Ferrocene-containing cationic lipids for the delivery of DNA: oxidation state determines transfection activity.

The ability of two redox-active, ferrocene-containing cationic lipids [11-(ferrocenylundecyl)trimethylammonium bromide (FTMA) and bis(11-ferrocenylundecyl)dimethylammonium bromide (BFDMA)] to transfect mammalian cells was investigated. This study sought to determine the range of conditions over which these lipids were capable of transfecting cells and whether the oxidation state of the ferrocenyl groups in these materials could be used to influence the extent of transfection. Experiments conducted in the COS-7 cell line demonstrated that reduced and oxidized FTMA were substantially cytotoxic and did not transfect cells. Subsequent experiments conducted using BFDMA and reporter plasmids encoding enhanced green fluorescent protein (EGFP) and firefly luciferase demonstrated that BFDMA was able to transfect cells. However, the extent of transfection depended significantly upon both the concentration of BFDMA and the oxidation state of the lipid. Quantitative characterization of cytotoxicity and gene expression demonstrated that a window of concentration existed over which reduced BFDMA was non-cytotoxic and yielded high levels of transfection, but over which electrochemically oxidized BFDMA yielded very low (background) levels of transfection. Characterization of lipoplexes using dynamic light scattering demonstrated that reduced and oxidized BFDMA formed small aggregates (ca. 90 to 250nm) at concentrations of lipid ranging from 2 to 10 microM. Taken together, these results demonstrate that the oxidation state of BFDMA, which can be controlled electrochemically, can be used to control the extent of cell transfection. These results could form the basis of transfection procedures that exploit the redox behavior of ferrocene-containing lipids to achieve active spatial and temporal control over transfection using electrochemical methods.     

Sociodemographic characteristics of the neighborhood and depressive symptoms in older adults: using multilevel modeling in geriatric psychiatry.

OBJECTIVE: Neighborhood sociodemographic characteristics may be important to the mental health of older adults who have decreased mobility and fewer resources. Our objective was to examine the association between neighborhood context and level of depressive symptomatology in older adults in a diverse geographic region of central North Carolina. METHODS: The sample included 2,998 adults 65 or older residing in 91 census tracts. Depressive symptoms were measured using the Center for Epidemiologic Studies-Depression scale (CES-D). Neighborhoods were characterized by five census-based characteristics: socioeconomic disadvantage, socioeconomic advantage, racial/ethnic heterogeneity, residential stability, and age structure. RESULTS: In ecologic level analyses, level of census tract socioeconomic disadvantage was associated with increased depressive symptoms. To determine whether neighborhood context was associated with depressive symptoms independently of individual characteristics, the authors used multilevel modeling. The authors examined the ability of each of five neighborhood (level 2) characteristics to predict a level 1 outcome (CES-D symptoms) controlling for the effects of individual (level 1) characteristics. Younger age, being widowed, lower income, and having some functional limitations were associated with increased depression symptoms conditional on census tract random effects. However, none of the neighborhood characteristics was significantly associated with depression symptoms, conditional on census tract random effects, either unadjusted or adjusted for individual characteristics. CONCLUSION: Any observed association between neighborhood sociodemographic characteristics and individual depressive symptoms in our sample may reflect the characteristics of the individuals who reside in the neighborhood rather than the neighborhood characteristics themselves. The use of multilevel modeling is important to separate these effects.     

Applying item response theory to enhance health outcomes assessment

Background Health-related quality of life (HRQL) is an accepted outcome measure in patients with mood and anxiety disorders. Yet, surprisingly little attention has been paid to the determinants. In this paper we test the hypothesis that it is associated with personality traits while controlling for mental disorders. Methods A large sample of outpatients (n=640) with mood and anxiety disorders was studied. The empirically supported five factor model of normal personality traits was assessed using the NEO-FFI and includes: neuroticism, extraversion, openness to experience, agreeableness, and conscientiousness. Mental disorders were assessed with the CIDI, and HRQL with the SF-36. Results Regression analyses revealed that the NEO-FFI scores, with the exception of conscientiousness, were significantly associated with SF-36 subscales and summary scores, independently from the mental disorders. The percentage of explained variance due to the personality traits was highest for the subscales Vitality (10.0%), Mental Health (13.3%) and the Mental Health Summary Score (9.5%). Furthermore, specific personality traits were related to specific SF-36 subscales. Conclusions A low HRQL of patients with mood or anxiety disorders is not only determined by the disease or the current health but is also shaped by personality traits that are relatively stable throughout an individual's life time.     

The kappa statistic for establishing interrater reliability in the secondary analysis of qualitative clinical data.

Analysis of extant clinical records is receiving increased emphasis in nursing investigations. Appropriate use of this approach to patient research requires careful attention to data management, including assessment of reliability. Percent agreement, phi, and Kappa all serve as estimates of interrater reliability in the analysis of data. Kappa has particular merit as a measure of interrater reliability; it also has some peculiar problems in implementation and interpretation. The nature and computation of Kappa and its application in analysis of clinical data are discussed.