Practices of Nurse Practitioners in Screening for Hepatitis C

Presented to the Faculty of the University of Alaska Anchorage in Partial Fulfillment of the Requirements for the Degree of MASTER OF SCIENCEThe purpose of this project was to determine both hepatitis C virus (HCV) screening rates and the percentage of cases diagnosed among adults born between 1945 and 1965 in a general practice clinic staffed by nurse practitioners (NPs). A descriptive study was conducted using a chart review of all patients born between 1945 and 1965 seen by NPs in a primary care clinic during a three month period of time. Data was collected on the total number of patients in the target group, those born between 1945 and 1965, as well as each patient’s gender, birth date, if screened for HCV, result of screening, and the reason for screening. Findings revealed that screening rates were suboptimal, with only six out of 178 patients in the target group having been screened for HCV. Age and gender did not appear to be a factor in whether or not a patient was screened

Integrated multiple mediation analysis: A robustness–specificity trade-off in causal structure

Recent methodological developments in causal mediation analysis have addressed several issues regarding multiple mediators. However, these developed methods differ in their definitions of causal parameters, assumptions for identification, and interpretations of causal effects, making it unclear which method ought to be selected when investigating a given causal effect. Thus, in this study, we construct an integrated framework, which unifies all existing methodologies, as a standard for mediation analysis with multiple mediators. To clarify the relationship between existing methods, we propose four strategies for effect decomposition: two-way, partially forward, partially backward, and complete decompositions. This study reveals how the direct and indirect effects of each strategy are explicitly and correctly interpreted as path-specific effects under different causal mediation structures. In the integrated framework, we further verify the utility of the interventional analogues of direct and indirect effects, especially when natural direct and indirect effects cannot be identified or when cross-world exchangeability is invalid. Consequently, this study yields a robustness–specificity trade-off in the choice of strategies. Inverse probability weighting is considered for estimation. The four strategies are further applied to a simulation study for performance evaluation and for analyzing the Risk Evaluation of Viral Load Elevation and Associated Liver Disease/Cancer data set from Taiwan to investigate the causal effect of hepatitis C virus infection on mortality

Gender disparities in primary education across siblings: is intra household disparity higher in regions with low child sex ratios?

Strong son preference in developing countries often motivates parents to undertake sex selection at birth, infanticide, and subsequent neglect of daughters, leading to low child sex ratios in these countries. An interesting question is whether such attitudes also lead to gender discrimination in primary education. While there is a vast literature on inter-household gender discrimination in education, studies of discrimination between siblings is comparatively rare. This paper asks the question: Do parents tend to educate sons more than daughters? Using unit level National Sample Survey Organization data for the 61st Round (2004-2005), we analyze disparity in primary educational attainments between siblings and examine whether such intra-household disparity is higher in areas where child sex ratios are low. Findings indicate that parental attitude towards education and practices may be more complicated and less uniformly negative at lower levels of education than commonly portrayed.Education, Gender, Sibling, India

Interpretable multiclass classification by MDL-based rule lists

Interpretable classifiers have recently witnessed an increase in attention from the data mining community because they are inherently easier to understand and explain than their more complex counterparts. Examples of interpretable classification models include decision trees, rule sets, and rule lists. Learning such models often involves optimizing hyperparameters, which typically requires substantial amounts of data and may result in relatively large models. In this paper, we consider the problem of learning compact yet accurate probabilistic rule lists for multiclass classification. Specifically, we propose a novel formalization based on probabilistic rule lists and the minimum description length (MDL) principle. This results in virtually parameter-free model selection that naturally allows to trade-off model complexity with goodness of fit, by which overfitting and the need for hyperparameter tuning are effectively avoided. Finally, we introduce the Classy algorithm, which greedily finds rule lists according to the proposed criterion. We empirically demonstrate that Classy selects small probabilistic rule lists that outperform state-of-the-art classifiers when it comes to the combination of predictive performance and interpretability. We show that Classy is insensitive to its only parameter, i.e., the candidate set, and that compression on the training set correlates with classification performance, validating our MDL-based selection criterion

HDV can constrain HBV genetic evolution in hbsag: Implications for the identification of innovative pharmacological targets

