False-positive HIV in a patient with SARS-CoV-2 infection; a case report

Salih, Rawezh Q.; Salih, Gasha A.; Abdulla, Berwn A.; Ahmed, Abdulla D.; Mohammed, Hawbash R.; Kakamad, Fahmi H.; Salih, Abdulwahid M.

doi:10.1016/j.amsu.2021.103027

Cited by 20 publications

(26 citation statements)

References 22 publications

(29 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The World Health Organization (WHO) first officially announced COVID-19 as a pandemic disease in March 2020 [ 4 ]. This infectious disease is highly contagious and mainly spreads by coughing, talking within proximity, or sneezing [ 5 ]. A large proportion of infected COVID-19 cases have very mild symptoms - such as loss of taste or smell, fever, fatigue, and dry cough - or are completely asymptomatic.…”

Section: Introductionmentioning

confidence: 99%

Post COVID-19 pulmonary fibrosis; a meta-analysis study

Amin¹,

Kakamad

Ahmed

et al. 2022

Annals of Medicine &Amp; Surgery

Self Cite

109

View full text Add to dashboard Cite

Section: Introductionmentioning

confidence: 99%

Post COVID-19 pulmonary fibrosis; a meta-analysis study

Amin¹,

Kakamad

Ahmed

et al. 2022

Annals of Medicine &Amp; Surgery

Self Cite

109

View full text Add to dashboard Cite

“…Cross-reactivity between SARS-CoV-2 and HIV has also been reported in studies describing false positive HIV results in COVID-19 patients. Such cases were associated with antibody cross-reaction during immunoassay screening tests, while cross-reactive CDR3 regions were detected between the two viruses [ 77 , 78 ]. Sequence analysis has shown that HIV and SARS-CoV proteins share common motifs that shape a degree of homology [ 79 ].…”

Section: Discussionmentioning

confidence: 99%

Machine-Learning-Assisted Analysis of TCR Profiling Data Unveils Cross-Reactivity between SARS-CoV-2 and a Wide Spectrum of Pathogens and Other Diseases

Γεωργακίλας

Galanopoulos

Tsinaris

et al. 2022

Biology

View full text Add to dashboard Cite

During the last two years, the emergence of SARS-CoV-2 has led to millions of deaths worldwide, with a devastating socio-economic impact on a global scale. The scientific community’s focus has recently shifted towards the association of the T cell immunological repertoire with COVID-19 progression and severity, by utilising T cell receptor sequencing (TCR-Seq) assays. The Multiplexed Identification of T cell Receptor Antigen (MIRA) dataset, which is a subset of the immunoACCESS study, provides thousands of TCRs that can specifically recognise SARS-CoV-2 epitopes. Our study proposes a novel Machine Learning (ML)-assisted approach for analysing TCR-Seq data from the antigens’ point of view, with the ability to unveil key antigens that can accurately distinguish between MIRA COVID-19-convalescent and healthy individuals based on differences in the triggered immune response. Some SARS-CoV-2 antigens were found to exhibit equal levels of recognition by MIRA TCRs in both convalescent and healthy cohorts, leading to the assumption of putative cross-reactivity between SARS-CoV-2 and other infectious agents. This hypothesis was tested by combining MIRA with other public TCR profiling repositories that host assays and sequencing data concerning a plethora of pathogens. Our study provides evidence regarding putative cross-reactivity between SARS-CoV-2 and a wide spectrum of pathogens and diseases, with M. tuberculosis and Influenza virus exhibiting the highest levels of cross-reactivity. These results can potentially shift the emphasis of immunological studies towards an increased application of TCR profiling assays that have the potential to uncover key mechanisms of cell-mediated immune response against pathogens and diseases.

show abstract

“…Cross-reactivity between SARS-CoV-2 and HIV has also been reported in studies describing false positive HIV results in COVID-19 patients. Such cases were associated with antibody cross-reaction during immunoassay screening tests, while cross-reactive CDR3 regions were detected between the two viruses 73,74 . Sequence analysis has shown that HIV and SARS-CoV proteins share common motifs that shape a degree of homology 75 .…”

Section: Discussionmentioning

confidence: 99%

Machine learning assisted analysis on TCR profiling data from COVID-19-convalescent and healthy individuals unveils cross-reactivity between SARS-CoV-2 and a wide spectrum of pathogens and other diseases

Γεωργακίλας

Galanopoulos

Tsinaris

et al. 2022

Preprint

View full text Add to dashboard Cite

During the last two years, the emergence of SARS-CoV-2 has led to millions of deaths worldwide, with a devastating socio-economic impact on a global scale. The scientific community’s focus has recently shifted towards the association of the T cell immunological repertoire with COVID-19 progression and severity, by utilising T cell receptor sequencing (TCR-Seq) assays. The Multiplexed Identification of T cell Receptor Antigen (MIRA) dataset, which is a subset of the immunoACCESS© study, provides thousands of TCRs that can specifically recognize SARS-CoV-2 epitopes. Our study proposes a novel Machine Learning (ML) assisted approach for analysing TCR-Seq data from the antigens’ point of view, with the ability to accurately distinguish between COVID-19-convalescent and healthy individuals in the case of MIRA dataset. Most SARS-CoV-2 antigens were found to exhibit equal levels of recognition by MIRA TCRs in both convalescent and healthy cohorts, leading to the assumption of putative cross-reactivity between SARS-CoV-2 and other infectious agents. This hypothesis was validated by combining MIRA with other public TCR profiling repositories that host assays and sequencing data concerning a plethora of pathogens. Our study provides evidence regarding the cross-reactivity between SARS-CoV-2 and a wide spectrum of pathogens and diseases, with M. tuberculosis and Influenza virus exhibiting the highest levels of cross-reactivity. These results can potentially shift the emphasis of immunological studies towards an increased application of TCR profiling assays that have the potential to uncover key mechanisms of cell-mediated immune response against pathogens and diseases.

show abstract

False-positive HIV in a patient with SARS-CoV-2 infection; a case report

Cited by 20 publications

References 22 publications

Post COVID-19 pulmonary fibrosis; a meta-analysis study

Post COVID-19 pulmonary fibrosis; a meta-analysis study

Machine-Learning-Assisted Analysis of TCR Profiling Data Unveils Cross-Reactivity between SARS-CoV-2 and a Wide Spectrum of Pathogens and Other Diseases

Machine learning assisted analysis on TCR profiling data from COVID-19-convalescent and healthy individuals unveils cross-reactivity between SARS-CoV-2 and a wide spectrum of pathogens and other diseases

Contact Info

Product

Resources

About