Evaluation of in vitro activity of herbal formula Di Er You and herb Coptis against bacteria isolated from dogs with otitis externa.

OBJECTIVE: To determine the in vitro activity of the herbal formula Di Er You (DEY) and the single-herb Coptis against bacteria cultured from dogs with otitis externa. ANIMALS: 32 client-owned dogs diagnosed with otitis externa. METHODS: A sample of otic debris from each patient was collected and plated onto a fresh Sheep's Blood Agar plate in the hospital. After bacterial growth was confirmed, 4 wells were created, numbered randomly, and treated with saline (placebo), DEY, Coptis, and Zymox Otic Enzymatic Solution with 1% Hydrocortisone (Zymox). After 24 hours of incubation, the diameter of the zone of inhibition (dZOI) of each treatment was measured and recorded, and compared among treatments. A sample of the bacterial colonies grown was submitted to an outside lab for bacterial identification. RESULTS: The mean ± SD dZOI values for saline, DEY, Coptis, and Zymox treated wells were 0.25 ± 1.41, 12.47 ± 3.92, 14.25 ± 7.12, and 3.22 ± 5.12, respectively. Post hoc multiple comparisons test revealed that (1) saline-treated wells had significantly smaller dZOI values than the other 3 groups (all P < .001), (2) Zymox treated wells had significantly smaller dZOI values than either herbal treated groups (both P < .001), and (3) DEY treated wells had significantly smaller dZOI values than those treated with Coptis (P = .0042). CLINICAL RELEVANCE: The results from this in vitro study suggested that both DEY and Coptis could be effective treatments in inhibiting the growth of bacteria in dogs with otitis externa. Prospective randomized controlled clinical trials are warranted to confirm these findings.

The serological response in dogs inoculated with canine distemper virus vaccine at the acupuncture point governing vessel-14: A randomized controlled trial.

The improvement of immunity to vaccination has historically focused on manipulation of antigen presentation rather than the host. Immune modulation by stimulating specific acupuncture points along the Meridian System has been practiced in Traditional Chinese Medicine. The purpose of this study was to quantitatively determine whether acupoint vaccination, in which vaccine is administered at an acupuncture point in dogs, has the potential to enhance the immune response. A randomized controlled trial was conducted to compare the effectiveness of acupoint vaccination versus a conventional method, based on humoral immune response in dogs given Canine Distemper Vaccine (CDV). One hundred client-owned dogs were admitted to the study with following characteristics: (1) passed a routine physical exam, (2) aged between 1 and 10 years old, (3) had no history of chronic disease, and (4) were not on immunomodulating medications. Dogs were randomly assigned to either the Acupuncture group inoculated at the acupoint Governing Vessel (GV)-14, or to the Control group inoculated conventionally at a non-acupuncture site. Mean changes from Day0 to Day14 of the response to CDV vaccination, measured by serum neutralization (SN) titers with log-transformation for reducing outlier effects, were compared between groups. No significant difference was found between groups in age, weight, or sex (all p >0.2). Both groups had significant increases of CDV SN titer post-vaccination (p < 0.001). The mean increase in Acupuncture group (0.72; SD = 0.79) was significantly greater than that of the Control group (0.36; SD = 0.67); p = 0.019. Inference on percentage of change in raw SN titer data further revealed that the effects in the Acupuncture group was significantly greater than the Control group (242% vs. 83%; p = 0.02). This study demonstrated that Acupoint vaccination at GV-14 resulted in a significantly elevated humoral immune response to CDV vaccine compared to Controls, which suggests the potential of acupoint vaccination to enhance the immune response.

Transient and permanent arterial occlusions modeling poststroke epilepsy in aging rats.

