Primary cardiac sarcomas are rare tumors with an unfavourable prognosis. Complete surgical resection is currently the only mode of therapy proven to show any benefit. We report the cases of two patients presenting with features of obstruction and embolism and a presumed diagnosis of left atrial myxoma. At operation they were unexpectedly found to have large tumours raising strong suspicions of malignancy. Due to the extensive involvement of intracardiac structures with little possibility of reconstruction together with poor general condition of the patient, debulking was deemed to be the only viable option. Subsequent histology confirmed the diagnosis of sarcoma in both patients. Surgery produced immediate and effective symptom relief. The first patient died four months after the operation and second patient is still alive at 12 months after her operation. A brief review of literature on cardiac sarcoma is presented.
At 8:52 am on 8 October 2005 a massive earthquake wracked northern Pakistan and Kashmir. Various teams were sent to Islamabad and the disaster region from the UK. We discuss the types of injury patterns seen and recommend that a central register of volunteers should be created to deal with similar situations in the future.
Nearly 3.5 billion humans have oral health issues, including dental caries, which requires dentist-patient exposure in oral examinations. The automated approaches identify and locate carious regions from dental images by localizing and processing either colored photographs or X-ray images taken via specialized dental photography cameras. The dentists’ interpretation of carious regions is difficult since the detected regions are masked using solid coloring and limited to a particular dental image type. The software-based automated tools to localize caries from dental images taken via ordinary cameras requires further investigation. This research provided a mixed dataset of dental photographic (colored or X-ray) images, instantiated a deep learning approach to enhance the existing dental image carious regions’ localization procedure, and implemented a full-fledged tool to present carious regions via simple dental images automatically. The instantiation mainly exploits the mixed dataset of dental images (colored photographs or X-rays) collected from multiple sources and pre-trained hybrid Mask RCNN to localize dental carious regions. The evaluations performed by the dentists showed that the correctness of annotated datasets is up to 96%, and the accuracy of the proposed system is between 78% and 92%. Moreover, the system achieved the overall satisfaction level of dentists above 80%.
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