Evidence map›Paper›PMID 40830432›Full record

ArticleBMC infectious diseases2025

The silent legacy of COVID-19: exploring genomic instability in long-term COVID-19 survivors.

Elaheh Abiri, Ali Abiri, Salman Daneshi, Rasoul Raesi

Abstract read
In one paragraph

Article in BMC infectious diseases, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

4 authors.

Elaheh AbiriDepartment of Cellular and Molecular Biology, School of Biology, Institute of Biological Sciences, Damghan University, Damghan, Iran.ORCID http://orcid.org/0000-0002-0739-9654
Ali AbiriDepartment of Computer, Da.C., Islamic Azad University, Damghan, Iran.
Salman DaneshiDepartment of Public Health, School of Health, Jiroft University of Medical Sciences, Jiroft, Iran.ORCID http://orcid.org/0000-0002-9199-0382
Rasoul RaesiDepartment of Public Health, Torbat Jam Faculty of Medical Sciences, School of Health, Torbat Jam, Iran. Raesi.br881@gmail.com.ORCID http://orcid.org/0000-0002-5998-5209

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundPersistent symptoms and complications reported by many patients for more than four weeks after contracting coronavirus disease 2019 (COVID-19) are referred to as post-COVID-19 syndrome. These persistent symptoms can occur in individuals with both mild and severe COVID-19, though the underlying pathophysiological mechanisms remain poorly understood. This study aims to explore post-COVID-19 syndrome from a biological perspective, focusing on genomic instability.

methodsIn this cross-sectional study, the comet assay method was employed in March 2024 to evaluate the level of DNA damage in 29 patients to examine the post-COVID-19 syndrome state at Kausar Semnan Hospital in Iran. Levels of DNA damage were assessed using the alkaline comet assay in patients hospitalized for COVID-19, four weeks after a positive RT-PCR test. Patients were categorized based on pneumonia severity: mild (11 patients in non-ICU), moderate (10 patients in ICU and non-intubated), and severe/critical (8 patients in ICU and intubated). Ten healthy individuals who tested negative for COVID-19 were considered as a control group. Data were analyzed using descriptive and inferential statistical tests at a significance level of p < 0.01 in GraphPad Prism 9 software.

resultsPost-COVID-19 patients exhibited significantly higher levels of DNA damage compared to healthy controls. The highest DNA damage was observed in intubated-ICU patients (mean DNA damage: 29.5%), followed by non-intubated-ICU patients (mean: 24.3%), non-ICU patients (mean: 19.1%), and healthy controls (mean: 9.4%). These findings suggest a clear correlation between COVID-19 severity and increased genomic instability.

conclusionThe results of this study highlight the prevalence of DNA damage in post-COVID-19 patients, which may explain long-term genomic instability and associated health complications. The findings underscore the importance of further research into the pathophysiological mechanisms of post-COVID-19 syndrome, particularly its impact on genomic stability. This study contributes to the growing evidence that post-COVID-19 syndrome is a complex condition with virus-specific abnormalities affecting multiple biological pathways. Future studies should focus on understanding these mechanisms to develop targeted interventions for long-term COVID-19 survivors.

Indexed as

COVID-19Genomic InstabilitySurvivorsAdultAgedComet AssayCross-Sectional StudiesDNA DamageFemaleHumansIranMaleMiddle AgedSARS-CoV-2Severity of Illness IndexComet assayCOVID-19DNA damageLong-term COVID-19LymphopeniaPost-COVID-19

Identifiers

PMID40830432
PMCPMC12366110

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.