Estimating systemic fibrosis by combining galectin-3 and ST2 provides powerful risk stratification value for patients after acute decompensated heart failure

Background: Two fibrosis biomarkers, galectin-3 (Gal-3) and suppression of tumorigenicity 2 (ST2), provide prognostic value additive to natriuretic peptides and traditional risk factors in patients with heart failure (HF). However, it is to be investigated whether their combined measurement before discharge provides incremental risk stratification for patients after acute HF. Methods: A total of 344 patients with acute HF were analyzed with Gal-3, and ST2 measured. Patients were prospectively followed for 3.7 ± 1.3 years for deaths, and composite events (death/HF-related re-hospitalizations). Results: The levels of Gal-3 and ST2 were only slightly related (r = 0.20, p < 0.001). The medians of Gal-3 and ST2 were 18 ng/mL and 32.4 ng/mL, respectively. These biomarkers compensated each other and characterized patients with different risk factors. According to the cutoff at median values, patients were separated into four subgroups based on high and low Gal-3 (HG and LG, respectively) and ST2 levels (HS and LS, respectively). Kaplan-Meier survival curves showed that HGHS powerfully identified patients at risk of mortality (Log rank = 21.27, p < 0.001). In multivariable analysis, combined log(Gal-3) and log(ST2) was an in­dependent predictor. For composite events, Kaplan-Meier survival curves showed a lower event- -free survival rate in the HGHS subgroup compared to others (Log rank = 34.62, p < 0.001; HGHS vs. HGLS, Log rank = 4.00, p = 0.045). In multivariable analysis, combined log(Gal-3) and log(ST2) was also an independent predictor. Conclusions: Combination of biomarkers involving heterogeneous fibrosis pathways may identify patients with high systemic fibrosis, providing powerful risk stratification value

A Practical and Secure Stateless Order Preserving Encryption for Outsourced Databases

Order-preserving encryption (OPE) plays an important role in securing outsourced databases. OPE schemes can be either Stateless or Stateful. Stateful schemes can achieve the ideal security of order-preserving encryption, i.e., “reveal no information about the plaintexts besides order.” However, comparing to stateless schemes, stateful schemes require maintaining some state information locally besides encryption keys and the ciphertexts are mutable. On the other hand, stateless schemes only require remembering encryption keys and thus is more efficient. It is a common belief that stateless schemes cannot provide the same level of security as stateful ones because stateless schemes reveal the relative distance among their corresponding plaintext. In real world applications, such security defects may lead to the leakage of statistical and sensitive information, e.g., the data distribution, or even negates the whole encryption. In this paper, we propose a practical and secure stateless order-preserving encryption scheme. With prior knowledge of the data to be encrypted, our scheme can achieve IND-CCPA (INDistinguishability under Committed ordered Chosen Plaintext Attacks) security for static data set. Though the IND-CCPA security can\u27t be met for dynamic data set, our new scheme can still significantly improve the security in real world applications. Along with the encryption scheme, in this paper we also provide methods to eliminate access pattern leakage in communications and thus prevents some common attacks to OPE schemes in practice