Depression as a risk factor for ischaemic heart disease in men: population based case-control study

Objective: To determine the relation between depression, anxiety, and use of antidepressants and the onset of ischaemic heart disease.

Hormone replacement therapy and risk of hip fracture: population based case-control study

Objective: To determine the relative risk of hip fracture associated with postmenopausal hormone replacement therapy including the effect of duration and recency of treatment, the addition of progestins, route of administration, and dose.

Population based case-control study of sick leave in postmenopausal women before diagnosis of hyperparathyroidism

Objective: To analyse sick leave in women at risk of primary hyperparathyroidism before its diagnosis.

Piperonylic Acid, a Selective, Mechanism-Based Inactivator of the trans-Cinnamate 4-Hydroxylase: A New Tool to Control the Flux of Metabolites in the Phenylpropanoid Pathway1

Piperonylic acid (PA) is a natural molecule bearing a methylenedioxy function that closely mimics the structure of trans-cinnamic acid. The CYP73A subfamily of plant P450s catalyzes trans-cinnamic acid 4-hydroxylation, the second step of the general phenylpropanoid pathway. We show that when incubated in vitro with yeast-expressed CYP73A1, PA behaves as a potent mechanism-based and quasi-irreversible inactivator of trans-cinnamate 4-hydroxylase. Inactivation requires NADPH, is time dependent and saturable (KI = 17 μm, kinact = 0.064 min−1), and results from the formation of a stable metabolite-P450 complex absorbing at 427 nm. The formation of this complex is reversible with substrate or other strong ligands of the enzyme. In plant microsomes PA seems to selectively inactivate the CYP73A P450 subpopulation. It does not form detectable complexes with other recombinant plant P450 enzymes. In vivo PA induces a sharp decrease in 4-coumaric acid concomitant to cinnamic acid accumulation in an elicited tobacco (Nicotiana tabacum) cell suspension. It also strongly decreases the formation of scopoletin in tobacco leaves infected with tobacco mosaic virus.

Molecular control of vertebrate iron metabolism: mRNA-based regulatory circuits operated by iron, nitric oxide, and oxidative stress.

As an essential nutrient and a potential toxin, iron poses an exquisite regulatory problem in biology and medicine. At the cellular level, the basic molecular framework for the regulation of iron uptake, storage, and utilization has been defined. Two cytoplasmic RNA-binding proteins, iron-regulatory protein-1 (IRP-1) and IRP-2, respond to changes in cellular iron availability and coordinate the expression of mRNAs that harbor IRP-binding sites, iron-responsive elements (IREs). Nitric oxide (NO) and oxidative stress in the form of H2O2 also signal to IRPs and thereby influence cellular iron metabolism. The recent discovery of two IRE-regulated mRNAs encoding enzymes of the mitochondrial citric acid cycle may represent the beginnings of elucidating regulatory coupling between iron and energy metabolism. In addition to providing insights into the regulation of iron metabolism and its connections with other cellular pathways, the IRE/IRP system has emerged as a prime example for the understanding of translational regulation and mRNA stability control. Finally, IRP-1 has highlighted an unexpected role for iron sulfur clusters as post-translational regulatory switches.

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.