Younger siblings, C-Reactive protein, and risk of age-related macular degeneration

In this study, we examined the relationship between exposure to siblings and 1) the risk of age-related macular degeneration (AMD) and 2) C-reactive protein levels. We retrospectively analyzed pooled cross-sectional data from 2 studies: the Cardiovascular Health and Age-Related Maculopathy Study (2001–2002) and the Age-Related Maculopathy Statin Study (2004–2006). Associations between number of siblings and AMD were assessed by using multinomial logistic regression. Associations between number of siblings and C-reactive protein levels were examined by using a generalized linear model for γ distribution. A higher number of younger siblings was associated with significantly lower odds of early AMD in those with a family history of AMD (odds ratio = 0.2, 95% confidence interval: 0.1, 0.8) (P = 0.022) but was unrelated to AMD for those who had no family history of the disease (odds ratio = 1.0, 95% confidence interval: 0.9, 1.2) (P = 0.874). A higher number of younger siblings correlated with lower C-reactive protein levels (β = −0.19, 95% confidence interval: −0.38, −0.01) (P = 0.036). This supports the theory that immune modulation contributes to AMD pathogenesis and suggests that exposure to younger siblings might be protective when there is a family history of AMD.

Predicting future purchases with the Poisson log-normal model

The negative binomial distribution (NBD) has been widely used in marketing for modeling purchase frequency counts, particularly in packaged goods contexts. A key managerially relevant use of this model is Conditional Trend Analysis (CTA)—a method of benchmarking future sales utilizing the NBD conditional expectation. CTA allows brand managers to identify whether the sales change in a second period is accounted for by previous non-, light, or heavy buyers of the brand. Although a useful tool, the conditional prediction of the NBD suffers from a bias: it under predicts what the period-one non-buyer class will do in period two and over predicts the sales contribution of existing buyers. In addition, the NBD's assumption of a gamma-distributed mean purchase rate lacks theoretical support—it is not possible to explain why a gamma distribution should hold. This paper therefore proposes an alternative model using a log-normal distribution in place of the gamma distribution, hence creating a Poisson log-normal (PLN) distribution. The PLN distribution has a stronger theoretical grounding than the NBD as it has a natural interpretation relying on the central limit theorem. Empirical analysis of brands in multiple categories shows that the PLN distribution gives better predictions than the NBD.

Can the direct medical cost of chronic disease be transferred across different countries? Using cost-of-illness studies on type 2 diabetes, epilepsy and schizophrenia as examples

OBJECTIVES: To systematically review cost of illness studies for schizophrenia (SC), epilepsy (EP) and type 2 diabetes mellitus (T2DM) and explore the transferability of direct medical cost across countries.

METHODS: A comprehensive literature search was performed to yield studies that estimated direct medical costs. A generalized linear model (GLM) with gamma distribution and log link was utilized to explore the variation in costs that accounted by the included factors. Both parametric (Random-effects model) and non-parametric (Boot-strapping) meta-analyses were performed to pool the converted raw cost data (expressed as percentage of GDP/capita of the country where the study was conducted).

RESULTS: In total, 93 articles were included (40 studies were for T2DM, 34 studies for EP and 19 studies for SC). Significant variances were detected inter- and intra-disease classes for the direct medical costs. Multivariate analysis identified that GDP/capita (p<0.05) was a significant factor contributing to the large variance in the cost results. Bootstrapping meta-analysis generated more conservative estimations with slightly wider 95% confidence intervals (CI) than the parametric meta-analysis, yielding a mean (95%CI) of 16.43% (11.32, 21.54) for T2DM, 36.17% (22.34, 50.00) for SC and 10.49% (7.86, 13.41) for EP.

CONCLUSIONS: Converting the raw cost data into percentage of GDP/capita of individual country was demonstrated to be a feasible approach to transfer the direct medical cost across countries. The approach from our study to obtain an estimated direct cost value along with the size of specific disease population from each jurisdiction could be used for a quick check on the economic burden of particular disease for countries without such data.

