ObjectiveWe aimed to investigate whether preoperative serum neutrophil gelatinase-associated lipocalin (sNGALpre-op) predicted postoperative acute kidney injury (AKI) during hospitalisation and 1-year cardiovascular and all-cause mortality following adult cardiac surgery.MethodsThis study was a post hoc analysis of the Effect of Remote Ischemic Preconditioning on Clinical Outcomes in Patient Undergoing Coronary Artery Bypass Graft Surgery trial involving adult patients undergoing coronary artery bypass graft. Postoperative AKI within 72 hours was defined using the International Kidney Disease: Improving Global Outcomes classification.Results1371 out of 1612 patients had data on sNGALpre-op. The overall 1-year cardiovascular and all-cause mortality was 5.2% (71/1371) and 7.7% (105/1371), respectively. There was an observed increase in the incidence of AKI from the first to the third tertile of sNGALpre-op (30.5%, 41.5% and 45.9%, respectively, p<0.001). There was also an increase in both cardiovascular and all-cause mortality from the first to the third tertile of sNGALpre-op, linear trend test with adjusted p=0.018 and p=0.013, respectively. The adjusted HRs for those in the second and third tertiles of sNGALpre-op compared with the first tertile were 1.60 (95% CI 0.78 to 3.25) and 2.22 (95% CI 1.13 to 4.35) for cardiovascular mortality, and 1.25 (95% CI 0.71 to 2.22) and 1.91 (95% CI 1.13 to 3.25) for all-cause mortality at 1 year.ConclusionIn a cohort of high-risk adult patients undergoing cardiac surgery, there was an increase in postoperative AKI and 1-year mortality from the first to the third tertile of preoperative serum NGAL. Those in the last tertile (>220 ng/L) had an estimated twofold increase risk of cardiovascular and all-cause mortality at 1 year.Clinical trial registrationNCT101247545; Post-results.

Social inequalities are ubiquitous, and here we show that the values of the Gini ([Formula: see text]) and Kolkata ([Formula: see text]) indices, two generic inequality indices, approach each other (starting from [Formula: see text] and [Formula: see text] for equality) as the competitions grow in various social institutions like markets, universities and elections. It is further shown that these two indices become equal and stabilize at a value (at [Formula: see text]) under unrestricted competitions. We propose to view this coincidence of inequality indices as a generalized version of the (more than a) century old 80-20 law of Pareto. Furthermore, the coincidence of the inequality indices noted here is very similar to the ones seen before for self-organized critical (SOC) systems. The observations here, therefore, stand as a quantitative support toward viewing interacting socio-economic systems in the framework of SOC, an idea conjectured for years.

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A popular measure for citation inequalities of individual scientists has been the Hirsch index (h). If for any scientist the number nc of citations is plotted against the serial number np of the papers having those many citations (when the papers are ordered from the highest cited to the lowest), then h corresponds to the nearest lower integer value of np below the fixed point of the non-linear citation function (or given by nc = h = np if both np and nc are a dense set of integers near the h value). The same index can be estimated (from h = s = ns) for the avalanche or cluster of size (s) distributions (ns) in the elastic fiber bundle or percolation models. Another such inequality index called the Kolkata index (k) says that (1 − k) fraction of papers attract k fraction of citations (k = 0.80 corresponds to the 80–20 law of Pareto). We find, for stress (σ), the lattice occupation probability (p) or the Kolkata Index (k) near the bundle failure threshold (σc) or percolation threshold (pc) or the critical value of the Kolkata Index kc a good fit to Widom–Stauffer like scaling h/[N/log⁡N] = f(N[σc−σ]α), h/[N/log⁡N]=f(N|pc−p|α) or h/[Nc/logNc]=f(Nc|kc−k|α), respectively, with the asymptotically defined scaling function f, for systems of size N (total number of fibers or lattice sites) or Nc (total number of citations), and α denoting the appropriate scaling exponent. We also show that if the number (Nm) of members of parliaments or national assemblies of different countries (with population N) is identified as their respective h − indexes, then the data fit the scaling relation Nm∼N/log⁡N, resolving a major recent controversy.

During the last three decades, integer-valued autoregressive process of order p [or INAR(p)] based on different operators have been proposed as a natural, intuitive and maybe efficient model for integer-valued time-series data. However, this literature is surprisingly mute on the usefulness of the standard AR(p) process, which is otherwise meant for continuous-valued time-series data. In this paper, we attempt to explore the usefulness of the standard AR(p) model for obtaining coherent forecasting from integer-valued time series. First, some advantages of this standard Box-Jenkins's type AR(p) process are discussed. We then carry out our some simulation experiments, which show the adequacy of the proposed method over the available alternatives. Our simulation results indicate that even when samples are generated from INAR(p) process, Box-Jenkins's model performs as good as the INAR(p) processes especially with respect to mean forecast. Two real data sets have been employed to study the expediency of the standard AR(p) model for integer-valued time-series data.