Evaluation of life quality and sleep problems in children presenting with headache to the pediatric neurology outpatient clinic

Aim: We aimed to investigate the quality of life (QL) and sleep habits (SH) of children presenting to the pediatric neurology outpatient clinic with headache. Methods: This prospective, cross-sectional and observational survey study included children aged 2-18 who presented to the pediatric neurology outpatient clinics of Dr. Ali Kemal Belviranlı Maternity and Children's Hospital or Konya City Hospital between April and August 2022. QL was assessed using the Pediatric Quality of Life Inventory (PedsQL) and sleep characteristics were evaluated using the Children’s Sleep Habits Questionnaire. Results: The study included 137 patients (56.2% girls) with a mean age of 153.54±34.5 months at presentation. All patients were diagnosed with primary headache; 51.8% had migraine and 48.2% had tension-type headache. Quality of life scores were 69.07±14.96 according to child self-assessment and 66.39±15.37 according to parental assessment. The mean score on the sleep habits questionnaire was 48.01±7.68, and 9.5% of the patients had good sleep quality. Subscale scores showed that the greatest adverse effects on QL were in the areas of emotional functioning and school functioning. Conclusions: Clinical assessment of patients’ QL and SH is important for individualizing treatment and approach in pediatric primary headache

VLSI Implementation of TDC Architectures Used in PET Imaging Systems

Positron emission tomography (PET) is a medical imaging method based on the measurement of concentrations of positron-emitting radionuclides in a living body. In the PET imaging system, glucose is labeled with a positron-emitting radionuclide and injected intravenously. Then, the positrons move through the tissue and collide with the electrons of the cells in which they interact. As a result of this interaction, two gamma rays are emitted in the opposite direction. Gama rays emitted from cancerous tissue that has retained radioactive glucose are detected through ring-shaped detectors. And the detected signals are converted into an electrical response. Subsequently, these responses are sampled with electronic circuits and recorded as histogram matrix to generate the image set. The gamma rays may not reach the detectors located in the opposite position in equal time. In PETs having TOF characteristics, it is aimed to obtain better positioning information by a method based on the principle of measuring the difference between the reach time of the two photons to detectors. The measurement of the flight time is carried out with TDC structures. The measurement of this time difference at the ps level is directly related to the spatial resolution of the PET system. In this study, 45 nm CMOS VLSI simulations of TDC structures that have various architectural approaches were performed for use in PET systems. With the designed TDC architectures, two gamma photons time reach to detectors have been simulated and the time difference has been successfully digitized. In addition, various performance metrics such as input and output voltages, time resolutions, measurement ranges, and power analysis of TDC architectures have been determined. Proposed Vernier oscillator-based TDC architecture has been reached 25 ps time resolution with a low power consumption of 1.62681 mW at 1V supply voltage.Comment: 8 pages, in Turkish language. 6 figures, conference paper,International Marmara Sciences Congess (IMASCON 2019 SPRING), https://www.imascon.com/dosyalar/imascon2019bahar/imascon_fen_bildiriler_ciltII_bahar_2019.pdf , https://avesis.kocaeli.edu.tr/yayin/99073ee1-45ff-495e-9cab-42de4d0fad71/vlsi-implementation-of-tdc-architectures-used-in-pet-imaging-system

Non-Syndromic Familial Unerupted Teeth: A Rare Contidion

A tooth that remains unerupted beyond the normal time of eruption and fails to erupt is called an impacted tooth. Maxillofacial surgeons encounter the problem of impacted teeth very often. Usually, impacted teeth involve the permanent dentition and they are rare in the primary dentition. Impaction of a primary tooth is a very rare entity. These rare cases are seen more frequently in primary second molars, followed by the order of the lower and upper centralincisor, lateral incisor and the canine teeth. Evolutionary and hereditary factors may cause uneruption. In this report, three cases of impacted primary teeth that belong to same familial members are presented. Father and sons. We extracted boy’s teeth but father refused treatment

Congenital Agenesis of Right Internal Carotid Artery: A Report of Two Cases

Congenital unilateral agenesis of the internal carotid artery (ICA) is a rare anomaly. Due to proper sufficient collateral circulation via the circle of Willis most cases are asymptomatic, but patients can also present with ischemic or hemorrhagic cerebrovascular insults. The absence of the bony carotid canal is essential to differentiate this anomaly from chronic ICA occlusion. Awareness of this situation by clinicians and radiologists is essential because these patients have an increased incidence of various intracranial pathologies. We report two cases of this rare developmental congenital abnormality occurring in two young patients and describe the presentation, diagnosis, determined developmental causes, imaging findings, and complications

Evaluation of the international severity score for FMF (ISSF) scores in Turkish children diagnosed with FMF: a single-center experience

Aims The aim of this study is to evaluate our patients with the newly developed international severity score for FMF (ISSF) and make comparisons with the literature