Endurance training in humans leads to fiber type-specific increases in levels of peroxisome proliferator-activated receptor-[gamma] coactivator-1 and peroxisome proliferator-activated receptor-[alpha] in skeletal muscle

The peroxisome proliferator-activated receptor (PPAR)-&gamma; coactivator-1 (PGC-1) can induce mitochondria biogenesis and has been implicated in the development of oxidative type I muscle fibers. The PPAR isoforms α, β/δ, and &gamma; control the transcription of genes involved in fatty acid and glucose metabolism. As endurance training increases skeletal muscle mitochondria and type I fiber content and fatty acid oxidative capacity, our aim was to determine whether these increases could be mediated by possible effects on PGC-1 or PPAR-α, -β/δ, and -&gamma;. Seven healthy men performed 6 weeks of endurance training and the expression levels of PGC-1 and PPAR-α, -β/δ, and -&gamma; mRNA as well as the fiber type distribution of the PGC-1 and PPAR-α proteins were measured in biopsies from their vastus lateralis muscle. PGC-1 and PPAR-α mRNA expression increased by 2.7- and 2.2-fold (P < 0.01), respectively, after endurance training. PGC-1 expression was 2.2- and 6-fold greater in the type IIa than in the type I and IIx fibers, respectively. It increased by 2.8-fold in the type IIa fibers and by 1.5-fold in both the type I and IIx fibers after endurance training (P < 0.015). PPAR-α was 1.9-fold greater in type I than in the II fibers and increased by 3.0-fold and 1.5-fold in these respective fibers after endurance training (P < 0.001). The increases in PGC-1 and PPAR-α levels reported in this study may play an important role in the changes in muscle mitochondria content, oxidative phenotype, and sensitivity to insulin known to be induced by endurance training.

Distribution of Gaba in the nerve ganglia of haliotis asinina linnaeus

Gamma-aminobutyric acid (GABA) is a major neurotransmitter and effective settlement inducer in abalone aquaculture. This study aimed to explore the distribution of GABA within neural tissues of Haliotis asinina. Gamma-aminobutyric acid was found in neuronal cell type 1 of 3 major ganglia (i.e., cerebral, pleuropedal, and visceral ganglia) of both sexes. The distribution of GABA-immunoreactive (-ir) cells in the cerebral ganglion was concentrated mostly in the cortex region of the dorsal horn, whereas it was scattered throughout the pleuropedal ganglion, with more in the upper half. Gamma-aminobutyric acid-ir nerve fibers were found throughout the neuropils of the ganglia. The visceral ganglion had the least numbers of GABA-ir neurons compared with the other 2 ganglia. The cells were distributed mainly in the dorsal horn. We also observed GABA to be colocalized with 2 other neurotransmitters: serotonin (5-HT) and dopamine (DA). In the cerebral ganglion, fluorescence double staining of GABA and 5-HT, and GABA and DA showed immunoreactivity in separate cells and was also colocalized in the same cells. In the pleuropedal ganglion, the staining pattern was similar to the cerebral ganglion, but positive-staining cells were less numerous. In the visceral ganglion, GABA and DA, and GABA and 5-HT were colocalized in the same cell types. Overall, we found that GABAergic cells were most numerous in the cerebral ganglion of H. asinina. Further studies are required to determine the functions of these neurotransmitters in relation to their distribution.

The role of thermomechanical routes on the distribution of grain boundary and interface plane orientations in transformed microstructures

In the current study, a series of thermomechanical routes were used to produce different microstructures (i.e., ferrite and martensite) in low-carbon low alloy steels. The five-parameter grain boundary character distribution was measured for all microstructures. The thermomechanical processing route altered the texture of the fully ferritic microstructure and significantly influenced the anisotropy of the grain boundary character distribution. Generally, the population of (111) planes increased with an increase in the &gamma;-fiber texture for the ferritic microstructure, but it did not change the shape of the grain boundary plane distribution at specific misorientations. The most commonly observed boundaries in the fully ferritic structures produced through different routes were {112} symmetric tilt boundaries with the Σ3 = 60 deg/[111] misorientation; this boundary also had a low energy. However, the grain boundary plane distribution was significantly changed by the phase transformation path (i.e., ferrite vs martensite) for a given misorientation. In the martensitic steel, the most populous Σ3 boundary was the {110} symmetric tilt boundary. This results from the crystallographic constraints associated with the shear transformation (i.e., martensite) rather than the low-energy interface that dominates in the diffusional phase transformation (i.e., ferrite).