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1.
J Electrocardiol ; 58: 113-118, 2020.
Artigo em Inglês | MEDLINE | ID: mdl-31816563

RESUMO

AIMS: One third of ischemic strokes are of unknown etiology. Interatrial block (IAB) is a marker of atrial electromechanical dysfunction that may predispose to the development of atrial fibrillation (AF). We hypothesized that IAB, especially in its advanced form, could be a marker of covert AF in patients with embolic stroke of undetermined source (ESUS). METHODS: We reviewed a single center cohort of ESUS patients with no prior history of AF. According to P-wave analysis of baseline ECG we distinguished 3 groups: normal P-wave duration (P-wave < 120 ms), partial IAB (P-IAB, P-wave ≥ 120 ms) and A-IAB (A-IAB, P-wave ≥ 120 ms with biphasic morphology in inferior leads). Follow-up was done 1, 6 and 12 months after discharge; then every 6 months. AF episodes, frequent premature atrial contractions (PACs) (>1%) and atrial tachyarrhythmias (runs of >3 consecutive PACs) were detected on 24 h Holter. The primary endpoint was new-onset AF detection on follow-up by any means. RESULTS: A high prevalence of both P-IAB (n = 30, 40%) and A-IAB (n = 23, 31%) was found in 75 ESUS patients. After a 521 day mean follow-up, 14 patients (19%) were diagnosed of AF. A-IAB independently predicted AF diagnosis (p =0.042) on follow-up. 24 h Holter analysis showed greater frequency of PACs and atrial tachyarrhythmia episodes in patients with IAB (p = 0.0275). CONCLUSIONS: In this hypothesis-generating study, A-IAB in the setting of ESUS is an independent risk predictor of covert AF. Although additional randomized clinical trials are warranted, A-IAB identifies ESUS patients with advanced atrial disease that could potentially benefit from early oral anticoagulation in secondary prevention.

2.
Ann Noninvasive Electrocardiol ; 24(5): e12685, 2019 09.
Artigo em Inglês | MEDLINE | ID: mdl-31490594

RESUMO

As medical education evolves, some traditional teaching methods often get forgotten. For generations, the Lewis ladder diagram (LLD) has helped students understand the mechanisms of cardiac arrhythmias and conduction disorders. Similarly, clinicians have used LLDs to communicate their proposed mechanisms to their colleagues and trainees. In this article, we revisit this technique of constructing the LLD and demonstrate this process by describing the mechanisms of various bigeminal rhythms.

3.
Sci Rep ; 9(1): 4729, 2019 Mar 18.
Artigo em Inglês | MEDLINE | ID: mdl-30894584

RESUMO

Thermal Imaging (Infrared-Imaging-IRI) is a promising new technique for psychophysiological research and application. Unlike traditional physiological measures (like skin conductance and heart rate), it is uniquely contact-free, substantially enhancing its ecological validity. Investigating facial regions and subsequent reliable signal extraction from IRI data is challenging due to head motion artefacts. Exploiting its potential thus depends on advances in analytical methods. Here, we developed a novel semi-automated thermal signal extraction method employing deep learning algorithms for facial landmark identification. We applied this method to physiological responses elicited by a sudden auditory stimulus, to determine if facial temperature changes induced by a stimulus of a loud sound can be detected. We compared thermal responses with psycho-physiological sensor-based tools of galvanic skin response (GSR) and electrocardiography (ECG). We found that the temperatures of selected facial regions, particularly the nose tip, significantly decreased after the auditory stimulus. Additionally, this response was quite rapid at around 4-5 seconds, starting less than 2 seconds following the GSR changes. These results demonstrate that our methodology offers a sensitive and robust tool to capture facial physiological changes with minimal manual intervention and manual pre-processing of signals. Newer methodological developments for reliable temperature extraction promise to boost IRI use as an ecologically-valid technique in social and affective neuroscience.

