Utilizing Data Mining Techniques and Ensemble Learning to Predict Development of Surgical Site Infections in Gynecologic Cancer Patients

Surgical site infections are costly to both patients and hospitals, increase patient mortality, and are the most common form of a hospital acquired infection. Gynecological cancer surgery patients are already at higher risk of developing an infection due to the suppression of their immune system. This research leverages popular data mining techniques to create a prediction model to identify high risk patients. Implemented techniques include logistic regression, naive Bayes, recursive partitioning and regression trees, random forest, feed forward neural network, k-nearest neighbor, and support vector machines with linear kernel. Weighted stacked generalization was implemented to improve upon the individual base level model’s performance. The chosen meta level classifiers were support vector machines with linear kernel, logistic regression, and k-nearest neighbor. The result is a model that identifies high-risk patients immediately following a surgical procedure with an AUC of 0.6864, accuracy of 0.6744, sensitivity of 0.7, and specificity of 0.6728

Mitochondrial Genetic Diversity and its Determinants in Island Melanesia

For a long time, many physical anthropologists and human geneticists considered Island Melanesian populations to be genetically impoverished, dominated by the effects of random genetic drift because of their small sizes, internally very homogeneous, and therefore of little relevance in reconstructing past human migrations. This view is changing. Here we present the developing detailed picture of mitochondrial DNA (mtDNA) variation in eastern New Guinea and Island Melanesia that reflects linguistic distinctions within the region as well as considerable island-by-island isolation. It also appears that the patterns of variation reflect marital migration distinctions between bush and beach populations. We have identified a number of regionally specific mtDNA variants. We also question the widely accepted hypothesis that the mtDNA variant referred to as the ‘Polynesian Motif’ (or alternatively the ‘Austronesian Motif’) developed outside this region somewhere to the west. It may well have first appeared among certain non-Austronesian speaking groups in eastern New Guinea or the Bismarcks. Overall, the developing mtDNA pattern appears to be more easily reconciled with that of other genetic and biometric variables

Policy Feedback and the Politics of the Affordable Care Act

There is a large body of literature devoted to how “policies create politics” and how feedback effects from existing policy legacies shape potential reforms in a particular area. Although much of this literature focuses on self‐reinforcing feedback effects that increase support for existing policies over time, Kent Weaver and his colleagues have recently drawn our attention to self‐undermining effects that can gradually weaken support for such policies. The following contribution explores both self‐reinforcing and self‐undermining policy feedback in relationship to the Affordable Care Act, the most important health‐care reform enacted in the United States since the mid‐1960s. More specifically, the paper draws on the concept of policy feedback to reflect on the political fate of the ACA since its adoption in 2010. We argue that, due in part to its sheer complexity and fragmentation, the ACA generates both self‐reinforcing and self‐undermining feedback effects that, depending of the aspect of the legislation at hand, can either facilitate or impede conservative retrenchment and restructuring. Simultaneously, through a discussion of partisan effects that shape Republican behavior in Congress, we acknowledge the limits of policy feedback in the explanation of policy stability and change

Association of immunophenotype with expression of topoisomerase II α and β in adult acute myeloid leukemia.

Anthracyclines used in the treatment of acute myelogenous leukemia (AML) inhibit the activity of the mammalian topoisomerase II (topo II) isoforms, topo II α and topo IIβ. In 230 patients with non-M3 AML who received frontline ara-C/daunorubicin we determined expression of topo IIα and topo IIβ by RT-PCR and its relationship to immunophenotype (IP) and outcomes. Treatment outcomes were analyzed by logistic or Cox regression. In 211 patients, available for analysis, topo IIα expression was significantly lower than topo IIβ (P \u3c 0.0001). In contrast to topo IIα, topo IIβ was significantly associated with blast percentage in marrow or blood (P = 0.0001), CD7 (P = 0.01), CD14 (P \u3c 0.0001) and CD54 (P \u3c 0.0001). Event free survival was worse for CD56-negative compared to CD56-high (HR = 1.9, 95% CI [1.0-3.5], p = 0.04), and overall survival was worse for CD-15 low as compared to CD15-high (HR = 2.2, 95% CI [1.1-4.2], p = 0.02). Ingenuity pathway analysis indicated topo IIβ and immunophenotype markers in a network associated with cell-to-cell signaling, hematological system development/function and inflammatory response. Topo IIβ expression reflects disease biology of highly proliferative disease and distinct IP but does not appear to be an independent variable influencing outcome in adult AML patients treated with anthracycline-based therapy

Scientific concepts and methods for moving persistence assessments into the 21st century

The evaluation of a chemical substance's persistence is key to understanding its environmental fate, exposure concentration, and, ultimately, environmental risk. Traditional biodegradation test methods were developed many years ago for soluble, nonvolatile, single-constituent test substances, which do not represent the wide range of manufactured chemical substances. In addition, the Organisation for Economic Co-operation and Development (OECD) screening and simulation test methods do not fully reflect the environmental conditions into which substances are released and, therefore, estimates of chemical degradation half-lives can be very uncertain and may misrepresent real environmental processes. In this paper, we address the challenges and limitations facing current test methods and the scientific advances that are helping to both understand and provide solutions to them. Some of these advancements include the following: (1) robust methods that provide a deeper understanding of microbial composition, diversity, and abundance to ensure consistency and/or interpret variability between tests; (2) benchmarking tools and reference substances that aid in persistence evaluations through comparison against substances with well-quantified degradation profiles; (3) analytical methods that allow quantification for parent and metabolites at environmentally relevant concentrations, and inform on test substance bioavailability, biochemical pathways, rates of primary versus overall degradation, and rates of metabolite formation and decay; (4) modeling tools that predict the likelihood of microbial biotransformation, as well as biochemical pathways; and (5) modeling approaches that allow for derivation of more generally applicable biotransformation rate constants, by accounting for physical and/or chemical processes and test system design when evaluating test data. We also identify that, while such advancements could improve the certainty and accuracy of persistence assessments, the mechanisms and processes by which they are translated into regulatory practice and development of new OECD test guidelines need improving and accelerating. Where uncertainty remains, holistic weight of evidence approaches may be required to accurately assess the persistence of chemicals. Integr Environ Assess Manag 2022;18:1454-1487. © 2022 The Authors. Integrated Environmental Assessment and Management published by Wiley Periodicals LLC on behalf of Society of Environmental Toxicology & Chemistry (SETAC). Keywords: Bioavailability; Biodegradability; Biodegradation; Degradation half-lives; Persistence assessment