No abstract

A complementary DNA clone has been isolated that encodes a coxsackievirus and adenovirus receptor (CAR). When transfected with CAR complementary DNA, nonpermissive hamster cells became susceptible to coxsackie B virus attachment and infection. Furthermore, consistent with previous studies demonstrating that adenovirus infection depends on attachment of a viral fiber to the target cell, CAR-transfected hamster cells bound adenovirus in a fiber-dependent fashion and showed a 100-fold increase in susceptibility to virus-mediated gene transfer. Identification of CAR as a receptor for these two unrelated and structurally distinct viral pathogens is important for understanding viral pathogenesis and has implications for therapeutic gene delivery with adenovirus vectors.

Differences in body composition have often been examined in conjunction with measurements of energy expenditure in men and women. Numerous studies during the past decade examined the relationship between resting energy expenditure (REE) and the components of a two-compartment model of composition, namely the fat-free mass (FFM) and the fat mass (FM). A synthetic review of these studies confirms a primary correlation between REE and FFM in adults over a broad range of body weights. A generalized prediction equation is proposed as REE = 370 +/- 21.6 x FFM. This equation explains 65-90% of the variation in REE. Several studies suggest, further, that FFM predicts total daily energy expenditure (TDEE) equally well. An independent contribution by FM to the prediction of either REE or TDEE is not supported for the general population, perhaps reflecting the relative constancy of the absolute FM in nonobese individuals. In the subset of obese women, FM may be a significant predictor.

A multiple regression analysis of several factors influencing basal metabolic rate (BMR) was performed using data for 223 subjects from the classic metabolism studies published by Harris and Benedict in 1919. These data had previously been analyzed by Kleiber using metabolic body size, the three-fourths power of body mass, as a predictor of BMR. His prediction equations were separated by sex and each contained components for age and height. Factors in the present analysis included sex, age, height, body mass, and estimated lean body mass (LBM). Lean body mass was found to be the single predictor of BMR. A best estimate prediction equation: BMR(cal/day) = 500 + 22 (LBM) is proposed. The previously presumed influences of sex and age are shown to add little to this estimation.