Overview of in vitro-in vivo extrapolation approaches for the risk assessment of nanomaterial toxicity.

Nanomaterials are increasingly used in many applications due to their enhanced properties. To ensure their safety for humans and the environment, nanomaterials need to be evaluated for their potential risk. The risk assessment analysis on the nanomaterials based on animal or in vivo studies is accompanied by several concerns, including animal welfare, time and cost needed for the studies.

Maternal perceived stress and green spaces during pregnancy are associated with adult offspring gene (NR3C1 and IGF2/H19) methylation patterns in adulthood: A pilot study.

Background: Changes in NR3C1 and IGF2/H19 methylation patterns have been associated with behavioural and psychiatric outcomes. Maternal mental state has been associated with offspring NR3C1 promotor and IGF2/H19 imprinting control region (ICR) methylation patterns. However, there is a lack of prospective studies with long-term follow-up.

Modeling the positive testing rate of COVID-19 in South Africa using a semi-parametric smoother for binomial data.

Identification and isolation of COVID-19 infected persons plays a significant role in the control of COVID-19 pandemic. A country's COVID-19 positive testing rate is useful in understanding and monitoring the disease transmission and spread for the planning of intervention policy. Using publicly available data collected between March 5th, 2020 and May 31st, 2021, we proposed to estimate both the positive testing rate and its daily rate of change in South Africa with a flexible semi-parametric smoothing model for discrete data.

Development of Microbiome Biomarkers in Intervention Studies.

Aims: There has been an increased interest in studying the association between microbial communities and different diseases and in discovering microbiome biomarkers. This association is pivotal to discover such biomarkers. In this paper, we present a unified modelling approach that can be used to detect and develop microbiome biomarkers for different clinical responses of interest at different levels of the microbiome ecosystem.

Exploring the Microbiome Analysis and Visualization Landscape.

Research on the microbiome has boomed recently, which resulted in a wide range of tools, packages, and algorithms to analyze microbiome data. Here we investigate and map currently existing tools that can be used to perform visual analysis on the microbiome, and associate the including methods, visual representations and data features to the research objectives currently of interest in microbiome research. The analysis is based on a combination of a literature review and workshops including a group of domain experts.

Modelling trends of CD4 counts for patients on antiretroviral therapy (ART): a comprehensive health care clinic in Nairobi, Kenya.

Background: In resource-limited settings, changes in CD4 counts constitute an important component in patient monitoring and evaluation of treatment response as these patients do not have access to routine viral load testing. In this study, we quantified trends on CD4 counts in patients on highly active antiretroviral therapy (HAART) in a comprehensive health care clinic in Kenya between 2011 and 2017. We evaluated the rate of change in CD4 cell count in response to antiretroviral treatment.

A Roadmap for Building Data Science Capacity for Health Discovery and Innovation in Africa.

Technological advances now make it possible to generate diverse, complex and varying sizes of data in a wide range of applications from business to engineering to medicine. In the health sciences, in particular, data are being produced at an unprecedented rate across the full spectrum of scientific inquiry spanning basic biology, clinical medicine, public health and health care systems. Leveraging these data can accelerate scientific advances, health discovery and innovations.

Epidemiology of Mycobacterium tuberculosis lineages and strain clustering within urban and peri-urban settings in Ethiopia.

Background: Previous work has shown differential predominance of certain Mycobacterium tuberculosis (M. tb) lineages and sub-lineages among different human populations in diverse geographic regions of Ethiopia. Nevertheless, how strain diversity is evolving under the ongoing rapid socio-economic and environmental changes is poorly understood.

Global prevalence of Mycobacterium bovis infections among human tuberculosis cases: Systematic review and meta-analysis.

Tuberculosis (TB) is a chronic communicable bacterial disease caused by Mycobacterium tuberculosis complex (MTBC) species. M. tuberculosis is the main causative agent of human TB, and cattle are the primary host of Mycobacterium bovis; due to close interaction between cattle and humans, M.

