratio, mean square error, dice coefficient, Jaccard similarity, Hausdorff distance, Peak signal-to-noise ratio (PSNR), segmentation accuracy and ROC curve. Discussion The results obtained conclude that the proposed model outperforms the existing approaches in all the evaluation metrics thereby achieving optimized classification of CAD. The proposed method removes the large number of background artifacts and obtains a better vascular structure.