The Rise of SARS-CoV-2 (COVID-19) Omicron Subvariant Pathogenicity

Analysis of Vocal Signatures of COVID-19 in Cough Sounds: A Newer Diagnostic Approach Using Artificial Intelligence

BACKGROUND

Artificial intelligence (AI) based models are explored increasingly in the medical field. The highly contagious pandemic of coronavirus disease 2019 (COVID-19) affected the world and availability of diagnostic tools high resolution computed tomography (HRCT) and/or real-time reverse transcriptase-polymerase chain reaction (RTPCR) was very limited, costly and time consuming. Therefore, the use of AI in COVID-19 for diagnosis using cough sounds can be efficacious and cost effective for screening in clinic or hospital and help in early diagnosis and further management of patients.

OBJECTIVES

To develop an accurate and fast voice-processing AI software to determine voice-based signatures in discriminating COVID-19 and non-COVID-19 cough sounds for screening of COVID-19.

METHODOLOGY

A prospective study involving 117 patients was performed based on online and/or offline voice data collection of cough sounds of COVID-19 patients in isolation ward of a tertiary care teaching hospital and non-COVID-19 participants using a smart phone. A website-based AI software was developed to identify the cough sounds as COVID-19 or non-COVID-19. The data were divided into three segments including training set, validation set and test set. A pre-processing algorithm was utilized and combined with Short Time Fourier Transform feature representation and Logistic regression model. A precise software was used to identify vocal signatures and K-fold cross validation was carried out.

RESULT

A total of 117 audio recordings of cough sounds were collected through the developed website after inclusion-exclusion criteria out of which 52 have been marked belonging to COVID-19 positive, while 65 were marked as COVID-19 negative/unsure /never had COVID-19, which were assumed to be COVID-19 negative based on RT-PCR test results. The mean and standard error values for the accuracies attained at the end of each experiment in training, validation and testing set were found to be 67.34%±0.22, 58.57%±1.11 and 64.60%±1.79 respectively. The weight values were found to be positive which were contributing towards predicting the samples as COVID-19 positive with large spikes around 7.5 kHz, 7.8 kHz, 8.6 kHz and 11 kHz which can be used for classification.

CONCLUSION

The proposed AI based approach can be a helpful screening tool for COVID-19 using vocal sounds of cough. It can help the health system by reducing the cost burden and improving overall diagnosis and management of the disease.

Successful salvage of a severe COVID-19 patient previously with lung cancer and radiation pneumonitis by mesenchymal stem cells: a case report and literature review

During the COVID-19 pandemic, elderly patients with underlying condition, such as tumors, had poor prognoses after progressing to severe pneumonia and often had poor response to standard treatment. Mesenchymal stem cells (MSCs) may be a promising treatment for patients with severe pneumonia, but MSCs are rarely used for patients with carcinoma. Here, we reported a 67-year-old female patient with lung adenocarcinoma who underwent osimertinib and radiotherapy and suffered from radiation pneumonitis. Unfortunately, she contracted COVID-19 and that rapidly progressed to severe pneumonia. She responded poorly to frontline treatment and was in danger. Subsequently, she received a salvage treatment with four doses of MSCs, and her symptoms surprisingly improved quickly. After a lung CT scan that presented with a significantly improved infection, she was discharged eventually. Her primary disease was stable after 6 months of follow-up, and no tumor recurrence or progression was observed. MSCs may be an effective treatment for hyperactive inflammation due to their ability related to immunomodulation and tissue repair. Our case suggests a potential value of MSCs for severe pneumonia that is unresponsive to conventional therapy after a COVID-19 infection. However, unless the situation is urgent, it needs to be considered with caution for patients with tumors. The safety in tumor patients still needs to be observed.

An ACE2 decamer viral trap as a durable intervention solution for current and future SARS-CoV

The capacity of SARS-CoV-2 to evolve poses challenges to conventional prevention and treatment options such as vaccination and monoclonal antibodies, as they rely on viral receptor binding domain (RBD) sequences from previous strains. Additionally, animal CoVs, especially those of the SARS family, are now appreciated as a constant pandemic threat. We present here a new antiviral approach featuring inhalation delivery of a recombinant viral trap composed of ten copies of angiotensin-converting enzyme 2 (ACE2) fused to the IgM Fc. This ACE2 decamer viral trap is designed to inhibit SARS-CoV-2 entry function, regardless of viral RBD sequence variations as shown by its high neutralization potency against all known SARS-CoV-2 variants, including Omicron BQ.1, BQ.1.1, XBB.1 and XBB.1.5. In addition, it demonstrates potency against SARS-CoV-1, human NL63, as well as bat and pangolin CoVs. The multivalent trap is effective in both prophylactic and therapeutic settings since a single intranasal dosing confers protection in human ACE2 transgenic mice against viral challenges. Lastly, this molecule is stable at ambient temperature for more than twelve weeks and can sustain physical stress from aerosolization. These results demonstrate the potential of a decameric ACE2 viral trap as an inhalation solution for ACE2-dependent coronaviruses of current and future pandemic concerns.

Epidemiology and analysis of SARS-CoV-2 Omicron subvariants BA.1 and 2 in Taiwan

The Omicron variant of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), first detected in October 2021, possessed many mutations compared to previous variants. We aimed to identify and analyze SARS-CoV-2 Omicron subvariants among coronavirus disease 2019 (COVID-19) patients between January 2022 and September 2022 in Taiwan. The results revealed that BA.2.3.7, featuring K97E and G1251V in the spike protein compared with BA.2, emerged in March 2022 and persistently dominated between April 2022 and August 2022, resulting in the largest COVID-19 outbreak since 2020. The accumulation of amino acid (AA) variations, mainly AA substitution, in the spike protein was accompanied by increasing severity in Omicron-related COVID-19 between April 2022 and January 2023. Older patients were more likely to have severe COVID-19, and comorbidity was a risk factor for COVID-19-related mortality. The accumulated case fatality rate (CFR) dropped drastically after Omicron variants, mainly BA.2.3.7, entered Taiwan after April 2022, and the CFR was 0.16% in Taiwan, which was lower than that worldwide (0.31%) between April 2021 and January 2023. The relatively low CFR in Omicron-related COVID-19 patients can be attributed to adjustments to public health policies, promotion of vaccination programs, effective antiviral drugs, and the lower severity of the Omicron variant.