Factors associated with localization of tuberculosis disease among patients in a high burden country: A health facility-based comparative study in Ethiopia.

Introduction: In contrast to most tuberculosis (TB) high burden countries, Ethiopia has for a long time reported a very high percentage of extra pulmonary TB (EPTB), which is also reflected in population based estimations reported by the World Health Organization (WHO). Particularly a steadily higher proportion of cervical tuberculous lymphadenitis (TBLN) has been described. Here we identify clinical and demographic factors associated with anatomic site of the TB disease.

Informal mhealth at scale in Africa: Opportunities and challenges.

The extraordinary global growth of digital connectivity has generated optimism that mobile technologies can help overcome infrastructural barriers to development, with 'mobile health' (mhealth) being a key component of this. However, while 'formal' (top-down) mhealth programmes continue to face challenges of scalability and sustainability, we know relatively little about how health-workers are using their own mobile phones in their work. Using data from Ghana, Ethiopia and Malawi, we document the reach, nature and perceived impacts of community health-workers' (CHWs') 'informal mhealth' practices, and ask how equitably these are distributed.

Short-term real-time prediction of total number of reported COVID-19 cases and deaths in South Africa: a data driven approach.

Background: The rising burden of the ongoing COVID-19 epidemic in South Africa has motivated the application of modeling strategies to predict the COVID-19 cases and deaths. Reliable and accurate short and long-term forecasts of COVID-19 cases and deaths, both at the national and provincial level, are a key aspect of the strategy to handle the COVID-19 epidemic in the country.

Methods: In this paper we apply the previously validated approach of phenomenological models, fitting several non-linear growth curves (Richards, 3 and 4 parameter logistic, Weibull and Gompertz), to produce short term forecasts of COVID-19 cases and deaths at the national level as well as the provincial level.

Correlations between the metabolic profile and F-FDG-Positron Emission Tomography-Computed Tomography parameters reveal the complexity of the metabolic reprogramming within lung cancer patients.

Several studies have demonstrated that the metabolite composition of plasma may indicate the presence of lung cancer. The metabolism of cancer is characterized by an enhanced glucose uptake and glycolysis which is exploited by F-FDG positron emission tomography (PET) in the work-up and management of cancer. This study aims to explore relationships between H-NMR spectroscopy derived plasma metabolite concentrations and the uptake of labeled glucose (F-FDG) in lung cancer tissue.

High dimensional surrogacy: computational aspects of an upscaled analysis.

Identification of genomic biomarkers is an important area of research in the context of drug discovery experiments. These experiments typically consist of several high dimensional datasets that contain information about a set of drugs (compounds) under development. This type of data structure introduces the challenge of multi-source data integration.

Model-based prediction of CD4 cells counts in HIV-infected adults on antiretroviral therapy in Northwest Ethiopia: A flexible mixed effects approach.

Background: CD4 cell counts is widely used as a biomarker for treatment progression when studying the efficacy of drugs to treat HIV-infected patients. In the past, it had been also used in determining eligibility to initiate antiretroviral therapy. The main aim of this was to model the evolution of CD4 counts over time and use this model for an early prediction of subject-specific time to cross a pre-specified CD4 threshold.

A multivariate linear model for investigating the association between gene-module co-expression and a continuous covariate.

A way to enhance our understanding of the development and progression of complex diseases is to investigate the influence of cellular environments on gene co-expression (i.e. gene-pair correlations).

Differential effects of two therapeutic cancer vaccines on short- and long-term survival populations among patients with advanced lung cancer.

Background: Progress in immunotherapy has revolutionized the treatment landscape for advanced lung cancer, with emerging evidence of patients experiencing long-term survivals. The goal of this study was to explore the existence of short- and long-term survival populations and to assess the effect of immunotherapy on them.

