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.

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.

Higher order spectra based support vector machine for arrhythmia classification

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. HRV analysis is an important tool to observe the heart’s ability to respond to normal regulatory impulses that affect its rhythm. 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. A computer-based arrhythmia detection system of cardiac states is very useful in diagnostics and disease management. In this work, we studied the identification of the HRV signals using features derived from HOS. These features were fed to the support vector machine (SVM) for classification. Our proposed system can classify the normal and other four classes of arrhythmia with an average accuracy of more than 85%.

The effect of topical adrenergic and anticholinergic agents on the choroidal thickness of young healthy adults

The human choroid is capable of rapidly changing its thickness in response to a variety of stimuli. However little is known about the role of the autonomic nervous system in the regulation of the thickness of the choroid. Therefore, we investigated the effect of topical parasympatholytic and sympathomimetic agents upon the choroidal thickness and ocular biometrics of young healthy adult subjects. Fourteen subjects (mean age 27.9 ± 4 years) participated in this randomized, single-masked, placebo-controlled study. Each subject had measurements of choroidal thickness (ChT) and ocular biometrics of their right eye taken before, and then 30 and 60 min following the administration of topical pharmacological agents. Three different drugs: 2% homatropine hydrobromide, 2.5% phenylephrine hydrochloride and a placebo (0.3% hydroxypropyl methylcellulose) were tested in all subjects; each on different days (at the same time of the day) in randomized order. Participants were masked to the pharmacological agent being used at each testing session. The instillation of 2% homatropine resulted in a small but significant increase in subfoveal ChT at 30 and 60 min after drug instillation (mean change 7 ± 3 μm and 14 ± 2 μm respectively; both p < 0.0001). The parafoveal choroid also exhibited a similar magnitude, significant increase in thickness with time after 2% homatropine (p < 0.001), with a mean change of 7 ± 0.3 μm and 13 ± 1 μm (in the region located 0.5 mm from the fovea center), 6 ± 1 μm and 12.5 ± 1 μm (1 mm from the fovea center) and 6 ± 2 μm and 12 ± 2 μm (1.5 mm from the fovea center) after 30 and 60 min respectively. Axial length decreased significantly 60 min after homatropine (p < 0.01). There were also significant changes in lens thickness (LT) and anterior chamber depth (ACD) (p < 0.05) associated with homatropine instillation. No significant changes in choroidal thickness, or ocular biometrics were found after 2.5% phenylephrine or placebo at any examination points (p > 0.05). In human subjects, significant increases in subfoveal and parafoveal choroidal thickness occurred after administration of 2% homatropine and this implies an involvement of the parasympathetic system in the control of choroidal thickness in humans.

The biology of the glutamatergic system and potential role in migraine

Migraine is a common genetically linked neurovascular disorder. Approximately ~12% of the Caucasian population are affected including 18% of adult women and 6% of adult men (1, 2). A notable female bias is observed in migraine prevalence studies with females affected ~3 times more than males and is credited to differences in hormone levels arising from reproductive achievements. Migraine is extremely debilitating with wide-ranging socioeconomic impact significantly affecting people's health and quality of life. A number of neurotransmitter systems have been implicated in migraine, the most studied include the serotonergic and dopaminergic systems. Extensive genetic research has been carried out to identify genetic variants that may alter the activity of a number of genes involved in synthesis and transport of neurotransmitters of these systems. The biology of the Glutamatergic system in migraine is the least studied however there is mounting evidence that its constituents could contribute to migraine. The discovery of antagonists that selectively block glutamate receptors has enabled studies on the physiologic role of glutamate, on one hand, and opened new perspectives pertaining to the potential therapeutic applications of glutamate receptor antagonists in diverse neurologic diseases. In this brief review, we discuss the biology of the Glutamatergic system in migraine outlining recent findings that support a role for altered Glutamatergic neurotransmission from biochemical and genetic studies in the manifestation of migraine and the implications of this on migraine treatment.