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Dr. Behnam  Farahdel  Md image

Dr. Behnam Farahdel Md

13652 Cantara St Kaiser Permanente-Business Office
Panorama City CA 91402
818 752-2809
Medical School: Other - 1998
Accepts Medicare: Yes
Participates In eRX: No
Participates In PQRS: No
Participates In EHR: No
License #: A76764
NPI: 1033229836
Taxonomy Codes:
2084N0400X

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Publications

Spectrum of DNA variants for non-syndromic deafness in a large cohort from multiple continents. - Human genetics
Hearing loss is the most common sensory deficit in humans with causative variants in over 140 genes. With few exceptions, however, the population-specific distribution for many of the identified variants/genes is unclear. Until recently, the extensive genetic and clinical heterogeneity of deafness precluded comprehensive genetic analysis. Here, using a custom capture panel (MiamiOtoGenes), we undertook a targeted sequencing of 180 genes in a multi-ethnic cohort of 342 GJB2 mutation-negative deaf probands from South Africa, Nigeria, Tunisia, Turkey, Iran, India, Guatemala, and the United States (South Florida). We detected causative DNA variants in 25 % of multiplex and 7 % of simplex families. The detection rate varied between 0 and 57 % based on ethnicity, with Guatemala and Iran at the lower and higher end of the spectrum, respectively. We detected causative variants within 27 genes without predominant recurring pathogenic variants. The most commonly implicated genes include MYO15A, SLC26A4, USH2A, MYO7A, MYO6, and TRIOBP. Overall, our study highlights the importance of family history and generation of databases for multiple ethnically discrete populations to improve our ability to detect and accurately interpret genetic variants for pathogenicity.
First report of inherited thyroxine-binding globulin deficiency in Iran caused by a known de novo mutation in SERPINA7. - Molecular genetics and metabolism reports
Thyroxine-binding globulin (TBG) is the main transporter of thyroid hormones in human serum, encoded by the gene TBG (SERPINA7), located in long arm of X-chromosome (Xq21-q22). Deficiency of SERPINA7 (serum protease inhibitor, clade A [alpha-1 antiproteinase, antitrypsin], member 7) leads to inherited TBG deficiency. Several mutations have been reported in the coding and noncoding regions of SERPINA7 in association with TGB deficiency.Automated chemiluminescence immunoassays were used to determine TSH, free and total T4 and T3 (fT4, TT4, TT3) and TBG. Direct DNA sequencing identified the mutation in SERPINA7.We present a 3 and 4/12 year old boy, born premature, who was mismanaged as hypothyroidism before referral to our center, and was diagnosed with TBG deficiency at our center with a hemizygous substitution in exon 1, position c.347T > A, leading to replacement of isoleucine for arginine in position 96 (considering the first 20 amino acid signal peptide).This known mutation, reported as the first SERPINA7 mutation in Iran, emphasizes the point that endocrinologists should pay more attention to inherited TBG to prevent unnecessary treatment.
Real-time seizure prediction using RLS filtering and interpolated histogram feature based on hybrid optimization algorithm of Bayesian classifier and Hunting search. - Computer methods and programs in biomedicine
Epileptic seizure prediction using EEG signal analysis is an important application for drug therapy and pediatric patient monitoring. Time series estimation to obtain the future samples of EEG signal has vital role for detecting seizure attack. In this paper, a novel density-based real-time seizure prediction algorithm based on a trained offline seizure detection algorithm is proposed.In the offline seizure detection procedure, after signal preprocessing, histogram-based statistical features are extracted from signal probability distribution. By defining a deterministic polynomial model on the normalized histogram, a novel syntactic feature that is named Interpolated Histogram Feature (IHF) is proposed. Moreover, with this feature, Seizure Distribution Model (SDM) as a descriptor of the seizure and non-seizure signals is presented. By using a novel hybrid optimization algorithm based on Bayesian classifier and Hunting Search (HuS) algorithm, the optimal features are selected. To detect the seizure attacks in the online mode, a Multi-Layer Perceptron (MLP) classifier is trained with the optimal features in the offline procedure. For online prediction, the enhanced Recursive Least Square (RLS) filter is applied to estimate sample-by-sample of the EEG signal. Also, a density-based signal tracking scenario is introduced to update and tune the parameters of RLS filtering algorithm.Our prediction algorithm is evaluated on 104 hours of EEG signals recorded from 23 pediatric patients. Our online signal prediction algorithm provides the accuracy rate of 86.56% and precision rate of 86.53% simultaneously using the trained MLP classifier from the offline mode. The recall rate of seizure prediction is 97.27% and the false prediction rate of 0.00215 per hour is achieved as well. Ultimately, the future samples of EEG signal are estimated, and the time of seizure signal prediction is also converged to 6.64 seconds.In our proposed real-time algorithm, by implementing a density-based signal tracking scenario, the future samples of signal with suitable time is predicted and the seizure is detected based on the optimal features from the IHF and histogram-based statistical features with acceptable performance.Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.
Health-related quality of life in patients with relapsing-remitting multiple sclerosis treated with subcutaneous interferon β-1a in Iran. - The International journal of neuroscience
Multiple sclerosis (MS) requires long-term therapy and can affect many aspects of a patient's life, including quality of life. MS patients score lower on health-related quality of life (HRQoL) measures. The efficacy of subcutaneous interferon β-1a has been extensively evaluated by using objective measures but its impact on HRQoL is currently unclear. In this observational study we evaluated HRQoL of Iranian patients with relapsing-remitting MS treated with IFN β-1a by using SF-36 and MusiQoL questionnaires.400 recruited RRMS patients were treated with HSA-free IFN β-1a for 1 year. Patients were required to fill in SF-36 and MusiQoL questionnaires at the first visit and at each follow-up visit. EDSS evaluation was performed at baseline and at each visit. Comparisons in HRQoL between visits were calculated using Cohen's d effect size. The relationship between change in EDSS score and the score of each questionnaire was calculated using Pearson correlation coefficients.383 completed the study. 239 were female. Mean (SD) age was 28.75 (±5.49). After 1 year, overall MusiQoL Index score effect size was -0.16 and SF-36 physical component and mental component showed overall effect sizes of -0.28 and -0.53 respectively. Mean (range) EDSS change was 1 (1-4). 374 were clinically stable with mean (range) EDSS change of 0.1 (-2-0.5). Increase in EDSS was linked to a decrease in both MusiQoL and SF-36.we found that, HRQoL did not change significantly over the first year of therapy. Furthermore, decreases in HRQoL were inversely correlated with increases in EDSS score.
Complex Partial Seizure as a Manifestation of Non-Ketotic Hyperglycemia: The Needle Recovered From Haystack? - Journal of clinical medicine research
We present a case of a 75-year-old gentleman with undiagnosed type 2 diabetes mellitus presenting with acute onset expressive dysphasia and right hemi-paresis with no prior history of seizure. He developed clusters of stereotypical complex partial seizures which were refractory to anti-epileptic agents. He was not known to have diabetes and his brain MRI was normal. His random blood sugar measurement on admission to hospital was 30 mmol/L with HbA1c measurement of 14.8%. His seizures terminated completely when his hyperglycemia was corrected with insulin and rehydration therapy.
Radio Frequency Ultrasound Time Series Signal Analysis to Evaluate High-intensity Focused Ultrasound Lesion Formation Status in Tissue. - Journal of medical signals and sensors
High-intensity focused ultrasound (HIFU) is a novel treatment modality used by scientists and clinicians in the recent decades. This modality has had a great and significant success as a noninvasive surgery technique applicable in tissue ablation therapy and cancer treatment. In this study, radio frequency (RF) ultrasound signals were acquired and registered in three stages of before, during, and after HIFU exposures. Different features of RF time series signals including the sum of amplitude spectrum in the four quarters of the frequency range, the slope, and intercept of the best-fit line to the entire power spectrum and the Shannon entropy were utilized to distinguish between the HIFU-induced thermal lesion and the normal tissue. We also examined the RF data, frame by frame to identify exposure effects on the formation and characteristics of a HIFU thermal lesion at different time steps throughout the treatment. The results obtained showed that the spectrum frequency quarters and the slope and intercept of the best fit line to the entire power spectrum both increased two times during the HIFU exposures. The Shannon entropy, however, decreased after the exposures. In conclusion, different characteristics of RF time series signal possess promising features that can be used to characterize ablated and nonablated tissues and to distinguish them from each other in a quasi-quantitative fashion.

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