Methods: Data from two randomized, multicenter, controlled clinical trials was used to evaluate the effect of two therapeutic vaccines (anti-idiotypic vaccine VAXIRA and anti-EGF vaccine CIMAVAX) on survival curves in advanced non-small cell lung cancer patients.

The Usage of Exon-Exon Splice Junctions for the Detection of Alternative Splicing using the REIDS model.

Alternative gene splicing is a common phenomenon in which a single gene gives rise to multiple transcript isoforms. The process is strictly guided and involves a multitude of proteins and regulatory complexes. Unfortunately, aberrant splicing events have been linked to genetic disorders.

Diffusion kurtosis imaging allows the early detection and longitudinal follow-up of amyloid-β-induced pathology.

Background: Alzheimer's disease (AD) is a progressive neurodegenerative disorder and the most common cause of dementia in the elderly population. In this study, we used the APP/PS1 transgenic mouse model to explore the feasibility of using diffusion kurtosis imaging (DKI) as a tool for the early detection of microstructural changes in the brain due to amyloid-β (Aβ) plaque deposition.

Methods: We longitudinally acquired DKI data of wild-type (WT) and APP/PS1 mice at 2, 4, 6 and 8 months of age, after which these mice were sacrificed for histological examination.

Choice of initial antiretroviral drugs and treatment outcomes among HIV-infected patients in sub-Saharan Africa: systematic review and meta-analysis of observational studies.

Background: The effectiveness of antiretroviral therapy (ART) depends on the choice of regimens during initiation. Most evidences from developed countries indicated that there is difference between efavirenz (EFV) and nevirapine (NVP). However, the evidences are limited in resource poor countries particularly in Africa.

Disease severity in patients with visceral leishmaniasis is not altered by co-infection with intestinal parasites.

Visceral leishmaniasis (VL) is a neglected tropical disease that affects the poorest communities and can cause substantial morbidity and mortality. Visceral leishmaniasis is characterized by the presence of Leishmania parasites in the spleen, liver and bone marrow, hepatosplenomegaly, pancytopenia, prolonged fever, systemic inflammation and low body mass index (BMI). The factors impacting on the severity of VL are poorly characterized.

Multi-state models for the analysis of time-to-treatment modification among HIV patients under highly active antiretroviral therapy in Southwest Ethiopia.

Background: Highly active antiretroviral therapy (HAART) has shown a dramatic change in controlling the burden of HIV/AIDS. However, the new challenge of HAART is to allow long-term sustainability. Toxicities, comorbidity, pregnancy, and treatment failure, among others, would result in frequent initial HAART regimen change.

A random effects model for the identification of differential splicing (REIDS) using exon and HTA arrays.

Background: Alternative gene splicing is a common phenomenon in which a single gene gives rise to multiple transcript isoforms. The process is strictly guided and involves a multitude of proteins and regulatory complexes. Unfortunately, aberrant splicing events do occur which have been linked to genetic disorders, such as several types of cancer and neurodegenerative diseases (Fan et al.

Identification of the minimum effective dose for normally distributed data using a Bayesian variable selection approach.

The identification of the minimum effective dose is of high importance in the drug development process. In early stage screening experiments, establishing the minimum effective dose can be translated into a model selection based on information criteria. The presented alternative, Bayesian variable selection approach, allows for selection of the minimum effective dose, while taking into account model uncertainty.

Multiple confidence intervals for selected parameters adjusted for the false coverage rate in monotone dose-response microarray experiments.

Benjamini and Yekutieli () introduced the concept of the false coverage-statement rate (FCR) to account for selection when the confidence intervals (CIs) are constructed only for the selected parameters. Dose-response analysis in dose-response microarray experiments is conducted only for genes having monotone dose-response relationship, which is a selection problem. In this paper, we consider multiple CIs for the mean gene expression difference between the highest dose and control in monotone dose-response microarray experiments for selected parameters adjusted for the FCR.