The surge in Covid related mucormycosis

Chandra, Somesh; Rawal, Rakesh

doi:10.1016/j.jinf.2021.06.008

Cited by 19 publications

(16 citation statements)

References 25 publications

(18 reference statements)

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“…Up-regulation of 78-kDa glucose-regulated protein (GRP78), a heat shock protein, in patients with COVID-19 (5 times more than controls) due to increased glucose and iron content caused by diabetic ketoacidosis (DKA) or induced by dexamethasone use also facilitates fungal entry. Besides, it promotes Mucorales’ pathogenicity and virulence [ 17 , 50 ].…”

Section: How Does Mucormycosis Affect Patients With Covid-19?mentioning

confidence: 99%

COVID-19–associated mucormycosis: Evidence-based critical review of an emerging infection burden during the pandemic’s second wave in India

et al. 2021

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Coronavirus Disease 2019 (COVID-19), during the second wave in early 2021, has caused devastating chaos in India. As daily infection rates rise alarmingly, the number of severe cases has increased dramatically. The country has encountered health infrastructure inadequacy and excessive demand for hospital beds, drugs, vaccines, and oxygen. Adding more burden to such a challenging situation, mucormycosis, an invasive fungal infection, has seen a sudden surge in patients with COVID-19. The rhino-orbital-cerebral form is the most common type observed. In particular, approximately three-fourths of them had diabetes as predisposing comorbidity and received corticosteroids to treat COVID-19. Possible mechanisms may involve immune and inflammatory processes. Diabetes, when coupled with COVID-19–induced systemic immune change, tends to cause decreased immunity and an increased risk of secondary infections. Since comprehensive data on this fatal opportunistic infection are evolving against the backdrop of a major pandemic, prevention strategies primarily involve managing comorbid conditions in high-risk groups. The recommended treatment strategies primarily included surgical debridement and antifungal therapy using Amphotericin B and selected azoles. Several India-centric clinical guidelines have emerged to rightly diagnose the infection, characterise the clinical presentation, understand the pathogenesis involved, and track the disease course. Code Mucor is the most comprehensive one, which proposes a simple but reliable staging system for the rhino-orbital-cerebral form. A staging system has recently been proposed, and a dedicated registry has been started. In this critical review, we extensively analyse recent evidence and guidance on COVID-19–associated mucormycosis in India.

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Section: How Does Mucormycosis Affect Patients With Covid-19?mentioning

confidence: 99%

COVID-19–associated mucormycosis: Evidence-based critical review of an emerging infection burden during the pandemic’s second wave in India

et al. 2021

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“…This probably could explain both the higher transmissibility of SARS CoV2 and the surge in CAROCM in the second wave. 3 , 14 …”

Section: Discussionmentioning

confidence: 99%

Pathogenetic factors fanning the flames of COVID-19 to cause rhino-orbito-cerebral mucormycosis: An observational study

Reddy¹,

Yeduguri²,

N³

et al. 2022

Journal of Medical Mycology

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“…6 Cases of mucormycosis have been found in patients who are recovering from coronavirus. 5,7,8 As coronavirus leaves it is patients' immune systems in a debilitating situation, they are more susceptible to mucormycosis. As of June 21, 2021, 31,216 cases of infection and 2109 deaths due to black fungus have been reported globally.…”

Section: Introductionmentioning

confidence: 99%

“…It is more likely to affect people having diabetes, cancer, HIV or AIDS, and organ transplant that means having compromised immune systems 6 . Cases of mucormycosis have been found in patients who are recovering from coronavirus 5,7,8 . As coronavirus leaves it is patients' immune systems in a debilitating situation, they are more susceptible to mucormycosis.…”

Section: Introductionmentioning

confidence: 99%

COVID‐19 and black fungus: Analysis of the public perceptions through machine learning

Islam¹,

Khan²,

Mahmud³

2021

Engineering Reports

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While COVID‐19 is ravaging the lives of millions of people across the globe, a second pandemic “black fungus” has surfaced robbing people of their lives especially people who are recovering from coronavirus. Thus, the objective of this article is to analyze public perceptions through sentiment analysis regarding black fungus during the COVID‐19 pandemic. To attain the objective, first, a support vector machine (SVM) model, with an average AUC of 82.75%, was developed to classify user sentiments in terms of anger, fear, joy, and sad. Next, this SVM model was used to predict the class labels of the public tweets ( n = 6477) related to COVID‐19 and black fungus. As outcome, this article found public perceptions towards black fungus during COVID‐19 pandemic belong mostly to sad ( n = 2370, 36.59%), followed by joy ( n = 2095, 32.34%), fear ( n = 1914, 29.55%) and anger ( n = 98, 1.51%). This article also found that public perceptions are varied to some critical concerns like education, lockdown, hospital, oxygen, quarantine, and vaccine. For example, people mostly exhibited fear in social media about education, hospital, vaccine while some people expressed joy about education, hospital, vaccine, and oxygen. Again, it was found that mass people have an ignorance tendency to lockdown, COVID‐19 restrictions, and prescribed hygiene rules although the coronavirus and black fungus infection rates broke the previous infection records.

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The surge in Covid related mucormycosis

Cited by 19 publications

References 25 publications

COVID-19–associated mucormycosis: Evidence-based critical review of an emerging infection burden during the pandemic’s second wave in India

COVID-19–associated mucormycosis: Evidence-based critical review of an emerging infection burden during the pandemic’s second wave in India

Pathogenetic factors fanning the flames of COVID-19 to cause rhino-orbito-cerebral mucormycosis: An observational study

COVID‐19 and black fungus: Analysis of the public perceptions through machine learning

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