Efficacy of Chloral Hydrate-Hydroxyzine and Chloral Hydrate-Midazolam in Pediatric Magnetic Resonance Imaging Sedation

How to Cite This Article: Fallah R, Fadavi N, Behdad Sh, Fallah Tafti M. Efficacy of Chloral Hydrate-Hydroxyzine and Chloral Hydrate-Midazolam in Pediatric Magnetic Resonance Imaging Sedation. Iran J Child Neurol. 2014 Spring 8(2):11-17.ObjectiveMagnetic resonance imaging (MRI) is a useful diagnostic tool for the evaluation of congenital or acquired brain lesions. But, in all of less than 8-year-old children, pharmacological agents and procedural sedation should be used to inducemotionless conditions for imaging studies. The purpose of this study was to compare the efficacy and safety of combination of chloral hydrate-hydroxyzine (CH+H) and chloral hydrate-midazolam (CH+M) in pediatric MRI sedation.Materials & MethodsIn a parallel single-blinded randomized clinical trial, sixty 1-7-year-old children who underwent brain MRI, were randomly assigned to receive chloral hydrate in a minimum dosage of 40 mg/kg in combination with either 2 mg/kg ofhydroxyzine or 0.5 mg/kg of midazolam. The primary outcomes were efficacy of adequate sedation (Ramsay sedation score of five) and completion of MRI examination. The secondary outcome was clinical side-effects.ResultsTwenty-eight girls (46.7%) and 32 boys (53.3%) with the mean age of 2.72±1.58 years were studied. Adequate sedation and completion of MRI were achieved in 76.7% of CH+H group. Mild and transient clinical side-effects, such as vomiting of one child in each group and agitation in 2 (6.6 %) children of CH+M group, were also seen. The adverse events were more frequent in CH+M group.ConclusionCombinations of chloral hydrate-hydroxyzine and chloral hydrate-midazolam were effective in pediatric MRI sedation; however, chloral hydrate-hydroxyzine was safer. References1. Lehman RK, Schor NF. Neurologic Evaluation. In:Kliegman RM, Stanton BF, Schor NF, St. Geme JW,Behrman RE, editors. Nelson Textbook of Pediatrics.19th ed. Philadelphia: Saunders; 2011. p. 2013-7.2. Sahyoun C, Krauss B. Clinical implications of pharmacokinetics and pharmacodynamics of procedural sedation agents in children. Curr Opin Pediatr 2012;24:225-32.3. Mason KP, Prescilla R, Fontaine PJ, Zurakowski D. Pediatric CT sedation: comparison of dexmedetomidine and pentobarbital. AJR Am J Roentgenol 2011;196(2):W194-8.4. Schulte-Uentrop L, Goepfert MS. Anaesthesia or sedation for MRI in children. Curr Opin Anaesthesiol 2010;23(4):513-7.5. Freeman JM. The risks of sedation for electroencephalograms: data at last. Pediatrics 2001; 108(1):178.6. Cortellazzi P, Lamperti M, Minati L, Falcone C, Pantaleoni C, Caldiroli D. Sedation of neurologically impaired children undergoing MRI: a sequential approach. Paediatr Anaesth 2007;17(7):630-6.7. Haselkorn T, Whittemore AS, Udaltsova N, Friedman GD. Short-term chloral hydrate administration and cancer in humans. Drug Saf 2006; 29(1):67-77.8. Costa LR, Costa PS, Brasileiro SV, Bendo CB, Viegas CM, Paiva SM. Post-Discharge Adverse Events following Pediatric Sedation with High Doses of Oral Medication. J Pediatr 2012;160(5):807-13.9. da Costa LR, da Costa PS, Lima AR. A randomized double-blinded trial of chloral hydrate with or