Biochemical aspirin resistance in stroke patients: a cross-sectional single centre study

Background: Aspirin use is known to reduce the recurrence of stroke. However, the clinical response to aspirin has been mixed. The rate of stroke recurrence whilst on aspirin treatment is still unacceptably high. A plausible explanation for this may be resistance to the effects of aspirin. The causes of aspirin resistance are manifold and multi-factorial. We conducted a study to investigate the prevalence rate of biochemical aspirin resistance in a cohort of aspirin-naïve stroke patients. We also sought to determine the inherent factors that may predispose towards the development of aspirin resistance. Method: This was a cross-sectional, observational study conducted on patients admitted to our centre with an acute stroke who were aspirin-naïve. The diagnosis of an acute stroke was confirmed by clinical history and brain imagi ng. Fifty consecutive patients were prospectively enrolled. Socio demographic data were collected and baseline blood investigations were performed. Patients were tested for biochemical aspirin resistance using Multiplate platelet analyser (Dynabyte, Munich, Germany) after 5 doses of aspirin, corresponding to a total dose of 900 mg. Results: The median age of patients was 65.5 years and 54 % of patients were female. There were 11 smokers; of these 10 were male. Twenty-six (52 %) patients were Chinese, 21 (41%) were Malay and 3 (6.0 %) were Indian. Aspirin resistance was present in 14 % of our patients.There was an inverse relationship between the presence of aspirin resistance and plasma HDL levels (r = -0.394; p = 0.005). There was no relationship observed between aspirin resistance and total cholesterol, triglycerides, LDL, HbA1c, ALT, ALP, urea and creatinine levels. There were no significant differences in demographic profiles or smoking status between the aspirin resistant and non-aspirin resistant groups. We did not find any link between ethnicity and aspirin resistance. Conclusions: Our results indicate that a lower HDL leve l is associated with biochemical aspi-rin resistance. This may increase platelet aggregation and consequently increase the risk of a recurrent stroke. The clinical implications for aspirin resistance are far reaching. Any evidence that correctable factors may negatively influence the action of aspirin warrants further investigation. The prevalence rate of biochemical aspirin resistance in our study is comparable to the findings in other studies performed in an Asian population. Further research is required to determine how our findings translate into clinical aspirin resistance and stroke recurrence

Biochemical aspirin resistance in stroke patients - a cross-sectional single centre study

Aspirin use is known to reduce the recurrence of stroke. However, the clinical response to aspirin has been mixed. The rate of stroke recurrence whilst on aspirin treatment is still unacceptably high. A plausible explanation for this may be resistance to the effects of aspirin. The causes of aspirin resistance are manifold and multi-factorial. We conducted a study to investigate the prevalence rate of biochemical aspirin resistance in a cohort of aspirin-naïve stroke patients. We also sought to determine the inherent factors that may predispose towards the development of aspirin resistance. Method: This was a cross-sectional, observational study conducted on patients admitted to our centre with an acute stroke who were aspirin-naïve. The diagnosis of an acute stroke was confirmed by clinical history and brain imaging. Fifty consecutive patients were prospectively enrolled. Socio-demographic data were collected and baseline blood investigations were performed. Patients were tested for biochemical aspirin resistance using Multiplate® platelet analyser (Dynabyte, Munich, Germany) after 5 doses of aspirin, corresponding to a total dose of 900 mg. Results: The median age of patients was 65.5 years and 54 % of patients were female. There were 11 smokers; of these 10 were male. Twenty-six (52 %) patients were Chinese, 21 (41 %) were Malay and 3 (6.0 %) were Indian. Aspirin resistance was present in 14 % of our patients. There was an inverse relationship between the presence of aspirin resistance and plasma HDL levels (r = -0.394; p = 0.005). There was no relationship observed between aspirin resistance and total cholesterol, triglycerides, LDL, HbA1c, ALT, ALP, urea and creatinine levels. There were no significant differences in demographic profiles or smoking status between the aspirin resistant and non-aspirin resistant groups. We did not find any link between ethnicity and aspirin resistance. Conclusions: Our results indicate that a lower HDL level is associated with biochemical aspirin resistance. This may increase platelet aggregation and consequently increase the risk of a recurrent stroke. The clinical implications for aspirin resistance are far reaching. Any evidence that correctable factors may negatively influence the action of aspirin warrants further investigation. The prevalence rate of biochemical aspirin resistance in our study is comparable to the findings in other studies performed in an Asian population. Further research is required to determine how our findings translate into clinical aspirin resistance and stroke recurrence