The pathophysiological mechanisms of epileptogenesis following ischemic stroke in the aged brain are not well understood, largely due to limited developments in animal modeling of poststroke epilepsy (PSE). A recent study in our laboratory (Kelly et al., 2018) using transient (3 h) unilateral middle cerebral artery (MCA) and common carotid artery (CCA) occlusion (MCA/CCAo) in 4- and 20-month-old Fischer (F344) rats resulted in epileptic seizures in both age groups; age and infarction factors independently had effects on seizure frequency. We hypothesized that permanent unilateral MCA/CCAo, a simpler model, was capable of producing results comparable to those of transient MCA/CCAo. In this study, we performed permanent MCA/CCAo and compared it to transient MCA/CCAo in 76 4-, 12-, and 20-month-old F344 rats; 41 (54%) animals experienced early, unexpected mortality. The remaining 35 (46%) animals had depth electrodes implanted. Prior to implantation of depth electrodes, 9 (26%) of these 35 animals (26%) were monitored periodically by video alone before video-EEG monitoring (17,837 h total) to assess the potential development of PSE. No EEG recordings were obtained from 12- or 20-month-old transient occlusion or 20-month-old permanent occlusion animals due to premature deaths. Five animals (14%) demonstrated epileptic seizure activity after MCA/CCAo: one 4-month-old transient occlusion animal, one 4-month-old permanent occlusion animal, and three 12-month-old permanent occlusion animals. Of these 5 animals, all but the 4-month-old permanent animal demonstrated 1-4 Hz spike-wave discharges variably associated with inactivity or frank motor arrest, and 2 animals (4- and 12-month-old permanent) demonstrated generalized ictal EEG discharges associated with grade 5 convulsive activity. All animals monitored with video-EEG demonstrated generalized 7-9 Hz spike-wave discharges, innate in F344 animals and distinct from lesion-induced epileptic seizures. Gross inspection of brains revealed variability in lesion presence and size among age groups and occlusion types. Comparison of infarct volumes of permanent MCA/CCAo animals (2.9 ± 1.29 mm3, n = 6) with those of transient MCA/CCAo animals (1.7 ± 0.31 mm3, n = 3) was not significant (p = 0.44) due to the small sample size. Timm staining revealed no evidence of mossy fiber sprouting in 7 animals tested, only one of which was known to be epileptic (4-month-old transient). These results provide evidence of focal nonconvulsive electrographic ictal discharges and behavioral seizures in both permanent and transient MCA/CCAo animals lesioned at 4- or 12-months-of-age and support the use of arterial ligation as a viable method for modeling PSE.

Poststroke epilepsy following transient unilateral middle cerebral and common carotid artery occlusion in young adult and aged F344 rats.

The mechanisms of injured brain that establish poststroke seizures and epilepsy are not well understood, largely because animal modeling has had limited development. The main objective of this study was to determine whether an arterial occlusion model of cortical stroke in young adult and aged rats was capable of generating either focal or generalized epileptic seizures within 2 months of lesioning. Four- and 20-month-old male Fischer 344 (F344) sham-operated controls and those lesioned by transient (3 h) unilateral middle cerebral artery (MCA) and common carotid artery (CCA) occlusion (MCA/CCAo) were studied by video-EEG recordings up to 2 months post-procedure. The main findings were: 1) seizures (grade 3 and above) were recorded within 2 months in both young (4-month; 0.23/h) and aged (20-month; 1.93/h) MCA/CCAo rat groups; both MCA/CCAo rat groups had more seizures recorded than the respective control groups, i.e., no seizures in young controls and 0.52/h in old controls; 2) both age and infarction independently had effects on seizure frequency; however, there was no demonstrated interaction between the two factors; and 3) there was no difference in infarct volumes comparing 4- to 20-month-old MCA/CCAo animals. In addition, all lesioned and sham-operated animals demonstrated intermittent solitary myoclonic convulsions arising out of sleep. Morbidity and mortality of animals limited the extent to which the animals could be evaluated, especially 20-month-old animals. These results suggest that transient unilateral MCA/CCAo can result in poststroke epileptic seizures in both young adult and aged F344 rats within a relatively brief period of time following lesioning.

Quantitative EEG analysis for automated detection of nonconvulsive seizures in intensive care units.

Because of increased awareness of the high prevalence of nonconvulsive seizures in critically ill patients, use of continuous EEG (cEEG) monitoring is rapidly increasing in ICUs. However, cEEG monitoring is labor intensive, and manual review and interpretation of the EEG are impractical in most ICUs. Effective methods to assist in rapid and accurate detection of nonconvulsive seizures would greatly reduce the cost of cEEG monitoring and enhance the quality of patient care. In this study, we report a preliminary investigation of a novel ICU EEG analysis and seizure detection algorithm. Twenty-four prolonged cEEG recordings were included in this study. Seizure detection sensitivity and specificity were assessed for the new algorithm and for the two commercial seizure detection software systems. The new algorithm performed with a mean sensitivity of 90.4% and a mean false detection rate of 0.066/hour. The two commercial detection products performed with low sensitivities (12.9 and 10.1%) and false detection rates of 1.036/hour and 0.013/hour, respectively. These findings suggest that the novel algorithm has potential to be the basis of clinically useful software that can assist ICU staff in timely identification of nonconvulsive seizures. This study also suggests that currently available seizure detection software does not perform sufficiently in detection of nonconvulsive seizures in critically ill patients. This article is part of a Supplemental Special Issue entitled The Future of Automated Seizure Detection and Prediction.

Effects of age and cortical infarction on EEG dynamic changes associated with spike wave discharges in F344 rats.

