Diverse biological functions of the renin-angiotensin system.

The renin-angiotensin system (RAS) has been widely known as a circulating endocrine system involved in the control of blood pressure. However, components of RAS have been found to be localized in rather unexpected sites in the body including the kidneys, brain, bone marrow, immune cells, and reproductive system. These discoveries have led to steady, growing evidence of the existence of independent tissue RAS specific to several parts of the body. It is important to understand how RAS regulates these systems for a variety of reasons: It gives a better overall picture of human physiology, helps to understand and mitigate the unintended consequences of RAS-inhibiting or activating drugs, and sets the stage for potential new therapies for a variety of ailments. This review fulfills the need for an updated overview of knowledge about local tissue RAS in several bodily systems, including their components, functions, and medical implications.

An orthodontic analysis of the smile dynamics with videography.

BACKGROUND AND OBJECTIVES: Smile is one of the most effective means by which people convey their emotions. The objective of this study was to capture, analyse and measure the parameters through videos clips for studying the dynamics of posed and unposed smile and to measure the parameters through video clips for studying the dynamics of speech. METHODOLOGY: A total of 100 subjects seeking orthodontic treatment with Angle's Class I malocclusion were included in the study. The principal investigator selected the frames for speech and a panel of five members selected the appropriate frames for posed smile and unposed smiles. Frames after videography were used for measurements. Parameters like Maximum incisor exposure, Lower lip to upper incisor, Gingival exposure, Inter-labial gap etc were measured in "mm" for posed and unposed smile frames. Categorical data was compared using McNemar's test. p â€‹< â€‹0.05 was considered significant. RESULTS: The median of maximum upper incisal exposure (p â€‹= â€‹2.2e-16), lower lip to upper incisor (p â€‹= â€‹2.422e-13A), inter-labial gap (p â€‹= â€‹2.2e-16 A), smile width (p â€‹= â€‹5.212e-16 A) and smile index were significant (p â€‹= â€‹0.0001 A). There was a significant change from not exposed to exposed gingival exposure over posed smile to unposed smile (p â€‹= â€‹0.0008). The most posterior maxillary tooth visible in the posed and unposed groups were the second premolars in 57% and 74% patients, respectively. In both the smile groups, 55% of subjects exhibited a consonant smile. CONCLUSION: It can be concluded that the information obtained from the current video graphic study can be used as a guideline for the diagnosis and as a part of comprehensive treatment planning.

Computational Causal Modeling of the Dynamic Biomarker Cascade in Alzheimer's Disease.

BACKGROUND: Alzheimer's disease (AD) is a major public health concern, and there is an urgent need to better understand its complex biology and develop effective therapies. AD progression can be tracked in patients through validated imaging and spinal fluid biomarkers of pathology and neuronal loss. We still, however, lack a coherent quantitative model that explains how these biomarkers interact and evolve over time. Such a model could potentially help identify the major drivers of disease in individual patients and simulate response to therapy prior to entry in clinical trials. A current theory of AD biomarker progression, known as the dynamic biomarker cascade model, hypothesizes AD biomarkers evolve in a sequential but temporally overlapping manner. A computational model incorporating assumptions about the underlying biology of this theory and its variations would be useful to test and refine its accuracy with longitudinal biomarker data from clinical trials. METHODS: We implemented a causal model to simulate time-dependent biomarker data under the descriptive assumptions of the dynamic biomarker cascade theory. We modeled pathologic biomarkers (beta-amyloid and tau), neuronal loss biomarkers, and cognitive impairment as nonlinear first-order ordinary differential equations (ODEs) to include amyloid-dependent and nondependent neurodegenerative cascades. We tested the feasibility of the model by adjusting its parameters to simulate three specific natural history scenarios in early-onset autosomal dominant AD and late-onset AD and determine whether computed biomarker trajectories agreed with current assumptions of AD biomarker progression. We also simulated the effects of antiamyloid therapy in late-onset AD. RESULTS: The computational model of early-onset AD demonstrated the initial appearance of amyloid, followed by biomarkers of tau and neurodegeneration and the onset of cognitive decline based on cognitive reserve, as predicted by the prior literature. Similarly, the late-onset AD computational models demonstrated the first appearance of amyloid or nonamyloid-related tauopathy, depending on the magnitude of comorbid pathology, and also closely matched the biomarker cascades predicted by the prior literature. Forward simulation of antiamyloid therapy in symptomatic late-onset AD failed to demonstrate any slowing in progression of cognitive decline, consistent with prior failed clinical trials in symptomatic patients. CONCLUSIONS: We have developed and computationally implemented a mathematical causal model of the dynamic biomarker cascade theory in AD. We demonstrate the feasibility of this model by simulating biomarker evolution and cognitive decline in early- and late-onset natural history scenarios, as well as in a treatment scenario targeted at core AD pathology. Models resulting from this causal approach can be further developed and refined using patient data from longitudinal biomarker studies and may in the future play a key role in personalizing approaches to treatment.