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.
The development of linear hypopigmentation after intralesional or intraarticular injection of triamcinolone acetonide has been reported in the literature as a very rare side effect. This case report describes a patient with linear hypopigmentation and discusses the possible pathophysiology. Clinicians involved in the care of hypertrophic scars and keloids need to be aware of this rare side effect so that they can guide their patients appropriately. They need to understand the pathogenesis of this complication better so that it may become avoidable.
BackgroundThere are limited data regarding the incidence of pneumothorax in COVID-19 patients as well as the impact of the same on patient outcomes.
MethodsA retrospective review of the medical records at three large tertiary care hospitals in Mumbai was performed to identify patients hospitalised with COVID-19 from March 2020 to October 2020. The presence of pneumothorax and/ or pneumomediastinum was noted when chest radiographs or CT scans were performed. Demographic and clinical characteristics of patients who developed air leak were recorded. Results 4,906 patients with COVID-19 were admitted, with 1,324 (27%) having severe COVID-19 disease. The overall incidence of pneumothorax and/or pneumomediastinum in patients with severe disease was 3.2% (42/1,324). Eighteen patients had pneumothorax, 16 had pneumomediastinum and 8 patients had both. Fourteen patients (33.3%) developed this complication breathing spontaneously, 28 patients (66.6%) developed it during mechanical ventilation. Overall mortality in this cohort was 74%, compared with 17% in the COVID-19 patients without pneumothorax (p<0.001).
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 (p < 0.05). 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.
The research direction of identifying acoustic bio-markers of respiratory diseases has received renewed interest following the onset of COVID-19 pandemic. In this paper, we design an approach to COVID-19 diagnostic using crowd-sourced multi-modal data. The data resource, consisting of acoustic signals like cough, breathing, and speech signals, along with the data of symptoms, are recorded using a web-application over a period of ten months. We investigate the use of statistical descriptors of simple time-frequency features for acoustic signals and binary features for the presence of symptoms. Unlike previous works, we primarily focus on the application of simple linear classifiers like logistic regression and support vector machines for acoustic data while decision tree models are employed on the symptoms data. We show that a multimodal integration of acoustics and symptoms classifiers achieves an area-under-curve (AUC) of 92.40, a significant improvement over any individual modality. Several ablation experiments are also provided which highlight the acoustic and symptom dimensions that are important for the task of COVID-19 diagnostics.
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