Novel TSC1 Gene Mutation in a Familial Case of Tuberous Sclerosis Complex: Case Report

Introduction: Tuberous sclerosis complex (TSC) is a genetic disorder that can arise from sporadic or inherited mutations in TSC1 or TSC2 genes. It is characterized by tumors in multiple organs that result in broad clinical manifestations, usually affecting the central nervous system. Diagnosis is based upon clinical criteria that require careful clinical examination along with imaging and the availability of genetic testing to assess gene mutations linked to this disorder. We present a familial case of tuberous sclerosis complex with multiple subtle clinical manifestations and novel TSC1 mutation that were diagnosed in adulthood. This report adds to the growing literature on TSC1 gene by correlating specific nucleotide substitutions with possible secondary clinical manifestations. Case presentation: A 21 year-old Hispanic female presented with history of epilepsy onset in early childhood. Upon clinical examination and imaging testing, she was found to have lesions on her scalp, eyes and brain. Her mother is 51 years old with long-standing history of epilepsy with multiple lesions on skin, eyes, and similar findings in brain imaging to daughter. No additional family history was obtained suggestive of tuberous sclerosis complex. Conclusion: Milder forms of tuberous sclerosis complex can remain undiagnosed until adolescence and adulthood, which may lead to treatment delay and complications due to the lack of regular follow-up care. In this case report, both individuals’ presenting symptom was epilepsy and later other clinical findings were linked to the disorder. Additionally, a novel DNA sequence variant was detected, which expands our knowledge of known TSC1 gene mutations. Although these genetic test results cannot be used alone to make a definite diagnosis, they suggest an association to TSC given the clinical manifestations and family history

Induced effects of transcranial magnetic stimulation on the autonomic nervous system and the cardiac rhythm

Several standard protocols based on repetitive transcranial magnetic stimulation (rTMS) have been employed for treatment of a variety of neurological disorders. Despite their advantages in patients that are retractable to medication, there is a lack of knowledge about the effects of rTMS on the autonomic nervous system that controls the cardiovascular system. Current understanding suggests that the shape of the so-called QRS complex together with the size of the different segments and intervals between the PQRST deflections of the heart could predict the nature of the different arrhythmias and ailments affecting the heart. This preliminary study involving 10 normal subjects from 20 to 30 years of age demonstrated that rTMS can induce changes in the heart rhythm. The autonomic activity that controls the cardiac rhythm was indeed altered by an rTMS session targeting the motor cortex using intensity below the subject\u27s motor threshold and lasting no more than 5 minutes. The rTMS activation resulted in a reduction of the RR intervals (cardioacceleration) in most cases. Most of these cases also showed significant changes in the Poincare plot descriptor SD2 (long-term variability), the area under the low frequency (LF) power spectrum density curve, and the low frequency to high frequency (LF/HF) ratio. The RR intervals changed significantly in specific instants of time during rTMS activation showing either heart rate acceleration or heart rate deceleration

A probabilistic approach for pediatric epilepsy diagnosis using brain functional connectivity networks

BACKGROUND: The lives of half a million children in the United States are severely affected due to the alterations in their functional and mental abilities which epilepsy causes. This study aims to introduce a novel decision support system for the diagnosis of pediatric epilepsy based on scalp EEG data in a clinical environment. METHODS: A new time varying approach for constructing functional connectivity networks (FCNs) of 18 subjects (7 subjects from pediatric control (PC) group and 11 subjects from pediatric epilepsy (PE) group) is implemented by moving a window with overlap to split the EEG signals into a total of 445 multi-channel EEG segments (91 for PC and 354 for PE) and finding the hypothetical functional connectivity strengths among EEG channels. FCNs are then mapped into the form of undirected graphs and subjected to extraction of graph theory based features. An unsupervised labeling technique based on Gaussian mixtures model (GMM) is then used to delineate the pediatric epilepsy group from the control group. RESULTS:The study results show the existence of a statistically significant difference (p \u3c 0.0001) between the mean FCNs of PC and PE groups. The system was able to diagnose pediatric epilepsy subjects with the accuracy of 88.8% with 81.8% sensitivity and 100% specificity purely based on exploration of associations among brain cortical regions and without a priori knowledge of diagnosis. CONCLUSIONS:The current study created the potential of diagnosing epilepsy without need for long EEG recording session and time-consuming visual inspection as conventionally employed

A probabilistic approach for pediatric epilepsy diagnosis using brain functional connectivity networks

