Biotechnological Perspectives to Combat the COVID-19 Pandemic: Precise Diagnostics and Inevitable Vaccine Paradigms

COVID-19 Vaccination Reporting and Adverse Event Analysis in Taiwan

The COVID-19 pandemic necessitated an urgent global response in vaccine deployment, achieving over 70.6% global vaccination coverage with at least one dose. This study focuses on Taiwan's vaccine administration and adverse event reporting, set against a global backdrop. Using data from Taiwan's Vaccine Adverse Event Reporting System (VAERS) and global vaccination data, this study investigates vaccine safety and the public health implications of vaccination strategies from local and global perspectives. Taiwan's proactive approach, resulting in high vaccination rates, provides a case study for the monitoring and management of vaccine-related adverse events. This study offers insights into the safety profiles of various COVID-19 vaccines and further explores the implications of adverse event reporting rates for vaccine policy and public health strategies. The comparative analysis reveals that, while vaccination has been effective in controlling the virus's spread, safety monitoring remains critical for maintaining public trust. It underscores the necessity of enhanced surveillance and the importance of transparent and tailored risk communication to support informed public health decisions. The findings aim to contribute to the global dialogue on vaccine safety, equitable distribution, evidence-based policy-making, and development of mitigation measures with consideration of local demographics in the ongoing fight against COVID-19.

Fully integrated sample-in-answer-out platform for viral detection using digital reverse transcription recombinase polymerase amplification (dRT-RPA)

Recombinase polymerase amplification (RPA) is one of the most promising diagnostic methods for pathogen detection, owing to the simplified isothermal amplification technique. Using one-step digital reverse transcription RPA (dRT-RPA) to detect viral RNA provides a fast diagnosis and absolute quantification. Here, we present a chip that purifies, digitalizes, and detects viral RNA of SARS-CoV-2 in a fully automated and sensitive manner. The chip purifies the RNA using the surface charge concept of magnet bead-RNA binding, then mixes the RNA with the amplification reagents, digitalizes the amplification mixture, and performs dRT-RPA. RNA-bead complex is transported among purification buffers that are separated by an oil phase. For reagent manipulation and mixing, a magnetic valve system is integrated on the chip, where an external magnet controls the reagent direction and time of addition. Besides, a novel vacuum system is suggested to drive and regulate the reagents into two fluid systems simultaneously in ∼2 min. We also developed a cost-effective way to perform fluorescent detection for dRT-RPA on chip by using EvaGreen® dye. With integrated heating and optical detection system, the on-chip dRT-RPA presents a sample-to-answer detection platform for absolute viral RNA quantitation in 37 min and a sensitivity as low as 10 RNA copies/μL. Hence, this platform is expected to be a useful tool for accurate and automated diagnosis of infectious diseases.

Update on the effectiveness of COVID-19 vaccines on different variants of SARS-CoV-2

It has been more than three years since the first emergence of coronavirus disease 2019 (COVID-19) and millions of lives have been taken to date. Like most pandemics caused by viral infections, massive public vaccination is the most promising approach to cease COVID-19 infection. In this regard, several vaccine platforms including inactivated virus, nucleic acid-based (mRNA and DNA vaccines), adenovirus-based, and protein-based vaccines have been designed and developed for COVID-19 prevention and many of them have received FDA or WHO approval. Fortunately, after global vaccination, the transmission rate, disease severity, and mortality rate of COVID-19 infection have diminished significantly. However, a rapid increase in COVID-19 cases due to the omicron variant in vaccinated countries has raised concerns about the effectiveness of these vaccines. In this review, articles published between January 2020 and January 2023 were reviewed using PubMed, Google Scholar, and Web of Science search engines with appropriate related keywords. The related papers were selected and discussed in detail. The current review mainly focuses on the effectiveness and safety of COVID-19 vaccines against SARS-CoV-2 variants. Along with discussing the available and approved vaccines, characteristics of different variants of COVID-19 have also been discussed in brief. Finally, the currently circulating COVID-19 variant i.e Omicron, along with the effectiveness of available COVID-19 vaccines against these new variants are discussed in detail. In conclusion, based on the available data, administration of newly developed bivalent mRNA COVID-19 vaccines, as booster shots, would be crucial to prevent further circulation of the newly developed variants.

Computational Portable Microscopes for Point-of-Care-Test and Tele-Diagnosis

In bio-medical mobile workstations, e.g., the prevention of epidemic viruses/bacteria, outdoor field medical treatment and bio-chemical pollution monitoring, the conventional bench-top microscopic imaging equipment is limited. The comprehensive multi-mode (bright/dark field imaging, fluorescence excitation imaging, polarized light imaging, and differential interference microscopy imaging, etc.) biomedical microscopy imaging systems are generally large in size and expensive. They also require professional operation, which means high labor-cost, money-cost and time-cost. These characteristics prevent them from being applied in bio-medical mobile workstations. The bio-medical mobile workstations need microscopy systems which are inexpensive and able to handle fast, timely and large-scale deployment. The development of lightweight, low-cost and portable microscopic imaging devices can meet these demands. Presently, for the increasing needs of point-of-care-test and tele-diagnosis, high-performance computational portable microscopes are widely developed. Bluetooth modules, WLAN modules and 3G/4G/5G modules generally feature very small sizes and low prices. And industrial imaging lens, microscopy objective lens, and CMOS/CCD photoelectric image sensors are also available in small sizes and at low prices. Here we review and discuss these typical computational, portable and low-cost microscopes by refined specifications and schematics, from the aspect of optics, electronic, algorithms principle and typical bio-medical applications.

