Emerging pulmonary histopathological findings in COVID-19 patients— a letter to the editor in response to Grosse et al. 2020

Parmar, Dilen; Ponnampalam, Jaya; Seligmann, George; Gandhi, Tanaya

doi:10.1016/j.carpath.2020.107302

Cited by 1 publication

(1 citation statement)

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“…Use of in silico modeling and simulation techniques could allow for visualization of lung micromechanics to better understand functional changes in COVID-19 [ 137 , 138 ]. There is currently great variation in procedures used in postmortem studies of COVID-19 pathology and in order to make better comparisons between studies, standardized procedures and classification systems would be beneficial [ 139 , 140 ].…”

Section: Resultsmentioning

confidence: 99%

Implications of microscale lung damage for COVID-19 pulmonary ventilation dynamics: A narrative review

et al. 2021

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The COVID-19 pandemic surges on as vast research is produced to study the novel SARS-CoV-2 virus and the disease state it induces. Still, little is known about the impact of COVID-19-induced microscale damage in the lung on global lung dynamics. This review summarizes the key histological features of SARS-CoV-2 infected alveoli and links the findings to structural tissue changes and surfactant dysfunction affecting tissue mechanical behavior similar to changes seen in other lung injury. Along with typical findings of diffuse alveolar damage affecting the interstitium of the alveolar walls and blood-gas barrier in the alveolar airspace, COVID-19 can cause extensive microangiopathy in alveolar capillaries that further contribute to mechanical changes in the tissues and may differentiate it from previously studied infectious lung injury. Understanding microlevel damage impact on tissue mechanics allows for better understanding of macroscale respiratory dynamics. Knowledge gained from studies into the relationship between microscale and macroscale lung mechanics can allow for optimized treatments to improve patient outcomes in case of COVID-19 and future respiratory-spread pandemics.

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Section: Resultsmentioning

confidence: 99%