Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) is a newly emerging virus well known as the major cause of the worldwide pandemic due to Coronavirus Disease 2019 (COVID-19). Major breakthroughs in the Next Generation Sequencing (NGS) field were elucidated following the first release of a full-length SARS-CoV-2 genome on the 10 January 2020, with the hope of turning the table against the worsening pandemic situation. Previous studies in respiratory virus characterization require mapping of raw sequences to the human genome in the downstream bioinformatics pipeline as part of metagenomic principles. Illumina, as the major player in the NGS arena, took action by releasing guidelines for improved enrichment kits called the Respiratory Virus Oligo Panel (RVOP) based on a hybridization capture method capable of capturing targeted respiratory viruses, including SARS-CoV-2; therefore, allowing a direct map of raw sequences data to SARS-CoV-2 genome in downstream bioinformatics pipeline. Consequently, two bioinformatics pipelines emerged with no previous studies benchmarking the pipelines. This study focuses on gaining insight and understanding of target enrichment workflow by Illumina through the utilization of different bioinformatics pipelines named as ‘Fast Pipeline’ and ‘Normal Pipeline’ to SARS-CoV-2 strains isolated from Yogyakarta and Central Java, Indonesia. Overall, both pipelines work well in the characterization of SARS-CoV-2 samples, including in the identification of major studied nucleotide substitutions and amino acid mutations. A higher number of reads mapped to the SARS-CoV-2 genome in Fast Pipeline and merely were discovered as a contributing factor in a higher number of coverage depth and identified variations (SNPs, insertion, and deletion). Fast Pipeline ultimately works well in a situation where time is a critical factor. On the other hand, Normal Pipeline would require a longer time as it mapped reads to the human genome. Certain limitations were identified in terms of pipeline algorithm, whereas it is highly recommended in future studies to design a pipeline in an integrated framework, for instance, by using NextFlow, a workflow framework to combine all scripts into one fully integrated pipeline.

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Introduction- Colorectal cancer (CRC) is a development of abnormal cells either in colon or rectum. CRC considered being the 3rd leading cause of death in 2018 only behind lung and breast cancer. It first arises during pre-cancerous stages called as polyps. The detection and removal of polyp is important to increase the survival rate of patient. Various method of polyp detection are available. However, only colonoscopy remains the gold standard in detection and removal of polyps. Several studies showed how Artificial Intelligence (AI) used in colonoscopy area particularly in detecting polyps, assessing physicians and predicting patient with high risk of CRC. The aim of this study is to describe the involvement of AI in colonoscopy and its impact in reducing the Materials and methods– Search for journal articles conducted between May and June 2016 from various resources including PubMed and Google Scholar.  6 research journals were reviewed and all the advantages and limitations were discussed throughout this study. Results– Various study showed that AI able to improve medical diagnostic of CRC in several ways, including in the improvement of adenoma detection rate (ADR) in terms of medical diagnostic, finding physicians associated with high Adenoma Detection Rate (ADR) and predicting patients with high risk of CRC. In addition, the use of AI in colonoscopy also associated with limitations including require large amount of datasets and advance computational resources in order to generate accurate output. Conclusion– The utilization of AI in colonoscopy shows how it able to improve the diagnosis accuracy and survival rate of patients associated with CRC despite several limitations that were identified during the study. However in the future, instead of allowing it to fully automatically conducting diagnosis, it still needs to be accompanied by physicians conducting the operation as there is no hundred percent perfect algorithms.

Purpose: Breast cancer is a major cause of death in occidental women. Mechanisms involved in its etiology remain misunderstood. Metabolomics is a powerful tool which may help elucidating novel biological pathways and identify new biomarkers in order to predict breast cancer well before symptoms appear. The aim of this study was to investigate whether untargeted metabolomic signatures from blood draws of healthy women could contribute to better understand and predict the long-term risk of developing breast cancer. Methods: A nested case-control study was conducted within the SU.VI.MAX prospective cohort (13 years of follow-up) to analyze baseline plasma samples of 211 incident breast cancer cases and 211 matched controls by LC-MS mass spectrometry. Multivariable conditional logistic regression models were computed. Results: 83 ions were significantly associated (corrected-pvalue <0.05) with breast cancer risk. Notably, we observed that a lower plasma level of O-succinyl-homoserine and higher plasma levels of valine/norvaline, glutamine/isoglutamine, 5-aminovaleric acid, phenylalanine, tryptophane, γ-glutamyl-threonine, ATBC, 2-amino-cyanobutanoic acid and pregnene-triol sulfate were associated with an increased risk of developing breast cancer during follow-up. Corrected-pvalues ranged from 0.009 (OR=1.43[1.14-1.78] for phenylalanine and OR=1.45[1.15-1.83] for valine/norvaline) to 0.03 (OR=1.28[1.03-1.58] for 2-amino-cyano-butanoic acid). Conclusion: Several pre-diagnostic plasmatic metabolites are strongly associated with long-term breast cancer risk. If confirmed in other independent cohort studies, these results could help to identify healthy women at higher risk of developing breast cancer in the subsequent decade and to propose a better understanding of the complex mechanisms involved in its etiology. Trial registration: SU.VI.MAX, clinicaltrials.gov NCT00272428. Registered 3 January 2006 Keywords: Metabolomics, breast cancer, mass spectrometry, plasma, prospective study Citation Format: Lucie L, Céline D, Bernard L, Aicha D, Adrien R, Marie-Paule V, Mélanie P, Marie L, Tom F, Delphine C, Laurent Z, Pilar G, Serge H, Mélanie D, Valentin P, Bernard S, Paule L-M, Emmanuelle K-G, Nathalie D-P, Claudine M, Stéphanie D, Estelle P-G, Mathilde T. Plasma metabolomic signatures associated with long-term breast cancer risk in the SU.VI.MAX prospective cohort [abstract]. In: Proceedings of the 2018 San Antonio Breast Cancer Symposium; 2018 Dec 4-8; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2019;79(4 Suppl):Abstract nr P4-10-17.

Both dengue and Zika viruses have infected millions of people worldwide, urging the development of efficacious drugs and vaccines to fight the infection. Unfortunately, current research is yet to elucidate the structural proteomics comparison of the NS2B/NS3 from both viruses. Therefore, the main objective of this study was to comparatively study the structural proteins between dengue and Zika viruses by leveraging standard homology modelling tools. Our data provide 3D molecular structure overviews of NS2B/NS3 derived from the foregoing viruses. Sequence alignment indicated that the viruses share a 56% similarity rate of protein structures. However, in terms of function, both have NS2B that is pivotal for the activation of NS3 proteases.