Drugs and the autonomic nervous system

The nervous systems can initially be divided up into the central and peripheral nervous systems. The central nervous system is the brain and spinal cord and drugs that modify the central nervous system are considered as a subject in systematic pharmacology (therapeutics) section. Everything neural, other that the central nervous system, can be considered peripheral nervous systems. The peripheral nervous systems can be divided into the autonomic(involuntary) nervous system, which is the system that performs without your conscious help, and the somatic or voluntary nervous system, which you can consciously control(Figure 7.1). In addition the autonomic nervous system is divided into the sympathetic and parasympathetic nervous systems...

A generative nervous system for the planet

Generative systems are now being proposed for addressing major ecological problems. The Complex Urban Systems Project (CUSP) founded in 2008 at the Queensland University of Technology, emphasises the ecological significance of the generative global networking of urban environments. It argues that the natural planetary systems for balancing global ecology are no longer able to respond sufficiently rapidly to the ecological damage caused by humankind and by dense urban conurbations in particular as evidenced by impacts such as climate change. The proposal of this research project is to provide a high speed generative nervous system for the planet by connecting major cities globally to interact directly with natural ecosystems to engender rapid ecological response. This would be achieved by active interactions of the global urban network with the natural ecosystem in the ecological principle of entropy. The key goal is to achieve ecologically positive cities by activating self-organising cities capable of full integration into natural eco-systems and to netowork the cities globally to provide the planet with a nervous system.

Drugs and the somatic nervous system

Drugs and the somatic nervous system 8.1 The somatic nervous system 8.2 Anticholinesterases 8.3 Neuromuscular blockers 8.4 Botox

EdU, a new thymidine analogue for labelling proliferating cells in the nervous system

The standard method of labelling proliferating cells uses the thymidine analogue, bromodeoxyuridine (BrdU), which incorporates into the DNA during S-phase of the cell cycle. A disadvantage of this method is that the immunochemical processing requires pre-treatment of the cells and tissue with heat or acid to reveal the antigen. This pre-treatment reduces reliability of the method and degrades the specimen, reducing the ability for multiple immuno-fluorescence labelling at high resolution. We report here the utility of a novel thymidine analogue, ethynyl deoxyuridine (EdU), detected with a fluorescent azide via the “click” chemistry reaction (the Huisgen 1,3-dipolar cycloaddition reaction of an organic azide to a terminal acetylene). The detection of EdU requires no heat or acid treatment and the incorporated EdU is covalently conjugated to fluorescent probe. The reaction is quick and compatible with fluorescence immunochemistry and other fluorescent probes. We show here that EdU is non-toxic in vitro and in vivo and can be used in place of BrdU to label cells during neurogenesis and the progeny identified at least 30 days later. The fluorescent labelling of EdU, markedly improves the detection of proliferating cells and allows concurrent high resolution fluorescence immunochemistry.

Pathogens penetrating the central nervous system: Infection pathways and the cellular and molecular mechanisms of invasion

The brain is well protected against microbial invasion by cellular barriers, such as the blood-brain barrier (BBB) and the blood-cerebrospinal fluid barrier (BCSFB). In addition, cells within the central nervous system (CNS) are capable of producing an immune response against invading pathogens. Nonetheless, a range of pathogenic microbes make their way to the CNS, and the resulting infections can cause significant morbidity and mortality. Bacteria, amoebae, fungi, and viruses are capable of CNS invasion, with the latter using axonal transport as a common route of infection. In this review, we compare the mechanisms by which bacterial pathogens reach the CNS and infect the brain. In particular, we focus on recent data regarding mechanisms of bacterial translocation from the nasal mucosa to the brain, which represents a little explored pathway of bacterial invasion but has been proposed as being particularly important in explaining how infection with Burkholderia pseudomallei can result in melioidosis encephalomyelitis.

