Prostaglandin E1 binding and adenylate cyclase activation in normal and transformed fibroblasts

The binding of [³H]prostaglandin E₁ to membranes of clones of normal rat kidney fibroblasts (NRK cells) has been measured. Cell lines that responded to prostaglandin E₁, such as NRK and NRK transformed with Schmitt-Ruppin strain of Rous sarcoma virus (SR-NRK cells), have a high affinity prostaglandin E₁ binding site. Murine-sarcoma-virus-transformed lines of NRK cells are unresponsive to prostaglandin E₁ and have reduced prostaglandin E₁ binding. Exposure of cells to prostaglandin E₁ results both in decreases prostaglandin E₁ responsiveness and reduced prostaglandin E₁ binding.Activation of adenylate cyclase is correlated to binding of prostaglandin E₁ to receptors in both NRK and SR-NRK cell membranes. Mathematical models suggest that GTP decreases the affinity of hormone for its receptor while increasing the catalytic efficiency of adenylate cyclase, and that aggregates of occupied receptors may play an important role in the activation of adenylate cyclase.     

Effect of hyperthermia on the plasma concentrations of prolactin and cortisol in the fetal lamb and pregnant ewe during late gestation

Exposing pregnant sheep to an ambient temperature of 43 +/- 1 degree C for 8 h was associated with a 1-1.5 degrees C increase of maternal and fetal core temperatures, and a 11-fold and 3-5 fold increase in maternal and fetal plasma prolactin concentrations respectively. Hyperthermia did not change maternal or fetal plasma cortisol concentrations. We conclude that maternal and fetal hyperprolactinaemia may occur in late pregnancy during hyperthermic conditions and that the increase in fetal plasma prolactin is due to either increased secretion or decreased metabolic clearance of prolactin in the feto-placental compartment.     

EPR spectroscopic detection of free radical outflow from an isolated muscle bed in exercising humans.

There is no direct evidence to support the contention that contracting skeletal muscle and/or associated vasculature generates free radicals in exercising humans. The unique combination of isolated quadriceps exercise and the measurement of femoral arterial and venous free radical concentrations with the use of electron paramagnetic resonance (EPR) spectroscopy enabled this assumption to be tested in seven healthy men. Application of ex vivo spin trapping using alpha-phenyl-tert-butylnitrone (PBN) resulted in the detection of oxygen- or carbon-centered free radicals (a(N) = 1.38 +/- 0.01 mT and a(beta)(H) = 0.17 +/-0.01 mT, where a(N) and a(beta)(H) are the nitrogen and beta-hydrogen coupling constants, respectively) with consistently higher EPR signal intensities of the PBN spin adduct observed in the venous compared with the arterial circulation (P < 0.05). Incremental exercise further increased the venoarterial intensity difference [85 +/- 58 arbitrary units (AU) at 24 +/- 6% maximal work rate (WR(max)) vs. 387 +/- 214 AU at 69 +/- 7% WR(max); P < 0.05]. When combined with measured changes in femoral venous blood flow (Q), this resulted in a net adduct outflow of 130 +/- 118 and 1,146 +/- 582 AU/min (P < 0.05), which was positively associated with leg oxygen uptake (r(2) = 0.47, P < 0.05) and Q (r(2) = 0.47, P < 0.05). These results provide the first evidence for oxygen- or carbon-centered free radical outflow from an active muscle bed in humans.     

A real-time computational model for estimating kinematics of ankle ligaments

Background: An accurate assessment of ankle ligament kinematics is crucial in understanding the injury mechanisms and can help to improve the treatment of an injured ankle, especially when used in conjunction with robot-assisted therapy. A number of computational models have been developed and validated for assessing the kinematics of ankle ligaments. However, few of them can do real-time assessment to allow for an input into robotic rehabilitation programs. Method: An ankle computational model was proposed and validated to quantify the kinematics of ankle ligaments as the foot moves in real-time. This model consists of three bone segments with three rotational degrees of freedom (DOFs) and 12 ankle ligaments. This model uses inputs for three position variables that can be measured from sensors in many ankle robotic devices that detect postures within the foot–ankle environment and outputs the kinematics of ankle ligaments. Validation of this model in terms of ligament length and strain was conducted by comparing it with published data on cadaver anatomy and magnetic resonance imaging. Results: The model based on ligament lengths and strains is in concurrence with those from the published studies but is sensitive to ligament attachment positions. Conclusions: This ankle computational model has the potential to be used in robot-assisted therapy for real-time assessment of ligament kinematics. The results provide information regarding the quantification of kinematics associated with ankle ligaments related to the disability level and can be used for optimizing the robotic training trajectory.