Do we need another heart failure biomarker. focus on soluble suppression of tumorigenicity 2 (sST2)

If sST2 indeed turns into the HbA1c of heart failure, its value should increase exponentially in our management of patients with heart failure. Serial sST2 levels should allow us to titrate therapy and monitor the clinical state of the patient. In addition, since sST2 is such a strong marker of the risk of death, it would not be surprising to see a level be used to make decisions when patients are on the cusp of such therapies as ICD, CRT, CardioMems implantation and even left ventricular assist devices. A discussion about the use of biomarkers would not be complete without mentioning the issue of surrogates for determining the therapy effectiveness of some of the newer heart failure drugs. Novartis’s EntrestoVR , the brand name for its recently CE marked and FDA approved ARNI1 drug (previously known as LCZ696) and Servier’s ivabradine drug CorlanorVR (marketed by Amgen in the USA), also CE marked and FDA approved, while offering exciting potential benefits to heart failure patients—even being hailed ‘game-changer’ drugs by some—raises the thorny issue of cost vs. benefit. These new drugs are several times the cost of the generics that have become the mainstay of heart failure treatment, i.e. ACE inhibitors, angiotensin receptor blocker (ARBs), beta-blockers, etc. Pushback is therefore expected from payers. Because sST2 changes rapidly with the underlying condition of the patient, is not affected by normal confounding factors, and has a single cut point, it may be ideally suited to help clinicians determine if these newer mediations are effective for each patient, are improving quality of life, and whether dosing needs to be titrated or changed. The new reality of heart failure care is that while more treatment options have opened up, which can literally be a lifesaver for millions of patients, the burden on healthcare systems has skyrocketed. Biomarkers, and particularly sST2, could offer physicians and payers a way to bring treatment down to an individual patient level, providing

Biomarkers in emergency medicine

Researchers navigate the ocean of biomarkers searching for proper targets and optimal utilization of them. Emergency medicine builds up the front line to maximize the utility of clinically validated biomarkers and is the cutting edge field to test the applicability of promising biomarkers emerging from thorough translational researches. The role of biomarkers in clinical decision making would be of greater significance for identification, risk stratification, monitoring, and prognostication of the patients in the critical- and acute-care settings. No doubt basic research to explore novel biomarkers in relation to the pathogenesis is as important as its clinical counterpart. This special issue includes five selected research papers that cover a variety of biomarker- and disease-related topics

Circulating Biologically Active Adrenomedullin Predicts Organ Failure and Mortality in Sepsis

BACKGROUND: Sepsis is a life-threatening organ dysfunction caused by a dysregulated host response to infection. Biologically active adrenomedullin (bio-ADM) is an emerging biomarker for sepsis. We explored whether bio-ADM concentration could predict severity, organ failure, and 30-day mortality in septic patients. METHODS: In 215 septic patients (109 patients with sepsis; 106 patients with septic shock), bio-ADM concentration was measured at diagnosis of sepsis, using sphingotest bio-ADM (Sphingotec GmbH, Hennigsdorf, Germany) and analyzed in terms of sepsis severity, vasopressor use, and 30-day mortality. The number of organ failures, sequential (sepsis-related) organ failure assessment (SOFA) score, and 30-day mortality were compared according to bio-ADM quartiles. RESULTS: Bio-ADM concentration was significantly higher in patients with septic shock, vasopressor use, and non-survivors than in patients with solitary sepsis, no vasopressor use, and survivors, respectively (all P<0.0001). Bio-ADM quartiles were associated with the number of organ failures (P<0.0001), as well as SOFA cardiovascular, renal, coagulation, and liver subscores (all P<0.05). The 30-day mortality rate showed a stepwise increase in each bio-ADM quartile (all P<0.0001). Bio-ADM concentration and SOFA score equally predicted the 30-day mortality (area under the curve: 0.827 vs 0.830). CONCLUSIONS: Bio-ADM could serve as a useful and objective biomarker to predict severity, organ failure, and 30-day mortality in septic patients

