SARS-COV-2 Infection in Children and Young People in Bulgaria - A Prospective, Single-Center, Cohort Study

Compared to other respiratory viruses, the proportion of hospitalizations due to SARS-CoV-2 among children is relatively low. While severe illness is not common among children and young individuals, a particular type of severe condition called multisystem inflammatory syndrome in children (MIS-C) has been reported. The aim of this prospective cohort study, which followed a group of individuals under the age of 19, was to examine the characteristics of patients who had contracted SARS-CoV-2, including their coexisting medical conditions, clinical symptoms, laboratory findings, and outcomes. The study also aimed to investigate the features of children who met the WHO case definition of MIS-C, as well as those who required intensive care. A total of 270 patients were included between March 2020 and December 2021. The eligible criteria were individuals between 0-18 with a confirmed SARS-CoV-2 infection at the Infectious Disease Hospital "Prof. Ivan Kirov"in Sofia, Bulgaria. Nearly 76% of the patients were <= 12 years old. In our study, at least one comorbidity was reported in 28.1% of the cases, with obesity being the most common one (8.9%). Less than 5% of children were transferred to an intensive care unit. We observed a statistically significant difference in the age groups, with children between 5 and 12 years old having a higher likelihood of requiring intensive care compared to other age groups. The median values of PaO2 and SatO2 were higher among patients admitted to the standard ward, while the values of granulocytes and C-reactive protein were higher among those transferred to the intensive care unit. Additionally, we identified 26 children who met the WHO case definition for MIS-C. Our study data supports the evidence of milder COVID-19 in children and young individuals as compared to adults. Older age groups were associated with higher incidence of both MIS-C and ICU admissions.Copyright © 2023 P. Velikov et al., published by Sciendo.

Recent Development in Plasmonic Nanobiosensors for Viral DNA/RNA Biomarkers.

Recently, due to the coronavirus pandemic, the need for early diagnosis of infectious diseases, including viruses, is emerging. Though early diagnosis is essential to prevent infection and progression to severe illness, there are few technologies that accurately measure low concentrations of biomarkers. Plasmonic nanomaterials are attracting materials that can effectively amplify various signals, including fluorescence, Raman, and other optical and electromagnetic output. In this review, we introduce recently developed plasmonic nanobiosensors for measuring viral DNA/RNA as potential biomarkers of viral diseases. In addition, we discuss the future perspective of plasmonic nanobiosensors for DNA/RNA detection. This review is expected to help the early diagnosis and pathological interpretation of viruses and other diseases.

A Deep Learning Approach for Viral DNA Sequence Classification using Genetic Algorithm

DNA sequence classification is one of the major challenges in biological data processing. The identification and classification of novel viral genome sequences drastically help in reducing the dangers of a viral outbreak like COVID-19. The more accurate the classification of these viruses, the faster a vaccine can be produced to counter them. Thus, more accurate methods should be utilized to classify the viral DNA. This research proposes a hybrid deep learning model for efficient viral DNA sequence classification. A genetic algorithm (GA) was utilized for weight optimization with Convolutional Neural Networks (CNN) architecture. Furthermore, Long Short-Term Memory (LSTM) as well as Bidirectional CNN-LSTM model architectures are employed. Encoding methods are needed to transform the DNA into numeric format for the proposed model. Three different encoding methods to represent DNA sequences as input to the proposed model were experimented: k-mer, label encoding, and one hot vector encoding. Furthermore, an efficient oversampling method was applied to overcome the imbalanced dataset issues. The performance of the proposed GA optimized CNN hybrid model using label encoding achieved the highest classification accuracy of 94.88% compared with other encoding methods © 2022, International Journal of Advanced Computer Science and Applications.All Rights Reserved.

Clustered Regularly Interspaced Short Palindromic Repeats-Mediated Amplification-Free Detection of Viral DNAs Using Surface-Enhanced Raman Spectroscopy-Active Nanoarray.

Nucleic acid biomarkers have been widely used to detect various viral-associated diseases, including the recent pandemic COVID-19. The CRISPR-Cas-based trans-activating phenomenon has shown excellent potential for developing sensitive and selective detection of nucleic acids. However, the nucleic acid amplification steps are typically required when sensitive and selective monitoring of the target nucleic acid is needed. To overcome the aforementioned challenges, we developed a CRISPR-Cas12a-based nucleic acid amplification-free biosensor by a surface-enhanced Raman spectroscopy (SERS)-assisted ultrasensitive detection system. We integrated the activated CRISPR-Cas12a by viral DNA with a Raman-sensitive system composed of ssDNA-immobilized Raman probe-functionalized Au nanoparticles (RAuNPs) on the graphene oxide (GO)/triangle Au nanoflower array. Using this CRISPR-based Raman-sensitive system improved the detection sensitivity of the multiviral DNAs such as hepatitis B virus (HBV), human papillomavirus 16 (HPV-16), and HPV-18 with an extremely low detection limit and vast detection range from 1 aM to 100 pM without the amplification steps. We suggest that this ultrasensitive amplification-free detection system for nucleic acids can be widely applied to the precise and early diagnosis of viral infections, cancers, and several genetic diseases.

Emerging Concepts and Technologies in Vaccine Development.

Despite the success of vaccination to greatly mitigate or eliminate threat of diseases caused by pathogens, there are still known diseases and emerging pathogens for which the development of successful vaccines against them is inherently difficult. In addition, vaccine development for people with compromised immunity and other pre-existing medical conditions has remained a major challenge. Besides the traditional inactivated or live attenuated, virus-vectored and subunit vaccines, emerging non-viral vaccine technologies, such as viral-like particle and nanoparticle vaccines, DNA/RNA vaccines, and rational vaccine design, offer innovative approaches to address existing challenges of vaccine development. They have also significantly advanced our understanding of vaccine immunology and can guide future vaccine development for many diseases, including rapidly emerging infectious diseases, such as COVID-19, and diseases that have not traditionally been addressed by vaccination, such as cancers and substance abuse. This review provides an integrative discussion of new non-viral vaccine development technologies and their use to address the most fundamental and ongoing challenges of vaccine development.