without hydroxyzine versus placebo for pediatric dental sedation. Braz Dent J 2007;18(4):334-40.10. Klein EJ, Brown JC, Kobayashi A, Osincup D, Seidel K. A randomized clinical trial comparing oral, aerosolized intranasal, and aerosolized buccal midazolam. Ann Emerg Med 2011;58(4):323-9.11. Johnson E, Briskie D, Majewski R, Edwards S, Reynolds P. The physiologic and behavioral effects of oral and intranasal midazolam in pediatric dental patients. Pediatr Dent 2010;32(3):229-38.12. Wetzel RC. Anesthesia, Perioperative Care, and Sedation. In: Kliegman RM, Stanton BF, Schor NF, St. Geme JW, Behrman RE, editors. Nelson Textbook of Pediatrics. 19th ed. Philadelphia: Saunders; 2011. p. 359-60.13. Cote CJ, Wilson S. Guidelines for monitoring and management of pediatric patients during and after sedation for diagnostic and therapeutic procedures: an update. Pediatrics 2006;118(6):2587-602.14. Ramsay MA, Savege TM, Simpson BR, Goodwin R. Controlled sedation with alphaxalone-alphadolone. Br Med J 1974;2(5920):656-9.15. Fallah R, Jalili Sh, Golestan M, Akhavan Karbasi S, Jarahzadeh MH. Efficacy of chloral hydrate and promethazine for sedation during electroencephalography in children; a randomised clinical trial. Iran J Pediatr 2013;23(1):27-31.16. Fallah R, Nakhaei MH, Behdad S, Moghaddam RN, Shamszadeh A. Oral chloral hydrate vs. intranasal midazolam for sedation during computerized tomography. Indian Pediatr 2013;50(2):233-5.17. Mason KP, Sanborn P, Zurakowski D, Karian VE, Connor L, Fontaine PJ, et al. Superiority of pentobarbital versus chloral hydrate for sedation in infants during imaging. Radiology 2004;230(2):537-42.18. Chowdhury J, Vargas KG. Comparison of chloral hydrate, meperidine, and hydroxyzine to midazolam regimens for oral sedation of pediatric dental patients. Pediatr Dent 2005;27(3):191-7.19. Roach CL, Husain N, Zabinsky J, Welch E, Garg R.Moderate sedation for echocardiography of preschoolers. Pediatr Cardiol 2010;31(4):469-73.20. Avalos-Arenas V, Moyao-García D, Nava-Ocampo AA, Zayas-Carranza RE, Fragoso-Ríos R. Is chloral hydrate/ hydroxyzine a good option for paediatric dental outpatient sedation? Curr Med Res Opin 1998;14(4):219-26.21. Torres-Pérez J, Tapia-García I, Rosales-Berber MA, Hernández-Sierra JF, Pozos-Guillén Ade J. Comparison of three conscious sedation regimens for pediatric dental patients. J Clin Pediatr Dent 2007;31:183-6.22. Lee YJ, Kim do K, Kwak YH, Kim HB, Park JH, Jung JH. Analysis of the appropriate age and weight for pediatric patient sedation for magnetic resonance imaging. Am J Emerg Med 2012;30(7):1189-95.23. Kannikeswaran N, Sethuraman U, Sivaswamy L, Chen X, Mahajan PV. Children with and without developmental disabilities: sedation medication requirements and adverse events related to sedation. Pediatr Emerg Care 2012;28(10):1036-40.24. Fávero ML, Ponce FA, Pio MR, Tabith Junior A, Carvalho e Silva FL. Chloral hydrate to study auditory brainstem response. Braz J Otorhinolaryngol 2010;76(4):433-6. [Article in English, Portuguese]25. Heistein LC, Ramaciotti C, Scott WA, Coursey M, Sheeran PW, Lemler MS. Chloral hydrate sedation for pediatric echocardiography: physiologic responses, adverse events, and risk factors. Pediatrics 2006;117(3):e434-41

