Direct measurement of DNA-mediated adhesion between lipid bilayers

Multivalent interactions between deformable mesoscopic units are ubiquitous in biology, where membrane macromolecules mediate the interactions between neighbouring living cells and between cells and solid substrates. Lately, analogous artificial materials have been synthesised by functionalising the outer surface of compliant Brownian units, for example emulsion droplets and lipid vesicles, with selective linkers, in particular short DNA sequences. This development extended the range of applicability of DNA as a selective glue, originally applied to solid nano and colloidal particles. On very deformable lipid vesicles, the coupling between statistical effects of multivalent interactions and mechanical deformation of the membranes gives rise to complex emergent behaviours, as we recently contributed to demonstrate [Parolini et al., Nature Communications, 2015, 6, 5948]. Several aspects of the complex phenomenology observed in these systems still lack a quantitative experimental characterisation and fundamental understanding. Here we focus on the DNA-mediated multivalent interactions of a single liposome adhering to a flat supported bilayer. This simplified geometry enables the estimate of the membrane tension induced by the DNA-mediated adhesive forces acting on the liposome. Our experimental investigation is completed by morphological measurements and the characterisation of the DNA-melting transition, probed by in-situ F\"{o}rster Resonant Energy Transfer spectroscopy. Experimental results are compared with the predictions of an analytical theory that couples the deformation of the vesicle to a full description of the statistical mechanics of mobile linkers. With at most one fitting parameter, our theory is capable of semi-quantitatively matching experimental data, confirming the quality of the underlying assumptions.Comment: 16 pages, 7 figure

Multiphysics simulation of corona discharge induced ionic wind

Ionic wind devices or electrostatic fluid accelerators are becoming of increasing interest as tools for thermal management, in particular for semiconductor devices. In this work, we present a numerical model for predicting the performance of such devices, whose main benefit is the ability to accurately predict the amount of charge injected at the corona electrode. Our multiphysics numerical model consists of a highly nonlinear strongly coupled set of PDEs including the Navier-Stokes equations for fluid flow, Poisson's equation for electrostatic potential, charge continuity and heat transfer equations. To solve this system we employ a staggered solution algorithm that generalizes Gummel's algorithm for charge transport in semiconductors. Predictions of our simulations are validated by comparison with experimental measurements and are shown to closely match. Finally, our simulation tool is used to estimate the effectiveness of the design of an electrohydrodynamic cooling apparatus for power electronics applications.Comment: 24 pages, 17 figure

Laser treatment in diabetic retinopathy

Diabetic retinopathy is a leading cause of visual impairment and blindness in developed countries due to macular edema and proliferative diabetic retinopathy (PDR). For both complications laser treatment may offer proven therapy: the Diabetic Retinopathy Study demonstrated that panretinal scatter photocoagulation reduces the risk of severe visual loss by >= 50% in eyes with high-risk characteristics. Pan-retinal scatter coagulation may also be beneficial in other PDR and severe nonproliferative diabetic retinopathy (NPDR) under certain conditions. For clinically significant macular edema the Early Treatment of Diabetic Retinopathy Study could show that immediate focal laser photocoagulation reduces the risk of moderate visual loss by at least 50%. When and how to perform laser treatment is described in detail, offering a proven treatment for many problems associated with diabetic retinopathy based on a high evidence level. Copyright (c) 2007 S. Karger AG, Basel

Severity of oxidative stress and inflammatory activation in end-stage heart failure patients are unaltered after 1 month of left ventricular mechanical assistance

This study investigates the impact of early left ventricular (LV)-mechanical unloading on systemic oxidative stress and inflammation in terminal heart failure patients and their impact both on multi organ failure and on intensive care unit (ICU) stay. Circulating levels of urinary 15-isoprostane-F2t (8-epi-PGF2a) and pro-inflammatory markers [plasma interleukin (IL)-6, IL-8, and urinary neopterin, a monocyte activation index] were analyzed in 20 healthy subjects, 22 stable end-stage heart failure (ESHF) patients and in 23 LV assist device (LVAD) recipients at pre-implant and during first post-LVAD (PL) month. Multiorgan function was evaluated by total Sequential Organ Failure Assessment (tSOFA) score. In LVAD recipients the levels of oxidative-inflammatory markers and tSOFA score were higher compared to other groups. After device implantation 8-epi-PGF2a levels were unchanged, while IL-6, and IL-8 levels increased during first week, and at 1 month returned to pre-implant values, while neopterin levels increased progressively during LVAD support. The tSOFA score worsened at 1 PL-week with respect to pre-implant value, but improved at 1 PL-month. The tSOFA score related with IL-6 and IL-8 levels, while length of ICU stay related with pre-implant IL-6 levels. These data suggest that hemodynamic instability in terminal HF is associated to worsening of systemic inflammatory and oxidative milieu that do not improve in the early phase of hemodynamic recovery and LV-unloading by LVAD, affecting multi-organ function and length of ICU stay. This data stimulate to evaluate the impact of inflammatory signals on long-term outcome of mechanical circulatory support

Association of pre-operative interleukin-6 levels with Interagency Registry for Mechanically Assisted Circulatory Support profiles and intensive care unit stay in left ventricular assist device patients

