Aortic root surgery in septuagenarians: impact of different surgical techniques

<p>Abstract</p> <p>Background</p> <p>To evaluate the impact and safety of different surgical techniques for aortic root replacement (ARR) on early and late morbidity and mortality in septuagenarians undergoing ARR.</p> <p>Methods</p> <p>Ninety-five patients (73.8 ± 3.2 years) were operated and divided into three groups according to the aortic root procedure; MECH-group (n = 51) patients with a mechanical composite graft, BIO-group (n = 22) patients with a customized biological composite graft, and REIMPL-group (n = 22) patients with a valve sparing aortic root reimplantation (David I). In 42.1% (40/95) of these patients the aortic arch was replaced. Follow-up was completed in 95.2% (79/83) of in-hospital survivors.</p> <p>Results</p> <p>Hospital mortality was 12.6% (12/95) in the entire population (MECH. 15.7% (8/51), BIO 19.7% (4/22), REIMPL 0% (0/22); p = 0.004). Two patients died intraoperatively. The most frequent postoperative complications were prolonged mechanical ventilation ((>48 h) in 16.8% (16/93) (MECH. 7% (7/51), BIO 36.4% (8/22), REIMPL 4.5% (1/22); p = 0.013) and rethoracotomy for postoperative bleeding in 12.6% (12/95) (MECH. 12% (6/51), BIO 22.7% (5/22), REIMPL 4.5% (1/22); p = 0.19). Nineteen late deaths (22.9%) (19/83) (MECH 34.8% (15/43), BIO 16.7% (3/18), REIMPL 4.5% (1/22); p = 0.012) occurred during a mean follow-up of 41 ± 42 months (MECH 48 ± 48 months, BIO 25 ± 37 months, REIMPL 40 ± 28 months, p = 0.028). Postoperative NYHA class decreased significantly (p = 0.017) and performance status (p = 0.027) increased for the entire group compared to preoperative values.</p> <p>Conclusion</p> <p>Our data indicate that valve sparing aortic root reimplantation is safe and effective in septuagenarians, and is associated with low early and late morbidity and mortality.</p

Functional Protein Network Activation Mapping Reveals New Potential Molecular Drug Targets for Poor Prognosis Pediatric BCP-ALL

Background: In spite of leukemia therapy improvements obtained over the last decades, therapy is not yet effective in all cases. Current approaches in Acute Lymphoblastic Leukemia (ALL) research focus on identifying new molecular targets to improve outcome for patients with a dismal prognosis. In this light phosphoproteomics seems to hold great promise for the identification of proteins suitable for targeted therapy. Methodology/Principal Findings: We employed Reverse Phase Protein Microarrays to identify aberrantly activated proteins in 118 pediatric B-cell precursor (BCP)-ALL patients. Signal transduction pathways were assayed for activation/expression status of 92 key signalling proteins. We observed an increased activation/expression of several pathways involved in cell proliferation in poor clinical prognosis patients. MLL-rearranged tumours revealed BCL-2 hyperphosphorylation through AMPK activation, which indicates that AMPK could provide a functional role in inhibiting apoptosis in MLL-rearranged patients, and could be considered as a new potential therapeutic target. Second, in patients with poor clinical response to prednisone we observed the up-modulation of LCK activity with respect to patients with good response. This tyrosine-kinase can be down-modulated with clinically used inhibitors, thus modulating LCK activity could be considered for further studies as a new additional therapy for prednisone-resistant patients. Further we also found an association between high levels of CYCLIN E and relapse incidence. Moreover, CYCLIN E is more expressed in early relapsed patients, who usually show an unfavourable prognosis. Conclusions/Significance: We conclude that functional protein pathway activation mapping revealed specific deranged signalling networks in BCP-ALL that could be potentially modulated to produce a better clinical outcome for patients resistant to standard-of-care therapies

