An investigation and comparison of the bioactivity of selected essential oils on human lice and house dust mites

The insecticidal potency of some essential oils suggests that they may find an application in the control of house dust mites, but current in vitro assays for mites do not appear to give consistent results. A simple, novel, mite chamber assay was therefore developed to carry out testing. Different species of insects are susceptible to different essential oil components, so we compared the relative acaricidal and pediculicidal activity of three essential oils: tea tree, lavender and lemon, because the activity of their constituents on lice ranges from highly active to virtually inactive. The most effective essential oil against both lice and mites was tea tree oil; lavender was the second most effective, and lemon oil the least, although it did show activity against mites, unlike lice. The assay proved simple and effective and gave reproducible results. (c) 2007 Elsevier B.V. All rights reserved.

The medicinal use of essential oils and their components for treating lice and mite infestations

Recent studies have demonstrated that essential oils, and in particular, pennyroyal, tea tree and anise, have potent insecticidal and acaricidal (mite-killing) activity. The individual components of essential oils are now being investigated in order to give a rational basis to discover which essential oils may prove to be the most effective all-round agents for killing headlice and their eggs, and treating scabies, and for eliminating house dust mites, a major cause of asthma.

Lethality of essential oil constituents towards the human louse, Pediculus humanus, and its eggs

Essential oils have been widely used in traditional medicine for the eradication of lice, including head lice, but due to the variability of their constitution the effects may not be reproducible. In an attempt to assess the contribution of their component monoterpenoids, a range of common individual compounds were tested in in vitro toxicity model against both human lice (Pediculus humanus, an accepted model of head lice lethality) and their eggs, at different concentrations. No detailed study into the relative potencies of their constituent terpenoids has so far been published. Adult lice were observed for lack of response to stimuli over 3 h and the LT50 calculated, and the percentage of eggs failing to hatch was used to generate ovicidal activity data. A ranking was compiled for adult lice and partially for eggs, enabling structure-activity relationships to be assessed for lethality to both, and showed that, for activity in both life-cycle stages, different structural criteria were required. (+)-Terpinen-4-ol was the most effective compound against adult lice, followed by other mono-oxygenated monocyclic compounds, whereas nerolidol was particularly lethal to eggs, but ineffective against adult lice. (c) 2006 Elsevier B.V. All rights reserved.

Upper limb tremor suppression in ADL via an orthosis incorporating a controllable double viscous beam actuator

Neuromuscular disorders affect millions of people world-wide. Upper limb tremor is a common symptom, and due to its complex aetiology it is difficult to compensate for except, in particular cases by surgical intervention or drug therapy. Wearable devices that mechanically compensate for limb tremor could benefit a considerable number of patients, but the technology to assist suffers in this way is under-developed. In this paper we propose an innovative orthosis that can dynamically suppress pathological tremor, by applying viscous damping to the affected limb in a controlled manner. The orthosis design utilises a new actuator design based on Magneto-Rheological Fluids that efficiently deliver damping action in response to the instantaneous tremor frequency and amplitude.

Prediction of Parkinson's disease tremor onset using artificial neural networks

In this paper we present the initial results using an artificial neural network to predict the onset of Parkinson's Disease tremors in a human subject. Data for the network was obtained from implanted deep brain electrodes. A tuned artificial neural network was shown to be able to identify the pattern of the onset tremor from these real time recordings.

Identification of Parkinson’s disease tremor onset using artificial neural networks

In this paper we consider the possibility of using an artificial neural network to accurately identify the onset of Parkinson’s Disease tremors in human subjects. Data for the network is obtained by means of deep brain implantation in the human brain. Results presented have been obtained from a practical study (i.e. real not simulated data) but should be regarded as initial trials to be discussed further. It can be seen that a tuned artificial neural network can act as an extremely effective predictor in these circumstances.

Empirical mode decomposition: a novel technique for the study of tremor time series

Tremor is a clinical feature characterized by oscillations of a part of the body. The detection and study of tremor is an important step in investigations seeking to explain underlying control strategies of the central nervous system under natural (or physiological) and pathological conditions. It is well established that tremorous activity is composed of deterministic and stochastic components. For this reason, the use of digital signal processing techniques (DSP) which take into account the nonlinearity and nonstationarity of such signals may bring new information into the signal analysis which is often obscured by traditional linear techniques (e.g. Fourier analysis). In this context, this paper introduces the application of the empirical mode decomposition (EMD) and Hilbert spectrum (HS), which are relatively new DSP techniques for the analysis of nonlinear and nonstationary time-series, for the study of tremor. Our results, obtained from the analysis of experimental signals collected from 31 patients with different neurological conditions, showed that the EMD could automatically decompose acquired signals into basic components, called intrinsic mode functions (IMFs), representing tremorous and voluntary activity. The identification of a physical meaning for IMFs in the context of tremor analysis suggests an alternative and new way of detecting tremorous activity. These results may be relevant for those applications requiring automatic detection of tremor. Furthermore, the energy of IMFs was visualized as a function of time and frequency by means of the HS. This analysis showed that the variation of energy of tremorous and voluntary activity could be distinguished and characterized on the HS. Such results may be relevant for those applications aiming to identify neurological disorders. In general, both the HS and EMD demonstrated to be very useful to perform objective analysis of any kind of tremor and can therefore be potentially used to perform functional assessment.

Prediction of Parkinson’s disease tremor onset using radial basis function neural networks

The possibility of using a radial basis function neural network (RBFNN) to accurately recognise and predict the onset of Parkinson’s disease tremors in human subjects is discussed in this paper. The data for training the RBFNN are obtained by means of deep brain electrodes implanted in a Parkinson disease patient’s brain. The effectiveness of a RBFNN is initially demonstrated by a real case study.

Prediction of Parkinson’s Disease tremor onset using a radial basis function neural network based on particle swarm optimization

Deep Brain Stimulation (DBS) has been successfully used throughout the world for the treatment of Parkinson's disease symptoms. To control abnormal spontaneous electrical activity in target brain areas DBS utilizes a continuous stimulation signal. This continuous power draw means that its implanted battery power source needs to be replaced every 18–24 months. To prolong the life span of the battery, a technique to accurately recognize and predict the onset of the Parkinson's disease tremors in human subjects and thus implement an on-demand stimulator is discussed here. The approach is to use a radial basis function neural network (RBFNN) based on particle swarm optimization (PSO) and principal component analysis (PCA) with Local Field Potential (LFP) data recorded via the stimulation electrodes to predict activity related to tremor onset. To test this approach, LFPs from the subthalamic nucleus (STN) obtained through deep brain electrodes implanted in a Parkinson patient are used to train the network. To validate the network's performance, electromyographic (EMG) signals from the patient's forearm are recorded in parallel with the LFPs to accurately determine occurrences of tremor, and these are compared to the performance of the network. It has been found that detection accuracies of up to 89% are possible. Performance comparisons have also been made between a conventional RBFNN and an RBFNN based on PSO which show a marginal decrease in performance but with notable reduction in computational overhead.