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Biomedical subjects

D Rodbard

Publications and source records attributed to D Rodbard.

At least 91 records · Page 5Linked to original sources
PubMed1981

Biosynthesis of proteoglycans by rat granuloma cells cultured in vitro: modulation by gonadotropins, steroid hormones, prostaglandins, and a cyclic nucleotide.

Rat ovarian granulosa cells were isolated from immature female rats 48 h after stimulation with 5 IU PMS gonadotropin and then maintained in culture. The effects of ovine (o)FSH, oLH, hCG, testosterone, 17 beta-estradiol, progesterone, prostaglandins E1, E2, F1 alpha, F2 alpha, N,O'-dibutyryl cAMP, and theophylline on proteoglycan synthesis by the granulosa cells in vitro were examined using [35S]sulfate as a precursor. oFSH, oLH, hCG, prostaglandins E1 and E2, N,O'-dibutyryl cAMP, theophylline and testosterone stimulated the production of 35S-labeled proteoglycans compared to control cultured. 17 beta-Estradiol, progesterone, and the prostaglandins F1 alpha, and F2 alpha, showed no stimulation. Two major species of proteoglycan are synthesized and secreted into the medium: 1) a population with large hydrodynamic size (Kav = 0.30 on Sepharose CL-2B); and 2) a population with relatively small size (Kav = 0.60 on Sepharose CL-2B). The stimulatory effect of the hormones was accounted for entirely by a net increase in synthesis of the smaller proteoglycan population. The hormones exert their stimulatory effects on proteoglycan synthesis at concentrations similar to those required for other in vitro biological responses of granulosa cells.

Animals↗
PubMed1981

Transport of steroid hormones: binding of 21 endogenous steroids to both testosterone-binding globulin and corticosteroid-binding globulin in human plasma.

This report describes a model of steroid transport in human plasma. The binding affinities of 21 endogenous steroids for both testosterone-binding globulin (TeBG) and corticosteroid-binding globulin (CBG) were determined under equilibrium conditions using a solid phase method at physiological pH and temperature. A computer program was used to solve the complex equilibrium interactions between these steroids and TeBG, CBG, and albumin. In this manner, we calculated the plasma distribution of each steroid into TeBG-bound, CBG-bound, albumin-bound, and unbound fractions in normal men, normal women during both the follicular and luteal phases of the ovarian cycle, and women during the third trimester of a normal pregnancy.

Adrenal Cortex Hormones↗
PubMed1981

Peripheral responses to thyroid hormone before and after L-thyroxine therapy in patients with subclinical hypothyroidism.

Twenty patients with serum levels of T4 and T3 within the normal range but with elevated serum concentrations of TSH were evaluated before and after treatment with L-T4. This therapy increased serum T4 (5.5 +/- 1.1 to 8.8 +/- 1.8 microgram/dl) and T3 (116 +/- 20 to 137 +/- 28 ng/dl) levels. Cardiac systolic time intervals (STI) were significantly (P less than 0.01) reduced by this therapy. The preejection period (123 +/- 18 to 114 +/- 14 msec; n = 12), the change in preejection period (+17 +/- 17 to +6 +/- 15 msec; n = 12), the ratio of preejection period to left ventricular ejection time (0.412 +/- 0.068 to 0.357 +/- 0.063 msec; n = 12), and the interval from the Q wave of the electrocardiogram to the pulse wave arrival time at the brachial artery (224 +/- 10 to 200 +/- 13 msec; n = 10) were consistently reduced. Cardiac STI were significantly correlated with serum TSH and T4 levels, but not with serum T3 levels. Normalization of serum TSH levels was associated with changes in QKd measurements even in those patients with minimal elevations in serum TSH. These studies demonstrate that patients having the combination of elevated TSH but T4 and T3 levels in the normal range have alterations in STI which can be changed significantly by L-T4 in doses which normalize TSH secretion. These data suggest that such patients have a mild form of primary hypothyroidism.

Adult↗
PubMed1980

Quantitative characterization of hormone receptors.