Results of Non-contrast Brain Computed Tomography Scans of 1-18 Year Old Epileptic Children

How to Cite this Article: Fallah R, Nafisi Moghadam R, Fallah Tafti M, Salmani Nodoushan M. Results of Noncontrast Brain Computed Tomography Scans of 1-18 Year Old Epileptic Children. Iran J Child Neurol 2012; 6(3): 33-38.ObjectiveThe advent of computed tomography (CT) scan revolutionized the diagnosticevaluation of neurologic patients. The aim of this study was to evaluate brain CTresults of epileptic children.Materials & MethodsIn a descriptive cross-sectional study, noncontrast brain CT scan of 150 consecutive1-18 year old epileptic children whom were referred to pediatric neurology clinic ofShahid Sadoughi University of Medical Sciences, from May 2008 to October 2010 inYazd-Iran, evaluated.ResultsSixty two girls and 88 boys with mean age of 6.6 ± 4.3 years were evaluated.In 38 (25.3 %) children, seizure onset age was under one year and 38 others hadabnormal mental / developmental status. Fifty three children (35.3 %) and 97 (64.7%)had partial and generalized seizures, respectively. Partial seizures were more prevalentin children with seizure onset in < 1 year [41.5% (22/53) vs. 16.5% (16/97)] Result of CT was normal in 74 % (n=111). Among the patients with abnormalresults, 18(46%) had brain atrophy, 10 (25.6%) structural CNS dysgenesia, six (15.4%)intracranial calcification, three (7.8%) hydrocephaly and two had (5.2%) brain tumor.Abnormal brain CT was more prevalent in patients with seizure onset in less than oneyear of age [60.5% (23 of 38) vs. 14.3% (16 of 112), p = 0.003], partial epilepsy [51% (27of 53) vs. 12% (12/97)], and abnormal developmental status [ 81.5% (31 of 38) vs.7% (8of 112]. Mean age of seizure onset in epileptic children with abnormal brain CT scanwas less (M ± SD:1/17 ± 0.6 years versus 4.02±1.9 years).ConclusionBrain CT scan might be considered in evaluation of epileptic children with partialseizures, seizure onset in less than one year of age or neurodevelopmental delay.ReferencesJagoda A, Gupta K. The emergency department evaluationof the adult patient who presents with a first-time seizure.Emerg Med Clin North Am 2011; 29(1):41-9.Camfield PR, Camfield CS. Pediatric epilepsy. In:Swaiman KF, Ashwal S, Ferriero D M. Pediatric Neurology: principles & practice. (4th ed). Philadelphia:Mosby Elsevier, 2006.P. 983.Gaillard WD, Chiron C, Cross JH, Harvey AS, Kuzniecky R, Hertz-Pannier L, Vezina LG; ILAE, Committee for Neuroimaging, Subcommittee for Pediatric. Guidelines for imaging infants and children with recent-onset epilepsy. Epilepsia 2009; 50(9):2147-53.Soto-Ares G, Jissendi Tchofo P, Szurhaj W, Trehan G,Leclerc X. Management of patients after a first seizure. J Neuroradiol 2004; 31(4):281-8. (in French)Hirtz D, Ashwal S, Berg A, et al. Practice parameter:evaluating a first nonfebrile seizure in children: report of the quality standards subcommittee of the American Academy of Neurology, the Child Neurology Society, and the American Epilepsy Society. Neurology 2000; 55:616– 623.Kuzniecky RI. Neuroimaging in pediatric epilepsy.Epilepsia 1996; 37, Suppl 1:S10-21.Adamsbaum C, Rolland Y, Husson B. Pediatric neuroimaging emergencies. J Neuroradiol 2004;31(4):272-80. (in French)Proposal for revised classification of epilepsies and epileptic syndromes. Commission on Classification and Terminology of the International League Against Epilepsy. Epilepsia 1989; 30:389–399.Hsieh DT, Chang T, Tsuchida TN, et al. New-onset afebrile seizures in infants: role of neuroimaging.Neurology 2010;12:74(2):150-6.Khodapanahandeh F, Hadizadeh H. Neuroimaging inchildren with first afebrile seizures: to order or not toorder? Arch Iran Med 2006;9(2):156-8.Berg AT, Testa FM, Levy SR, Shinnar S. Neuroimaging in children with newly diagnosed epilepsy: A community based study. Pediatrics 2000; 106(3):527-32.Maytal J, Krauss JM, Novak G, Nagelberg J, Patel M. Therole of brain computed tomography in evaluating children with new onset of seizures in the emergency department.Epilepsia 2000; 41(8):950-4.Kumar R, Navjivan S, Kohli N, Sharma B. Clinicalcorrelates of CT abnormality in generalized childhood epilepsy in India. J Trop Pediatr 1997;43(4):199-203.Aguilar-Rebolledo F, Sosa-Villalobos R, del Castillo- Troncoso C. Should computed axial tomography of theskull be done in all pediatric patients with epilepsy?. BolMed Hosp Infant Mex 1992;49(12):845-50. (in Spanish)Obajimi MO, Fatunde OJ, Ogunseyinde AO, OmigbodunOO, Atalabi OM, Joel RU. Computed tomography and childhood seizure disorder in Ibadan. West 2004;23(2):167-72.Wammanda RD, Anyiam JO, Hamidu AU, Chom ND,Eseigbe EE. Computerized tomography of children with seizure disorders. Niger J Clin Pract 2009;12(1):25-8.Korff C, Nordli DR Jr. Do generalized tonic-clonic seizures in infancy exist? Neurology 2005, 65:17501753.Vanderver A, Chang T, Kennedy C, et al. MR Imaging forthe diagnosis of cerebral dysplasia in new onset seizuresin children. Ann Neurol 2003,54:S114

