Pankey Institute

GPER is a membrane‐bound estrogen receptor, distinct from ERα or ERβ, and exerts genomic and non‐genomic effects. GPER's effect on the cardiovascular system has been controversial; evidence indicate it relaxes arteries, whereas other findings suggest it contracts arteries. Our objective is to better understand the dual nature of GPER. Previously, our work demonstrated that G‐1 stimulates cAMP production. We hypothesize GPER mediates relaxation response through cAMP and constriction via ERK1/2. Isometric tension studies were used to measure GPER‐mediated coronary tone response in porcine coronary arteries. Western blots were applied to detect pERK1/2 in primary cell culture of smooth muscle cells. The identity of smooth muscle cells was validated by immunohistochemistry techniques using α actin as a marker. Under adenylate cyclase inhibition by SQ22536, G‐1 stimulated phosphorylation of ERK1/2. The effect of G‐1 was blocked by G36, a GPER inhibitor. A time course of G‐1 (1 nM) demonstrated that detection of pERK1/2 peaked at 2 and 5 min, decreased at 15 min, and returned below baseline by 30 min. Similar results were found for a time course of E2 (1 nM) detecting pERK1/2, however, under no adenylate cyclase inhibition. Tension studies demonstrated that G‐1 caused concentration‐dependent relaxation of PGF2α (1 μM) precontracted, endothelium denuded coronary arteries. PD98059, a MEK 44/42 inhibitor which blocks the phosphorylation of ERK1/2, led to further relaxation than G‐1 alone. We conclude that phosphorylation of ERK1/2 lessens the coronary artery relaxation caused by GPER.Support or Funding InformationAmerican Heart Association

Occlusal splints are used to protect teeth, relieve orofacial pain, and preview the patient response to a simulated occlusal correction. This chapter outlines proper occlusal splint fabrication that employs T-Scan analysis to verify a therapeutic occlusion. The T-Scan provides objective relative occlusal force and timing data that guides the refinement of a splint's occlusal scheme. Therefore, this chapter explains adjusting an occlusal splint's contact pattern with ink ribbon followed by the T-Scan. It also addresses the controversy regarding the existence of, or lack thereof, a relationship between occlusal interferences and masticatory muscle dysfunction. The author postulates that the research studies that argue against the existence of a relationship are absent of occlusal measurement and lack a scientific basis to deny a relationship exists. Lastly, recommendations are made to include the T-Scan in Temporomandibular Disorder treatment studies with both occlusal splints and natural teeth, so that researchers might resolve this controversy for dental clinicians.

The T-Scan is an effective patient education tool for illustrating existing occlusal pathology. It presents complex occlusal information in a visual format that is easily understood. The T-Scan applies to all stages of the teaching/learning process because its recorded data forms the framework upon which a doctor/patient discussion can begin regarding the patient's occlusal disease manifestations, the potential benefit of treatments, and the risks of not undergoing corrective treatment. When used as part of an educational strategy, the T-Scan can lead the patient to accept procedures that would benefit their long-term dental health. This chapter outlines the four stages of creating optimum dental health, the steps required to perform effective teaching and learning, the differing styles of teaching and learning utilized in educational forums, and how to best employ the technique of Feature, Function, and Benefit. A case study illustrates how T-Scan data can educate a patient about their own occlusal problems.

Ventricular‐vascular interaction is very complex, because pulsatile pressures depend on vascular properties and three cardiac parameters—heart rate, ventricular compliance and contractility. There is a growing interest in modeling the interaction of the vasculature and continuous‐flow pumps to optimize the design, control, and pharmacological support of a continuous‐flow total artificial heart. The lack of pulsatility that makes the interaction easier to model, however, also introduces a new constraint: continuous‐flow pumps depend only on two parameters—pump speed and an internal resistance. The purpose of this work is to predict flows and pressures in terms of vascular and pump properties. The vasculature was characterized by parameters for arterial and venous compliances, as well as systemic and pulmonary resistances. Through linearization and simplification, we were able to develop simple algebraic formulas predicting total flow, systemic arterial and venous pressure, pulmonary arterial and venous pressure, as well as pump power. With fewer parameters than a ventricle, a pump does not have enough flexibility to adjust the limited set of pump parameters to independently control pressures while maintaining normal blood volume and vascular resistances. Our algebraic solution, however, suggests that pump and vascular parameter values can be adjusted to optimize hemodynamic homeostasis.

To characterize the interaction of the heart with the pulmonary and systemic circulations, engineers developed a minimal closed‐loop cardiovascular model, predicting cardiac output and pressures from 18 critical parameters. Despite the need for numerical solutions, it has potential to be used by clinical investigators to study the mechanical basis of altered hemodynamic status and by physiologists to study homeostatic mechanisms. To capture the complex interplay between hemodynamics and structural adaptation, we modified the standard minimal model to make structural parameters adapt to local hemodynamics. First, we linearized all pressure‐volume and pressure‐flow relationships. Then we made the simplifying assumption that end‐systolic pressure approximates mean arterial pressure. Finally, we assumed that each parameter adapts as a linear function of regional pressure. Yielding cardiac output and mean pressures, our model reproduces basic adaptive behaviors such as cardiomegaly in response to decreases in contractility and diastolic dysfunction in response to decreases in sensitivity of the adaptive process. More importantly, by judicious use of simplifying assumptions, all solutions can be expressed algebraically, which makes this fundamental model accessible to physiologists, clinical investigators, and students.