GABRA1 and GABRA6 gene mutations in idiopathic generalized epilepsy patients.

The GABA receptor is an important epilepsy-associated candidate gene, and has always been a focus in etiology and in the treatment of epilepsy. This study explores the genetic association between GABA receptor gene polymorphisms and epilepsy in a cohort of the Pakistani population. A case-control study was conducted on 150 patients with idiopathic generalized epilepsy (IGE) and 150 controls. Blood samples were collected, and genomic DNA was extracted and amplified using polymerase chain reaction (PCR). The amplified products were subsequently genotyped by Sanger sequencing and the results were analyzed using the chi-square test. Among the five mutational sites observed, two GABRA1 (rs2279020 and novel c.1016_1017insT) and two GABRA6 (rs3219151 and novel c.1344C>G) were found to be significantly associated with IGE. Amino acid alignment showed that a novel insertion mutation, c.1016_1017insT, in GABRA1 disrupted the reading frame and was possibly damaging, whereas c.1344C>G in GABRA6 was responsible for a synonymous mutation. Therefore, both the GABA receptor genes may play critical roles in the development of epilepsy in Pakistani patients.

Overt long QT syndrome in children presenting with seizure disorders in Pakistan.

Background and Objective: The long QT syndrome (LQTS) is a repolarization defect of heart involving potassium linked channels and it usually manifests clinically as seizures, syncope, or sudden cardiac death syndrome in children secondary to its characteristic ventricular tachy-arrhythmia like torsades de pointes. The reason behind epilepsy or seizures like activity in this disease is the sequelae of prolonged cerebral hypoperfusion secondary to the cardiac dysrhythmia. The aim of study is to look for clinical spectrum and risk factors associated with LQTS among children presenting with epilepsy, which can predict the early diagnosis of LQTS. Materials and Methods: For this observational study, 422 patients having epilepsy presenting for the first time in a 3-year period were enrolled. Demographical profile, LQTS measures, and various factors under observation were recorded. Results: Among the 422 enrolled children (M: F 1.8:1) with age ranging from 4 to 87 months (median 23 months), 8 (1.9%) children who presented with epilepsy had LQTS. Among those, mean QTc on electrocardiogram was 454 ± 31 msec and mean Schwartz score >3. Half of the patients with LQTS had deafness (P = 0.002) and 37.5% had a positive family history (P = 0.0045). Nearly a third (37.5%) presented with syncope and 87.5% patients with LQTS had no postictal drowsiness or sleep (P ≤ 0.004). Conclusions: LQTS is underestimated in children presenting with epilepsy and LQTS should be considered as an alternate diagnosis in children with recurrent seizures or syncopal attacks. The brief period of seizures with no postictal drowsiness, syncope, and strong family history are the features which may help in segregating LQTS from epilepsy.

Evaluation of an automated thresholding algorithm for the quantification of paraspinal muscle composition from MRI images.

BACKGROUND: The imaging assessment of paraspinal muscle morphology and fatty infiltration has gained considerable attention in the past decades, with reports suggesting an association between muscle degenerative changes and low back pain (LBP). To date, qualitative and quantitative approaches have been used to assess paraspinal muscle composition. Though highly reliable, manual thresholding techniques are time consuming and not always feasible in a clinical setting. The tedious and rater-dependent nature of such manual thresholding techniques provides the impetus for the development of automated or semi-automated segmentation methods. The purpose of the present study was to develop and evaluate an automated thresholding algorithm for the assessment of paraspinal muscle composition. The reliability and validity of the muscle measurements using the new automated thresholding algorithm were investigated through repeated measurements and comparison with measurements from an established, highly reliable manual thresholding technique. METHODS: Magnetic resonance images of 30 patients with LBP were randomly selected cohort of patients participating in a project on commonly diagnosed lumbar pathologies in patients attending spine surgeon clinics. A series of T2-weighted MR images were used to train the algorithm; preprocessing techniques including adaptive histogram equalization method image adjustment scheme were used to enhance the quality and contrast of the images. All muscle measurements were repeated twice using a manual thresholding technique and the novel automated thresholding algorithm, from axial T2-weigthed images, at least 5 days apart. The rater was blinded to all earlier measurements. Inter-method agreement and intra-rater reliability for each measurement method were assessed. The study did not received external funding and the authors have no disclosures. RESULTS: There was excellent agreement between the two methods with inter-method reliability coefficients (intraclass correlation coefficients) varying from 0.79 to 0.99. Bland and Altman plots further confirmed the agreement between the two methods. Intra-rater reliability and standard error of measurements were comparable between methods, with reliability coefficient varying between 0.95 and 0.99 for the manual thresholding and 0.97-0.99 for the automated algorithm. CONCLUSION: The proposed automated thresholding algorithm to assess paraspinal muscle size and composition measurements was highly reliable, with excellent agreement with the reference manual thresholding method.

Identification of CpG islands in DNA sequences using statistically optimal null filters.

: CpG dinucleotide clusters also referred to as CpG islands (CGIs) are usually located in the promoter regions of genes in a deoxyribonucleic acid (DNA) sequence. CGIs play a crucial role in gene expression and cell differentiation, as such, they are normally used as gene markers. The earlier CGI identification methods used the rich CpG dinucleotide content in CGIs, as a characteristic measure to identify the locations of CGIs. The fact, that the probability of nucleotide G following nucleotide C in a CGI is greater as compared to a non-CGI, is employed by some of the recent methods. These methods use the difference in transition probabilities between subsequent nucleotides to distinguish between a CGI from a non-CGI. These transition probabilities vary with the data being analyzed and several of them have been reported in the literature sometimes leading to contradictory results. In this article, we propose a new and efficient scheme for identification of CGIs using statistically optimal null filters. We formulate a new CGI identification characteristic to reliably and efficiently identify CGIs in a given DNA sequence which is devoid of any ambiguities. Our proposed scheme combines maximum signal-to-noise ratio and least squares optimization criteria to estimate the CGI identification characteristic in the DNA sequence. The proposed scheme is tested on a number of DNA sequences taken from human chromosomes 21 and 22, and proved to be highly reliable as well as efficient in identifying the CGIs.