A Viral Dynamic Model for Treatment Regimens with Direct-acting Antivirals for Chronic Hepatitis C Infection

We propose an integrative, mechanistic model that integrates in vitro virology data, pharmacokinetics, and viral response to a combination regimen of a direct-acting antiviral (telaprevir, an HCV NS3-4A protease inhibitor) and peginterferon alfa-2a/ribavirin (PR) in patients with genotype 1 chronic hepatitis C (CHC). This model, which was parameterized with on-treatment data from early phase clinical studies in treatment-naïve patients, prospectively predicted sustained virologic response (SVR) rates that were comparable to observed rates in subsequent clinical trials of regimens with different treatment durations in treatment-naïve and treatment-experienced populations. The model explains the clinically-observed responses, taking into account the IC50, fitness, and prevalence prior to treatment of viral resistant variants and patient diversity in treatment responses, which result in different eradication times of each variant. The proposed model provides a framework to optimize treatment strategies and to integrate multifaceted mechanistic information and give insight into novel CHC treatments that include direct-acting antiviral agents

Analysis of genetic systems using experimental evolution and whole-genome sequencing

The application of whole-genome sequencing to the study of microbial evolution promises to reveal the complex functional networks of mutations that underlie adaptation. A recent study of parallel evolution in populations of Escherichia coli shows how adaptation involves both functional changes to specific proteins as well as global changes in regulation

Genotype-dependent lifespan effects in peptone deprived Caenorhabditis elegans

Dietary restriction appears to act as a general non-genetic mechanism that can robustly prolong lifespan. There have however been reports in many systems of cases where restricted food intake either shortens, or does not affect, lifespan. Here we analyze lifespan and the effect of food restriction via deprived peptone levels on lifespan in wild isolates and introgression lines (ILs) of the nematode Caenorhabditis elegans. These analyses identify genetic variation in lifespan, in the effect of this variation in diet on lifespan and also in the likelihood of maternal, matricidal, hatching. Importantly, in the wild isolates and the ILs, we identify genotypes in which peptone deprivation mediated dietary restriction reduces lifespan. We also identify, in recombinant inbred lines, a locus that affects maternal hatching, a phenotype closely linked to dietary restriction in C. elegans. These results indicate that peptone deprivation mediated dietary restriction affects lifespan in C. elegans in a genotype-dependent manner, reducing lifespan in some genotypes. This may operate by a mechanism similar to dietary restriction

Fit between humanitarian professionals and project requirements: hybrid group decision procedure to reduce uncertainty in decision-making

Choosing the right professional that has to meet indeterminate requirements is a critical aspect in humanitarian development and implementation projects. This paper proposes a hybrid evaluation methodology for some non-governmental organizations enabling them to select the most competent expert who can properly and adequately develop and implement humanitarian projects. This methodology accommodates various stakeholders’ perspectives in satisfying the unique requirements of humanitarian projects that are capable of handling a range of uncertain issues from both stakeholders and project requirements. The criteria weights are calculated using a two-step multi-criteria decision-making method: (1) Fuzzy Analytical Hierarchy Process for the evaluation of the decision maker weights coupled with (2) Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) to rank the alternatives which provide the ability to take into account both quantitative and qualitative evaluations. Sensitivity analysis have been developed and discussed by means of a real case of expert selection problem for a non-profit organisation. The results show that the approach allows a decrease in the uncertainty associated with decision-making, which proves that the approach provides robust solutions in terms of sensitivity analysis

Comparative effects of whey and casein proteins on satiety in overweight and obese individuals: A randomized controlled trial

Background/Objective: Dairy protein seems to reduce appetite by increasing satiety and delaying the return of hunger and subsequently lowering energy intake compared with fat or carbohydrate. The aim of this study was to compare the effect of whey with that of casein proteins on satiety in overweight/obese individuals. Methods/Subjects: This was a randomized, parallel-design 12-week-long study. Seventy subjects with a body mass index between 25 and 40 kg/m2 and aged 18–65 years were randomized into one of three supplement groups: glucose control (n=25), casein (n=20) or whey (n=25) protein. Before commencing the study, at weeks 6 and 12 of the treatment, a Visual Analogue Scale (VAS) was used to measure subjective sensations of appetite before lunch and before dinner. Results: Rating for VAS (mm) at 6 and 12 weeks showed significantly higher satiety in the whey group compared with the casein (P=0.017 and P=0.025, respectively) or control (P=0.024 and P=0.032, respectively) groups when measured before lunch. Similarly, at 6 and 12 weeks, the score for fullness was also significantly higher in the whey group compared with both casein (P=0.038 and P=0.022, respectively) and control (P=0.020 and P=0.030, respectively) groups. However, these short-term effects on satiety from dairy whey proteins did not have any long-term effects on energy intake or body weight over 12 weeks compared with casein. Conclusions: Collectively, whey protein supplementation appears to have a positive and acute postprandial effect on satiety and fullness compared with casein and carbohydrate supplementation in overweight and obese individuals

A conceptual framework for the adoption of big data analytics by e-commerce startups: a case-based approach

E-commerce start-ups have ventured into emerging economies and are growing at a significantly faster pace. Big data has acted like a catalyst in their growth story. Big data analytics (BDA) has attracted e-commerce firms to invest in the tools and gain cutting edge over their competitors. The process of adoption of these BDA tools by e-commerce start-ups has been an area of interest as successful adoption would lead to better results. The present study aims to develop an interpretive structural model (ISM) which would act as a framework for efficient implementation of BDA. The study uses hybrid multi criteria decision making processes to develop the framework and test the same using a real-life case study. Systematic review of literature and discussion with experts resulted in exploring 11 enablers of adoption of BDA tools. Primary data collection was done from industry experts to develop an ISM framework and fuzzy MICMAC analysis is used to categorize the enablers of the adoption process. The framework is then tested by using a case study. Thematic clustering is performed to develop a simple ISM framework followed by fuzzy analytical network process (ANP) to discuss the association and ranking of enablers. The results indicate that access to relevant data forms the base of the framework and would act as the strongest enabler in the adoption process while the company rates technical skillset of employees as the most important enabler. It was also found that there is a positive correlation between the ranking of enablers emerging out of ISM and ANP. The framework helps in simplifying the strategies any e-commerce company would follow to adopt BDA in future. © 2019, Springer-Verlag GmbH Germany, part of Springer Nature