ST2 and Multimarker Testing in Acute Decompensated Heart Failure

Most data on heart failure biomarkers have been derived from patient cohorts with chronic disease. However, risk prediction in patients admitted with acute decompensated heart failure (ADHF) remains a challenge. ADHF is not a single disease: it presents in various manners, and different causes may underlie ADHF, which may be reflected by different biomarkers. Soluble suppression of tumorigenicity 2 (ST2) has been shown to be a strong independent predictor of short-, mid-, and long-term outcome in ADHF. Furthermore, combining biomarkers may help further improve the prognostic power of ST2. The ProBNP Investigation of Dyspnea in the Emergency Department study showed that elevated plasma levels of ST2 together with elevated levels of 4 other biomarkers have clear incremental values to predict outcome in ADHF. The Multinational Observational Cohort on Acute Heart Failure study is an international collaborative network that recruited 5,306 patients hospitalized for ADHF that demonstrated that ST2 and midregional pro-adrenomedulin had independently strong value to predict 30-day and 1-year outcome in patients with ADHF. The Multinational Observational Cohort on Acute Heart Failure study also showed that C-reactive protein plus ST2 better classified risk in patients with ADHFs than ST2 alone. Combining biomarkers for risk prediction or risk stratification might have clinical and more importantly pathophysiological meaning

Spectral Gap Amplification

A large number of problems in science can be solved by preparing a specific eigenstate of some Hamiltonian H. The generic cost of quantum algorithms for these problems is determined by the inverse spectral gap of H for that eigenstate and the cost of evolving with H for some fixed time. The goal of spectral gap amplification is to construct a Hamiltonian H' with the same eigenstate as H but a bigger spectral gap, requiring that constant-time evolutions with H' and H are implemented with nearly the same cost. We show that a quadratic spectral gap amplification is possible when H satisfies a frustration-free property and give H' for these cases. This results in quantum speedups for optimization problems. It also yields improved constructions for adiabatic simulations of quantum circuits and for the preparation of projected entangled pair states (PEPS), which play an important role in quantum many-body physics. Defining a suitable black-box model, we establish that the quadratic amplification is optimal for frustration-free Hamiltonians and that no spectral gap amplification is possible, in general, if the frustration-free property is removed. A corollary is that finding a similarity transformation between a stoquastic Hamiltonian and the corresponding stochastic matrix is hard in the black-box model, setting limits to the power of some classical methods that simulate quantum adiabatic evolutions.Comment: 14 pages. New version has an improved section on adiabatic simulations of quantum circuit

Mpemba effect and phase transitions in the adiabatic cooling of water before freezing

An accurate experimental investigation on the Mpemba effect (that is, the freezing of initially hot water before cold one) is carried out, showing that in the adiabatic cooling of water a relevant role is played by supercooling as well as by phase transitions taking place at 6 +/- 1 oC, 3.5 +/- 0.5 oC and 1.3 +/- 0.6 oC, respectively. The last transition, occurring with a non negligible probability of 0.21, has not been detected earlier. Supported by the experimental results achieved, a thorough theoretical analysis of supercooling and such phase transitions, which are interpreted in terms of different ordering of clusters of molecules in water, is given.Comment: revtex, 4 pages, 2 figure

Parameter Estimation with Mixed-State Quantum Computation

We present a quantum algorithm to estimate parameters at the quantum metrology limit using deterministic quantum computation with one bit. When the interactions occurring in a quantum system are described by a Hamiltonian $H= \theta H_0$, we estimate $\theta$ by zooming in on previous estimations and by implementing an adaptive Bayesian procedure. The final result of the algorithm is an updated estimation of $\theta$ whose variance has been decreased in proportion to the time of evolution under H. For the problem of estimating several parameters, we implement dynamical-decoupling techniques and use the results of single parameter estimation. The cases of discrete-time evolution and reference-frame alignment are also discussed within the adaptive approach.Comment: 12 pages. Improved introduction and technical details moved to Appendi

A subsystem-independent generalization of entanglement

We introduce a generalization of entanglement based on the idea that entanglement is relative to a distinguished subspace of observables rather than a distinguished subsystem decomposition. A pure quantum state is entangled relative to such a subspace if its expectations are a proper mixture of those of other states. Many information-theoretic aspects of entanglement can be extended to the general setting, suggesting new ways of measuring and classifying entanglement in multipartite systems. By going beyond the distinguishable-subsystem framework, generalized entanglement also provides novel tools for probing quantum correlations in interacting many-body systems.Comment: 5 pages, 1 encapsulated color figure, REVTeX4 styl