The role of periosteum : a neglected aspect of osteoporosis

Most current studies on the pathogenesis of osteoporosis emphasize the bone metabolic activities occurring on endosteal surfaces, whereas the periosteal aspect is somewhat neglected. In terms of bone physiology, periosteum plays a determining role in de novo cortical bone formation and cortical bone expansion through periosteum is the most efficient way of increasing bone strength against fractures. Despite the important role of periosteum in the pathogenesis and treatment of osteoporosis, little is known about the structural and cellular features of periosteum in osteoporosis. This chapter will focus on the major changes occurring in the periosteum of osteoporosis and possible implications of these changes in the pathogenesis of osteoporosis. The changes identified in the periosteum of osteoporosis are mainly located in the metaphyseal compartment, which include: (a) much thicker and more cellular cambial layer; (b) increased number of TRAP (tartrate resistant acid phosphatase), VEGF (vascular endothelial growth factor) cells and the degree of vascularization; and (c) enhanced expression of sympathetic nerve fibers. The structural and cellular changes of osteoporotic periosteum indicate that periosteum plays an important role in the cortical bone resorption in metaphyseal areas and this pathological process may be regulated by the sympathetic nervous system.

Structural and cellular features in metaphyseal and diaphyseal periosteum of osteoporotic rats

Despite the important physiological role of periosteum in the pathogenesis and treatment of osteoporosis, little is known about the structural and cellular characteristics of periosteum in osteoporosis. To study the structural and cellular differences in both diaphyseal and metaphyseal periosteum of osteoporotic rats, samples from the right femur of osteoporotic and normal female Lewis rats were collected and tissue sections were stained with hematoxylin and eosin, antibodies or staining kit against tartrate resistant acid phosphatase (TRAP), alkaline phosphatase (ALP), vascular endothelial growth factor (VEGF), von Willebrand (vWF), tyrosine hydroxylase (TH) and calcitonin gene-related peptide (CGRP). The results showed that the osteoporotic rats had much thicker and more cellular cambial layer of metaphyseal periosteum compared with other periosteal areas and normal rats (P\0.001). The number of TRAP? osteoclasts in bone resorption pits, VEGF? cells and the degree of vascularization were found to be greater in the cambial layer of metaphyseal periosteum of osteoporotic rats (P\0.05), while no significant difference was detected in the number of ALP? cells between the two groups. Sympathetic nerve fibers identified by TH staining were predominantly located in the cambial layer of metaphyseal periosteum of osteoporotic rats. No obvious difference in the expression of CGRP between the two groups was found. In conclusion, periosteum may play an important role in the cortical bone resorption in osteoporotic rats and this pathological process may be regulated by the sympathetic nervous system.

