NaHCO3 loading causes increased arterial pressure and kidney damage in rats with chronic kidney disease.

Sodium bicarbonate (NaHCO3) is commonly utilized as a therapeutic to treat metabolic acidosis in people with chronic kidney disease (CKD). While increased dietary sodium chloride (NaCl) is known to promote volume retention and increase blood pressure, the effects of NaHCO3 loading on blood pressure and volume retention in CKD remain unclear. In the present study, we compared the effects of NaCl and NaHCO3 loading on volume retention, blood pressure, and kidney injury in both 2/3 and 5/6 nephrectomy remnant kidney rats, a well-established rodent model of CKD. We tested the hypothesis that NaCl loading promotes greater volume retention and increases in blood pressure than equimolar NaHCO3. Blood pressure was measured 24 h daily using radio telemetry. NaCl and NaHCO3 were administered in drinking water ad libitum or infused via indwelling catheters. Rats were housed in metabolic cages to determine volume retention. Our data indicate that both NaHCO3 and NaCl promote hypertension and volume retention in remnant kidney rats, with salt-sensitivity increasing with greater renal mass reduction. Importantly, while NaHCO3 intake was less pro-hypertensive than equimolar NaCl intake, NaHCO3 was not benign. NaHCO3 loading significantly elevated blood pressure and promoted volume retention in rats with CKD when compared with control rats receiving tap water. Our findings provide important insight into the effects of sodium loading with NaHCO3 in CKD and indicate that NaHCO3 loading in patients with CKD is unlikely to be benign.

Manipulating and Basic Analysis of Tabular Metagenomics Datasets Using R.

Handling and manipulating tabular datasets is a critical step in every metagenomics analysis pipeline. The R statistical programming language offers a variety of versatile tools for working with tabular data that allow for the development of computationally efficient and reproducible workflows. Here we outline the basics of the R programming language and showcase a number of tools for data manipulation and basic analysis of metagenomics datasets.

Metagenomics Data Visualization Using R.

Communicating key finds is a crucial part of the research process. Data visualization is the field of graphically representing data to help communicate key findings. Building on previous chapters around data manipulating using the R programming language this, chapter will explore how to use R to plot data and generate high-quality graphics. It will cover plotting using the base R plotting functionality and introduce the famous ggplot2 package [2] that is widely used for data visualization in R. After this general introduction to data visualization tools, the chapter will explore more specific data visualization techniques for metagenomics data and their use cases using these basic packages.

A dynamic microsimulation model for epidemics.

A large evidence base demonstrates that the outcomes of COVID-19 and national and local interventions are not distributed equally across different communities. The need to inform policies and mitigation measures aimed at reducing the spread of COVID-19 highlights the need to understand the complex links between our daily activities and COVID-19 transmission that reflect the characteristics of British society. As a result of a partnership between academic and private sector researchers, we introduce a novel data driven modelling framework together with a computationally efficient approach to running complex simulation models of this type. We demonstrate the power and spatial flexibility of the framework to assess the effects of different interventions in a case study where the effects of the first UK national lockdown are estimated for the county of Devon. Here we find that an earlier lockdown is estimated to result in a lower peak in COVID-19 cases and 47% fewer infections overall during the initial COVID-19 outbreak. The framework we outline here will be crucial in gaining a greater understanding of the effects of policy interventions in different areas and within different populations.

Contribution of the KSHV and EBV lytic cycles to tumourigenesis.

Kaposi's Sarcoma-associated herpesvirus (KSHV) and Epstein Barr virus (EBV) are the causative agents of several malignancies. Like all herpesviruses, KSHV and EBV undergo distinct latent and lytic replication programmes. The transition between these states allows the establishment of a lifelong persistent infection, dissemination to sites of disease and the spread to new hosts. Latency-associated viral proteins have been well characterised in transformation and tumourigenesis pathways; however, a number of studies have shown that abrogation of KSHV and EBV lytic gene expression impairs the oncogenesis of several cancers. Furthermore, several lytically expressed proteins have been functionally tethered to the angioproliferative and anti-apoptotic phenotypes of virus-infected cells. As a result, the investigation and therapeutic targeting of KSHV and EBV lytic cycles may be essential for the treatment of their associated malignancies.

Overexpressing components of the nuclear transport apparatus causes severe growth symptoms in tobacco leaves.

Regulating nucleo-cytoplasmic transport of RNA and protein is a key cellular control point. Perturbing the function of plant nuclear transport components can cause significant developmental defects and in this report we add an important line to this evidence. Overexpression of AtRAN1 or AtNUP62 in Nicotiana benthamiana causes significant damage to leaf tissue. This demonstrates that the precise control of nuclear transport is an important aspect of maintaining tissue integrity.