ABSTRACT
Background Malignant pericardial effusion (MPE) is a rare presentation in cancer, associated with high morbidity and mortality. Pericardial effusion may cause cardiac tamponade and sudden death without timely intervention. Management of MPE in rural setting during coronavirus disease 2019 (COVID-19) pandemic would require a multidisciplinary team in a center with expertise and could be a challenge in rural India with limited resources. Methods Here we present a case of MPE of unknown origin in a 40-year-old woman, complicated by COVID-19 infection, which was successfully managed in a rural health setting in southern India. Results She was subjected to prompt pericardiocentesis to relieve symptoms and dose-dense palliative chemotherapy followed by metronomic chemotherapy and pro-anakoinosis therapy during COVID-19 home isolation. She currently has no evidence of disease and is tolerating treatment well. Conclusion Complex oncological emergencies like MPE of unknown origin can be managed in rural setting in India, with a slight modification of existing facility resulting in successful outcomes. This case of MPE in a 40-year-old lady is a glaring example of how the same can be achieved. Principle of pro-anakoinosis can be of value not only during pandemics and lockdowns but also otherwise, the feasibility of which has to be elucidated in larger studies.
ABSTRACT
The prime objective of this research is to develop an automatic tool 'Lung-Infection Visualizer' for marking the Region of Infection and cropping of the marked region in chest radiographs. The tool is also integrated with the feature extractor, feature visualization algorithm, and deep learning-based classifier. Thus, it facilitates the radiology experts where they can easily mark the infected region and visualize the region of infection. In this manuscript, the authors employ the template-based and Brute Force approach of feature mapping. Further, they applied the ResNet, Faster Recurrent Neural Network, XceptionNet, and VGG-16 deep learning-based classifiers for classifying the chest radiographs into bacterial pneumonia, viral pneumonia, COVID-19, and Normal classes. The authors also fine-tune the model parameters and hyperparameters for optimizing the performance of the deep learning-based models. The comparison in the performance proves that the VGG-16 model reports the highest accuracy of 90.07% and outperforms the other models on the dataset of 5,499 chest radiographs used for this research. The cropping tool is registered as Intellectual Property Rights in the name of authors with the registration number SW-14092/2021. And the title 'AutoCrop Tool'. © 2021 ACM.
ABSTRACT
OBJECTIVE: Coronavirus disease 2019 and the consequent public health and social distancing measures significantly impacted on service continuity for mental health patients. This article reports on contingency planning initiative in the Australian public sector. METHODS: Ninety-word care synopses were developed for each patient. These formed the basis for guided conversations between case managers and consultant psychiatrists to ensure safe service provision and retain a person-centred focus amidst the threat of major staffing shortfalls. RESULTS: This process identified vulnerable patient groups with specific communication needs and those most at risk through service contraction. The challenges and opportunities for promoting safety and self-management through proactive telehealth came up repeatedly. The guided conversations also raised awareness of the shared experience between patients and professionals of coronavirus disease 2019. CONCLUSION: There is a parallel pandemic of anxiety which creates a unique opportunity to connect at a human level.