Droplets generated from toilets during urination as a possible vehicle of carbapenem-resistant Klebsiella pneumoniae.

BACKGROUND: In the health care setting, infection control actions are fundamental for containing the dissemination of multidrug-resistant bacteria (MDR). Carbapenemase-producing Enterobacterales (CPE), especially Klebsiella pneumoniae (CR-KP), can spread among patients, although the dynamics of transmission are not fully known. Since CR-KP is present in wastewater and microorganisms are not completely removed from the toilet bowl by flushing, the risk of transmission in settings where toilets are shared should be addressed. We investigated whether urinating generates droplets that can be a vehicle for bacteria and explored the use of an innovative foam to control and eliminate this phenomenon. METHODS: To study droplet formation during urination, we set up an experiment in which different geometrical configurations of toilets could be reproduced and customized. To demonstrate that droplets can mobilize bacteria from the toilet bowl, a standard ceramic toilet was contaminated with a KPC-producing Klebsiella pneumoniae ST101 isolate. Then, we reproduced urination and attached culture dishes to the bottom of the toilet lid for bacterial colony recovery with and without foam. RESULTS: Rebound droplets invariably formed, irrespective of the geometrical configuration of the toilet. In microbiological experiments, we demonstrated that bacteria are always mobilized from the toilet bowl (mean value: 0.11 ± 0.05 CFU/cm2) and showed that a specific foam layer can completely suppress mobilization. CONCLUSIONS: Our study demonstrated that droplets generated from toilets during urination can be a hidden source of CR-KP transmission in settings where toilets are shared among colonized and noncolonized patients.

Modeling mitral valve stenosis: A parametric study on the stenosis severity level.

New computational techniques providing more accurate representation of human heart pathologies could help uncovering relevant physical phenomena and improve the outcome of medical therapies. In this framework, the present work describes an efficient computational model for the evaluation of the ventricular flow alteration in presence of mitral valve stenosis. The model is based on the direct numerical simulation of the Navier-Stokes equations two-way coupled with a structural solver for the left ventricle and mitral valve dynamics. The presence of mitral valve stenosis is mimicked by a single-parameter constraint acting on the kinematics of the mitral leaflets. Four different degrees of mitral valve stenosis are considered focusing on the hemodynamic alterations occurring in pathologic conditions. The mitral jet, generated during diastole, is seen to shrink and strengthen when the stenosis gets more severe. As a consequence, the kinetic energy of the flow, the tissues shear stresses, the transvalvular pressure drop and mitral regurgitation increase. It results that, as the stenosis severity level increases, the geometric and effective orifice areas decrease up to 50% with respect the normal case due to the reduced leaflets mobility and stronger blood acceleration during the diastolic phase. The modified intraventricular hemodynamics is also related to a stronger pressure gradient that, for severe stenosis, can be more than ten times larger than the healthy valve case. These computational results are fully consistent with the available clinical literature and open the way to the virtual assessment of surgical procedures and to the evaluation of prosthetic devices.

Effects of mitral chordae tendineae on the flow in the left heart ventricle.

In this paper a computational model for the ventricular flow with a mitral valve and modeled chordae tendineae is presented. The results are compared with an analogous case in which the chordae are not included and their presence is replaced by kinematic boundary conditions. The problem is studied using direct numerical simulation of the Navier-Stokes equations, two-way coupled with a structural solver for the ventricle and mitral valve dynamics. An experimental validation of the model is performed by a comparison of the results with a companion dedicated experiment. It is found that the inclusion of the chordae tendineae makes the model self-consistent thus avoiding the use of ad hoc kinematic constraints to mimic their effect. In this way it is possible to simulate the correct system dynamics without user-defined parameters. More in detail, the results have shown that the mitral valve dynamics can be described also without chordae with the help of ad hoc kinematic constrains, whereas the changes produced in the intra-ventricular flow need the explicit consideration of the chordae in the model. On the other hand, the computational load increases owing to the presence of additional structures that, being thin filaments, are also demanding for the spatial resolution requirements. Since the presence of the chordae tendineae produces only specific differences in the overall flow structure, we conclude that their explicit modeling should be limited to those cases in which their presence is unavoidable.

Stochastic modelling of PTEN regulation in brain tumors: A model for glioblastoma multiforme.

This work is the outcome of the partnership between the mathematical group of Department DISBEF and the biochemical group of Department DISB of the University of Urbino "Carlo Bo" in order to better understand some crucial aspects of brain cancer oncogenesis. Throughout our collaboration we discovered that biochemists are mainly attracted to the instantaneous behaviour of the whole cell, while mathematicians are mostly interested in the evolution along time of small and different parts of it. This collaboration has thus been very challenging. Starting from [23,24,25], we introduce a competitive stochastic model for post-transcriptional regulation of PTEN, including interactions with the miRNA and concurrent genes. Our model also covers protein formation and the backward mechanism going from the protein back to the miRNA. The numerical simulations show that the model reproduces the expected dynamics of normal glial cells. Moreover, the introduction of translational and transcriptional delays offers some interesting insights for the PTEN low expression as observed in brain tumor cells.