4.
IEEE J Biomed Health Inform ; 23(6): 2583-2591, 2019 11.
Artigo em Inglês | MEDLINE | ID: mdl-30714935

RESUMO

A substantial proportion of patients with functional neurological disorders (FND) are being incorrectly diagnosed with epilepsy because their semiology resembles that of epileptic seizures (ES). Misdiagnosis may lead to unnecessary treatment and its associated complications. Diagnostic errors often result from an overreliance on specific clinical features. Furthermore, the lack of electrophysiological changes in patients with FND can also be seen in some forms of epilepsy, making diagnosis extremely challenging. Therefore, understanding semiology is an essential step for differentiating between ES and FND. Existing sensor-based and marker-based systems require physical contact with the body and are vulnerable to clinical situations such as patient positions, illumination changes, and motion discontinuities. Computer vision and deep learning are advancing to overcome these limitations encountered in the assessment of diseases and patient monitoring; however, they have not been investigated for seizure disorder scenarios. Here, we propose and compare two marker-free deep learning models, a landmark-based and a region-based model, both of which are capable of distinguishing between seizures from video recordings. We quantify semiology by using either a fusion of reference points and flow fields, or through the complete analysis of the body. Average leave-one-subject-out cross-validation accuracies for the landmark-based and region-based approaches of 68.1% and 79.6% in our dataset collected from 35 patients, reveal the benefit of video analytics to support automated identification of semiology in the challenging conditions of a hospital setting.


Assuntos
Epilepsia/diagnóstico por imagem , Interpretação de Imagem Assistida por Computador/métodos , Monitorização Fisiológica/métodos , Gravação em Vídeo/métodos , Aprendizado Profundo , Humanos
5.
Seizure ; 65: 65-71, 2019 Feb.
Artigo em Inglês | MEDLINE | ID: mdl-30616221

RESUMO

PURPOSE: The recent explosion of artificial intelligence techniques in video analytics has highlighted the clinical relevance in capturing and quantifying semiology during epileptic seizures; however, we lack an automated anomaly identification system for aberrant behaviors. In this paper, we describe a novel system that is trained with known clinical manifestations from patients with mesial temporal and extra-temporal lobe epilepsy and presents aberrant semiology to physicians. METHODS: We propose a simple end-to-end-architecture based on convolutional and recurrent neural networks to extract spatiotemporal representations and to create motion capture libraries from 119 seizures of 28 patients. The cosine similarity distance between a test representation and the libraries from five aberrant seizures separate to the main dataset is subsequently used to identify test seizures with unusual patterns that do not conform to known behavior. RESULTS: Cross-validation evaluations are performed to validate the quantification of motion features and to demonstrate the robustness of the motion capture libraries for identifying epilepsy types. The system to identify unusual epileptic seizures successfully detects out of the five seizures categorized as aberrant cases. CONCLUSIONS: The proposed approach is capable of modeling clinical manifestations of known behaviors in natural clinical settings, and effectively identify aberrant seizures using a simple strategy based on motion capture libraries of spatiotemporal representations and similarities between hidden states. Detecting anomalies is essential to alert clinicians to the occurrence of unusual events, and we show how this can be achieved using pre-learned database of semiology stored in health records.


Assuntos
Encéfalo/fisiopatologia , Diagnóstico por Computador/métodos , Epilepsia do Lobo Temporal/diagnóstico , Epilepsia do Lobo Temporal/fisiopatologia , Convulsões/diagnóstico , Eletroencefalografia , Feminino , Humanos , Masculino , Reprodutibilidade dos Testes , Convulsões/fisiopatologia , Gravação em Vídeo
6.
Conf Proc IEEE Eng Med Biol Soc ; 2019: 6529-6532, 2019 Jul.
Artigo em Inglês | MEDLINE | ID: mdl-31947337

RESUMO

Recent breakthroughs in computer vision offer an exciting avenue to develop new remote, and non-intrusive patient monitoring techniques. A very challenging topic to address is the automated recognition of breathing disorders during sleep. Due to its complexity, this task has rarely been explored in the literature on real patients using such marker-free approaches. Here, we propose an approach based on deep learning architectures capable of classifying breathing disorders. The classification is performed on depth maps recorded with 3D cameras from 76 patients referred to a sleep laboratory that present a range of breathing disorders. Our system is capable of classifying individual breathing events as normal or abnormal with an accuracy of 61.8%, hence our results show that computer vision and deep learning are viable tools for assessing locally or remotely breathing quality during sleep.