Novel and heteroplasmic mutations in mitochondrial tRNA genes in Brugada syndrome

  Background: Brugada syndrome (BrS) is a rare cardiac arrhythmia characterized by sudden death associated with electrocardiogram patterns characterized by incomplete right bundle-branch block and ST-segment elevations in the anterior precordial leads. This syndrome predominantly is seen in younger males with structurally normal hearts. Mitochondrial variants particularly mt-tRNA mutations, are hot spots that lead to cardiological disorders. Previous studies have shown that mutations in mitochondrial tRNA genes play an important causal or modifying role in BrS. The present study aims to evaluate the involvement of mitochondrial tRNA genes in arrhythmogenic BrS. Methods: In this study, 40 Iranian patients were investigated for the presence of the mutations in 6 mitochondrial tRNA genes (tRNA Ile, Met, Gln, Asn, Ala and Trp) by PCR-SSCP analysis. Results: There were 4 mutations in tRNA genes, that for first time, were found in BrS patients and these mutations were not in controls. Three of them were heteroplasmic and located in tRNAGln (T4377A) and tRNAMet (G4407A and C4456T) which were assessed as pathogenic mutations. A homo­plasmic variant (5580T > C) in tRNATrp gene was located within the junction region between tRNATrp and tRNAAla genes. This mutation may disturb the processing of mt-tRNATrp. Conclusions: The results of this study suggest that mutations in mitochondrial tRNA genes might lead to deficiencies in translational process of critical proteins of the respiratory chain and potentially lead to BrS in Iranian subjects. (Cardiol J 2018; 25, 1: 113–119

Recurrent Interval Type-2 Fuzzy Wavelet Neural Network with Stable Learning Algorithm: Application to Model-Based Predictive Control

Fuzzy neural networks, with suitable learning strategy, have been demonstrated as an effective tool for online data modeling. However, it is a challenging task to construct a model to ensure its quality and stability for non-stationary dynamic systems with some uncertainties. To solve this problem, this paper presents a novel identification model based on recurrent interval type-2 fuzzy wavelet neural network (RIT2FWNN) with new learning algorithm. The model benefits from both advantages of recurrent and wavelet neural networks such as use of temporal data and fast convergence properties. The proposed antecedent and consequent parameters update rules are derived using sliding-mode-control-theory. To evaluate the proposed fuzzy model, it is utilized to design a nonlinear model-based predictive controller and is applied for the synchronization of fractional-order time-delay chaotic systems. Using Lyapunov stability analysis, it is shown that all update rules of the parameters are uniformly ultimately bounded. The adaptation laws obtained in this method are very simple and have closed forms. Some stability conditions are derived to prove learning dynamics and asymptotic stability of the network by using an appropriate Lyapunov function. The efficacy and performance of the proposed method is verified by simulation examples

Churg-Strauss syndrome following cessation of allergic desensitization vaccination: a case report

<p>Abstract</p> <p>Introduction</p> <p>Churg-Strauss syndrome is a vasculitis of medium to small sized vessels. Diagnosis is mainly clinical with findings of asthma, eosinophilia, rhinosinusitis and signs of vasculitis in major organs.</p> <p>Case presentation</p> <p>We present a case of a 19-year-old Persian male who developed signs and symptoms of this syndrome related to hyposensitization treatments for allergy control.</p> <p>Conclusions</p> <p>No unifying etiology for the disease can be presented as it is found associated with environmental factors, medications, infections and is even considered a variant of asthma with predisposition to vasculitic involvement. Therefore, it is important to recognize this disease and be aware of underdiagnosis because of emphasis on pathologic evidence. Here, we present a case of allergic desensitization causing Churg-Strauss syndrome in the absence of other known factors.</p

