University of Siena

About: University of Siena is a education organization based out in Siena, Italy. It is known for research contribution in the topics: Population & Cancer. The organization has 12179 authors who have published 33334 publications receiving 1008287 citations. The organization is also known as: Università degli studi di Siena & Universita degli studi di Siena.

Cardio-renal syndromes: report from the consensus conference of the acute dialysis quality initiative.

Abstract: A consensus conference on cardio-renal syndromes (CRS) was held in Venice Italy, in September 2008 under the auspices of the Acute Dialysis Quality Initiative (ADQI). The following topics were matter of discussion after a systematic literature review and the appraisal of the best available evidence: definition/classification system; epidemiology; diagnostic criteria and biomarkers; prevention/protection strategies; management and therapy. The umbrella term CRS was used to identify a disorder of the heart and kidneys whereby acute or chronic dysfunction in one organ may induce acute or chronic dysfunction in the other organ. Different syndromes were identified and classified into five subtypes. Acute CRS (type 1): acute worsening of heart function (AHF–ACS) leading to kidney injury and/or dysfunction. Chronic cardio-renal syndrome (type 2): chronic abnormalities in heart function (CHF-CHD) leading to kidney injury and/or dysfunction. Acute reno-cardiac syndrome (type 3): acute worsening of kidney function (AKI) leading to heart injury and/or dysfunction. Chronic reno-cardiac syndrome (type 4): chronic kidney disease leading to heart injury, disease, and/or dysfunction. Secondary CRS (type 5): systemic conditions leading to simultaneous injury and/or dysfunction of heart and kidney. Consensus statements concerning epidemiology, diagnosis, prevention, and management strategies are discussed in the paper for each of the syndromes.

Protein carbonylation, cellular dysfunction, and disease progression

Abstract: Carbonylation of proteins is an irreversible oxidative damage, often leading to a loss of protein function, which is considered a widespread indicator of severe oxidative damage and disease-derived protein dysfunction. Whereas moderately carbonylated proteins are degraded by the proteasomal system, heavily carbonylated proteins tend to form high-molecular-weight aggregates that are resistant to degradation and accumulate as damaged or unfolded proteins. Such aggregates of carbonylated proteins can inhibit proteasome activity. A large number of neurodegenerative diseases are directly associated with the accumulation of proteolysis-resistant aggregates of carbonylated proteins in tissues. Identification of specific carbonylated protein(s) functionally impaired and development of selective carbonyl blockers should lead to the definitive assessment of the causative, correlative or consequential role of protein carbonylation in disease onset and/or progression, possibly providing new therapeutic aproaches.

Model predictive control based on linear programming - the explicit solution

Abstract: We study model predictive control (MPC) schemes for discrete-time linear time-invariant systems with constraints on inputs and states, that can be formulated using a linear program (LP). In particular, we focus our attention on performance criteria based on a mixed 1 -norm, namely, 1-norm with respect to time and -norm with respect to space. First we provide a method to compute the terminal weight so that closed-loop stability is achieved. We then show that the optimal control profile is a piecewise affine and continuous function of the initial state and briefly describe the algorithm to compute it. The piecewise affine form allows to eliminate online LP, as the computation associated with MPC becomes a simple function evaluation. Besides practical advantages, the availability of the explicit structure of the MPC controller provides an insight into the type of control action in different regions of the state space, and highlights possible conditions of degeneracies of the LP, such as multiple optima.

Inflammation and Pregnancy

Abstract: Inflammation is a process by which tissues respond to various insults. It is characterized by upregulation of chemokines, cytokines, and pattern recognition receptors that sense microbes and tissue breakdown products. During pregnancy, the balance of Th1 (cell-mediated immunity) and Th2 (humoral immunity) cytokines is characterized by an initial prevalence of Th2 cytokines, followed by a progressive shift toward Th1 predominance late in gestation, that when is abnormal, may initiate and intensify the cascade of inflammatory cytokine production involved in adverse pregnancy outcomes. Maternal and placental hormones may affect the inflammatory pathway. Hypoxia and the innate immune response are 2 adaptive mechanisms by which organisms respond to perturbation in organ function, playing a major role in spontaneous abortion, intrauterine growth restriction, preeclampsia, and preterm delivery. The interaction between tissue remodeling factors, like matrix metalloproteinases, and vasoactive/hemostatic factors, like prostaglandin and coagulation factors, mediates this adaptive response.

Determination of jet energy calibration and transverse momentum resolution in CMS

Abstract: Measurements of the jet energy calibration and transverse momentum resolution in CMS are presented, performed with a data sample collected in proton-proton collisions at a centre-of-mass energy of 7TeV, corresponding to an integrated luminosity of 36pb−1. The transverse momentum balance in dijet and γ/Z+jets events is used to measure the jet energy response in the CMS detector, as well as the transverse momentum resolution. The results are presented for three different methods to reconstruct jets: a calorimeter-based approach, the ``Jet-Plus-Track'' approach, which improves the measurement of calorimeter jets by exploiting the associated tracks, and the ``Particle Flow'' approach, which attempts to reconstruct individually each particle in the event, prior to the jet clustering, based on information from all relevant subdetectors