Rodent models of absence seizures are used to investigate the network properties and regulatory mechanisms of the seizure's generalized spike and wave discharge (SWD). As rats age, SWDs occur more frequently, suggesting aging-related changes in the regulation of the corticothalamic mechanisms generating the SWD. We hypothesized that brain resetting mechanisms - how the brain "resets" itself to a more normal functional state following a transient period of abnormal function, e.g., a SWD - are impaired in aged animals and that brain infarction would further affect these resetting mechanisms. The main objective of this study was to determine the effects of aging, infarction, and their potential interaction on the resetting of EEG dynamics assessed by quantitative EEG (qEEG) measures of linear (signal energy measured by amplitude variation; signal frequency measured by mean zero-crossings) and nonlinear (signal complexity measured by the pattern match regularity statistic and the short-term maximum Lyapunov exponent) brain EEG dynamics in 4- and 20-month-old F344 rats with and without brain infarction. The main findings of the study were: 1) dynamic resetting of both linear and nonlinear EEG characteristics occurred following SWDs; 2) animal age significantly affected the degree of dynamic resetting in all four qEEG measures: SWDs in older rats exhibited a lower degree of dynamic resetting; 3) infarction significantly affected the degree of dynamic resetting only in terms of EEG signal complexity: SWDs in infarcted rats exhibited a lower degree of dynamic resetting; and 4) in all four qEEG measures, there was no significant interaction effect between age and infarction on dynamic resetting. We conclude that recovery of the brain to its interictal state following SWDs was better in young adult animals compared with aged animals, and to a lesser degree, in age-matched controls compared with infarction-injured animal groups, suggesting possible effects of brain resetting mechanisms and/or the disruption of the epileptogenic network that triggers SWDs.

SIGNAL REGULARITY-BASED AUTOMATED SEIZURE DETECTION SYSTEM FOR SCALP EEG MONITORING.

The purpose of the present study was to build a clinically useful automated seizure detection system for scalp EEG recordings. To achieve this, a computer algorithm was designed to translate complex multichannel scalp EEG signals into several dynamical descriptors, followed by the investigations of their spatiotemporal properties that relate to the ictal (seizure) EEG patterns as well as to normal physiologic and artifact signals. This paper describes in detail this novel seizure detection algorithm and reports its performance in a large clinical dataset.

Seizure Detection Software Used to Complement the Visual Screening Process for Long-Term EEG Monitoring.

It is widely recognized that visual screening of long-term EEG recordings can be time-consuming and labor-intensive due to the large volume of patient data produced daily in most Epilepsy Monitoring Units (EMUs). As a result, seizures, especially those with only electrographic changes, are sometimes overlooked, which for some patients could result in missed information for diagnosis, an unnecessarily prolonged hospital stay, and unavailable EMU beds for others. In this report, we propose that a better solution for identifying seizures in long-term EEG recording is to combine detection results from a reliable (high sensitivity and low false detection rate) automated detection system with EEG technologists' visual screening process. Using commercially available detection software, we present case studies that demonstrate potential benefits of this method that could help improve detection rates and bring greater efficiency to the seizure identification process in long-term EEG monitoring.

An investigation of EEG dynamics in an animal model of temporal lobe epilepsy using the maximum Lyapunov exponent.

Analysis of intracranial electroencephalographic (iEEG) recordings in patients with temporal lobe epilepsy (TLE) has revealed characteristic dynamical features that distinguish the interictal, ictal, and postictal states and inter-state transitions. Experimental investigations into the mechanisms underlying these observations require the use of an animal model. A rat TLE model was used to test for differences in iEEG dynamics between well-defined states and to test specific hypotheses: 1) the short-term maximum Lyapunov exponent (STL(max)), a measure of signal order, is lowest and closest in value among cortical sites during the ictal state, and highest and most divergent during the postictal state; 2) STL(max) values estimated from the stimulated hippocampus are the lowest among all cortical sites; and 3) the transition from the interictal to ictal state is associated with a convergence in STL(max) values among cortical sites. iEEGs were recorded from bilateral frontal cortices and hippocampi. STL(max) and T-index (a measure of convergence/divergence of STL(max) between recorded brain areas) were compared among the four different periods. Statistical tests (ANOVA and multiple comparisons) revealed that ictal STL(max) was lower (p<0.05) than other periods, STL(max) values corresponding to the stimulated hippocampus were lower than those estimated from other cortical regions, and T-index values were highest during the postictal period and lowest during the ictal period. Also, the T-index values corresponding to the preictal period were lower than those during the interictal period (p<0.05). These results indicate that a rat TLE model demonstrates several important dynamical signal characteristics similar to those found in human TLE and support future use of the model to study epileptic state transitions.

Quantitative complexity analysis in multi-channel intracranial EEG recordings form epilepsy brains.

