Next Generation Infectious Diseases Monitoring Gages via Incremental Federated Learning: Current Trends and Future Possibilities

Infectious diseases are always alarming for the survival of human life and are a key concern in the public health domain. Therefore, early diagnosis of these infectious diseases is a high demand for modern-era healthcare systems. Novel general infectious diseases such as coronavirus are infectious diseases that cause millions of human deaths across the globe in 2020. Therefore, early, robust recognition of general infectious diseases is the desirable requirement of modern intelligent healthcare systems. This systematic study is designed under Kitchenham guidelines and sets different RQs (research questions) for robust recognition of general infectious diseases. From 2018 to 2021, four electronic databases, IEEE, ACM, Springer, and ScienceDirect, are used for the extraction of research work. These extracted studies delivered different schemes for the accurate recognition of general infectious diseases through different machine learning techniques with the inclusion of deep learning and federated learning models. A framework is also introduced to share the process of detection of infectious diseases by using machine learning models. After the filtration process, 21 studies are extracted and mapped to defined RQs. In the future, early diagnosis of infectious diseases will be possible through wearable health monitoring cages. Moreover, these gages will help to reduce the time and death rate by detection of severe diseases at starting stage.

Post-acute sequelae of SARS-CoV-2 syndrome presenting as postural orthostatic tachycardia syndrome.

The novel SARS-CoV-2 emerged in 2019, and the global COVID-19 pandemic continues into 2022. It has been known that a subset of patients develops chronic, debilitating symptoms after otherwise complete recovery from acute infection of COVID-19. Multiple terms have been used to describe this constellation of symptoms, including long COVID, long-haul COVID, and postacute sequelae of SARS-CoV-2 syndrome (PASC). PASC is broadly defined as a wide range of new, returning, or ongoing symptoms at least four weeks after infection. Those patients are often seen in emergency departments after acute COVID-19 infection, but their symptoms are not adequately managed because the underlying pathophysiology of PASC is not well understood. Among patients with PASC, postural orthostatic tachycardic syndrome (POTS) has been increasingly recognized. POTS is one of the most common forms of autonomic dysfunction and defined by a sustained orthostatic tachycardia during active standing or head-up tilt test in the absence of orthostatic hypotension or other cardiopulmonary diseases. Because POTS is a treatable condition, it is important to recognize POTS among PASC patients. Herein, we reviewed the current literature on POTS and dysautonomia in PASC in order to better understand the overlap and distinction between these pathologies.

Next Generation Infectious Diseases Monitoring Gages via Incremental Federated Learning: Current Trends and Future Possibilities.

Infectious diseases are always alarming for the survival of human life and are a key concern in the public health domain. Therefore, early diagnosis of these infectious diseases is a high demand for modern-era healthcare systems. Novel general infectious diseases such as coronavirus are infectious diseases that cause millions of human deaths across the globe in 2020. Therefore, early, robust recognition of general infectious diseases is the desirable requirement of modern intelligent healthcare systems. This systematic study is designed under Kitchenham guidelines and sets different RQs (research questions) for robust recognition of general infectious diseases. From 2018 to 2021, four electronic databases, IEEE, ACM, Springer, and ScienceDirect, are used for the extraction of research work. These extracted studies delivered different schemes for the accurate recognition of general infectious diseases through different machine learning techniques with the inclusion of deep learning and federated learning models. A framework is also introduced to share the process of detection of infectious diseases by using machine learning models. After the filtration process, 21 studies are extracted and mapped to defined RQs. In the future, early diagnosis of infectious diseases will be possible through wearable health monitoring cages. Moreover, these gages will help to reduce the time and death rate by detection of severe diseases at starting stage.

Autonomic Nerve Involvement in Post-Acute Sequelae of SARS-CoV-2 Syndrome (PASC).

The novel SARS-CoV-2 virus and resulting COVID-19 global pandemic emerged in 2019 and continues into 2022. While mortality from COVID-19 is slowly declining, a subset of patients have developed chronic, debilitating symptoms following complete recovery from acute infection with COVID-19. Termed as post-acute sequelae of SARS-CoV-2 syndrome (PASC), the underlying pathophysiology of PASC is still not well understood. Given the similarity between the clinical phenotypes of PASC and postural orthostatic tachycardia syndrome (POTS), it has been postulated that dysautonomia may play a role in the pathophysiology of PASC. However, there have been only a few studies that have examined autonomic function in PASC. In this retrospective study, we performed an analysis of autonomic nerve function testing in PASC patients and compared the results with those of POTS patients and healthy controls. Our results suggest that a significant number of PASC patients have abnormal autonomic function tests, and their clinical features are indistinguishable from POTS.