ABSTRACT
BACKGROUND: The COVID-19 pandemic has highlighted the need to invent alternative respiratory health diagnosis methodologies which provide improvement with respect to time, cost, physical distancing and detection performance. In this context, identifying acoustic bio-markers of respiratory diseases has received renewed interest. OBJECTIVE: In this paper, we aim to design COVID-19 diagnostics based on analyzing the acoustics and symptoms data. Towards this, the data is composed of cough, breathing, and speech signals, and health symptoms record, collected using a web-application over a period of twenty months. METHODS: We investigate the use of time-frequency features for acoustic signals and binary features for encoding different health symptoms. We experiment with use of classifiers like logistic regression, support vector machines and long-short term memory (LSTM) network models on the acoustic data, while decision tree models are proposed for the symptoms data. RESULTS: We show that a multi-modal integration of inference from different acoustic signal categories and symptoms achieves an area-under-curve (AUC) of 96.3%, a statistically significant improvement when compared against any individual modality ([Formula: see text]). Experimentation with different feature representations suggests that the mel-spectrogram acoustic features performs relatively better across the three kinds of acoustic signals. Further, a score analysis with data recorded from newer SARS-CoV-2 variants highlights the generalization ability of the proposed diagnostic approach for COVID-19 detection. CONCLUSION: The proposed method shows a promising direction for COVID-19 detection using a multi-modal dataset, while generalizing to new COVID variants.
Subject(s)
COVID-19 , Humans , Pandemics , SARS-CoV-2 , Acoustics , COVID-19 TestingABSTRACT
With the increasing cohort of COVID-19 survivors worldwide, we now realize the proportionate rise in post-COVID-19 syndrome. In this review article, we try to define, summarize, and classify this syndrome systematically. This would help clinicians to identify and manage this condition more efficiently. We propose a tool kit that might be useful in recording follow-up data of COVID-19 survivors.
ABSTRACT
Reinfections in COVID-19 are being reported all around the world and are a cause for concern, considering that a lot of our assumptions and modeling (including vaccination) related to the disease have relied on long-term immunity. We were one of the first groups to report a series of 4 healthcare workers to have been reinfected. This review article reports a scoping review of the available literature on reinfections, with a discussion of the implications of reinfections.
Subject(s)
Antitubercular Agents/therapeutic use , Drug Resistance, Bacterial , Mycobacterium tuberculosis , Tuberculosis, Multidrug-Resistant/drug therapy , Tuberculosis, Pulmonary/drug therapy , Cohort Studies , Communicable Disease Control , Genotype , Humans , India , Infectious Disease Medicine/methods , Linezolid/therapeutic use , Microbial Sensitivity Tests , Predictive Value of Tests , RifampinABSTRACT
The presence of a non-resolving pneumonia warrants the suspicion of a possible malignancy. While pulmonary involvement in Hodgkin's disease can present as a non-resolving pneumonia, the clinical clues of dyspnoea, stridor and wheeze point to a possible endobronchial involvement. A bronchoscopy in such a situation can be valuable for diagnosis, and can aid in staging of the disease. The true incidence of endobronchial involvement in Hodgkin's disease is not known, but when diagnosed early and treated appropriately, the prognosis is usually good, and a complete cure is possible.
