Antioxidant and cytotoxic activities of sulfated polysaccharides from five different edible seaweeds

In recent times, there has been a growing interest in the exploration of antioxidants and global trend toward the usage of seaweeds in the food industries. The low molecular weight up to 14 kDa sulfated polysaccharides of seaweeds (Portieria hornemannii, Spyridia hypnoides, Asparagopsis taxiformis, Centroceras clavulatum and Padina pavonica) were evaluated for in vitro antioxidant activities and cytotoxic assay using HeLa cell line and also characterized by FTIR. The high yield (7.74% alga dry wt.) of sulfated polysaccharide was observed in P. hornemannii followed by S. hypnoides (0.69%), C. clavulaum (0.55%) and A. taxiformis (0.17%). In the brown seaweed P. pavonica, the sulfated polysaccharide yield was 2.07%. High amount of sulfate was recorded in the polysaccharide of A. taxiformis followed by C. clavulaum, P. pavonica, S. hypnoides and P. hornemannii as indicative for bioactivity. The FTIR spectroscopic analysis supports the sulfated polysaccharides of S. hypnoides, C. clavulatum and A. taxiformis are similar to agar polymer whereas the spectral characteristics of P. hornemannii have similarities to carrageenan. The higher DPPH activity and reducing power were recorded in the polysaccharide of brown seaweed P. pavonica than the red seaweeds as follows: DPPH activities: S. hypnoides > A. taxiformis > C. clavulatum > P. hornimanii; Reducing power: A. taxiformis > P. hornimanii > S. hypnoides > C. clavulatum. The polysaccharide fractions contain up to 14 kDa from red seaweeds P. hornemannii and S. hypnoides followed by brown seaweed P. pavonica exhibit cytotoxic activity in HeLa cancer cell line (and are similar to structural properties of carrageenan extracted from P. hornemannii). The low molecular weight agar like polymer of S. hypnoides and alginate like brown seaweed P. pavonica showing better in vitro antioxidant activities that are capable of exhibiting cytotoxicity against HeLa cell line can be taken up further in-depth investigation for nutraceutical study.University of Algarve: DL 57/2016info:eu-repo/semantics/publishedVersio

Geographic variation in the aetiology, epidemiology and microbiology of bronchiectasis

Bronchiectasis is a disease associated with chronic progressive and irreversible dilatation of the bronchi and is characterised by chronic infection and associated inflammation. The prevalence of bronchiectasis is age-related and there is some geographical variation in incidence, prevalence and clinical features. Most bronchiectasis is reported to be idiopathic however post-infectious aetiologies dominate across Asia especially secondary to tuberculosis. Most focus to date has been on the study of airway bacteria, both as colonisers and causes of exacerbations. Modern molecular technologies including next generation sequencing (NGS) have become invaluable tools to identify microorganisms directly from sputum and which are difficult to culture using traditional agar based methods. These have provided important insight into our understanding of emerging pathogens in the airways of people with bronchiectasis and the geographical differences that occur. The contribution of the lung microbiome, its ethnic variation, and subsequent roles in disease progression and response to therapy across geographic regions warrant further investigation. This review summarises the known geographical differences in the aetiology, epidemiology and microbiology of bronchiectasis. Further, we highlight the opportunities offered by emerging molecular technologies such as -omics to further dissect out important ethnic differences in the prognosis and management of bronchiectasis.NMRC (Natl Medical Research Council, S’pore)MOH (Min. of Health, S’pore)Published versio

Pan-cancer analysis of whole genomes

Cancer is driven by genetic change, and the advent of massively parallel sequencing has enabled systematic documentation of this variation at the whole-genome scale(1-3). Here we report the integrative analysis of 2,658 whole-cancer genomes and their matching normal tissues across 38 tumour types from the Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium of the International Cancer Genome Consortium (ICGC) and The Cancer Genome Atlas (TCGA). We describe the generation of the PCAWG resource, facilitated by international data sharing using compute clouds. On average, cancer genomes contained 4-5 driver mutations when combining coding and non-coding genomic elements; however, in around 5% of cases no drivers were identified, suggesting that cancer driver discovery is not yet complete. Chromothripsis, in which many clustered structural variants arise in a single catastrophic event, is frequently an early event in tumour evolution; in acral melanoma, for example, these events precede most somatic point mutations and affect several cancer-associated genes simultaneously. Cancers with abnormal telomere maintenance often originate from tissues with low replicative activity and show several mechanisms of preventing telomere attrition to critical levels. Common and rare germline variants affect patterns of somatic mutation, including point mutations, structural variants and somatic retrotransposition. A collection of papers from the PCAWG Consortium describes non-coding mutations that drive cancer beyond those in the TERT promoter(4); identifies new signatures of mutational processes that cause base substitutions, small insertions and deletions and structural variation(5,6); analyses timings and patterns of tumour evolution(7); describes the diverse transcriptional consequences of somatic mutation on splicing, expression levels, fusion genes and promoter activity(8,9); and evaluates a range of more-specialized features of cancer genomes(8,10-18).Peer reviewe

Community assessment to advance computational prediction of cancer drug combinations in a pharmacogenomic screen

The effectiveness of most cancer targeted therapies is short-lived. Tumors often develop resistance that might be overcome with drug combinations. However, the number of possible combinations is vast, necessitating data-driven approaches to find optimal patient-specific treatments. Here we report AstraZeneca’s large drug combination dataset, consisting of 11,576 experiments from 910 combinations across 85 molecularly characterized cancer cell lines, and results of a DREAM Challenge to evaluate computational strategies for predicting synergistic drug pairs and biomarkers. 160 teams participated to provide a comprehensive methodological development and benchmarking. Winning methods incorporate prior knowledge of drug-target interactions. Synergy is predicted with an accuracy matching biological replicates for >60% of combinations. However, 20% of drug combinations are poorly predicted by all methods. Genomic rationale for synergy predictions are identified, including ADAM17 inhibitor antagonism when combined with PIK3CB/D inhibition contrasting to synergy when combined with other PI3K-pathway inhibitors in PIK3CA mutant cells.Peer reviewe

Artificial intelligence models for suspended river sediment prediction : state-of-the art, modeling framework appraisal, and proposed future research directions

202303 bcwwVersion of RecordSelf-fundedPublishe