Expansion of profibrotic monocyte-derived alveolar macrophages in patients with persistent respiratory symptoms and radiographic abnormalities after COVID-19 (preprint)

As many as 10-30% of the over 760 million survivors of COVID-19 develop persistent symptoms, of which respiratory symptoms are among the most common. To understand the cellular and molecular basis for respiratory PASC, we combined a machine learning-based analysis of lung computed tomography (CT) with flow cytometry, single-cell RNA-sequencing analysis of bronchoalveolar lavage fluid and nasal curettage samples, and alveolar cytokine profiling in a cohort of thirty-five patients with respiratory symptoms and radiographic abnormalities more than 90 days after infection with COVID-19. CT images from patients with PASC revealed abnormalities involving 73% of the lung, which improved on subsequent imaging. Interstitial abnormalities suggestive of fibrosis on CT were associated with the increased numbers of neutrophils and presence of profibrotic monocyte-derived alveolar macrophages in BAL fluid, reflecting unresolved epithelial injury. Persistent infection with SARS-CoV-2 was identified in six patients and secondary bacterial or viral infections in two others. These findings suggest that despite its heterogenous clinical presentations, respiratory PASC with radiographic abnormalities results from a common pathobiology characterized by the ongoing recruitment of neutrophils and profibrotic monocyte-derived alveolar macrophages driving lung fibrosis with implications for diagnosis and therapy.

Aerosol optical depth retrieval over land from OCEANSAT-2/ OCM- 2 data – A simple physics based approach

The decoupling of aerosol signals from the at-sensor reflectance measured through a space-borne sensor is a complex task due to the involved coupling mechanism of the interaction of light with the Earth's surface and the atmosphere. Specifically, the retrieval becomes more challenging over the land surface due to appreciable reflectance from the target. This paper presents a simplistic physics-based approach to retrieve Aerosol Optical Depth (AOD) (at a spatial resolution of 0.01°) over the land surface from the top-of-atmosphere (TOA) signals measured from an Indian space-borne sensor, Ocean Color Monitor-2 (onboard OCEANSAT-2 satellite). The estimated AOD from OCM-2 has been compared with that from Moderate Resolution Spectroradiometer (MODIS) onboard AQUA satellite (Collection 6.1 Deep Blue/Dark Target product). The comparison is illustrated for two different conditions prevalent over the Indian region, i.e., smoke-dominated (post-monsoonal) and dust-laden (pre-monsoonal) periods. A case study is also illustrated for the pre- and post-COVID-19 lockdown time period for two different zones of the Indian region. The validation (comparison) of OCM-2 AOD is performed with respect to the data from AERONET stations (MODIS), and we found a correlation coefficient (R2) of 0.84 (0.83) with root mean square error (RMSE) of 0.14 (0.20). Also, ∼87% (70%) of the retrieved OCM-2 (MODIS) AOD values were observed within the expected error (EE) envelope. The sensitivity of the retrieval algorithm with respect to the input parameters is also presented.