Non-SARS Non-MERS Human Coronaviruses: Clinical Characteristics and Outcome

Saib, Israa; Aleisa, Saud Mohammed Saud; Ardah, Husam I.; Mahmoud, Ebrahim; Alharbi, Ahmad O.; Alsaedy, Abdulrahman; Aljohani, Sameera; Alshehri, Ahmed; Alharbi, Naif Khalaf; Bosaeed, Mohammad

doi:10.3390/pathogens10121549

Cited by 5 publications

(3 citation statements)

References 24 publications

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“…Along with HCoV-229E and HCoV-NL63, HCoV-OC43 has also been shown to be a primary etiological agent for mild upper respiratory tract infections in children [ 12 , 20 , 21 , 22 ]. In line with this, a higher rate of infections with HCoV-OC43 has been consistently reported [ 23 , 24 , 25 , 26 , 27 ]. Although HCoV-OC43 infections are generally mild, subclinical, and self-limited [ 28 ], more severe respiratory tract infections including bronchiolitis and pneumonia have been reported in high-risk groups.…”

Section: Clinical Manifestations Of Hcov-oc43 Infectionsupporting

confidence: 68%

Human Coronavirus OC43 as a Low-Risk Model to Study COVID-19

Kim

Lee

2023

Viruses

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The coronavirus disease 2019 (COVID-19) pandemic has had irreversible and devastating impacts on every aspect of human life. To better prepare for the next similar pandemic, a clear understanding of coronavirus biology is a prerequisite. Nevertheless, the high-risk nature of the causative agent of COVID-19, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), requires the use of a cumbersome biosafety level-3 (BSL-3) confinement facility. To facilitate the development of preventive and therapeutic measures against SARS-CoV-2, one of the endemic strains of low-risk coronaviruses has gained attention as a useful research alternative: human coronavirus OC43 (HCoV-OC43). In this review, its history, classification, and clinical manifestations are first summarized. The characteristics of its viral genomes, genes, and evolution process are then further explained. In addition, the host factors necessary to support the life cycle of HCoV-OC43 and the innate, as well as adaptive, immunological responses to HCoV-OC43 infection are discussed. Finally, the development of in vitro and in vivo systems to study HCoV-OC43 and its application to the discovery of potential antivirals for COVID-19 by using HCoV-OC43 models are also presented. This review should serve as a concise guide for those who wish to use HCoV-OC43 to study coronaviruses in a low-risk research setting.

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Section: Clinical Manifestations Of Hcov-oc43 Infectionsupporting

confidence: 68%

Human Coronavirus OC43 as a Low-Risk Model to Study COVID-19

Kim

Lee

2023

Viruses

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“…Typically, these comorbidities comprise hypertension, diabetes, and cardiovascular diseases, among which hypertension is predominant [ 29 , 30 , 31 ]. In fact, hypertension has been listed as one of the most common comorbidities in patients with other coronavirus (CoV) infections, such as those with severe acute respiratory syndrome CoV, and Middle East respiratory syndrome CoV, and human CoV 229E [ 32 , 33 ].…”

Section: Discussionmentioning

confidence: 99%

Effect of Hypertension Comorbidity on Clinical Characteristics of COVID-19 Patients Infected by the Wild-Type, the Delta or Omicron Variant SARS-CoV-2

Zhang¹,

Zhang²,

Tao³

2022

Rev. Cardiovasc. Med.

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Background: Hypertension was the most common comorbidity in patients with the coronavirus disease 2019 . We aim to study the effect of comorbid hypertension on the clinical characteristics of COVID-19 patients with the underlying mechanism. Methods: We retrospectively analyzed 459, 336 and 659 COVID-19 patients who were infected by the wild-type, the delta and omicron variant, respectively, including their demographic information, medical history, immunization record (if available), and laboratory parameters, to investigate the clinical differences between COVID-19 patients with and without hypertension. Results: In this study 26.1%, 26.8%, and 12.9% of COVID-19 patients had pre-existing hypertension in the cohort of wild-type, delta, and omicron variant, respectively. Compared to non-hypertensive peers, hypertension patients demonstrated older age, higher occurrence of other major comorbidities, and poorer blood or coagulation parameters, showing worse prognosis. In case of the delta or omicron variant of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection, hypertension patients produced robust antibody responses, although indistinguishable whether it was due to vaccination or natural infection and resembled those of non-hypertensive peers in blood cell and coagulation profiles with still varying viremic damages to major organs. Conclusions: Resultantly, COVID-19 infection promoted pro-inflammatory and pro-thrombotic states in hypertension patients, whereas vaccinated individuals would exhibit favorable prognoses.

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“…For specific binary prediction, we limited our train dataset to include species that has both human pathogenic and non pathogenic virus. For coronavirus, SARS, MERS, OC43, NL63, 229E, HKU1 strains were considered as pathogenic (Saib et al, 2021). Bovine Coronavirus, Canine coronavirus, Feline coronavirus, Equine Coronavirus, Bat coronavirus, Mink coronavirus, Murine coronavirus, Avian coronavirus were considered as nonpathogenic.…”

Section: Data Collection Eskapee Genomementioning

confidence: 99%

PathoLM: Identifying pathogenicity from the DNA sequence through the Genome Foundation Model

Dip,

Shuvo,

Chau

et al. 2024

Preprint

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Pathogen identification is pivotal in diagnosing, treating, and preventing diseases, crucial for controlling infections and safeguarding public health. Traditional alignment-based methods, though widely used, are computationally intense and reliant on extensive reference databases, often failing to detect novel pathogens due to their low sensitivity and specificity. Similarly, conventional machine learning techniques, while promising, require large annotated datasets and extensive feature engineering and are prone to overfitting. Addressing these challenges, we introduce PathoLM, a cutting-edge pathogen language model optimized for the identification of pathogenicity in bacterial and viral sequences. Leveraging the strengths of pre-trained DNA models such as the Nucleotide Transformer, PathoLM requires minimal data for fine-tuning, thereby enhancing pathogen detection capabilities. It effectively captures a broader genomic context, significantly improving the identification of novel and divergent pathogens. We developed a comprehensive data set comprising approximately 30 species of viruses and bacteria, including ESKAPEE pathogens, seven notably virulent bacterial strains resistant to antibiotics. Additionally, we curated a species classification dataset centered specifically on the ESKAPEE group. In comparative assessments, PathoLM dramatically outperforms existing models like DciPatho, demonstrating robust zero-shot and few-shot capabilities. Furthermore, we expanded PathoLM-Sp for ESKAPEE species classification, where it showed superior performance compared to other advanced deep learning methods, despite the complexities of the task.

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Non-SARS Non-MERS Human Coronaviruses: Clinical Characteristics and Outcome

Cited by 5 publications

References 24 publications

Human Coronavirus OC43 as a Low-Risk Model to Study COVID-19

Human Coronavirus OC43 as a Low-Risk Model to Study COVID-19

Effect of Hypertension Comorbidity on Clinical Characteristics of COVID-19 Patients Infected by the Wild-Type, the Delta or Omicron Variant SARS-CoV-2

PathoLM: Identifying pathogenicity from the DNA sequence through the Genome Foundation Model

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