Background The lives of half a million children in the United States are severely affected due to the alterations in their functional and mental abilities which epilepsy causes. This study aims to introduce a novel decision support system for the diagnosis of pediatric epilepsy based on scalp EEG data in a clinical environment. Methods A new time varying approach for constructing functional connectivity networks (FCNs) of 18 subjects (7 subjects from pediatric control (PC) group and 11 subjects from pediatric epilepsy (PE) group) is implemented by moving a window with overlap to split the EEG signals into a total of 445 multi-channel EEG segments (91 for PC and 354 for PE) and finding the hypothetical functional connectivity strengths among EEG channels. FCNs are then mapped into the form of undirected graphs and subjected to extraction of graph theory based features. An unsupervised labeling technique based on Gaussian mixtures model (GMM) is then used to delineate the pediatric epilepsy group from the control group. Results The study results show the existence of a statistically significant difference (p \u3c 0.0001) between the mean FCNs of PC and PE groups. The system was able to diagnose pediatric epilepsy subjects with the accuracy of 88.8% with 81.8% sensitivity and 100% specificity purely based on exploration of associations among brain cortical regions and without a priori knowledge of diagnosis. Conclusions The current study created the potential of diagnosing epilepsy without need for long EEG recording session and time-consuming visual inspection as conventionally employed

Country characteristics and acute diarrhea in children from developing nations: A multilevel study

Background: Each year 2.5 billion cases of diarrheal disease are reported in children under five years, and over 1,000 die. Country characteristics could play a role on this situation. We explored associations between country characteristics and diarrheal disease in children under 5 years of age, adjusting by child, mother and household attributes in developing countries. Methods: This study included 348,706 children from 40 nations. We conducted a multilevel analysis of data from the Demographic and Health Surveys and the World Bank. Results: The prevalence of acute diarrhea was 14 %. Country inequalities (OR = 1.335; 95 % CI 1.117-1.663) and country's low income (OR = 1.488; 95 % CI 1.024-2.163) were associated with diarrhea, and these country characteristics changed the associations of well-known determinants of diarrhea. Specifically, living in poor countries strengthens the association of poor household wealth and mother's lack of education with the disease. Other factors associated with diarrhea were female sex of the child (OR = 0.922; 95 % CI 0.900-0.944), age of the child (OR = 0.978; 95 % CI 0.978-0.979), immunization status (OR = 0.821; 95 % CI 0.799-0.843), normal birthweight (OR = 0.879; 95 % CI 0.834-0.926), maternal age (OR = 0.987; 95 % CI 0.985-0.989), lack of maternal education (OR = 1.416; 95 % CI 1.283-1.564), working status of the mother (OR = 1.136; 95 % CI 1.106-1.167), planned pregnancy (OR = 0.774; 95 % CI 0.753-0.795), a nuclear family structure (OR = 0.949; 95 % CI 0.923-0.975), and household wealth (OR = 0.948; 95 % CI 0.921-0.977). Conclusions: Inequalities and lack of resources at the country level in developing countries -but not health expenditure- were associated with acute diarrhea, independently of child, family and household features. The broad environment considerably modifies well-known social determinants of acute diarrhea and public health campaigns designed to target diarrhea should consider macro characteristics of the country. © 2015 Pinzón-Rondón et al

Estimating Intracranial Volume in Brain Research: An Evaluation of Methods

Intracranial volume (ICV) is a standard measure often used in morphometric analyses to correct for head size in brain studies. Inaccurate ICV estimation could introduce bias in the outcome. The current study provides a decision aid in defining protocols for ICV estimation across different subject groups in terms of sampling frequencies that can be optimally used on the volumetric MRI data, and type of software most suitable for use in estimating the ICV measure. Four groups of 53 subjects are considered, including adult controls (AC, adults with Alzheimer's disease (AD), pediatric controls (PC) and group of pediatric epilepsy subjects (PE). Reference measurements were calculated for each subject by manually tracing intracranial cavity without sub-sampling. The reliability of reference measurements were assured through intra- and inter- variation analyses. Three publicly well-known software packages (FreeSurfer Ver. 5.3.0, FSL Ver. 5.0, SPM8 and SPM12) were examined in their ability to automatically estimate ICV across the groups. Results on sub-sampling studies with a 95 % confidence showed that in order to keep the accuracy of the inter-leaved slice sampling protocol above 99 %, sampling period cannot exceed 20 mm for AC, 25 mm for PC, 15 mm for AD and 17 mm for the PE groups. The study assumes a priori knowledge about the population under study into the automated ICV estimation. Tuning of the parameters in FSL and the use of proper atlas in SPM showed significant reduction in the systematic bias and the error in ICV estimation via these automated tools. SPM12 with the use of pediatric template is found to be a more suitable candidate for PE group. SPM12 and FSL subjected to tuning are the more appropriate tools for the PC group. The random error is minimized for FS in AD group and SPM8 showed less systematic bias. Across the AC group, both SPM12 and FS performed well but SPM12 reported lesser amount of systematic bias