Epilepsy is a brain disorder characterized clinically by temporary but recurrent disturbances of brain function that may or may not be associated with destruction or loss of consciousness and abnormal behavior. Human brain is composed of more than 10 to the power 10 neurons, each of which receives electrical impulses known as action potentials from others neurons via synapses and sends electrical impulses via a sing output line to a similar (the axon) number of neurons. When neuronal networks are active, they produced a change in voltage potential, which can be captured by an electroencephalogram (EEG). The EEG recordings represent the time series that match up to neurological activity as a function of time. By analyzing the EEG recordings, we sought to evaluate the degree of underlining dynamical complexity prior to progression of seizure onset. Through the utilization of the dynamical measurements, it is possible to classify the state of the brain according to the underlying dynamical properties of EEG recordings. The results from two patients with temporal lobe epilepsy (TLE), the degree of complexity start converging to lower value prior to the epileptic seizures was observed from epileptic regions as well as non-epileptic regions. The dynamical measurements appear to reflect the changes of EEG's dynamical structure. We suggest that the nonlinear dynamical analysis can provide a useful information for detecting relative changes in brain dynamics, which cannot be detected by conventional linear analysis.

Predictability analysis for an automated seizure prediction algorithm.

Epileptic seizures of mesial temporal origin are preceded by changes in signal properties detectable in the intracranial EEG. A series of computer algorithms designed to detect the changes in spatiotemporal dynamics of the EEG signals and to warn of impending seizures have been developed. In this study, we evaluated the performance of a novel adaptive threshold seizure warning algorithm (ATSWA), which detects the convergence in Short-Term Maximum Lyapunov Exponent (STLmax) values among critical intracranial EEG electrode sites, as a function of different seizure warning horizons (SWHs). The ATSWA algorithm was compared to two statistical based naïve prediction algorithms (periodic and random) that do not employ EEG information. For comparison purposes, three performance indices "area above ROC curve" (AAC), "predictability power" (PP) and "fraction of time under false warnings" (FTF) were defined and the effect of SWHs on these indices was evaluated. The results demonstrate that this EEG based seizure warning method performed significantly better (P < 0.05) than both naïve prediction schemes. Our results also show that the performance indexes are dependent on the length of the SWH. These results suggest that the EEG based analysis has the potential to be a useful tool for seizure warning.

Effects of acute hippocampal stimulation on EEG dynamics.

Progressive preictal dynamical convergence and postictal divergence of dynamical EEG descriptors among brain regions has been reported in human temporal lobe epilepsy (TLE) and in a rodent model of TLE. There are also reports of anticonvulsant effects of high frequency stimulation of the hippocampus in humans. We postulate that this anticonvulsant effect is due to dynamical resetting by the electrical stimulation. The following study investigated the effects of acute hippocampal electrical stimulation on dynamical transitions in the brain of a spontaneously seizing animal model of TLE to test the hypothesis of divergence in dynamical values by electrical stimulation of the hippocampus.

Dynamical resetting of the human brain at epileptic seizures: application of nonlinear dynamics and global optimization techniques.

Epileptic seizures occur intermittently as a result of complex dynamical interactions among many regions of the brain. By applying signal processing techniques from the theory of nonlinear dynamics and global optimization to the analysis of long-term (3.6 to 12 days) continuous multichannel electroencephalographic recordings from four epileptic patients, we present evidence that epileptic seizures appear to serve as dynamical resetting mechanisms of the brain, that is the dynamically entrained brain areas before seizures disentrain faster and more frequently (p < 0.05) at epileptic seizures than any other periods. We expect these results to shed light into the mechanisms of epileptogenesis, seizure intervention and control, as well as into investigations of intermittent spatiotemporal state transitions in other complex biological and physical systems.

Adaptive epileptic seizure prediction system.

Current epileptic seizure "prediction" algorithms are generally based on the knowledge of seizure occurring time and analyze the electroencephalogram (EEG) recordings retrospectively. It is then obvious that, although these analyses provide evidence of brain activity changes prior to epileptic seizures, they cannot be applied to develop implantable devices for diagnostic and therapeutic purposes. In this paper, we describe an adaptive procedure to prospectively analyze continuous, long-term EEG recordings when only the occurring time of the first seizure is known. The algorithm is based on the convergence and divergence of short-term maximum Lyapunov exponents (STLmax) among critical electrode sites selected adaptively. A warning of an impending seizure is then issued. Global optimization techniques are applied for selecting the critical groups of electrode sites. The adaptive seizure prediction algorithm (ASPA) was tested in continuous 0.76 to 5.84 days intracranial EEG recordings from a group of five patients with refractory temporal lobe epilepsy. A fixed parameter setting applied to all cases predicted 82% of seizures with a false prediction rate of 0.16/h. Seizure warnings occurred an average of 71.7 min before ictal onset. Similar results were produced by dividing the available EEG recordings into half training and testing portions. Optimizing the parameters for individual patients improved sensitivity (84% overall) and reduced false prediction rate (0.12/h overall). These results indicate that ASPA can be applied to implantable devices for diagnostic and therapeutic purposes.