The coincidence of low vitamin K status and high expression of growth differentiation factor 15 may indicate increased mortality risk in stable coronary heart disease patients

Background and aimsMatrix Gla protein (MGP) is a natural inhibitor of vascular calcification critically dependent on circulating vitamin K status. Growth differentiation factor 15 (GDF-15) is a regulatory cytokine mainly of the inflammatory and angiogenesis pathways, but potentially also involved in bone mineralization. We sought to determine whether these two circulating biomarkers jointly influenced morbidity and mortality risk in patients with chronic coronary heart disease (CHD).Methods and results894 patients ≥6 months after myocardial infarction and/or coronary revascularization at baseline were followed in a prospective study. All-cause and cardiovascular mortality, non-fatal cardiovascular events (myocardial infarction, stroke, any revascularization), and hospitalization for heart failure (HF) were followed as outcomes. Desphospho-uncarboxylated MGP (dp-ucMGP) was used as a biomarker of vitamin K status.Both, increased concentrations of dp-ucMGP (≥884 pmol/L) and GDF-15 (≥1339 pg/mL) were identified as independent predictors of 5-year all-cause or cardiovascular mortality. However, their coincidence further increased mortality risk. The highest risk was observed in patients with high dp-ucMGP plus high GDF-15, not only when compared with those with “normal” concentrations of both biomarkers [HR 5.51 (95% CI 2.91–10.44), p < 0.0001 and 6.79 (95% CI 3.06–15.08), p < 0.0001 for all-cause and cardiovascular mortality, respectively], but even when compared with patients with only one factor increased. This pattern was less convincing with non-fatal cardiovascular events or hospitalization for HF.ConclusionsThe individual coincidence of low vitamin K status (high dp-ucMGP) and high GDF-15 expression predicts poor survival of stable CHD patients.     

Safety and efficacy of once-daily didanosine, tenofovir and nevirapine as a simplification antiretroviral approach

OBJECTIVE: To assess the efficacy and safety of a once-daily antiretroviral regimen in HAART-experienced subjects with long-lasting viral suppression. METHODS: One-hundred-and-sixty-nine patients with chronically suppressed viral load (limit of detection <50 copies/ml) were recruited. Based on patient willingness to simplify treatment, 84 of them continued receiving their usual treatment (BID Group) and 85 switched to once-daily didanosine/tenofovir/nevirapine (QD Group) in a non-randomized fashion. RESULTS: At week 48, the proportion of patients with viral suppression in the QD and in the BID Group, respectively, was 97 vs 100% in the per-protocol analysis (P = 0.497), and 76 vs 86% for the intention-to-treat analysis (P = 0.176). Nevertheless, CD4 count decreased in the QD Group, with a mean decline of 95 cells/mm3 (95% CI: 45-145). Twelve subjects in the QD Group (14%) discontinued treatment due to adverse events, mainly nevirapine-related hepatitis (6%). No significant differences regarding the rate of acute pancreatitis or peripheral neuropathy were observed between both groups. A significant improvement in the lipid profile was only seen in the QD Group. High levels of adherence were observed in both groups during follow-up, as well as a good quality of life. At week 48, a reduction in effort to take medication (P < or = 0.001) and an increment in the satisfaction with the treatment (P < 0.001) was only seen in the QD group. No differences were observed in median nevirapine trough levels between patients on twice-daily nevirapine at baseline (4820 ng/ml) and subjects in the QD Group (6090 ng/ml, P = 0.30). CONCLUSION: Treatment simplification to a once-daily antiretroviral regimen based on didanosine, tenofovir and nevirapine may be a valid approach in HIV-infected subjects with long-lasting viral suppression. Combination of standard doses of didanosine and tenofovir may have contributed to the CD4 cell decline observed with this QD regimen.     

Mutational spectrum by phenotype: panel-based NGS testing of patients with clinical suspicion of RASopathy and children with multiple café-au-lait macules

Children with neurofibromatosis type 1 (NF1) may exhibit an incomplete clinical presentation, making difficult to reach a clinical diagnosis. A phenotypic overlap may exist in children with other RASopathies or with other genetic conditions if only multiple café-au-lait macules (CALMs) are present. The syndromes that can converge in these inconclusive phenotypes have different clinical courses. In this context, an early genetic testing has been proposed to be clinically useful to manage these patients. We present the validation and implementation into diagnostics of a custom NGS panel (I2HCP, ICO-IMPPC Hereditary Cancer Panel) for testing patients with a clinical suspicion of a RASopathy (n = 48) and children presenting multiple CALMs (n = 102). We describe the mutational spectrum and the detection rates identified in these two groups of individuals. We identified pathogenic variants in 21 out of 48 patients with clinical suspicion of RASopathy, with mutations in NF1 accounting for 10% of cases. Furthermore, we identified pathogenic mutations mainly in the NF1 gene, but also in SPRED1, in more than 50% of children with multiple CALMs, exhibiting an NF1 mutational spectrum different from a group of clinically diagnosed NF1 patients (n = 80). An NGS panel strategy for the genetic testing of these two phenotype-defined groups outperforms previous strategies.     