7.
Conf Proc IEEE Eng Med Biol Soc ; 2019: 2099-2105, 2019 07.
Artigo em Inglês | MEDLINE | ID: mdl-31946315

RESUMO

In epilepsy, semiology refers to the study of patient behavior and movement, and their temporal evolution during epileptic seizures. Understanding semiology provides clues to the cerebral networks underpinning the epileptic episode and is a vital resource in the pre-surgical evaluation. Recent advances in video analytics have been helpful in capturing and quantifying epileptic seizures. Nevertheless, the automated representation of the evolution of semiology, as examined by neurologists, has not been appropriately investigated. From initial seizure symptoms until seizure termination, motion patterns of isolated clinical manifestations vary over time. Furthermore, epileptic seizures frequently evolve from one clinical manifestation to another, and their understanding cannot be overlooked during a presurgery evaluation. Here, we propose a system capable of computing motion signatures from videos of face and hand semiology to provide quantitative information on the motion, and the correlation between motions. Each signature is derived from a sparse saliency representation established by the magnitude of the optical flow field. The developed computer-aided tool provides a novel approach for physicians to analyze semiology as a flow of signals without interfering in the healthcare environment. We detect and quantify semiology using detectors based on deep learning and via a novel signature scheme, which is independent of the amount of data and seizure differences. The system reinforces the benefits of computer vision for non-obstructive clinical applications to quantify epileptic seizures recorded in real-life healthcare conditions.

8.
Conf Proc IEEE Eng Med Biol Soc ; 2019: 1625-1629, 2019 07.
Artigo em Inglês | MEDLINE | ID: mdl-31946208

RESUMO

Epilepsy monitoring involves the study of videos to assess clinical signs (semiology) to assist with the diagnosis of seizures. Recent advances in the application of vision-based approaches to epilepsy analysis have demonstrated significant potential to automate this assessment. Nevertheless, current proposed computer vision based techniques are unable to accurately quantify specific facial modifications, e.g. mouth motions, which are examined by neurologists to distinguish between seizure types. 2D approaches that analyse facial landmarks have been proposed to quantify mouth motions, however, they are unable to fully represent motions in the mouth and cheeks (ictal pouting) due to a lack of landmarks in the the cheek regions. Additionally, 2D region-based techniques based on the detection of the mouth have limitations when dealing with large pose variations, and thus make a fair comparison between samples difficult due to the variety of poses present. 3D approaches, on the other hand, retain rich information about the shape and appearance of faces, simplifying alignment for comparison between sequences. In this paper, we propose a novel network method based on a 3D reconstruction of the face and deep learning to detect and quantify mouth semiology in our video dataset of 20 seizures, recorded from patients with mesial temporal and extra-temporal lobe epilepsy. The proposed network is capable of distinguishing between seizures of both types of epilepsy. An average classification accuracy of 89% demonstrates the benefits of computer vision and deep learning for clinical applications of non-contact systems to identify semiology commonly encountered in a natural clinical setting.

9.
J Electrocardiol ; 51(6): 1091-1093, 2018.
Artigo em Inglês | MEDLINE | ID: mdl-30497736

RESUMO

The diagnosis of advanced interatrial block (A-IAB) is done by surface ECG analysis when the P-wave ≥120 ms with biphasic (±) morphology in leads II, III and aVF. In this brief communication, we advance a new concept involving atypical patterns of A-IAB due to changes about the morphology or duration of the P-wave. It remains to be determined its real prevalence in different clinical scenarios, and whether these atypical ECG patterns should be considered as predictors of atrial fibrillation/stroke.


Assuntos
Eletrocardiografia , Bloqueio Interatrial/diagnóstico , Humanos
10.
Conf Proc IEEE Eng Med Biol Soc ; 2018: 332-335, 2018 07.
Artigo em Inglês | MEDLINE | ID: mdl-30440405

RESUMO

Electrophysiological observation plays a major role in epilepsy evaluation. However, human interpretation of brain signals is subjective and prone to misdiagnosis. Automating this process, especially seizure detection relying on scalpbased Electroencephalography (EEG) and intracranial EEG, has been the focus of research over recent decades. Nevertheless, its numerous challenges have inhibited a definitive solution. Inspired by recent advances in deep learning, here we describe a new classification approach for EEG time series based on Recurrent Neural Networks (RNNs) via the use of Long- Short Term Memory (LSTM) networks. The proposed deep network effectively learns and models discriminative temporal patterns from EEG sequential data. Especially, the features are automatically discovered from the raw EEG data without any pre-processing step, eliminating humans from laborious feature design task. Our light-weight system has a low computational complexity and reduced memory requirement for large training datasets. On a public dataset, a multi-fold cross-validation scheme of the proposed architecture exhibited an average validation accuracy of 95.54% and an average AUC of 0.9582 of the ROC curve among all sets defined in the experiment. This work reinforces the benefits of deep learning to be further attended in clinical applications and neuroscientific research.