BACKGROUND: Inflammatory mechanisms are associated with worse prognosis in end-stage heart failure (ESHF) patients who require left ventricular assist device (LVAD) support. Interagency Registry for Mechanically Assisted Circulatory Support (INTERMACS) profiles describe patient condition at pre-implant and outcome. This study assessed the relationship among inflammation patterns and INTERMACS profiles in LVAD recipients. METHOD: Thirty ESHF patients undergoing LVAD implantation as bridge to transplant were enrolled. Blood and urine samples were collected pre-operatively and serially up to 2 weeks post-operatively for assessment of inflammatory markers (plasma levels of interleukin [IL]-6, IL-8, IL-10, and osteopontin, a cardiac inflammatory-remodeling marker; and the urine neopterin/creatinine ratio, a monocyte activation marker). Multiorgan function was evaluated by the total sequential organ failure assessment (tSOFA) score. Outcomes of interest were early survival, post-LVAD tSOFA score, and intensive care unit (ICU) length of stay. RESULTS: Fifteen patients had INTERMACS profiles 1 or 2 (Group A), and 15 had profiles 3 or 4 (Group B). At pre-implant, only IL-6 levels and the IL-6/IL-10 ratio were higher in Group A vs B. After LVAD implantation, neopterin/creatinine ratio and IL-8 levels increased more in Group A vs B. Osteopontin levels increased significantly only in Group B. The tSOFA score at 2 weeks post-LVAD and ICU duration were related with pre-implant IL-6 levels. CONCLUSIONS: The INTERMACS profiles reflect the severity of the pre-operative inflammatory activation and the post-implant inflammatory response, affecting post-operative tSOFA score and ICU stay. Therefore, inflammation may contribute to poor outcome in patients with severe INTERMACS profile

Quantification of the environmental impact of lumpfish farming through a life cycle assessment

Infestations by the salmon louse (Lepeophtheirus salmonis Krøyer) represents the major fish health problem that the Atlantic salmon (Salmo salar) industry has to face. Sea lice infestation has a large impact on the economy of fish farmers, which are looking for a cost-effective and environmentally sustainable alternative to chemical or mechanical treatments to delouse fish. The biological control of sea lice using the so-called cleaner fish has been individuated as a feasible delousing approach of Atlantic salmons. In particular, in recent years the lumpfish (Cyclopterus lumpus) has been extensively farmed to be used as a ‘biological weapon’ in salmon farming because of its effectiveness in delousing also in harsh environmental conditions. However, the environmental impact of lumpfish farming is still largely unknown. Thus, the present study aimed at assessing the potential environmental impact of lumpfish production through a life cycle assessment (LCA) approach. Feed and electricity consumption, both for 8 of the 18 evaluated midpoint indicators, are the main responsible of the environmental load while for the Freshwater and Marine eutrophication about 90% of the impact is related to the emission of nitrogen and phosphorous compounds by fishes. These data lay the foundation for further, sustainable improvement of lumpfish farming

A model learning framework for inferring the dynamics of transmission rate depending on exogenous variables for epidemic forecasts

In this work, we aim to formalize a novel scientific machine learning framework to reconstruct the hidden dynamics of the transmission rate, whose inaccurate extrapolation can significantly impair the quality of the epidemic forecasts, by incorporating the influence of exogenous variables (such as environmental conditions and strain-specific characteristics). We propose a hybrid model that blends a data-driven layer with a physics-based one. The data-driven layer is based on a neural ordinary differential equation that learns the dynamics of the transmission rate, conditioned on the meteorological data and wave-specific latent parameters. The physics-based layer, instead, consists of a standard SEIR compartmental model, wherein the transmission rate represents an input. The learning strategy follows an end-to-end approach: the loss function quantifies the mismatch between the actual numbers of infections and its numerical prediction obtained from the SEIR model incorporating as an input the transmission rate predicted by the neural ordinary differential equation. We apply this original approach to both a synthetic test case and a realistic test case based on meteorological data (temperature and humidity) and influenza data from Italy between 2010 and 2020. In both scenarios, we achieve low generalization error on the test set and observe strong alignment between the reconstructed model and established findings on the influence of meteorological factors on epidemic spread. Finally, we implement a data assimilation strategy to adapt the neural equation to the specific characteristics of an epidemic wave under investigation, and we conduct sensitivity tests on the network's hyperparameters

The mutual interplay of gut microbiota, diet and human disease

The intestinal milieu harbours the gut microbiota, consisting of a complex community of bacteria, archaea, fungi, viruses, and protozoans that bring to the host organism an endowment of cells and genes more numerous than its own. In the last ten years, mounting evidence has highlighted the prominent influence of the gut mutualistic bacterial communities on human health. Microbial colonization occurs alongside with immune system development and plays a role in intestinal physiology. The community of the gut microbiota does not undergo significant fluctuations throughout adult life. However, bacterial infections, antibiotic treatment, lifestyle, surgery, and diet might profoundly affect it. Gut microbiota dysbiosis, defined as marked alterations in the amount and function of the intestinal microorganisms, is correlated with the aetiology of chronic non-communicable diseases, ranging from cardiovascular, neurologic, respiratory, and metabolic illnesses to cancer. In this review, we focus on the interplay among gut microbiota, diet, and host to provide a perspective on the role of microbiota and their unique metabolites in the pathogenesis and/or progression of various human disorders. We discuss interventions based on microbiome studies, i.e. faecal microbiota transplantation, probiotics, and prebiotics, to introduce the concept that correcting gut dysbiosis can ameliorate disease symptoms, thus offering a new approach toward disease treatment