The major urinary metabolites were quantified in 24-h collections by capillary gas chromatography. A single examiner measured weights, heights, and skinfold thicknesses.

Results: mTOR inhibitor drugs The median (IQR) age of patients was 24 y (21-26 y), and the duration of AN was 4.0 y (3.3-8.0 y). Body mass index (BMI; in kg/m(2)) increased from 12.8 (12.7-13.1) to 18.6 (18.0-19.6) after refeeding

(P < 0.0001). Steroid values [median pre-, post-refeeding (P value)] were as follows: androgen metabolites [472, 1017 mu g/24 h (0.93)], cortisol metabolites [1960, 3912 mu g/24 h (0.60)], and ratios of androsterone (5 alpha)/etiocholanolone (5 beta) [0.28, 0.63 (<0.001)], 5 alpha-/5 beta-tetrahydrocortisol [0.20, 0.48 (0.02)], tetrahydrocortisols/tetrahydrocortisone [0.87, 0.61 (0.09)], 20-hydroxy-/20-oxocortisol metabolites [0.29, 0.47 (0.01)], and 20 alpha-/20 beta-reduced cortisol metabolites [1.18, 1.89 (>= 1.00)]. BMI change was positively correlated with 5 alpha-/5 beta-tetrahydrocortisol (r = 0.95, P < 0.001). Before refeeding, the following metabolites were lower in patients than in control subjects: androsterone, 5 alpha-tetrahydrocortisol, NVP-HSP990 nmr alpha-cortolone and alpha-cortol, 5 alpha-/5 beta-tetrahydrocortisol, androsterone/etiocholanolone,

and 20-hydroxy/20-oxocortisol (all P < 0.05). After refeeding, all steroid metabolites in patients were at concentrations that were comparable with those in control subjects.

Conclusions: Significant changes in urine steroid-metabolite excretion occurred upon starvation, which were reversed upon refeeding. For cortisol, there were decreases in 5 alpha-/5 beta-tetrahydrocortisol and 20-hydroxy-/20-oxometabolites; for androgen, there was a decrease in androsterone/etiocholanolone. Am J Clin Nutr

2011;93:911-7.”
“Background: Despite an emerging consensus that the DSM-IV diagnostic criteria for cannabis abuse and dependence are best represented by a single underlying liability, it remains unknown if latent class or hybrid models Cytoskeletal Signaling inhibitor can better explain the data.

Method: Using structured interviews, 7316 adult male and female twins provided complete data on DSM-IV symptoms of cannabis abuse and dependence. Our aim was to derive a parsimonious, best-fitting cannabis use disorder (CUD) phenotype based on DSM-III-R/IV criteria by comparing an array of psychometric models (latent factor analysis, latent class analysis and factor mixture modeling) using full information maximum likelihood ordinal data methods in Mx.

Results: We found little evidence to support population heterogeneity since neither latent class nor hybrid factor mixture models provided a consistently good fit to the data.