Determination of sin2 θeff w using jet charge measurements in hadronic Z decays

The electroweak mixing angle is determined with high precision from measurements of the mean difference between forward and backward hemisphere charges in hadronic decays of the Z. A data sample of 2.5 million hadronic Z decays recorded over the period 1990 to 1994 in the ALEPH detector at LEP is used. The mean charge separation between event hemispheres containing the original quark and antiquark is measured for bb̄ and cc̄ events in subsamples selected by their long lifetimes or using fast D*'s. The corresponding average charge separation for light quarks is measured in an inclusive sample from the anticorrelation between charges of opposite hemispheres and agrees with predictions of hadronisation models with a precision of 2%. It is shown that differences between light quark charge separations and the measured average can be determined using hadronisation models, with systematic uncertainties constrained by measurements of inclusive production of kaons, protons and A's. The separations are used to measure the electroweak mixing angle precisely as sin2 θeff w = 0.2322 ± 0.0008(exp. stat.) ±0.0007(exp. syst.) ± 0.0008(sep.). The first two errors are due to purely experimental sources whereas the third stems from uncertainties in the quark charge separations

Search for the bcb_c meson in hadronic Z decays

A search for the Bc meson decaying into the channels J/psi pi+ and J/psi l nu (l = e or mu) is performed in a sample of 3.9 million hadronic Z decays collected by the ALEPH detector. This search results in the observation of 0 and 2 candidates in each of these channels, respectively, while 0.44 and 0.81 background events are expected. The following 90\% confidence level upper limits are derived: Br(Z->Bc X)/Br(Z->q q )*Br(Bc->J/psi pi+) 3.6 10^-5 Br(Z->Bc X)/Br(Z->q q )*Br(Bc->J/psi l nu) 5.2 10^-5 An additional Bc->J/psi(e+e-) mu nu candidate with very low background probability, found in an independent analysis, is also described in detail

Complex dynamics in learning complicated games.

Game theory is the standard tool used to model strategic interactions in evolutionary biology and social science. Traditionally, game theory studies the equilibria of simple games. However, is this useful if the game is complicated, and if not, what is? We define a complicated game as one with many possible moves, and therefore many possible payoffs conditional on those moves. We investigate two-person games in which the players learn based on a type of reinforcement learning called experience-weighted attraction (EWA). By generating games at random, we characterize the learning dynamics under EWA and show that there are three clearly separated regimes: (i) convergence to a unique fixed point, (ii) a huge multiplicity of stable fixed points, and (iii) chaotic behavior. In case (iii), the dimension of the chaotic attractors can be very high, implying that the learning dynamics are effectively random. In the chaotic regime, the total payoffs fluctuate intermittently, showing bursts of rapid change punctuated by periods of quiescence, with heavy tails similar to what is observed in fluid turbulence and financial markets. Our results suggest that, at least for some learning algorithms, there is a large parameter regime for which complicated strategic interactions generate inherently unpredictable behavior that is best described in the language of dynamical systems theory

The prevalence of chaotic dynamics in games with many players

We study adaptive learning in a typical p-player game. The payoffs of the games are randomly generated and then held fixed. The strategies of the players evolve through time as the players learn. The trajectories in the strategy space display a range of qualitatively different behaviours, with attractors that include unique fixed points, multiple fixed points, limit cycles and chaos. In the limit where the game is complicated, in the sense that the players can take many possible actions, we use a generating-functional approach to establish the parameter range in which learning dynamics converge to a stable fixed point. The size of this region goes to zero as the number of players goes to infinity, suggesting that complex non-equilibrium behaviour, exemplified by chaos, is the norm for complicated games with many players.</p

The prevalence of chaotic dynamics in games with many players

We study adaptive learning in a typical p-player game. The payoffs of the games are randomly generated and then held fixed. The strategies of the players evolve through time as the players learn. The trajectories in the strategy space display a range of qualitatively different behaviours, with attractors that include unique fixed points, multiple fixed points, limit cycles and chaos. In the limit where the game is complicated, in the sense that the players can take many possible actions, we use a generating-functional approach to establish the parameter range in which learning dynamics converge to a stable fixed point. The size of this region goes to zero as the number of players goes to infinity, suggesting that complex non-equilibrium behaviour, exemplified by chaos, is the norm for complicated games with many players.</p