Declining brain activity in cognitively normal apolipoprotein E ɛ4 heterozygotes: A foundation for using positron emission tomography to efficiently test treatments to prevent Alzheimer's disease

Cross-sectional positron emission tomography (PET) studies find that cognitively normal carriers of the apolipoprotein E (APOE) ɛ4 allele, a common Alzheimer's susceptibility gene, have abnormally low measurements of the cerebral metabolic rate for glucose (CMRgl) in the same regions as patients with Alzheimer's dementia. In this article, we characterize longitudinal CMRgl declines in cognitively normal ɛ4 heterozygotes, estimate the power of PET to test the efficacy of treatments to attenuate these declines in 2 years, and consider how this paradigm could be used to efficiently test the potential of candidate therapies for the prevention of Alzheimer's disease. We studied 10 cognitively normal ɛ4 heterozygotes and 15 ɛ4 noncarriers 50–63 years of age with a reported family history of Alzheimer's dementia before and after an interval of approximately 2 years. The ɛ4 heterozygotes had significant CMRgl declines in the vicinity of temporal, posterior cingulate, and prefrontal cortex, basal forebrain, parahippocampal gyrus, and thalamus, and these declines were significantly greater than those in the ɛ4 noncarriers. In testing candidate primary prevention therapies, we estimate that between 50 and 115 cognitively normal ɛ4 heterozygotes are needed per active and placebo treatment group to detect a 25% attenuation in these CMRgl declines with 80% power and P = 0.005 in 2 years. Assuming these CMRgl declines are related to the predisposition to Alzheimer's dementia, this study provides a paradigm for testing the potential of treatments to prevent the disorder without having to study thousands of research subjects or wait many years to determine whether or when treated individuals develop symptoms.

Floral Scent Production in Clarkia breweri1 : III. Enzymatic Synthesis and Emission of Benzenoid Esters

The fragrance of Clarkia breweri (Onagraceae), a California annual plant, includes three benzenoid esters: benzylacetate, benzylbenzoate, and methylsalicylate. Here we report that petal tissue was responsible for the benzylacetate and methylsalicylate emission, whereas the pistil was the main source of benzylbenzoate. The activities of two novel enzymes, acetyl-coenzyme A:benzylalcohol acetyltransferase (BEAT), which catalyzes the acetyl esterification of benzylalcohol, and S-adenosyl-l-methionine:salicylic acid carboxyl methyltransferase, which catalyzes the methyl esterification of salicylic acid, were also highest in petal tissue and absent in leaves. In addition, the activity of both enzymes in the various floral organs was developmentally and differentially regulated. S-Adenosyl-l-methionine:salicylic acid carboxyl methyltransferase activity in petals peaked in mature buds and declined during the next few days after anthesis, and it showed a strong, positive correlation with the emission of methylsalicylate. The levels of BEAT activity and benzylacetate emission in petals also increased in parallel as the buds matured and the flowers opened, but as emission began to decline on the 2nd d after anthesis, BEAT activity continued to increase and remained high until the end of the lifespan of the flower.

Neural-network-based classification of cognitively normal, demented, Alzheimer disease and vascular dementia from single photon emission with computed tomography image data from brain.

Single photon emission with computed tomography (SPECT) hexamethylphenylethyleneamineoxime technetium-99 images were analyzed by an optimal interpolative neural network (OINN) algorithm to determine whether the network could discriminate among clinically diagnosed groups of elderly normal, Alzheimer disease (AD), and vascular dementia (VD) subjects. After initial image preprocessing and registration, image features were obtained that were representative of the mean regional tissue uptake. These features were extracted from a given image by averaging the intensities over various regions defined by suitable masks. After training, the network classified independent trials of patients whose clinical diagnoses conformed to published criteria for probable AD or probable/possible VD. For the SPECT data used in the current tests, the OINN agreement was 80 and 86% for probable AD and probable/possible VD, respectively. These results suggest that artificial neural network methods offer potential in diagnoses from brain images and possibly in other areas of scientific research where complex patterns of data may have scientifically meaningful groupings that are not easily identifiable by the researcher.