The spread of the COVID-19 infection in Russia's Baltic macro-region: internal differences

This article explores the spread of the Covid-19 infection in Russia’s Baltic macro-region. The monthly excess mortality rate in the Baltic region is analysed along with regional and municipal Covid-19 response acts to identify regional features affecting the spread of the disease. The spatial characteristics of Russia’s Baltic regions, germane to the propagation of Covid-19, were distinguished by examining selected social and economic statistical indicators. Based on the space of places/space of flows dichotomy, Russia’s Baltic regions can be divided into three spaces: 1) St. Petersburg, the Leningrad and Kaliningrad regions (dominated by spaces of flows; highly permeable space); 2) the Republic of Karelia and the Murmansk region (the key factors are rotational employment and the introduction of the virus from without); 3) the Novgorod and Pskov regions (lowly permeable spaces of places; the central role of local foci of the disease). The principal risk factor for the space of flows is the rapid spread of Covid-19 along transport arteries, whilst, within the space of places, the coronavirus spreads through spatial diffusion from isolated foci along short radii. In the former case, local authorities counteracted spatial diffusion by restricting movement in the local labour market; in the latter, by limiting travel between the centre and the periphery. The traditional ideas about positive (openness, centrality) and negative (closedness, peripherality) characteristics of space are reversed in the context of the pandemic: periphery gains the benefit of natural protection from the pandemic, whilst centres become acutely vulnerable

Quantitative Structure - Skin permeability Relationships

This paper reviews in silico models currently available for the prediction of skin permeability with the main focus on the quantitative structure-permeability relationship (QSPR) models. A comprehensive analysis of the main achievements in the field in the last decade is provided. In addition, the mechanistic models are discussed and comparative studies that analyse different models are discussed

The application of molecular modelling in the safety assessment of chemicals: A case study on ligand-dependent PPARγ dysregulation.

The aim of this paper was to provide a proof of concept demonstrating that molecular modelling methodologies can be employed as a part of an integrated strategy to support toxicity prediction consistent with the mode of action/adverse outcome pathway (MoA/AOP) framework. To illustrate the role of molecular modelling in predictive toxicology, a case study was undertaken in which molecular modelling methodologies were employed to predict the activation of the peroxisome proliferator-activated nuclear receptor γ (PPARγ) as a potential molecular initiating event (MIE) for liver steatosis. A stepwise procedure combining different in silico approaches (virtual screening based on docking and pharmacophore filtering, and molecular field analysis) was developed to screen for PPARγ full agonists and to predict their transactivation activity (EC50). The performance metrics of the classification model to predict PPARγ full agonists were balanced accuracy=81%, sensitivity=85% and specificity=76%. The 3D QSAR model developed to predict EC50 of PPARγ full agonists had the following statistical parameters: q(2)cv=0.610, Nopt=7, SEPcv=0.505, r(2)pr=0.552. To support the linkage of PPARγ agonism predictions to prosteatotic potential, molecular modelling was combined with independently performed mechanistic mining of available in vivo toxicity data followed by ToxPrint chemotypes analysis. The approaches investigated demonstrated a potential to predict the MIE, to facilitate the process of MoA/AOP elaboration, to increase the scientific confidence in AOP, and to become a basis for 3D chemotype development