COVID-19 in patients with paracoccidioidomycosis.

INTRODUCTION: In 2020, we reported the first patient with concomitant COVID-19 and paracoccidioidomycosis (PCM). Since then, no other cases have been recorded in the literature. We aim to update information on the occurrence of COVID-19 in patients with PCM followed at a reference center for infectious diseases at Rio de Janeiro, Brazil. METHODS: We reviewed the medical records from patients diagnosed with PCM who presented with clinical symptoms, radiological findings, and/or laboratory diagnosis of COVID-19 at any time during their acute or follow-up care. The clinical profiles of these patients were described. RESULTS: Between March 2020 and September 2022, we identified six individuals with COVID-19 among the 117 patients with PCM evaluated. The median age was 38 years and the male to female ratio 2:1. Most patients (n = 5) presented for evaluation due to acute PCM. The severity of COVID-19 ranged from mild to severe in acute PCM and only the single patient with chronic PCM died. CONCLUSIONS: There is a range of disease severity in COVID-19 and PCM co-infection and concomitant disease may represent a severe association, especially in the chronic type of the mycosis with pulmonary involvement. As COVID-19 and chronic PCM share similar clinical aspects and PCM is neglected, it is probable that COVID-19 has been hampering simultaneous PCM diagnosis, which can explain the absence of new co-infection reports. With the continued persistence of COVID-19 globally, these findings further suggest that more attention by providers is necessary to identify co-infections with Paracoccidioides.

Automatic algorithm for quantifying lung involvement in patients with chronic obstructive pulmonary disease, infection with SARS-CoV-2, paracoccidioidomycosis and no lung disease patients.

In this work, we aimed to develop an automatic algorithm for the quantification of total volume and lung impairments in four different diseases. The quantification was completely automatic based upon high resolution computed tomography exams. The algorithm was capable of measuring volume and differentiating pulmonary involvement including inflammatory process and fibrosis, emphysema, and ground-glass opacities. The algorithm classifies the percentage of each pulmonary involvement when compared to the entire lung volume. Our algorithm was applied to four different patients groups: no lung disease patients, patients diagnosed with SARS-CoV-2, patients with chronic obstructive pulmonary disease, and patients with paracoccidioidomycosis. The quantification results were compared with a semi-automatic algorithm previously validated. Results confirmed that the automatic approach has a good agreement with the semi-automatic. Bland-Altman (B&A) demonstrated a low dispersion when comparing total lung volume, and also when comparing each lung impairment individually. Linear regression adjustment achieved an R value of 0.81 when comparing total lung volume between both methods. Our approach provides a reliable quantification process for physicians, thus impairments measurements contributes to support prognostic decisions in important lung diseases including the infection of SARS-CoV-2.