Colorimetric and fluorometric reverse transcription loop-mediated isothermal amplification (RT-LAMP) assay for diagnosis of SARS-CoV-2

The coronavirus disease 2019 (COVID-19) caused by the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) has caused millions of infections and deaths worldwide since it infected humans almost 3 years ago. Improvements of current assays and the development of new rapid tests or to diagnose SARS-CoV-2 are urgent. Reverse transcription loop-mediated isothermal amplification (RT-LAMP) is a rapid and propitious assay, allowing to detect both colorimetric and/or fluorometric nucleic acid amplifications. This study describes the analytical and clinical evaluation of RT-LAMP assay for detection of SARS-CoV-2, by designing LAMP primers targeting N (nucleocapsid phosphoprotein), RdRp (polyprotein), S (surface glycoprotein), and E (envelope protein) genes. The assay’s performance was compared with the gold standard RT-PCR, yielding 94.6% sensitivity and 92.9% specificity. Among the tested primer sets, the ones for S and N genes had the highest analytical sensitivity, showing results in about 20 min. The colorimetric and fluorometric comparisons revealed that the latter is faster than the former. The limit of detection (LoD) of RT-LAMP reaction in both assays is 50 copies/µl of the reaction mixture. However, the simple eye-observation advantage of the colorimetric assay (with a color change from yellow to red) serves a promising on-site point-of-care testing method anywhere, including, for instance, laboratory and in-house applications.

Development and application of ribonucleic acid therapy strategies against COVID-19

The Coronavirus disease 2019 (COVID-19) pandemic is caused by the severe acute respiratory syndrome 2 coronavirus (SARS-CoV-2), remaining a global health crisis since its outbreak until now. Advanced biotechnology and research findings have revealed many suitable viral and host targets for a wide range of therapeutic strategies. The emerging ribonucleic acid therapy can modulate gene expression by post-transcriptional gene silencing (PTGS) based on Watson-Crick base pairing. RNA therapies, including antisense oligonucleotides (ASO), ribozymes, RNA interference (RNAi), aptamers, etc., were used to treat SARS-CoV whose genome is similar to SARV-CoV-2, and the past experience also applies for the treatment of COVID-19. Several studies against SARS-CoV-2 based on RNA therapeutic strategy have been reported, and a dozen of relevant preclinical or clinical trials are in process globally. RNA therapy has been a very active and important part of COVID-19 treatment. In this review, we focus on the progress of ribonucleic acid therapeutic strategies development and application, discuss corresponding problems and challenges, and suggest new strategies and solutions.

SARS-CoV-2: phenotype, genotype, and characterization of different variants

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the cause of coronavirus disease 2019 (COVID-19), a major international public health concern. Because of very similar amino acid sequences of the seven domain names, SARS-CoV-2 belongs to the Coronavirinae subfamily of the family Coronaviridae, order Nidovirales, and realm Riboviria, placed in exceptional clusters, but categorized as a SARS-like species. As the RNA virus family with the longest genome, the Coronaviridae genome consists of a single strand of positive RNA (25–32 kb in length). Four major structural proteins of this genome include the spike (S), membrane (M), envelope (E), and the nucleocapsid (N) protein, all of which are encoded within the 3′ end of the genome. By engaging with its receptor, angiotensin-converting enzyme 2 (ACE2), SARS-CoV-2 infects host cells. According to the most recent epidemiological data, as the illness spread globally, several genetic variations of SARS-CoV-2 appeared quickly, with the World Health Organization (WHO) naming 11 of them. Among these, seven SARS-CoV-2 subtypes have received the most attention. Alpha (B.1.1.7), Beta (B.1.351), Gamma (P.1), Delta (B.1.617.2), and Omicron (B.1.617.2) are now designated as variations of concern (VOC) (B.1.1.529). Lambda (C.37) and Mu are variations of interest (VOI) (B.1.621). The remaining six are either being monitored or are no longer considered a threat. On the basis of studies done so far, antiviral drugs, antibiotics, glucocorticoids, recombinant intravenous immunoglobulin, plasma therapy, and IFN-α2b have been used to treat patients. Moreover, full vaccination is associated with lower infection and helps prevent transmission, but the risk of infection cannot be eliminated completely in vaccinated people.

Deep Learning Models for COVID-19 Detection

Healthcare is one of the crucial aspects of the Internet of things. Connected machine learning-based systems provide faster healthcare services. Doctors and radiologists can also use these systems for collaboration to provide better help to patients. The recently emerged Coronavirus (COVID-19) is known to have strong infectious ability. Reverse transcription-polymerase chain reaction (RT-PCR) is recognised as being one of the primary diagnostic tools. However, RT-PCR tests might not be accurate. In contrast, doctors can employ artificial intelligence techniques on X-ray and CT scans for analysis. Artificial intelligent methods need a large number of images; however, this might not be possible during a pandemic. In this paper, a novel data-efficient deep network is proposed for the identification of COVID-19 on CT images. This method increases the small number of available CT scans by generating synthetic versions of CT scans using the generative adversarial network (GAN). Then, we estimate the parameters of convolutional and fully connected layers of the deep networks using synthetic and augmented data. The method shows that the GAN-based deep learning model provides higher performance than classic deep learning models for COVID-19 detection. The performance evaluation is performed on COVID19-CT and Mosmed datasets. The best performing models are ResNet-18 and MobileNetV2 on COVID19-CT and Mosmed, respectively. The area under curve values of ResNet-18 and MobileNetV2 are 0.89% and 0.84%, respectively.