Parameter sensitivity analysis of surface plasmon resonance biosensor through numerical simulation

Surface based analytical tools have gained more importance for rapid, sensitive and label-free monitoring of molecular recognition events. Surface plasmon resonance (SPR) has played a prominent role in real time monitoring of surface binding events. SPR is increasing its significance especially for the study of ultrathin dielectric layer. This paper investigates the role of thin films of gold, silver and aluminium for protein detection in SPR biosensors. It is shown that the sensitivity, which is indicated by the shift of plasmon dip, is not linearly related to the thickness of protein but quadratic over a specific range. The approach involves a plot of a reflectivity curve as a function of the angle of incidence.

Oral sensitivity to fatty acids, food consumption and BMI in human subjects

Fatty acids are the chemical moieties that are thought to stimulate oral nutrient sensors, which detect the fat content of foods. In animals, oral hypersensitivity to fatty acids is associated with decreased fat intake and body weight. The aims of the present study were to investigate oral fatty acid sensitivity, food selection and BMI in human subjects. The study included two parts; study 1 established in thirty-one subjects (29 (sem 1·4) years, 22·8 (sem 0·5) kg/m²) taste thresholds using 3-AFC (3-Alternate Forced Choice Methodology) for oleic, linoleic and lauric acids, and quantified oral lipase activity. During study 2, fifty-four subjects (20 (sem 0·3) years, 21·5 (sem 0·4) kg/m2) were screened for oral fatty acid sensitivity using oleic acid (1·4 mm), and they were defined as hypo- or hypersensitive via triplicate triangle tests. Habitual energy and macronutrient intakes were quantified from 2 d diet records, and BMI was calculated from height and weight. Subjects also completed a fat ranking task using custard containing varying amounts (0, 2, 6 and 10 %) of fat. Study 1 reported median lipase activity as 2 μmol fatty acids/min per l, and detection thresholds for oleic, linoleic and lauric acids were 2·2 (sem 0·1), 1·5 (sem 0·1) and 2·6 (sem 0·3) mm. Study 2 identified twelve hypersensitive subjects, and hypersensitivity was associated with lower energy and fat intakes, lower BMI (P < 0·05) and an increased ability to rank custards based on fat content (P < 0·05). Sensitivity to oleic acid was correlated to performance in the fat ranking task (r 0·4, P < 0·05). These data suggest that oral fatty acid hypersensitivity is associated with lower energy and fat intakes and BMI, and it may serve as a factor that influences fat consumption in human subjects.

Fat taste sensitivity

Hyposensitivity to fatty acid taste is associated with greater intake of fat, higher BMI and attenuated gastrointestinal (GI) function. These observations are consistent amongst healthy and overweight/obese subjects, who display attenuated taste and GI function, and consume excess fat.

Association of high-sensitivity C-reactive protein with de novo major depression

Background: Although there is cross-sectional evidence that changes in the immune system contribute to the pathophysiology of depression, longitudinal data capable of elucidating cause and effect relationships are lacking.

Aims: We aimed to determine whether subclinical systemic inflammation, as measured by serum high-sensitivity C-reactive protein (hsCRP) concentration, is associated with an increased risk of de novo major depressive disorder.

Method: Major depressive disorder was diagnosed using a clinical interview (SCID-I/NP). This is a retrospective cohort study; from a population-based sample of 1494 randomly selected women recruited at baseline during the period 1994-7, 822 were followed for a decade and provided measures of both exposure and outcome. Of these women, 644 (aged 20-84 years) had no prior history of depression at baseline and were eligible for analysis.

Results: During 5827 person-years of follow-up, 48 cases of de novo major depressive disorder were identified. The hazard ratio (HR) for depression increased by 44% for each standard deviation increase in log-transformed hsCRP (ln-hsCRP) (HR = 1.44, 95% CI 1.04-1.99), after adjusting for weight, smoking and use of non-steroidal anti-inflammatory drugs. Further adjustment for other lifestyle factors, medications and comorbidity failed to explain the observed increased risk for depression.

Conclusions: Serum hsCRP is an independent risk marker for de novo major depressive disorder in women. This supports an aetiological role for inflammatory activity in the pathophysiology of depression.

Sequential fixed-width confidence bands for kernel regression estimation

We consider a random design model based on independent and identically distributed (iid) pairs of observations (Xi, Yi), where the regression function m(x) is given by m(x) = E(Yi|Xi = x) with one independent variable. In a nonparametric setting the aim is to produce a reasonable approximation to the unknown function m(x) when we have no precise information about the form of the true density, f(x) of X. We describe an estimation procedure of non-parametric regression model at a given point by some appropriately constructed fixed-width (2d) confidence interval with the confidence coefficient of at least 1−. Here, d(> 0) and 2 (0, 1) are two preassigned values. Fixed-width confidence intervals are developed using both Nadaraya-Watson and local linear kernel estimators of nonparametric regression with data-driven bandwidths.

The sample size was optimized using the purely and two-stage sequential procedure together with asymptotic properties of the Nadaraya-Watson and local linear estimators. A large scale simulation study was performed to compare their coverage accuracy. The numerical results indicate that the confidence bands based on the local linear estimator have the best performance than those constructed by using Nadaraya-Watson estimator. However both estimators are shown to have asymptotically correct coverage properties.

Sample size determination for kernel regression estimation using sequential fixed-width confidence bands

We consider a random design model based on independent and identically distributed pairs of observations (Xi, Yi), where the regression function m(x) is given by m(x) = E(Yi|Xi = x) with one independent variable. In a nonparametric setting the aim is to produce a reasonable approximation to the unknown function m(x) when we have no precise information about the form of the true density, f(x) of X. We describe an estimation procedure of non-parametric regression model at a given point by some appropriately constructed fixed-width (2d) confidence interval with the confidence coefficient of at least 1−. Here, d(> 0) and 2 (0, 1) are two preassigned values. Fixed-width confidence intervals are developed using both Nadaraya-Watson and local linear kernel estimators of nonparametric regression with data-driven bandwidths. The sample size was optimized using the purely and two-stage sequential procedures together with asymptotic properties of the Nadaraya-Watson and local linear estimators. A large scale simulation study was performed to compare their coverage accuracy. The numerical results indicate that the confi dence bands based on the local linear estimator have the better performance than those constructed by using Nadaraya-Watson estimator. However both estimators are shown to have asymptotically correct coverage properties.