Search

This work performs two-dimensional Computational Fluid Dynamics (CFD) simulations of pulsatile bubbly flow in a column resembling the flow inside human vena cava during Decompression Sickness (DCS), aiming to illustrate the effect of certain parameters in bubbly blood flow and so facilitate the design of the: a) corresponding in-vitro bubbly flow experiments under pulsatile flow conditions inside a flow loop and b) in-vivo trials on swines for assessing a novel electrical impedance spectroscopy technique on the detection of bubbles (as those found during DCS) in their bloodstream.The commercially available ANSYS 2019-R3 CFD code was employed to simulate the pulsatile bubbly flow that resembled DCS. Simulations were validated against experiments conducted in a vertical co-current upward pulsatile bubbly flow provided by a flow loop equipped with electrical, optical and pressure diagnostics.CFD simulations under pulsatile conditions were initially validated by oscillatory in-vitro bubbly flow experiments. Then, the influence of pulsation parameters on void fraction, α, and flow velocity, U, profiles was computationally investigated. Intense periodic fluctuations of void fraction were observed along the column and their intensity increases with pulsation amplitude. Moreover, U and α radial profiles were uniform for bubbles 30 μm but showed a core-peaking profile for bubbles 300 μm.CFD simulations of pulsatile bubbly flow resembling DCS provided unconventional information about the influence of different-sized sub-millimetre bubbles on the flow velocity and void fraction profiles, which are expected to improve the design of in-vitro and in-vivo trials for the detection of bubbles such as those found in DCS.

Epidemic spread models are useful tools to study the spread and the effectiveness of the interventions at a population level, to an epidemic. The workhorse of spatially homogeneous class models is the SIR-type ones comprising ordinary differential equations for the unknown state variables. The transition between different states is expressed through rate functions. Inspired by -but not restricted to- features of the COVID-19 pandemic, a new framework for modeling a disease spread is proposed. The main concept refers to the assignment of properties to each individual person as regards his response to the disease. A multidimensional distribution of these properties represents the whole population. The temporal evolution of this distribution is the only dependent variable of the problem. All other variables can be extracted by post-processing of this distribution. It is noteworthy that the new concept allows an improved consideration of vaccination modeling because it recognizes vaccination as a modifier of individuals response to the disease and not as a means for individuals to totally defeat the disease. At the heart of the new approach is an infection age model engaging a sharp cut-off. This model is analyzed in detail, and it is shown to admit self-similar solutions. A hierarchy of models based on the new approach, from a generalized one to a specific one with three dominant properties, is derived. The latter is implemented as an example and indicative results are presented and discussed. It appears that the new framework is general and versatile enough to simulate disease spread processes and to predict the evolution of several variables of the population during this spread.

Conventional SARS-CoV-2 surveillance based on genotyping of clinical samples is characterized by challenges related to the available sequencing capacity, population sampling methodologies, and is time, labor, and resource-demanding. Wastewater-based variant surveillance constitutes a valuable supplementary practice, since it does not require extensive sampling, and provides information on virus prevalence in a timely and cost-effective manner. Consequently, we developed a sensitive real-time RT-PCR-based approach that exclusively amplifies and quantifies SARS-CoV-2 genomic regions carrying the S:Δ69/70 deletion, indicative of the Omicron BA.1 variant, in wastewater. The method was incorporated in the analysis of composite daily samples taken from the main Wastewater Treatment Plant of Thessaloniki, Greece, from 1 December 2021. The applicability of the methodology is dependent on the epidemiological situation. During Omicron BA.1 global emergence, Thessaloniki was experiencing a massive epidemic wave attributed solely to the Delta variant, according to genomic surveillance data. Since Delta does not possess the S:Δ69/70, the emergence of Omicron BA.1 could be monitored via the described methodology. Omicron BA.1 was detected in sewage samples on 19 December 2021 and a rapid increase of its viral load was observed in the following 10-day period, with an estimated early doubling time of 1.86 days. The proportion of the total SARS-CoV-2 load attributed to BA.1 reached 91.09 % on 7 January, revealing a fast Delta-to-Omicron transition pattern. The detection of Omicron BA.1 subclade in wastewater preceded the outburst of reported (presumable) Omicron cases in the city by approximately 7 days. The proposed wastewater surveillance approach based on selective PCR amplification of a genomic region carrying a deletion signature enabled rapid, real-time data acquisition on Omicron BA.1 prevalence and dynamics during the slow remission of the Delta wave. Timely provision of these results to State authorities readily influences the decision-making process for targeted public health interventions, including control measures, awareness, and preparedness.

The COVID-19 pandemic represents an unprecedented global crisis necessitating novel approaches for, amongst others, early detection of emerging variants relating to the evolution and spread of the virus. Recently, the detection of SARS-CoV-2 RNA in wastewater has emerged as a useful tool to monitor the prevalence of the virus in the community. Here, we propose a novel methodology, called lineagespot, for the monitoring of mutations and the detection of SARS-CoV-2 lineages in wastewater samples using next-generation sequencing (NGS). Our proposed method was tested and evaluated using NGS data produced by the sequencing of 14 wastewater samples from the municipality of Thessaloniki, Greece, covering a 6-month period. The results showed the presence of SARS-CoV-2 variants in wastewater data. lineagespot was able to record the evolution and rapid domination of the Alpha variant (B.1.1.7) in the community, and allowed the correlation between the mutations evident through our approach and the mutations observed in patients from the same area and time periods. lineagespot is an open-source tool, implemented in R, and is freely available on GitHub and registered on bio.tools.