Radiation sterilization of medicinal formulations of doxorubicin bound to poly(butylcyanoacrylate) nanoparticles

Polybutylcyanoacrylate nanoparticles represent a potential system for delivering medicinal agents. The present report describes development of a method for radiation sterilization of medicinal formulations of doxorubicin based on polybutylcyanoacrylate nanoparticles. The physicochemical properties of the medicinal formulation were studied after gamma irradiation and irradiation with accelerated electrons over the dose range 10–35 kGy. The chemical structure of doxorubicin and the polymer carrier was found to be intact after irradiation at a dose of 25 kGy. Irradiation at these doses also had no effect on the colloidal properties of polymer nanoparticles, which retained their stability to aggregation and sedimentation. The optimum sterilizing dose was 15 kGy. __________ Translated from Khimiko-Farmatsevticheskii Zhurnal, Vol. 42, No. 6, pp. 52–56, June, 2008.     

Skin sensitivity reactions to some allergens in different population groups of Ethiopian subjects

The prevalence of allergic diseases to a variety of allergens has only been studied in a few countries and it has never been studied in Ethiopia. This study was aimed at assessing the prevalence of skin sensitivity reactions to allergens in Ethiopian subjects. A total of 216 subjects were tested with a skin scratch test using five types of allergens and also for total and differential white blood cell counts. Positive reaction to one or more allergens was detected in 49.5% of the subjects, the most prevalent allergen being mite extract. Some 27% showed a positive reaction to multiple allergens. The mean eosinophil count was higher in the subjects reacting to at least one of the allergens compared to those with no reaction (p=0.038). The results demonstrate a high prevalence of allergic reactions to the few allergens tested. Further studies using several allergens are recommended to substantiate this finding.     

Topological clustering of multilayer networks

Multilayer networks continue to gain significant attention in many areas of study, particularly due to their high utility in modeling interdependent systems such as critical infrastructures, human brain connectome, and socioenvironmental ecosystems. However, clustering of multilayer networks, especially using the information on higher-order interactions of the system entities, still remains in its infancy. In turn, higher-order connectivity is often the key in such multilayer network applications as developing optimal partitioning of critical infrastructures in order to isolate unhealthy system components under cyber-physical threats and simultaneous identification of multiple brain regions affected by trauma or mental illness. In this paper, we introduce the concepts of topological data analysis to studies of complex multilayer networks and propose a topological approach for network clustering. The key rationale is to group nodes based not on pairwise connectivity patterns or relationships between observations recorded at two individual nodes but based on how similar in shape their local neighborhoods are at various resolution scales. Since shapes of local node neighborhoods are quantified using a topological summary in terms of persistence diagrams, we refer to the approach as clustering using persistence diagrams (CPD). CPD systematically accounts for the important heterogeneous higher-order properties of node interactions within and in-between network layers and integrates information from the node neighbors. We illustrate the utility of CPD by applying it to an emerging problem of societal importance: vulnerability zoning of residential properties to weather- and climate-induced risks in the context of house insurance claim dynamics.