Ryanodine receptor phosphorylation in human heart failure

Heart failure is a complex disorder, characterized by activation of the sympathetic nervous system, leading to dysregulated Ca2+ homeostasis in cardiac myocytes and tissue remodeling. In a variety of diseases, cardiac malfunction is associated with aberrant fluxes of Ca2+ across both the surface membrane and the internal Ca2+ store, the sarcoplasmic reticulum (SR). One prominent hypothesis residues is that in heart failure, the activity of the ryanodine receptor (RyR2) Ca2+ release channel in the SR is increased due to excess phosphorylation and that this contributes to excess SR Ca2+ leak in diastole, reduced SR Ca2+ load and decreased contractility (Huke & Bers, 2008). There is controversy over which serine residues in RyR2 are hyperphosphorylated in animal models of heart failure and whether this is via the CaMKII or the PKA-linked signaling pathway. S2808, S2814 and S2030 in RyR2 have been variously claimed to be hyperphosphorylated. Our aim was to examine the degree of phosphorylation of these residues in RyR2 from failing human hearts. The use of human tissue was approved by the Human Research Ethics Committee, The Prince Charles Hospital, EC28114. Left ventricular tissue samples were obtained from an explanted heart of a patient with endstage heart failure (Emery Dreifuss Muscular Dystrophy with cardiomyopathy) and non-failing tissue was from a patient with cystic fibrosis undergoing heart-lung transplantation with no history of heart disease. SR vesicles were prepared as described by Laver et al. (1995) and examined with SDS-Page and Western Blot. Transferred proteins were probed with antibodies to detect total protein phosphorylation, phosphorylation of RyR2 serine residues S2808, S2814, S2030 and for the key proteins calsequestrin, triadin, junctin and FKBP12.6. To avoid membrane stripping artifact, each membrane was exposed to one phosphorylation-specific antibody and signal densities quantified using Bio-Rad Quantity One software. We found no distinguishable difference between failing and healthy hearts in the protein expression levels of RyR2, triadin, junctin or calsequestrin. We found an expected upregulation of total RyR2 phosphorylation in the failing heart sample, compared to a matched amount of RyR2 (quantified using densiometry) in healthy heart. Probing with antibodies detecting only the phosphorylated form of the specific RyR2 residues showed that the increase in total RyR2 phosphorylation in the failing heart was due to hyperphosphorylation of S2808 and S2814. We found that S2030 phosphorylation levels were unchanged in human heart failure. Interestingly, we found that S2030 has a basal level of phosphorylation in the healthy human heart, different from the absence of basal phosphorylation recently reported in rodent heart (Huke & Bers, 2008). Finally, preliminary results indicate that less FKBP 12.6 is associated with RyR2 in the failing heart, possibly as a consequence of PKA activation. In conclusion, residues S2808 and S2814 are hyperphosphorylated in human heart failure, presumably due to upregulation of the CaMKII and/or PKA signaling pathway as a result of chronic activation of the sympathetic nervous system. Such changes in RyR2 phosphorylation are believed to contribute to the leaky RyR2 phenotype associated with heart failure, which increases the incidence of arrhythmia and contributes to the severely impaired contractile performance of the failing heart. Huke S & Bers DM. (2008). Ryanodine receptor phosphorylation at serine 2030, 2808 and 2814 in rat cardiomyocytes. Biochemical and Biophysical Research Communications 376, 80-85. Laver DR, Roden LD, Ahern GP, Eager KR, Junankar PR & Dulhunty AF. (1995). Cytoplasmic Ca2+ inhibits the ryanodine receptor from cardiac muscle. Journal of Membrane Biology 147, 7-22. Proceedings

Cardiac state diagnosis using higher order spectra of heart rate variability

Heart rate variability (HRV) refers to the regulation of the sinoatrial node, the natural pacemaker of the heart, by the sympathetic and parasympathetic branches of the autonomic nervous system. Heart rate variability analysis is an important tool to observe the heart's ability to respond to normal regulatory impulses that affect its rhythm. A computer-based intelligent system for analysis of cardiac states is very useful in diagnostics and disease management. Like many bio-signals, HRV signals are nonlinear in nature. Higher order spectral analysis (HOS) is known to be a good tool for the analysis of nonlinear systems and provides good noise immunity. In this work, we studied the HOS of the HRV signals of normal heartbeat and seven classes of arrhythmia. We present some general characteristics for each of these classes of HRV signals in the bispectrum and bicoherence plots. We also extracted features from the HOS and performed an analysis of variance (ANOVA) test. The results are very promising for cardiac arrhythmia classification with a number of features yielding a p-value < 0.02 in the ANOVA test.