Most workers characterize steroid (and other hormone) receptors by graphical analysis of Scatchard plots or by simple linear regression. Unfortunately, these methods are suboptimal from a statistical point of view. The Scatchard plot, B/F vs. [Bound], does not satisfy the assumptions underlying simple linear regression: both variables are subject to error, and these errors are intimately interdependent. Accordingly, nether B/F nor [Bound] is an appropriate independent variable. Furthermore, both variables (B/F and [Bound] show non-uniformity of variance. Thus, even when the Scatchard plot is liner, one should estimate the binding parameters (affinity, K, and binding capacity, R) by means of weighted nonlinear least-squares regression, using the Total ligand concentration as the independent variable, and either B/T or [Bound] as the dependent variable. In the case of a nonlinear Scatchard plot, one should also use weighted nonlinear least-squares curve fitting to estimate the K and R values for the high and low affinity classes of sites. Allowing the computer program to provide the best estimate of the nonspecific or nonsaturable binding is also desirable. The program should provide estimates of the standard errors and/or 95% confidence limits for the estimated parameters, and the joint 95% confidence limits for K and R. One should routinely attempt to fit several models of varying degrees of complexity (e.g., 1,2, or 3 classes of sites), provide estimates of the goodness-of-fit for each, and then select the best model by statistical criteria. Sometimes, we encounter Scatchard plots that are obviously nonlinear but provide insufficient information within any one experiment to permit reliable characterization of two or more classes of sites. In this case, we may employ any of several alternative techniques, including 1) use of the " limiting slopes" technique to obtain approximate estimates of parameters; 2) use of a Continuous Affinity Distribution, with consideration of only the receptors with an affinity above an arbitrarily selected cutoff value of k; 3) use of a Discrete Affinity Distribution, by assigning values to the affinities (K19 K2) based on prior information, and then estimating the binding capacities; 4) pooling information over several specimens within an assay or over several assays by use of normalizing or scaling factors. The best estimates of these scaling factors can be obtained by the use of a general least-squares method for pooling data from different specimens or experiments. A series of computer programs to perform these analyses has been developed. They have been applied successfully to analysis of steroid receptors in specimens from breast carcinoma.

Breast Neoplasms↗
PubMed1980

Radioimmunoassay data processing with a small programmable calculator.

We have developed three programs for data processing for radioimmunoassays RIA implemented on a small, inexpensive, programmable calculator (Texas Instrument Co., Model 59): 1) The first program performs a weighted logit-log regression for analysis of the RIA standard curve, and provides several descriptive statistics: slope, intercept, ED50 (and their corresponding standard errors), residual variance, and correlation coefficient. This program also provides: dose interpolation for unknowns; an estimate of the precision (percent coefficient of variation) for each result; corrections for variable sample volumes or recoveries; and the mean and standard error of the mean for samples analyzed in replicate. 2) The second program uses the "four parameter logistic model" to describe the dose responses curve and perform dose interpolation. This is the equivalent to the use of the logit-log method, with provision for adjustment for the position of the 0% and 100% response. As such, it is more flexible and versatile than the logit-log method. 3) A third program is used for routine Within-Lab Quality Control: it calculates within-assay and between-assay precision, utilizing an analysis of variance (ANOVA) with a components of variance estimation. These programs incorporate the most important features of programs previously developed in this laboratory for an IBM 370 or a DECsystem-10 computer, and demonstrate the availability of adequate statistical analyses to laboratories without access to large centralized computer facilities.

Analysis of Variance↗
PubMed1980

Limited adrenal reserve in paracoccidioidomycosis: cortisol and aldosterone responses to 1-24 ACTH.

Adrenal function in twenty-three patients with paracoccidioidomycosis (South American Blastomycosis) has been assessed by measuring the response to adrenocortical stimulation with 1-24 ACTH. Two patients with overt Addison's disease showed very low basal levels and the complete absence of an increase in either cortisol or aldosterone secretion. Six patients showed probable diminished adrenal reserve in terms of cortisol and three patients showed diminished reserve in terms of aldosterone function. These findings indicate an incidence of significant hypoadrenalism in 44% of hospitalized patients with disseminated paracoccidioidomycosis.

Adolescent↗
PubMed1980

Kinetic analysis of cooperative ligand binding: applications to the insulin receptor.

We have developed a mathematical model for the kinetics of cooperative binding, assuming a linear relationship between the dissociation rate constant kd' and receptor occupancy, and have extensively explored its properties. Under ideal conditions changes in receptor occupancy by the hormone are coupled to changes in its dissociation rate. However, the correlation between receptor occupancy and the apparent dissociation rate can be obscured or even reversed in the presence of minor nonspecific binding, incomplete separation of bound and free ligand, and/or when there is a short lag period between the true onset of dissociation and measurement of "initial" binding. The present model and computer simulation studies suggest ways to reconcile otherwise conflicting observations. These results provide a basis for improved experimental design, to test the ability of this class of cooperative models to describe hormone binding systems.

Allosteric Regulation↗