Serum leptin levels in patients with childhood immune thrombocytopenic purpura

Acute immune thrombocytopenic purpura (ITP) induces thrombocytopenia by means of an autoimmune mechanism. Recent studies suggested that T helper immune response is responsible for the pathogenesis of chronic ITP. Despite several studies that were carried out, we do not have a clue as to what triggers the autoimmunity. Leptin is a 16-kd protein secreted from the adipose tissue. Leptin is structurally similar to interleukin (IL)-2, IL-6, and IL-15. The structural similarities between leptin receptor and hernatopoietic cytokine receptors suggested that leptin could play a role in hematopoiesis and immune function. Recent studies suggested that leptin could play an important role in autoimmunity. We made a prospective analysis of a series of 39 newly diagnosed acute childhood ITP in a year period. Serum leptin levels were obtained after diagnosis and before treatment and all patients were followed up at least 6 months to designate acute or chronic event. We conclude that in childhood acute ITP, leptin did not play a role in the pathophysiology of the disease. Further investigations are needed to examine what triggers T cells and how the autoimmune disease became

EEG-based emotion recognition with deep convolutional neural networks

The emotional state of people plays a key role in physiological and behavioral human interaction. Emotional state analysis entails many fields such as neuroscience, cognitive sciences, and biomedical engineering because the parameters of interest contain the complex neuronal activities of the brain. Electroencephalogram (EEG) signals are processed to communicate brain signals with external systems and make predictions over emotional states. This paper proposes a novel method for emotion recognition based on deep convolutional neural networks (CNNs) that are used to classify Valence, Arousal, Dominance, and Liking emotional states. Hence, a novel approach is proposed for emotion recognition with time series of multi-channel EEG signals from a Database for Emotion Analysis and Using Physiological Signals (DEAP). We propose a new approach to emotional state estimation utilizing CNN-based classification of multi-spectral topology images obtained from EEG signals. In contrast to most of the EEG-based approaches that eliminate spatial information of EEG signals, converting EEG signals into a sequence of multi-spectral topology images, temporal, spectral, and spatial information of EEG signals are preserved. The deep recurrent convolutional network is trained to learn important representations from a sequence of three-channel topographical images. We have achieved test accuracy of 90.62% for negative and positive Valence, 86.13% for high and low Arousal, 88.48% for high and low Dominance, and finally 86.23% for like-unlike. The evaluations of this method on emotion recognition problem revealed significant improvements in the classification accuracy when compared with other studies using deep neural networks (DNNs) and one-dimensional CNNs

Cardiac arrhythmia detection from 2d ecg images by using deep learning technique

Arrhythmia is irregular changes of normal heart rhythm and effective manual identifying of them require a lot of time and depends on experience of clinicians. This paper proposes deep learning-based novel 2-D convolutional neural network (CNN) approach for accurate classification of five different arrhythmia types. The performance of the proposed architecture is tested on Electrocardiogram (ECG) signals that are taken from MIT-BIH arrhythmia benchmark database. ECG signals was segmented into heartbeats and each of the heartbeats was converted into 2-D grayscale images as an input data for CNN structure. The accuracy of the proposed architecture was found as 97.42% on the training results revealed that the proposed 2-D CNN architecture with transformed 2-D ECG images can achieve highest accuracy without any preprocessing and feature extraction and feature selection stages for ECG signals

ECG Arrhythmia Detection with Deep Learning Derin Ogrenme ile EKG Aritmi Tespiti

Arrhythmia is any irregularity of heart rate that cause an abnormality in your heart rhythm. Manual analysis of Electrocardiogram (ECG) signal is not enough for quickly identify abnormalities in the heart rhythm. This paper proposes a deep learning approach for detection of five different arrhythmia types based on 2D convolutional neural networks (CNN) architecture. ECG signals were obtained from MIT-BIH arrhythmia database. For CNN architecture, each ECG signal was segmented into heartbeats, then each heartbeat was transformed into 2D grayscale heartbeat image. 2D CNN model was used due to success of image recognition. The proposed model result demonstrate that CNN and ECG image formation give highest result when classified different types of ECG arrhythmic signals

EEG based emotional state estimation using 2-D deep learning technique

Emotion detection is very crucial role on diagnosis of brain disorders and psychological disorders. Electroencephalogram (EEG) is useful tool that obtain complex physiological brain signals from human. In this paper, we proposed a novel approach for emotional state estimation using convolutional neural network (CNN) based deep learning technique from EEG signals. Firstly, we convert 32 lead EEG signals to 2D EEG images with Azimuthal Equidistant Projection (AEP) technique. Then, 2D images that represented measurements of EEG signals sent to CNN based deep neural network for classification. In this study, we have achieved accuracy of 95.96% two classes as negative and positive valence, 96.09% two classes as high and low arousal and 95.90% two classes as high and low arousal dominance