Assuntos
Epilepsia , Encéfalo , Eletroencefalografia , Humanos , Convulsões
11.
Conf Proc IEEE Eng Med Biol Soc ; 2018: 3578-3581, 2018 07.
Artigo em Inglês | MEDLINE | ID: mdl-30441151

RESUMO

Visual motion clues such as facial expression and pose are natural semiology features which an epileptologist observes to identify epileptic seizures. However, these cues have not been effectively exploited for automatic detection due to the diverse variations in seizure appearance within and between patients. Here we present a multi-modal analysis approach to quantitatively classify patients with mesial temporal lobe (MTLE) and extra-temporal lobe (ETLE) epilepsy, relying on the fusion of facial expressions and pose dynamics. We propose a new deep learning approach that leverages recent advances in Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks to automatically extract spatiotemporal features from facial and pose semiology using recorded videos. A video dataset from 12 patients with MTLE and 6 patients with ETLEin an Australian hospital has been collected for experiments. Our experiments show that facial semiology and body movements can be effectively recognized and tracked, and that they provide useful evidence to identify the type of epilepsy. A multi-fold cross-validation of the fusion model exhibited an average test accuracy of 92.10%, while a leave-one-subject-out cross-validation scheme, which is the first in the literature, achieves an accuracy of 58.49%. The proposed approach is capable of modelling semiology features which effectively discriminate between seizures arising from temporal and extra-temporal brain areas. Our approach can be used as a virtual assistant, which will save time, improve patient safety and provide objective clinical analysis to assist with clinical decision making.


Assuntos
Epilepsia , Convulsões , Humanos
12.
Epilepsy Behav ; 87: 46-58, 2018 10.
Artigo em Inglês | MEDLINE | ID: mdl-30173017

RESUMO

During seizures, a myriad of clinical manifestations may occur. The analysis of these signs, known as seizure semiology, gives clues to the underlying cerebral networks involved. When patients with drug-resistant epilepsy are monitored to assess their suitability for epilepsy surgery, semiology is a vital component to the presurgical evaluation. Specific patterns of facial movements, head motions, limb posturing and articulations, and hand and finger automatisms may be useful in distinguishing between mesial temporal lobe epilepsy (MTLE) and extratemporal lobe epilepsy (ETLE). However, this analysis is time-consuming and dependent on clinical experience and training. Given this limitation, an automated analysis of semiological patterns, i.e., detection, quantification, and recognition of body movement patterns, has the potential to help increase the diagnostic precision of localization. While a few single modal quantitative approaches are available to assess seizure semiology, the automated quantification of patients' behavior across multiple modalities has seen limited advances in the literature. This is largely due to multiple complicated variables commonly encountered in the clinical setting, such as analyzing subtle physical movements when the patient is covered or room lighting is inadequate. Semiology encompasses the stepwise/temporal progression of signs that is reflective of the integration of connected neuronal networks. Thus, single signs in isolation are far less informative. Taking this into account, here, we describe a novel modular, hierarchical, multimodal system that aims to detect and quantify semiologic signs recorded in 2D monitoring videos. Our approach can jointly learn semiologic features from facial, body, and hand motions based on computer vision and deep learning architectures. A dataset collected from an Australian quaternary referral epilepsy unit analyzing 161 seizures arising from the temporal (n = 90) and extratemporal (n = 71) brain regions has been used in our system to quantitatively classify these types of epilepsy according to the semiology detected. A leave-one-subject-out (LOSO) cross-validation of semiological patterns from the face, body, and hands reached classification accuracies ranging between 12% and 83.4%, 41.2% and 80.1%, and 32.8% and 69.3%, respectively. The proposed hierarchical multimodal system is a potential stepping-stone towards developing a fully automated semiology analysis system to support the assessment of epilepsy.