First Reported Case of Neuroleptospirosis Complicated With Anton's Syndrome

Leptospirosis is a spirochetal zoonotic disease with a wide clinical spectrum, often underdiagnosed especially when presented as an acute neurological manifestation. We report a case of a 24-year-old man with serologically positive leptospirosis, who presented with altered sensorium, seizures and subsequently developed cortical blindness. His brain MRI revealed bilateral occipital and later parietal lobe cerebritis

Labrune’s Syndrome Presenting With Stereotypy-Like Movements and Psychosis : A Case Report and Review

Labrune’s syndrome, or leukoencephalopathy with brain calcifications and cysts (LCC), is a rare genetic syndrome with variable neurological presentations. Psychiatric manifestations and involuntary movements are uncommonly reported. We report the case of a 19-year-old female, initially diagnosed with Fahr’s syndrome, who presented to us with acute psychosis, abnormal behavior and involuntary movements. Her brain computed tomography showed extensive bilateral intracranial calcifications without cysts. Genetic testing detected two compound heterozygous variants, NR_033294.1 n.*9C>T and n.24C>T, in the SNORD118 gene, confirming the diagnosis of LCC. We discuss the expanding phenotypic spectrum of LCC and provide a literature review on the current diagnosis and management of this rare syndrome

Impulse control behaviours in a Malaysian Parkinson’s disease population

Background: Impulse control behaviours are repetitive and excessive activities that may be sub-syndromal and not fulfill the criteria for impulse control disorder. These activities have potential to negatively impact on the daily lives of sufferers. We conducted a study to investigate the prevalence of impulse control behaviors and its associated features in Parkinson’s disease in our population. Methods: We conducted a prospective cross-sectional study on consecutive patients attending neurology clinic. Inclusion criteria include idiopathic Parkinson’s disease patients with Hoehn & Yahr stage I-IV. Eighty patients were enrolled and screened for impulse control behaviors using the Questionnaire for Impulsive-Compulsive Disorder for Parkinson’s disease (QUIP). Results: Prevalence of impulse control behaviors among our cohort was 11.3%; the features significantly associated with it were higher level of education (p=0.02), advanced stage of disease (p=0.03) and higher levodopa dosage (p= 0.01). The commonest impulse control behavior in our cohort was compulsive medication use (7.5%), followed by hobbyism (6.3%), hypersexuality (5%), compulsive buying (3.75%), punding (2.5%), walkabout (2.5%), compulsive eating (1.25%) and pathological gambling (1.3%). Conclusions: There is an association between impulse control behavior and higher levodopa dosage in a study on patients with Parkinson’s disease in Malaysia. We also found a low prevalence of pathological gambling as compared to studies performed in the West

A Classification Model of EEG Signals Based on RNN-LSTM for Diagnosing Focal and Generalized Epilepsy

Epilepsy is a chronic neurological disorder caused by abnormal neuronal activity that is diagnosed visually by analyzing electroencephalography (EEG) signals. Background: Surgical operations are the only option for epilepsy treatment when patients are refractory to treatment, which highlights the role of classifying focal and generalized epilepsy syndrome. Therefore, developing a model to be used for diagnosing focal and generalized epilepsy automatically is important. Methods: A classification model based on longitudinal bipolar montage (LB), discrete wavelet transform (DWT), feature extraction techniques, and statistical analysis in feature selection for RNN combined with long short-term memory (LSTM) is proposed in this work for identifying epilepsy. Initially, normal and epileptic LB channels were decomposed into three levels, and 15 various features were extracted. The selected features were extracted from each segment of the signals and fed into LSTM for the classification approach. Results: The proposed algorithm achieved a 96.1% accuracy, a 96.8% sensitivity, and a 97.4% specificity in distinguishing normal subjects from subjects with epilepsy. This optimal model was used to analyze the channels of subjects with focal and generalized epilepsy for diagnosing purposes, relying on statistical parameters. Conclusions: The proposed approach is promising, as it can be used to detect epilepsy with satisfactory classification performance and diagnose focal and generalized epilepsy

