Serum protein fingerprinting by PEA immunoassay coupled with a pattern-recognition algorithms distinguishes MGUS and multiple myeloma

Serum protein fingerprints associated with MGUS and MM and their changes in MM after autologous stem cell transplantation (MM-ASCT, day 100) remain unexplored. Using highly-sensitive Proximity Extension ImmunoAssay on 92 cancer biomarkers (Proseek Multiplex, Olink), enhanced serum levels of Adrenomedullin (ADM, P-corr=.0004), Growth differentiation factor 15 (GDF15, P-corr=.003), and soluble Major histocompatibility complex class I-related chain A (sMICA, P-corr=.023), all prosurvival and chemoprotective factors for myeloma cells, were detected in MM comparing to MGUS. Comparison of MGUS and healthy subjects revealed elevation of angiogenic and antia-poptotic midkine (P-corr=.0007) and downregulation of Transforming growth factor beta 1 (TGFB1, P-corr=.005) in MGUS. Importantly, altered serum pattern was associated with MM-ASCT compared to paired MM at the diagnosis as well as to healthy controls, namely by upregulated B-Cell Activating Factor (sBAFF) (P-corr<.006) and sustained elevation of other pro-tumorigenic factors. In conclusion, the serum fingerprints of MM and MM-ASCT were characteristic by elevated levels of prosurvival and chemoprotective factors for myeloma cells.Web of Science841694216940

Nonlinear dielectric susceptibilities in supercooled liquids: a toy model

The dielectric response of supercooled liquids is phenomenologically modeled by a set of Asymmetric Double Wells (ADW), where each ADW contains a dynamical heterogeneity of $N_{corr}$ molecules. We find that the linear macroscopic susceptibility $\chi_1$ does not depend on $N_{corr}$ contrary to all higher order susceptibilities $\chi_{2k+1}$. We show that $\chi_{2k+1}$ is proportional to the $k^{th}$ moment of $N_{corr}$, which could pave the way for new experiments on glass transition. In particular, as predicted by Bouchaud and Biroli on general grounds [Phys. Rev. B, {\bf 72}, 064204 (2005)], we find that $\chi_3$ is proportional to the average value of $N_{corr}$. We fully calculate $\chi_3$ and, with plausible values of few parameters our model accounts for the salient features of the experimental behavior of $\chi_3$ of supercooled glycerol.Comment: 13 pages, 5 figure

Superconductivity in an Exactly Solvable Model of the Pseudogap State: Absence of Self Averaging

We analyze the anomalies of superconducting state within a simple exactly solvable model of the pseudogap state, induced by fluctuations of ``dielectric'' short range order, for the model of the Fermi surface with ``hot'' patches. The analysis is performed for the arbitrary values of the correlation length xi_{corr} of this short range order. It is shown that superconducting energy gap averaged over these fluctuations is non zero in a wide temperature range above T_c - the temperature of homogeneous superconducting transition. This follows from the absence of self averaging of the gap over the random field of fluctuations. For temperatures T>T_c superconductivity apparently appears in separate regions of space (``drops''). These effects become weaker for shorter correlation lengths xi_{corr} and the region of ``drops'' on the phase diagram becomes narrower and disappears for xi_{corr}-->0, however, for the finite values of xi_{corr} the complete self averaging is absent.Comment: 20 pages, 6 figures, RevTeX 3.0, submitted to JETP, minor misprints correcte

Limiting Behavior of High Order Correlations for Simple Random Sampling

For N=1,2,..., let S_N be a simple random sample of size n=n_N from a population A_N of size N, where 0<=n<=N. Then with f_N=n/N, the sampling fraction, and 1_A the inclusion indicator that A is in S_N, for any H a subset of A_N of size k>= 0, the high order correlations Corr(k) = E (\prod_{A \in H} (1_A-f_N)) depend only on k, and if the sampling fraction f_N -> f as N -> infinity, then N^{k/2}Corr(k) -> [f(f-1)]^{k/2}EZ^k, k even and N^{(k+1)/2}Corr(k) -> [f(f-1)]^{(k-1)/2}(2f-1)(1/3)(k-1)EZ^{k+1}, k odd where Z is a standard normal random variable. This proves a conjecture given in [2].Comment: 32 page

Acceptance dependence of fluctuation measures near the QCD critical point

We argue that a crucial determinant of the acceptance dependence of fluctuation measures in heavy-ion collisions is the range of correlations in the momentum space, e.g., in rapidity, $\Delta y_{\rm corr}$. The value of $\Delta y_{\rm corr}\sim1$ for critical thermal fluctuations is determined by the thermal rapidity spread of the particles at freezeout, and has little to do with position space correlations, even near the critical point where the spatial correlation length $\xi$ becomes as large as $2-3$ fm (this is in contrast to the magnitudes of the cumulants, which are sensitive to $\xi$). When the acceptance window is large, $\Delta y\gg\Delta y_{\rm corr}$, the cumulants of a given particle multiplicity, $\kappa_k$, scale linearly with $\Delta y$, or mean multiplicity in acceptance, $\langle N\rangle$, and cumulant ratios are acceptance independent. While in the opposite regime, $\Delta y\ll\Delta y_{\rm corr}$, the factorial cumulants, $\hat\kappa_k$, scale as $(\Delta y)^k$, or $\langle N\rangle^k$. We demonstrate this general behavior quantitatively in a model for critical point fluctuations, which also shows that the dependence on transverse momentum acceptance is very significant. We conclude that extension of rapidity coverage proposed by STAR should significantly increase the magnitude of the critical point fluctuation signatures.Comment: 9 pages, 4 figures, references adde

Correlation Widths in Quantum--Chaotic Scattering

An important parameter to characterize the scattering matrix S for quantum-chaotic scattering is the width Gamma_{corr} of the S-matrix autocorrelation function. We show that the "Weisskopf estimate" d/(2pi) sum_c T_c (where d is the mean resonance spacing, T_c with 0 <= T_c <= 1 the "transmission coefficient" in channel c and where the sum runs over all channels) provides a very good approximation to Gamma_{corr} even when the number of channels is small. That same conclusion applies also to the cross-section correlation function