Experimental investigation of the effect of using different aggregate types on WMA mixtures

In recent years, production of warm mix asphalt (WMA) mixtures with the help of chemical additives has been developed due to obvious advantages, such as reduction of pollution emissions, construction temperature and the possibility of carrying asphalt in long distances. Various additives can have positive or negative effects on the performance characteristics of WMA mixtures made from different types of aggregates. Although, effects of different types of aggregates have been more investigated on the performance of hot mix asphalt (HMA), the effects on WMA have been less studied. Therefore, in this study, three types of aggregates including: limestone (Li), siliceous (Si) and slag (Sl) from the metal production factories together with Sasobit and Zeolite additives were provided to be used for the WMA mixtures. After constructing the asphalt samples and determining the optimum binder, Marshall Stability, indirect tensile strength tests and resilient modulus test and the durability parameter determination were performed. Test results indicated that WMA-Sasobit mixtures have the greatest impact on reducing consumed percentage of binder in slag and siliceous aggregates compared to limestone aggregates. For both additives, WMA mixtures containing limestone aggregates showed higher resilient modulus and siliceous aggregates showed lower resilient modulus. Moreover, the results of indirect tensile strength of specimens containing limestone aggregates showed the highest value and siliceous aggregates showed the lowest one. TSR in the limestone and slag aggregates was improved using both additives, but Zeolite additives reduce TSR in the siliceous aggregates. Keywords: Type of aggregate, Warm mix asphalt, Sasobit, Zeolite, Performance characteristics, Moisture susceptibilit

Musculoskeletal Disorders among Faculty Members and Impact of Work-Related Factors on Its Prevalen

Introduction: Nowadays, musculoskeletal disorders (MSDs) are one of the most common work-related problems in the world. The aim of this present study was to determine the prevalence of MSDs and their relationship with work-related factors among tutors working at Shahid Sadoughi University of Medical Sciences in Yazd, Iran. Materials and Methods: This cross sectional study was conducted on 113 tutors at the Shahid Sadoughi University of Medical Sciences in 2015. Data were collected by a standard self-administered questionnaire consisting of three major parts: a) demographic and work-related variables, b) musculoskeletal symptoms, c) a modified version of the Standard Nordic Questionnaire. The data were analyzed by SPSS (version 16) using appropriate statistical tests. P-values less than 0.05 were considered statistically significant. Results: Pain in the low back (27.9%) and neck (25%) was the most common complaint among the subjects. Overall, 80 tutors (70.79%) had symptoms of pain in at least one part of the body in the past year. The highest rate of complaints was related to laboratory tutors (94.73%). There was a statistically significant association between prevalence of MSDs and teaching time. Conclusions: The prevalence of MSDs among faculty members of Shahid Sadoughi University of Medical Sciences is high and more than the general population. The musculoskeletal complaints are significantly associated with teaching time

Detection of the rotator cuff tears using a novel convolutional neural network from magnetic resonance image (MRI)

The rotator cuff tear is a common situation for basketballers, handballers, or other athletes that strongly use their shoulders. This injury can be diagnosed precisely from a magnetic resonance (MR) image. In this paper, a novel deep learning-based framework is proposed to diagnose rotator cuff tear from MRI images of patients suspected of the rotator cuff tear. First, we collected 150 shoulders MRI images from two classes of rotator cuff tear patients and healthy ones with the same numbers. These images were observed by an orthopedic specialist and then tagged and used as input in the various configurations of the Convolutional Neural Network (CNN). At this stage, five different configurations of convolutional networks have been examined. Then, in the next step, the selected network with the highest accuracy is used to extract the deep features and classify the two classes of rotator cuff tear and healthy. Also, MRI images are feed to two quick pre-trained CNNs (MobileNetv2 and SqueezeNet) to compare with the proposed CNN. Finally, the evaluation is performed using the 5-fold cross-validation method. Also, a specific Graphical User Interface (GUI) was designed in the MATLAB environment for simplicity, which allows for testing by detecting the image class. The proposed CNN achieved higher accuracy than the two mentioned pre-trained CNNs. The average accuracy, precision, sensitivity, and specificity achieved by the best selected CNN configuration are equal to 92.67%, 91.13%, 91.75%, and 92.22%, respectively. The deep learning algorithm could accurately rule out significant rotator cuff tear based on shoulder MRI