Assuntos
Automatismo/fisiopatologia , Aprendizado Profundo , Epilepsia do Lobo Temporal/diagnóstico , Epilepsia/diagnóstico , Face/fisiopatologia , Mãos/fisiopatologia , Movimento/fisiologia , Monitorização Neurofisiológica/métodos , Convulsões/diagnóstico , Fenômenos Biomecânicos , Conjuntos de Dados como Assunto , Humanos
13.
Epilepsy Behav ; 82: 17-24, 2018 05.
Artigo em Inglês | MEDLINE | ID: mdl-29574299

RESUMO

Semiology observation and characterization play a major role in the presurgical evaluation of epilepsy. However, the interpretation of patient movements has subjective and intrinsic challenges. In this paper, we develop approaches to attempt to automatically extract and classify semiological patterns from facial expressions. We address limitations of existing computer-based analytical approaches of epilepsy monitoring, where facial movements have largely been ignored. This is an area that has seen limited advances in the literature. Inspired by recent advances in deep learning, we propose two deep learning models, landmark-based and region-based, to quantitatively identify changes in facial semiology in patients with mesial temporal lobe epilepsy (MTLE) from spontaneous expressions during phase I monitoring. A dataset has been collected from the Mater Advanced Epilepsy Unit (Brisbane, Australia) and is used to evaluate our proposed approach. Our experiments show that a landmark-based approach achieves promising results in analyzing facial semiology, where movements can be effectively marked and tracked when there is a frontal face on visualization. However, the region-based counterpart with spatiotemporal features achieves more accurate results when confronted with extreme head positions. A multifold cross-validation of the region-based approach exhibited an average test accuracy of 95.19% and an average AUC of 0.98 of the ROC curve. Conversely, a leave-one-subject-out cross-validation scheme for the same approach reveals a reduction in accuracy for the model as it is affected by data limitations and achieves an average test accuracy of 50.85%. Overall, the proposed deep learning models have shown promise in quantifying ictal facial movements in patients with MTLE. In turn, this may serve to enhance the automated presurgical epilepsy evaluation by allowing for standardization, mitigating bias, and assessing key features. The computer-aided diagnosis may help to support clinical decision-making and prevent erroneous localization and surgery.


Assuntos
Identificação Biométrica/métodos , Diagnóstico por Computador/métodos , Epilepsia/diagnóstico , Gravação em Vídeo/métodos , Austrália/epidemiologia , Identificação Biométrica/normas , Diagnóstico por Computador/normas , Epilepsia/epidemiologia , Epilepsia/fisiopatologia , Face/anatomia & histologia , Face/fisiologia , Humanos , Masculino , Movimento/fisiologia , Exame Neurológico/métodos , Exame Neurológico/normas , Reprodutibilidade dos Testes , Gravação em Vídeo/normas
14.
Epilepsia ; 58(11): 1817-1831, 2017 11.
Artigo em Inglês | MEDLINE | ID: mdl-28990168

RESUMO

Epilepsy being one of the most prevalent neurological disorders, affecting approximately 50 million people worldwide, and with almost 30-40% of patients experiencing partial epilepsy being nonresponsive to medication, epilepsy surgery is widely accepted as an effective therapeutic option. Presurgical evaluation has advanced significantly using noninvasive techniques based on video monitoring, neuroimaging, and electrophysiological and neuropsychological tests; however, certain clinical settings call for invasive intracranial recordings such as stereoelectroencephalography (SEEG), aiming to accurately map the eloquent brain networks involved during a seizure. Most of the current presurgical evaluation procedures focus on semiautomatic techniques, where surgery diagnosis relies immensely on neurologists' experience and their time-consuming subjective interpretation of semiology or the manifestations of epilepsy and their correlation with the brain's electrical activity. Because surgery misdiagnosis reaches a rate of 30%, and more than one-third of all epilepsies are poorly understood, there is an evident keen interest in improving diagnostic precision using computer-based methodologies that in the past few years have shown near-human performance. Among them, deep learning has excelled in many biological and medical applications, but has advanced insufficiently in epilepsy evaluation and automated understanding of neural bases of semiology. In this paper, we systematically review the automatic applications in epilepsy for human motion analysis, brain electrical activity, and the anatomoelectroclinical correlation to attribute anatomical localization of the epileptogenic network to distinctive epilepsy patterns. Notably, recent advances in deep learning techniques will be investigated in the contexts of epilepsy to address the challenges exhibited by traditional machine learning techniques. Finally, we discuss and propose future research on epilepsy surgery assessment that can jointly learn across visually observed semiologic patterns and recorded brain electrical activity.