Analysis of cardiac and epileptic signals using higher order spectra

The theory of nonlinear dyamic systems provides some new methods to handle complex systems. Chaos theory offers new concepts, algorithms and methods for processing, enhancing and analyzing the measured signals. In recent years, researchers are applying the concepts from this theory to bio-signal analysis. In this work, the complex dynamics of the bio-signals such as electrocardiogram (ECG) and electroencephalogram (EEG) are analyzed using the tools of nonlinear systems theory. In the modern industrialized countries every year several hundred thousands of people die due to sudden cardiac death. The Electrocardiogram (ECG) is an important biosignal representing the sum total of millions of cardiac cell depolarization potentials. It contains important insight into the state of health and nature of the disease afflicting the heart. Heart rate variability (HRV) refers to the regulation of the sinoatrial node, the natural pacemaker of the heart by the sympathetic and parasympathetic branches of the autonomic nervous system. Heart rate variability analysis is an important tool to observe the heart's ability to respond to normal regulatory impulses that affect its rhythm. A computerbased intelligent system for analysis of cardiac states is very useful in diagnostics and disease management. Like many bio-signals, HRV signals are non-linear in nature. Higher order spectral analysis (HOS) is known to be a good tool for the analysis of non-linear systems and provides good noise immunity. In this work, we studied the HOS of the HRV signals of normal heartbeat and four classes of arrhythmia. This thesis presents some general characteristics for each of these classes of HRV signals in the bispectrum and bicoherence plots. Several features were extracted from the HOS and subjected an Analysis of Variance (ANOVA) test. The results are very promising for cardiac arrhythmia classification with a number of features yielding a p-value < 0.02 in the ANOVA test. An automated intelligent system for the identification of cardiac health is very useful in healthcare technology. In this work, seven features were extracted from the heart rate signals using HOS and fed to a support vector machine (SVM) for classification. The performance evaluation protocol in this thesis uses 330 subjects consisting of five different kinds of cardiac disease conditions. The classifier achieved a sensitivity of 90% and a specificity of 89%. This system is ready to run on larger data sets. In EEG analysis, the search for hidden information for identification of seizures has a long history. Epilepsy is a pathological condition characterized by spontaneous and unforeseeable occurrence of seizures, during which the perception or behavior of patients is disturbed. An automatic early detection of the seizure onsets would help the patients and observers to take appropriate precautions. Various methods have been proposed to predict the onset of seizures based on EEG recordings. The use of nonlinear features motivated by the higher order spectra (HOS) has been reported to be a promising approach to differentiate between normal, background (pre-ictal) and epileptic EEG signals. In this work, these features are used to train both a Gaussian mixture model (GMM) classifier and a Support Vector Machine (SVM) classifier. Results show that the classifiers were able to achieve 93.11% and 92.67% classification accuracy, respectively, with selected HOS based features. About 2 hours of EEG recordings from 10 patients were used in this study. This thesis introduces unique bispectrum and bicoherence plots for various cardiac conditions and for normal, background and epileptic EEG signals. These plots reveal distinct patterns. The patterns are useful for visual interpretation by those without a deep understanding of spectral analysis such as medical practitioners. It includes original contributions in extracting features from HRV and EEG signals using HOS and entropy, in analyzing the statistical properties of such features on real data and in automated classification using these features with GMM and SVM classifiers.

Cardiac health diagnosis using higher order spectra and support vector machine

The Electrocardiogram (ECG) is an important bio-signal representing the sum total of millions of cardiac cell depolarization potentials. It contains important insight into the state of health and nature of the disease afflicting the heart. Heart rate variability (HRV) refers to the regulation of the sinoatrial node, the natural pacemaker of the heart by the sympathetic and parasympathetic branches of the autonomic nervous system. The HRV signal can be used as a base signal to observe the heart's functioning. These signals are non-linear and non-stationary in nature. So, higher order spectral (HOS) analysis, which is more suitable for non-linear systems and is robust to noise, was used. An automated intelligent system for the identification of cardiac health is very useful in healthcare technology. In this work, we have extracted seven features from the heart rate signals using HOS and fed them to a support vector machine (SVM) for classification. Our performance evaluation protocol uses 330 subjects consisting of five different kinds of cardiac disease conditions. We demonstrate a sensitivity of 90% for the classifier with a specificity of 87.93%. Our system is ready to run on larger data sets.