Chronic HBV + HDV infection is associated with greater risk of liver fibrosis, earlier hepatic decompensation, and liver cirrhosis hepatocellular carcinoma compared to HBV mono-infection. However, to-date no direct anti-HDV drugs are available in clinical practice. Here, we identified conserved and variable regions in HBsAg and HDAg domains in HBV + HDV infection, a critical finding for the design of innovative therapeutic agents. The extent of amino-acid variability was measured by Shannon-Entropy (Sn) in HBsAg genotype-D sequences from 31 HBV + HDV infected and 62 HBV mono-infected patients (comparable for demographics and virological-parameters), and in 47 HDAg genotype-1 sequences. Positions with Sn = 0 were defined as conserved. The percentage of conserved HBsAg-positions was significantly higher in HBV + HDV infection than HBV mono-infection (p = 0.001). Results were confirmed after stratification for HBeAg-status and patients’ age. A Sn = 0 at specific positions in the C-terminus HBsAg were correlated with higher HDV-RNA, suggesting that conservation of these positions can preserve HDV-fitness. Conversely, HDAg was characterized by a lower percentage of conserved-residues than HBsAg (p < 0.001), indicating higher functional plasticity. Furthermore, specific HDAg-mutations were significantly correlated with higher HDV-RNA, suggesting a role in conferring HDV replicative-advantage. Among HDAg-domains, only the virus-assembly signal exhibited a high genetic conservation (75% of conserved-residues). In conclusion, HDV can constrain HBsAg genetic evolution to preserve its fitness. The identification of conserved regions in HDAg poses the basis for designing innovative targets against HDV-infection

Cell surface-specific N-glycan profiling in breast cancer

Aberrant changes in specific glycans have been shown to be associated with immunosurveillance, tumorigenesis, tumor progression and metastasis. In this study, the N-glycan profiling of membrane proteins from human breast cancer cell lines and tissues was detected using modified DNA sequencer-assisted fluorophore-assisted carbohydrate electrophoresis (DSA-FACE). The N-glycan profiles of membrane proteins were analyzed from 7 breast cancer cell lines and MCF 10A, as well as from 100 pairs of breast cancer and corresponding adjacent tissues. The results showed that, compared with the matched adjacent normal tissue samples, two biantennary N-glycans (NA2 and NA2FB) were significantly decreased (p <0.0001) in the breast cancer tissue samples, while the triantennary glycan (NA3FB) and a high-mannose glycan (M8) were dramatically increased (p = 0.001 and p <0.0001, respectively). Moreover, the alterations in these specific N-glycans occurred through the oncogenesis and progression of breast cancer. These results suggested that the modified method based on DSA-FACE is a high-throughput detection technology that is suited for analyzing cell surface N-glycans. These cell surface-specific N-glycans may be helpful in recognizing the mechanisms of tumor cell immunologic escape and could be potential targets for new breast cancer drugs

Diagnostic value of two dimensional shear wave elastography combined with texture analysis in early liver fibrosis.

BACKGROUND: Staging diagnosis of liver fibrosis is a prerequisite for timely diagnosis and therapy in patients with chronic hepatitis B. In recent years, ultrasound elastography has become an important method for clinical noninvasive assessment of liver fibrosis stage, but its diagnostic value for early liver fibrosis still needs to be further improved. In this study, the texture analysis was carried out on the basis of two dimensional shear wave elastography (2D-SWE), and the feasibility of 2D-SWE plus texture analysis in the diagnosis of early liver fibrosis was discussed. AIM: To assess the diagnostic value of 2D-SWE combined with textural analysis in liver fibrosis staging. METHODS: This study recruited 46 patients with chronic hepatitis B. Patients underwent 2D-SWE and texture analysis; Young\u27s modulus values and textural patterns were obtained, respectively. Textural pattern was analyzed with regard to contrast, correlation, angular second moment (ASM), and homogeneity. Pathological results of biopsy specimens were the gold standard; comparison and assessment of the diagnosis efficiency were conducted for 2D-SWE, texture analysis and their combination. RESULTS: 2D-SWE displayed diagnosis efficiency in early fibrosis, significant fibrosis, severe fibrosis, and early cirrhosis (AUC \u3e 0.7, P \u3c 0.05) with respective AUC values of 0.823 (0.678-0.921), 0.808 (0.662-0.911), 0.920 (0.798-0.980), and 0.855 (0.716-0.943). Contrast and homogeneity displayed independent diagnosis efficiency in liver fibrosis stage (AUC \u3e 0.7, P \u3c 0.05), whereas correlation and ASM showed limited values. AUC of contrast and homogeneity were respectively 0.906 (0.779-0.973), 0.835 (0.693-0.930), 0.807 (0.660-0.910) and 0.925 (0.805-0.983), 0.789 (0.639-0.897), 0.736 (0.582-0.858), 0.705 (0.549-0.883) and 0.798 (0.650-0.904) in four liver fibrosis stages, which exhibited equivalence to 2D-SWE in diagnostic efficiency (P \u3e 0.05). Combined diagnosis (PRE) displayed diagnostic efficiency (AUC \u3e 0.7, P \u3c 0.01) for all fibrosis stages with respective AUC of 0.952 (0.841-0.994), 0.896 (0.766-0.967), 0.978 (0.881-0.999), 0.947 (0.835-0.992). The combined diagnosis showed higher diagnosis efficiency over 2D-SWE in early liver fibrosis (P \u3c 0.05), whereas no significant differences were observed in other comparisons (P \u3e 0.05). CONCLUSION: Texture analysis was capable of diagnosing liver fibrosis stage, combined diagnosis had obvious advantages in early liver fibrosis, liver fibrosis stage might be related to the hepatic tissue hardness distribution