G. R. Arunodaya

Bio: G. R. Arunodaya is an academic researcher from National Institute of Mental Health and Neurosciences. The author has contributed to research in topics: Dysautonomia & Wilson's disease. The author has an hindex of 19, co-authored 43 publications receiving 1453 citations.

Wilson’s disease: cranial MRI observations and clinical correlation

Abstract: Study of MRI changes may be useful in diagnosis, prognosis and better understanding of the pathophysiology of Wilson’s disease (WD). We aimed to describe and correlate the MRI abnormalities of the brain with clinical features in WD. MRI evaluation was carried out in 100 patients (57 males, 43 females; mean age 19.3±8.9 years) using standard protocols. All but 18 patients were on de-coppering agents. Their history, clinical manifestations and scores for severity of disease were noted. The mean duration of illness and treatment were 8.3±10.8 years and 7.5±7.1 years respectively. MRI of the brain was abnormal in all the 93 symptomatic patients. The most conspicuous observations were atrophy of the cerebrum (70%), brainstem (66%) and cerebellum (52%). Signal abnormalities were also noted: putamen (72%), caudate (61%), thalami (58%), midbrain (49%), pons (20%), cerebral white matter (25%), cortex (9%), medulla (12%) and cerebellum (10%). The characteristic T2-W globus pallidal hypointensity (34%), “Face of giant panda” sign (12%), T1-W striatal hyperintensity (6%), central pontine myelinosis (7%), and bright claustral sign (4%) were also detected. MRI changes correlated with disease severity scores (P<0.001) but did not correlate with the duration of illness. MRI changes were universal but diverse and involved almost all the structures of the brain in symptomatic patients. A fair correlation between MRI observations and various clinical features provides an explanation for the protean manifestations of the disease.

Wilson’s disease: diagnostic errors and clinical implications

Abstract: Background: Therapeutic outcome of Wilson’s disease (WD) significantly depends upon its early recognition. However, because of its rarity in community and protean manifestations, the diagnosis and treatment are often delayed. Aim: To ascertain diagnostic errors at initial evaluation in these patients. Methods: Analysis of medical records of 307 patients of WD registered over 30 years was done regarding presenting manifestations, initial diagnostic omissions, and interval between onset of symptoms to diagnosis and treatment. Results: Of the 307 patients of WD diagnostic errors by referring doctors from different specialties of health care were detected in 192 patients. These were diverse and multiple and included schizophrenia, juvenile polyarthritis, rheumatic chorea, nephrotic syndrome, metachromatic leucodystrophy, congenital myopathies, subacute sclerosing panencephalitis, neurodegenerative disease among others. The mean (SD) delay was two (three) years (range: 0.08–30 years). Some of the interventions before establishment of correct diagnosis were electroconvulsive therapy, thalamotomy, antipsychotics, and surgical correction for bony deformity. While 98 patients were referred with correct diagnosis, only 16 were given specific treatment. Conclusion: Awareness among health professionals about varied presenting features of WD and high index of suspicion may have prognostic implications.

Automatic detection of epileptic seizures in EEG using discrete wavelet transform and approximate entropy

Abstract: In this study, a new scheme was presented for detecting epileptic seizures from electro-encephalo-gram (EEG) data recorded from normal subjects and epileptic patients. The new scheme was based on approximate entropy (ApEn) and discrete wavelet transform (DWT) analysis of EEG signals. Seizure detection was accomplished in two stages. In the first stage, EEG signals were decomposed into approximation and detail coefficients using DWT. In the second stage, ApEn values of the approximation and detail coefficients were computed. Significant differences were found between the ApEn values of the epileptic and the normal EEG allowing us to detect seizures with over 96% accuracy. Without DWT as preprocessing step, it was shown that the detection rate was reduced to 73%. The analysis results depicted that during seizure activity EEG had lower ApEn values compared to normal EEG. This suggested that epileptic EEG was more predictable or less complex than the normal EEG. The data was further analyzed with surrogate data analysis methods to test for evidence of nonlinearities. It was shown that epileptic EEG had significant nonlinearity whereas normal EEG behaved similar to Gaussian linear stochastic process.

EASL Clinical Practice Guidelines: Wilson's disease

Abstract: This Clinical Practice Guideline (CPG) has been developed to assist physicians and other healthcare providers in the diagnosis and management of patients with Wilson's disease. The goal is to describe a number of generally accepted approaches for diagnosis, prevention, and treatment of Wilson's disease. Recommendations are based on a systematic literature review in the Medline (PubMed version), Embase (Dialog version), and the Cochrane Library databases using entries from 1966 to 2011. The Grades of Recommendation, Assessment, Development, and Evaluation (GRADE) system used in other EASL CPGs was used and set against the somewhat different grading system used in the AASLD guidelines (Table 1A and B). Unfortunately, there is not a single randomized controlled trial conducted in Wilson's disease which has an optimal design. Thus, it is impossible to assign a high or even a moderate quality of evidence to any of the questions dealt with in these guidelines. The evaluation is mostly based on large case series which have been reported within the last decades.

Wilson's disease and other neurological copper disorders.

Abstract: The copper metabolism disorder Wilson's disease was first defined in 1912. Wilson's disease can present with hepatic and neurological deficits, including dystonia and parkinsonism. Early-onset presentations in infancy and late-onset manifestations in adults older than 70 years of age are now well recognised. Direct genetic testing for ATP7B mutations are increasingly available to confirm the clinical diagnosis of Wilson's disease, and results from biochemical and genetic prevalence studies suggest that Wilson's disease might be much more common than previously estimated. Early diagnosis of Wilson's disease is crucial to ensure that patients can be started on adequate treatment, but uncertainty remains about the best possible choice of medication. Furthermore, Wilson's disease needs to be differentiated from other conditions that also present clinically with hepatolenticular degeneration or share biochemical abnormalities with Wilson's disease, such as reduced serum ceruloplasmin concentrations. Disordered copper metabolism is also associated with other neurological conditions, including a subtype of axonal neuropathy due to ATP7A mutations and the late-onset neurodegenerative disorders Alzheimer's disease and Parkinson's disease.