COVID-19 outbreak caused by the newly discovered SARS-CoV-2 has become a major public health threat around the world and has create a tremendous effect on the global economy. Hence, there is a high demand for rapid and accurate diagnosis to contain the spread of the disease. The Reverse-Transcription Polymerase Chain Reaction (RTPCR), the current standard for diagnosis of COVID-19 however possesses certain drawbacks that limits its application to meet the high demand of the continually increasing COVID-19 cases. Conversely, Loop-Mediated Isothermal Amplification (LAMP) is another nucleic acid amplification method that shows a great potential as an alternative tool in rapid diagnosis of COVID-19 due to its simplicity and rapidity. This review summarized the recent published research articles related to the application and modification of RT-LAMP assay for the rapid detection of COVID-19 in comparison with other available diagnostic methods.

Introduction: Prediction and identification of miRNAs target genes are crucial for understanding the biology of miRNAs. Amidst reported long-coding RNA (lncRNA), the microRNA 195-497 cluster host gene (MIR497HG) regulation is mediated by multiple non-coding RNAs (ncRNAs) such as microRNAs (miRNAs). MIR497HG has been implicated as a tumour suppressor in various cancers. However, the impact of MIR497HG and its derived miRNAs is largely unknown and still needs to be further explored. Employing an experimental approach is often challenging since some lncRNAs are difficult to identify and isolate by the current isolation technique. Thus, bioinformatic tools are introduced to aid these problems. This study sought to search and identify the miRNAs targeting the 3’untranslated region (3’UTR) of MIR497HG. Methods: Here, bioinformatic tools were adopted to identify a unique list of miRNAs that potentially target the 3’UTR of MIR497HG. Results: A total of 57 candidate miRNAs that target the 3’UTR of MIR497HG were extracted using the miRDB. Meanwhile, STarMir predicted 291 miRNAs that potentially target the 3’UTR of MIR497HG. A common list of 36 miRNAs was obtained using the Venny 2.1.0 and further narrowed down using the LogitProb score of StarMir. Finally, a total 4 miRNAs (hsa-miR-3182, hsa-miR-7156-5p, hsa-miR-452-3p and hsa-miR-2117) were identified. The mRNA target of identified miRNAs was identified by TargetScan. Finally, Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis of mRNA target was done using Enrichr. Conclusion: This finding could be useful in understanding the complex interaction between MIR497HG and its regulatory miRNA. In addition, a comparative analysis of computational miRNA-target predictions is provided in this study would potentially lay the foundations for miRNAs to be used for biomarkers in cancer research.