Summary:Purpose: To ascertain the prevalence and pattern of epilepsy and to characterize and quantify knowledge, attitude, and practice (KAP) toward epilepsy among the people of the state of Kerala, which is distinguished from the rest of India by a high level of literacy and health awareness of its population.Methods: We conducted a door-to-door survey covering the entire population of 238, I02 people residing in 43,68 I households in a seniiurban area of central Kerala. The screening questionnaire administered by medical social workers had a sensitivity of 100% for identifying persons with epilepsy. Neurologists examined all the individuals suspected of having epilepsy. We evaluated KAP toward epilepsy among 1,118 subjects (439 males and 679 females; mean age, 33.3 years; age range, 15-85 years) from households without epilepsy in the study area.Results: Through a three-phased survey, we ascertained 1,175 cases (6 16 males and 559 females) with active epilepsy, providing a crude point prevalence ratio of 4.9 cases per 1,000 people and an age-adjusted prevalence ratio of 4.7 cases per 1,000 population. The highest age-specific prevalence rate of 6.5 per 1,000 occurred in the 10-to 19-year-old age group. Sex-specific prevalence rates did not significantly differ. The proportion of generalized and localization-related epilepsies was 58.8% and 30.6%, respectively. Ninety-nine percent of the KAP respondents had read or heard about epilepsy. Thirty-one percent and 27% thought epilepsy was a hereditary disorder and a form of insanity, respectively. About 40% of the respondents felt that individuals with epilepsy could not be properly educated or employed. Eleven percent would object to their children having contact with epileptic children.Conclusions: The prevalence and pattern of epilepsy in central Kerala, South India, do not differ from that of developed countries. Although the awareness of epilepsy among the people of Kerala was comparable to that of developed countries, the attitudes were much more negative. The need for educating the people of Kerala on epilepsy and for incorporating an adequate knowledge of epilepsy in the school curricula cannot be overemphasized. Key Words: Epidemiology-Epilepsy-Knowledge, attitude and practice-KAP-Prevalence.Population-based studies, in contrast to hospital-based studies, provide a relatively unbiased picture of various aspects of the disease in question. Prevalence is a measure of the disease burden in the community, which has to be considered when planning the health needs at local, regional, and national levels. Knowing the degree of awareness and attitude toward a disease in a society is a necessary first step in eliminating discrimination against persons with that disease.

UI is quite common in Egypt and prevalence rates are higher when compared to other reports.

A total of 437 patients with epilepsy were identified, 30.7% of whom (n=134/437) were uncontrolled, with a prevalence of 2.1/1000. A total of 52.2% of uncontrolled patients (n=70/134) were inappropriately treated, while 47.8% (n=64/134) were compliant with appropriate treatments. Video monitoring EEG of compliant uncontrolled patients demonstrated that 78.1% patients (n=50/64) had definite epilepsy, while 21.9% (n=14/64) had psychogenic non-epileptic seizures (PNES). A logistic regression analysis revealed that status epilepticus, focal seizures, and mixed seizure types were risk factors for intractability.

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The perfect alignment between three or more sequences of Protein, RNA or DNA is a very difficult task in bioinformatics. There are many techniques for alignment multiple sequences. Many techniques maximize speed and do not concern with the accuracy of the resulting alignment. Likewise, many techniques maximize accuracy and do not concern with the speed. Reducing memory and execution time requirements and increasing the accuracy of multiple sequence alignment on large-scale datasets are the vital goal of any technique. The paper introduces the comparative analysis of the most well-known programs (CLUSTAL-OMEGA, MAFFT, BROBCONS, KALIGN, RETALIGN, and MUSCLE). For programs' testing and evaluating, benchmark protein datasets are used. Both the execution time and alignment quality are two important metrics. The obtained results show that no single MSA tool can always achieve the best alignment for all datasets.