Exploring the Kinh Vietnamese genomic database for the polymorphisms of the P450 genes towards precision public health.

BACKGROUND: Human cytochrome P450 (CYPs) genes are essential in metabolising drugs. Due to their high polymorphism, population-specific studies are of great interest. AIM: This research examined the six CYP genes, including CYP2B6, CYP2C9, CYP2C19, CYP2D6, CYP3A5, and CYP4F2 in the Kinh Vietnamese (KHV) for population-scale precision medicine. SUBJECTS AND METHODS: We processed data from a genomics database of 206 healthy and unrelated KHV individuals to calculate CYP allele frequencies. First, we compared the CYP genes of the KHV to six other populations retrieved from the 1000 Genomes Project. Second, we searched the PharmGBK database for drug-CYP interaction data to compile a drug dosage recommendation for the KHV. RESULTS: We observed the diverging trends in genetic variations of CYP2B6, CYP2D6, and CYP3A5 in the KHV. Regarding phenotypic drug responses in the KHV, CYP2C19 exhibited all metabolic phenotypes at a non-trivial frequency. In addition, CYP3A5 metabolised drugs at a lower rate compared to the other five CYPs. CONCLUSION: This is the first large-scale study to investigate multiple CYP genes in the KHV for precision medicine from a public health perspective. Differences found in the distributions of metabolizers for the KHV suggest careful prescriptions for CYP2C19 and CYP3A5-metabolised drugs.

Early Essential Newborn Care can still be used with mothers who have COVID-19 if effective infection control measures are applied.

We describe the first infant born to a woman with COVID-19 in Vietnam, by Caesarean section at 36 weeks and 5 days of gestation. The mother and baby remained together during their hospital stay and prolonged skin-to-skin contact and early and exclusive breastfeeding were achieved. This was in line with the World Health Organization's Early Essential Newborn Care (EENC) recommendations, the national Vietnamese standard of care since 2014. The baby remained virus-free throughout the 34-day postpartum follow-up. CONCLUSION: The EENC approach can still be used with mothers who have COVID-19 if effective infection control measures are applied.

MPBoot: fast phylogenetic maximum parsimony tree inference and bootstrap approximation.

BACKGROUND: The nonparametric bootstrap is widely used to measure the branch support of phylogenetic trees. However, bootstrapping is computationally expensive and remains a bottleneck in phylogenetic analyses. Recently, an ultrafast bootstrap approximation (UFBoot) approach was proposed for maximum likelihood analyses. However, such an approach is still missing for maximum parsimony. RESULTS: To close this gap we present MPBoot, an adaptation and extension of UFBoot to compute branch supports under the maximum parsimony principle. MPBoot works for both uniform and non-uniform cost matrices. Our analyses on biological DNA and protein showed that under uniform cost matrices, MPBoot runs on average 4.7 (DNA) to 7 times (protein data) (range: 1.2-20.7) faster than the standard parsimony bootstrap implemented in PAUP*; but 1.6 (DNA) to 4.1 times (protein data) slower than the standard bootstrap with a fast search routine in TNT (fast-TNT). However, for non-uniform cost matrices MPBoot is 5 (DNA) to 13 times (protein data) (range:0.3-63.9) faster than fast-TNT. We note that MPBoot achieves better scores more frequently than PAUP* and fast-TNT. However, this effect is less pronounced if an intensive but slower search in TNT is invoked. Moreover, experiments on large-scale simulated data show that while both PAUP* and TNT bootstrap estimates are too conservative, MPBoot bootstrap estimates appear more unbiased. CONCLUSIONS: MPBoot provides an efficient alternative to the standard maximum parsimony bootstrap procedure. It shows favorable performance in terms of run time, the capability of finding a maximum parsimony tree, and high bootstrap accuracy on simulated as well as empirical data sets. MPBoot is easy-to-use, open-source and available at http://www.cibiv.at/software/mpboot .

UFBoot2: Improving the Ultrafast Bootstrap Approximation.

The standard bootstrap (SBS), despite being computationally intensive, is widely used in maximum likelihood phylogenetic analyses. We recently proposed the ultrafast bootstrap approximation (UFBoot) to reduce computing time while achieving more unbiased branch supports than SBS under mild model violations. UFBoot has been steadily adopted as an efficient alternative to SBS and other bootstrap approaches. Here, we present UFBoot2, which substantially accelerates UFBoot and reduces the risk of overestimating branch supports due to polytomies or severe model violations. Additionally, UFBoot2 provides suitable bootstrap resampling strategies for phylogenomic data. UFBoot2 is 778 times (median) faster than SBS and 8.4 times (median) faster than RAxML rapid bootstrap on tested data sets. UFBoot2 is implemented in the IQ-TREE software package version 1.6 and freely available at http://www.iqtree.org.

Scholarometer: a social framework for analyzing impact across disciplines.

The use of quantitative metrics to gauge the impact of scholarly publications, authors, and disciplines is predicated on the availability of reliable usage and annotation data. Citation and download counts are widely available from digital libraries. However, current annotation systems rely on proprietary labels, refer to journals but not articles or authors, and are manually curated. To address these limitations, we propose a social framework based on crowdsourced annotations of scholars, designed to keep up with the rapidly evolving disciplinary and interdisciplinary landscape. We describe a system called Scholarometer, which provides a service to scholars by computing citation-based impact measures. This creates an incentive for users to provide disciplinary annotations of authors, which in turn can be used to compute disciplinary metrics. We first present the system architecture and several heuristics to deal with noisy bibliographic and annotation data. We report on data sharing and interactive visualization services enabled by Scholarometer. Usage statistics, illustrating the data collected and shared through the framework, suggest that the proposed crowdsourcing approach can be successful. Secondly, we illustrate how the disciplinary bibliometric indicators elicited by Scholarometer allow us to implement for the first time a universal impact measure proposed in the literature. Our evaluation suggests that this metric provides an effective means for comparing scholarly impact across disciplinary boundaries.