The role of brain signal processing and neuronal modelling in epilepsy – a review

Epilepsy is a neurological disorder characterized by recurrent seizures due to spontaneous changes of chemical synaptic coupling within the central nervous system. Numerous studies have been done in order to increase the level of cognition in epilepsy. Electroencephalography (EEG) as a non-invasive technique with the ability of presenting potentials on the head surface due to neural activity is widely used in epilepsy studies. The signals have been analyzed by brain signal processing techniques which mainly are categorized in feature extraction, feature dimensionally reduction and classification. The limitations such as inapproachability to intracranial in vivo and few seizure occurrences during sampling led to investigate on a model of signals and neural activity. This paper reviews the fundamentals of epilepsy toward using brain signal processing and neuronal modeling in three major branches; detection, prediction and source localization. It resulted a rare number of investigations on seizure epilepsy prediction due to the lack of long-term epilepsy EEG recording ending to the seizure. Subsequently, this review paper suggests to consider brain signal processing techniques in sub-branches of epilepsy detection; status, type, markers and surface localization, whilst it plays a remarkable role targeting to the source localization by neuronal modeling

Brain Dynamics in Response to Intermittent Photic Stimulation in Epilepsy

Purpose: Routine electroencephalogram (EEG) examinations uses intermittent photic stimulation (IPS) for investigation of the visual cortex EEG responses during resting time. This study aimed to discover brain dynamics effects of IPS in 28 generalized epilepsy patients and 28 healthy subjects. Methodology: Signal processing techniques were used in feature extraction by Fast Fourier transform (FFT), feature dimension reduction by t-test (significant, p<0.05) and classification by nearest neighbor (k-NN) and support vector machine (SVM). Results: The epilepsy group had higher level of amplitude in Theta waves compared to the healthy group. The Alpha waves in the resting time and for all IPS frequencies were observed with lower level of amplitude in healthy subjects compared to the epilepsy group. The k-NN (85.7% accuracy) classifier had the best discrimination of epilepsy from healthy group for resting time versus during IPS at 18 Hz IPS. However, using SVM (75.0% accuracy), IPS at 25 Hz yielded the best discrimination between resting time versus IPS in epilepsy where the healthy group responded similarly in all IPS frequencies. Conclusions: This study shows that IPS at 18 Hz and 25 Hz are suitable IPS frequencies for k-NN and SVM, respectively, to discriminate non-photosensitive generalized epilepsy from normal subjects during interictal

Assessment of a 16-Channel Ambulatory Dry Electrode EEG for Remote Monitoring

Ambulatory EEGs began emerging in the healthcare industry over the years, setting a new norm for long-term monitoring services. The present devices in the market are neither meant for remote monitoring due to their technical complexity nor for meeting clinical setting needs in epilepsy patient monitoring. In this paper, we propose an ambulatory EEG device, OptiEEG, that has low setup complexity, for the remote EEG monitoring of epilepsy patients. OptiEEG’s signal quality was compared with a gold standard clinical device, Natus. The experiment between OptiEEG and Natus included three different tests: eye open/close (EOC); hyperventilation (HV); and photic stimulation (PS). Statistical and wavelet analysis of retrieved data were presented when evaluating the performance of OptiEEG. The SNR and PSNR of OptiEEG were slightly lower than Natus, but within an acceptable bound. The standard deviations of MSE for both devices were almost in a similar range for the three tests. The frequency band energy analysis is consistent between the two devices. A rhythmic slowdown of theta and delta was observed in HV, whereas photic driving was observed during PS in both devices. The results validated the performance of OptiEEG as an acceptable EEG device for remote monitoring away from clinical environments