Suchergebnisse

A biomarker is a measurable indicator of the severity or presence of some disease. A biomarker is anything that can be used as an indicator of a particular disease state or some other physiological state of an organism. The space Decomposition-Gradient-Regression (DGR) method has been developed (Li et al., 2012; Li et al., 2015) to select biomarkers for schizophrenia. This study performs the DGR approach on data for bipolar disorder patients, which contains 56 biomarkers and 8 infectious agent's antibodies. Serum specimens were collected from 132 United States military service members (118 males and 14 females) with a diagnosis of bipolar disorder from 1992 to 2005 and their matched healthy controls. Trefoil Factor3 (TFF3), Gliadin, prolactin (PRL), Apolipoprotein A-II (Apo A-II) and Immunoglobulin A (IGA) were found to be significant predictors of Bipolar Disorder (BD) in males. Macrophage-Derived Chemokine (MDC), Alpha-1-Antitrypsin (AAT), Gliadin, Beta-2-Microglobulin (B2M) and Monocyte Chemotactic Protein 2 (MCP-2) might be used to identify bipolar disorder in females. A predictive biomarker panel for BD offers the potential to aid in the diagnosis, initiate treatment earlier and ideally alter the course of disease with reduced morbidity and functional impairment.

Biomarkers have been examined in schizophrenia research for decades. Medical morbidity and mortality rates, as well as personal and societal costs, are associated with schizophrenia patients. The identification of biomarkers and alleles, which often have a small effect individually, may help to develop new diagnostic tests for early identification and treatment. Currently, there is not a commonly accepted statistical approach to identify predictive biomarkers from high dimensional data. We used space decomposition-gradient-regression (DGR) method to select biomarkers, which are associated with the risk of schizophrenia. Then, we used the gradient scores, generated from the selected biomarkers, as the prediction factor in regression to estimate their effects. We also used an alternative approach, classification and regression tree, to compare the biomarker selected by DGR and found about 70% of the selected biomarkers were the same. However, the advantage of DGR is that it can evaluate individual effects for each biomarker from their combined effect. In DGR analysis of serum specimens of US military service members with a diagnosis of schizophrenia from 1992 to 2005 and their controls, Alpha-1-Antitrypsin (AAT), Interleukin-6 receptor (IL-6r) and connective tissue growth factor were selected to identify schizophrenia for males; and AAT, Apolipoprotein B and Sortilin were selected for females. If these findings from military subjects are replicated by other studies, they suggest the possibility of a novel biomarker panel as an adjunct to earlier diagnosis and initiation of treatment.

OBJECTIVES: Condoms are highly effective in preventing sexually transmitted infections (STIs) but implementation is often inconsistent with use rarely examined across travel transition periods. We examined the prevalence of condom use among ship-assigned US military personnel across an overseas deployment cycle and identified factors associated with condom non-use. METHODS: Longitudinal survey data were collected from ship-assigned US Navy/Marine Corps personnel on 11 ships before (T1), during (T2) and after (T3) an overseas deployment. The anonymous, self-completed survey included demographics, condom use at last sex, STI diagnosis, alcohol misuse and drug use with sex. Descriptive and generalised regression model analyses were conducted. RESULTS: Analysis included 1900 (T1), 549 (T2) and 1168 (T3) personnel who reported age, sex and condom use/non-use at last sex. The proportion reporting condom use was significantly higher during T2 (53%, p<0.0001) than T1 (27%) or T3 (28%), with STI prevalences of 1% (T1), 7% (T2) or 2% (T3), with fewer (29%) sexually active individuals at T2. In adjusted models, condom non-use was associated with hazardous alcohol use (OR 1.44, 95% CI 1.21 to 1.71), or drug use to enhance sex (OR 1.37, 95% CI 1.06 to 1.77), but transactional sex was negatively associated (OR 0.69, 95% CI 0.50 to 0.84). CONCLUSIONS: Condom use was highest during deployment, as was STI prevalence (among non-users), possibly reflecting concentration of high-risk sexual activities/individuals and/or sexual partners more likely to be infected. Higher condom use with transactional sex likely reflects awareness of higher STI risk. These data can be used to facilitate targeted interventions to reduce STI transmission and may extend to similarly aged cohorts travelling outside the US (eg, college students on spring break).