Assuntos
Mapeamento Encefálico/métodos , Encéfalo/fisiopatologia , Eletroencefalografia/métodos , Cuidados Pré-Operatórios/métodos , Convulsões/fisiopatologia , Eletrodos Implantados , Epilepsia/diagnóstico , Epilepsia/fisiopatologia , Humanos , Aprendizado de Máquina , Convulsões/diagnóstico , Inquéritos e Questionários
15.
CES odontol ; 27(2): 37-46, jul.-dic. 2014. graf, tab
Artigo em Espanhol | LILACS-Express | ID: lil-755597

RESUMO

Resumen Introducción y objetivo: La reabsorción radicular apical externa (RRAE) asociada al movimiento dental ha sido tema de investigación en el campo de la ortodoncia y la endodoncia. La detección temprana traería un beneficio para el paciente y el profesional; ha sido descrita como una complicación o secuela del tratamiento de ortodoncia que resulta en la pérdida permanente de estructura radicular El dignóstico eficiente es un aspecto esencial para definir la terapéutica adecuada y el éxito del tratamiento. El propósito de este estudio fue evaluar los cambios en la longitud radicular y del conducto como consecuencia del tratamiento ortodóncico.< Materiales y métodos: Se evaluaron 42 dientes , incisivos centrales superiores de 21 pacientes (12 mujeres, 9 hombres). Los cambios en la longitud radicular y amplitud del conducto fueron determinadas por medio de mediciones radiográficas obtenidas antes de iniciar el tratamiento (T1) y posteriormente a los 6 (T2), 12 (T3) y 18 meses (T4) utilizando un posicionador de técnica paralela RINN® XCP® (DENTSPLY) y el Sistema de radiovisiografía CDR de SCHICK ®. las imágenes obtenidas se importaron y procesaron con el sistema DBS WIN®, aquí se llevaron a cabo las mediciones correspondientes. Resultados: Todos los Incisivos evaluados mostraron disminución de la longitud y de la amplitud del conducto radicular durante los 18 meses de tratamiento. Conclusión: La RRAE se puede detectar en etapas tempranas del tratamiento de Ortodoncia, se presenta más significativamente en los primeros 6 meses, parece estar relacionado con las fuerzas de la fase de alineación y nivelación.


Abstract Introduction and objective: External apical root resorption ( RRAE ) associated with tooth movement has been the subject of research in the field of orthodontics and endodontics. Early detection would bring a benefit to the patient and the professional; It has been described as a complication of orthodontic treatment resulting in permanent loss of root structure. Efficient diagnosis is an essential aspect in order to define appropriate therapeutic and treatment success. The purpose of this study was to assess changes in root length and canal as a result of orthodontic treatment. Materials and methods: 42 teeth, maxillary central incisors of 21 patients (12 women, 9 men ) were evaluated. Changes in root length and width of the canal were determined with radiographic measurements obtained before starting treatment (T1 ) and at 6 ( T2 ), 12 ( T3 ) and 18 months ( T4 ) using a RINN® XCP® ( DENTSPLY) positioner technique and CDR System radiovisiography SCHICK®. Images obtained were imported and processed with the WIN® DBS system with which the corresponding measurements were made. Results: All tested incisors showed decreased length and breadth of the root canal during the 18 months of treatment. Conclusion: RRAE can be detected at early stages of orthodontic treatment , it appears most significantly during the first 6 monthsof treatment and seems to be related to the forces during alignment and leveling phases.

16.
CES odontol ; 23(2): 57-57, jul.-dic. 2010.
Artigo em Espanhol | LILACS | ID: lil-612564

RESUMO

En la actualidad, se han venido desarrollando y utilizando, una serie de herramientas interactivasa nivel odontológico, que mejoran y facilitan el proceso de enseñanza y aprendizaje, proporcionando bases para el desarrollo profesional del alumno y el odontólogo.


Assuntos
Cefalometria , Radiografia
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