Many modern human-made systems, e.g. critical infrastructures integrating operations of vital societal physical and cyber services such as power systems, telecommunication, and transportation, as well as a broad range of natural phenomena from human brain functionality to socioenvironmental ecosystems and virus–host interactomes, exhibit a sophisticated, highly interdependent structure (1–7). Modeling such interdependency can be addressed with multilayer graphs, resulting in a recent surge of interest in the interdisciplinary analysis of complex multilayer networks. A multilayer network accounts for relationships among multiple layers of connectivity (i.e., networks), where each layer represents a system or subsystem. Dictated by emerging applications in security and resilience of critical infrastructures to natural hazards, terrorist activities, and cyber threats (8–14), one of the primary goals of such studies nowadays is to better understand which segments of the multilayer network are most vulnerable to a particular hazard and to develop proactive strategies for optimal partitioning, thereby isolating unhealthy components and mitigating the risk of further failure propagation (15–17).Similar to the case of unilayer networks, the objective of optimal partitioning, or clustering, of multilayer networks is to unveil meaningful patterns of node groupings and to divide nodes into communities, by accounting for the different interaction types nodes can be involved both within and in-between layers of the considered multilayer graph. Still, contrary to unilayer networks, clustering of multilayer graphs remains a substantially less-developed area (18–20), and most currently existing methods are based on an adaptation of conventional clustering approaches for unilayer networks such as stochastic block models (21–24) and layer aggregation in the spectral domain (25–28) to the multilayer case. However, the clustering of multilayer graphs poses a number of new, specific research challenges. First, partitioning of multilayer graphs requires accounting for both the important relationships between nodes in the same layer and interactions among nodes in different layers. Second, such layers, as, for instance, in the case of critical infrastructures formed by transportation and power grid networks, may exhibit disparate local and global structural properties, making application of clustering methods originating in the unilayer network analysis and based on an aggregation of the layer information infeasible. Finally, higher-order network structures, in the context of both unilayer and multilayer graphs, are often shown to exhibit stronger signals of community existence than lower-order pairwise connectivity patterns which are assessed at the level of individual nodes and edges (29, 30). This phenomenon becomes particularly important in conjunction with resilience analysis of highly interdependent systems such as critical infrastructures when the focus is on evaluating how multiple interconnected entities of the systems, for example electric power substations, transportation hubs, and telecommunication towers, jointly respond to natural disasters and cyber attacks. Nevertheless, clustering of multilayer networks while accounting for higher-order connectivity structures remains in its infancy.To address these challenges, we introduce the concepts of topological data analysis (TDA) to studies of complex multilayer networks and propose a topological approach to network clustering. TDA is an emerging methodology at the interface of algebraic topology and data science (31–34) offering a mathematically rigorous machinery for analysis of data shape. In particular, TDA allows one to glean a deeper insight into hidden mechanisms behind the data-generating process by analyzing both topological and geometric properties of the observed data through multiple-resolution lenses. While TDA has been proven to deliver high utility in a very diverse set of applications, from cancer gene expression to financial fraud to ichthyology (35–38), TDA concepts have not yet propagated into clustering analysis of complex networks. The key idea behind our topological network clustering is to group nodes based on how similar in shape their local neighborhoods are. In particular, the proposed topological approach is based on the comparison of local topology and geometry around each node using persistence diagrams and, hence, is termed “clustering using persistence diagrams” (CPD). The topological CPD approach to network clustering allows both for systematic accounting of heterogeneous higher-order properties of within and in-between network layers and for integrating the important information from the node neighbors and their interactions. In contrast to earlier (not network-focused) TDA-based clustering approaches such as Mapper (39) and ToMATo (40), which both act in conjunction with some additional clustering algorithms, CPD is a stand-alone clustering approach and does not require a filter function, which is characteristic of Mapper. Furthermore, compared to clustering using Betti numbers, a TDA-based clustering algorithm for spatiotemporal data (41), CPD simultaneously accounts for multiple topological summaries and their interdependencies and, as a result, shows more stable performance, especially in application to sparse heterogeneous graph-structured data. Finally, the area of the CPD applicability is well beyond complex networks and also includes multivariate point clouds and sets of functions.We illustrate the application of our CPD algorithm and the utility of topological concepts for clustering of complex networks in application to a multilayer climate-insurance network. The insurance industry currently experiences major challenges due to the impact of climate dynamics expressed in the rising frequency and intensity of adverse weather events, including the so-called low-individual but high-cumulative-impact events such as higher-than-normal precipitation and stronger-than-usual wind speeds. For example, ref. 42 shows that 38% of insurance companies view climate risk as a core business issue, with implications for governance, strategy, risk management, and operations, while 29% of the companies consider climate risk as a sustainability issue which is evolving to a core business issue. In turn, often-neglected low-individual but high-cumulative-impact adverse weather events, coupled with aging critical city infrastructures, increasingly lead to accidents of various scales and property depreciation. One of the first tasks toward better assessment of climate risks and development of more efficient mitigation strategies is the identification of areas that show higher vulnerability not only due to the magnitude of climate trends but also due to economic and sociodemographic patterns. However, climate variables, insured property characteristics, and associated insurance claim dynamics tend to exhibit complex dependence structures that are often nonlinear and nonstationary in space and time. As a result, similarity measures based on Euclidean distances and conventional geographic proximity might not be appropriate metrics for optimal partitioning of such data. As shown by refs. 43–48, such a sophisticated dependence structure in climate variables can be addressed with complex networks. However, no analysis has been done to capture the multivariate spatiotemporal dependency for classifying the insurance risk exposure and informing the risk mitigation strategies. We address this knowledge gap by introducing a multilayer complex network based on climate and home insurance variables and by developing vulnerability zoning based on the topological CPD approach. The proposed peril map based on shape similarities in environmental and sociodemographic characteristics allows for more accurate modeling of climate risk than conventional tools based on simple geographic proximity.