897 resultados para SLEEP DEPRIVATION
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Little is known about how sleep disruption impacts physical health among the homeless. The association between homelessness, quality of sleep and physical health were investigated in the current study. Convenience sampling was used to select participants from a pool of people attending the programs of Ecclesia Ministries. Interviews were conducted with 32 persons from the Boston metropolitan area, of whom 23 were currently homeless. The researcher assessed level of sleep disturbance, number of health problems and degree of homelessness using a standard demographic questionnaire, the General Health Questionnaire-12 (GHQ-12) and the Pittsburgh Sleep Quality Index (PSQI). Our results found evidence of significant sleep disturbance as well as significant mental and physical health problems in the sample. Correlational analyses provided partial support for the hypothesis that degree of homelessness impacts both sleep quality and physical health. Future work should investigate whether change in homelessness status alters sleep quality and physical health and also whether interventions may be utilized in this understudied and vulnerable population.
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The prevalence of sleep difficulties among the patients seen in the primary care settings is about 30%. This problem increases with age and is more common among females than males. Variations are noticed in prescription choices for different patients with sleep difficulties. Many factors affect a physician's prescription decision while chosen from a wide array of available medications. Both pharmacological and behavioral therapies are available for the treatment of sleep difficulties. It is important to know the impact of use of different types of prescriptions on health outcomes related to sleep difficulties. Thus the knowledge of prescription patterns among different types of patients (e.g. age, gender, race, insurance type etc.) becomes important for determining a clinical guideline. This study is designed to assist in evidence-based policymaking on understanding the variations in physician prescriptions for sleep difficulties and reasons for such variations. ^ A modified version of the model suggested by Eisenberg was used as a theoretical framework for this study to predict the factors influencing treatment of sleep difficulties. Multivariate logistic regression methods were used to analyze the 1996–2001 National Ambulatory Medical Care Survey data. ^ This study found that increased age, female gender, white race, established patients, and mental comorbidity were associated with significantly increased likelihood for prescription of some type of therapy for sleep difficulties in US outpatient settings. Patients with private insurance were associated with lower likelihood of receipt of many therapies. Psychiatrists were more likely to prescribe some kind of treatment as well as more expensive therapies for sleep difficulty as compared to other physician specialties. HMO enrolled patient visits were more likely to be associated with receipt of behavioral therapy. This study also found that 32% of patients with sleep difficulties received no type of therapy during their visits. Only 5% of the patients received behavioral therapy only. Almost three-quarters of the patients receiving some kind of medication prescription were prescribed benzodiazepines. The study results also suggest a need for wider coverage of behavioral therapy by payers in US outpatient settings. ^
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Background. Insufficient and poor quality sleep among adolescents affects not only the cognitive functioning, but overall health of the individual. Existing research suggests that adolescents from varying ethnic groups exhibit differing sleep patterns. However, little research focuses on sleep patterns and associated factors (i.e. tobacco use, mental health indicators) among Hispanic youth. ^ Methods. The study population (n=2,536) included students in grades 9-12 who attended one of the three public high schools along the Texas-Mexico border in 2003. This was a cross sectional study using secondary data collected via a web-based, confidential, self-administered survey. Separate logistic regression models were estimated to identify factors associated with reduced (<9 hours/night) and poor quality sleep on average during weeknights. ^ Results. Of participants, 49.5% reported reduced sleep while 12.8% reported poor quality sleep. Factors significantly (p<0.05) associated with poor quality sleep were: often feeling stressed or anxious (OR=5.49), being born in Mexico (OR=0.65), using a computer/playing video games 15+ hours per week (OR=2.29), working (OR=1.37), being a current smoker (OR=2.16), and being a current alcohol user (OR=1.64). Factors significantly associated with reduced quantity of sleep were: often feeling stressed or anxious (OR=2.74), often having headaches/stomachaches (OR=1.77), being a current marijuana user (OR=1.70), being a current methamphetamine user (OR=4.92), and being a current alcohol user (OR=1.27). ^ Discussion. Previous research suggests that there are several factors that can influence sleep quality and quantity in adolescents. This paper discusses these factors (i.e. work, smoking, alcohol, etc.) found to be associated with poor sleep quality and reduced sleep quantity in the Hispanic adolescent population. A reduced quantity of sleep (81.20% of the participants) and a poor quality of sleep (12.80% of the participants) were also found in high school students from South Texas. ^
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Researchers have historically emphasized the contribution of caspase-3 to apoptotic but not necrotic cell death, while calpain has been implicated primarily in necrosis and, to a lesser extent, in apoptosis. Activation of these proteases occurs in vivo following various CNS insults including ischemia. In addition, both necrotic and apoptotic cell death phenotypes are detected following ischemia. However, the contributions of calpain and caspase-3 to apoptotic and necrotic cell death phenotypes following CNS insults are relatively unexplored. To date, no study has examined the concurrent activation of calpain and caspase-3 in necrotic and apoptotic cell death phenotypes following any CNS insult. The present study employed oxygen-glucose deprivation (OGD) to determine the relative contributions of caspase-3 and calpain to apoptotic and necrotic cell death following OGD. Experiments characterized a model of OGD by evaluating cell viability and characterizing the cell death phenotypes following OGD in primary septo-hippocampal co-cultures. Furthermore, cell markers (NeuN and MAP2 or GFAP) assessed the effects of OGD on neuronal and astroglial viability, respectively. In addition, calpain and caspase-3 mediated proteolysis of α-spectrin was examined using Western blot techniques. Activation of these proteases in individual cells phenotypically characterized as apoptotic and necrotic was also evaluated by using antibodies specific for calpain or caspase-3 mediated breakdown products to α-spectrin. Administration of appropriate caspase-3 and calpain inhibitors also examined the effects of protease inhibition on cell death. OGD produced prominent expression of apoptotic cell death phenotypes primarily in neurons, with relatively little damage to astroglia. Although Western blot data suggested greater proteolysis of α-spectrin by calpain than caspase-3, co-activation of both proteases was usually detected in cells exhibiting apoptotic or necrotic cell death phenotypes. While inhibition of calpain and caspase-3 activity decreased LDH release following OGD, it was not clear whether this effect was also associated with a decrease in cell death and the appearance of apoptotic cell death phenotypes. These data demonstrate that both calpain and caspase-3 contribute to the expression of apoptotic cell death phenotypes following OGD, and that calpain could potentially have a larger role in the expression of apoptotic cell death than previously thought. ^
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This paper showed the basic educational status of slum children between 5 and 14 years old. The attendance ratio of slum children is much lower than that of children in Delhi as a whole. Parental perception of education and financing education are the major constraints. Even if children are attending schools, the majority of them are over-aged. There are both demand and supply side reasons for discouraging slum children from attending schooling. As opposed to school-based surveys in previous literature, children in slums are more likely to go to government schools rather than low-fee paying private schools. Some policies are suggested.
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This work is part of an on-going collaborative project between the medical and signal processing communities to promote new research efforts on automatic OSA (Obstructive Apnea Syndrome) diagnosis. In this paper, we explore the differences noted in phonetic classes (interphoneme) across groups (control/apnoea) and analyze their utility for OSA detection
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We present a novel approach for detecting severe obstructive sleep apnea (OSA) cases by introducing non-linear analysis into sustained speech characterization. The proposed scheme was designed for providing additional information into our baseline system, built on top of state-of-the-art cepstral domain modeling techniques, aiming to improve accuracy rates. This new information is lightly correlated with our previous MFCC modeling of sustained speech and uncorrelated with the information in our continuous speech modeling scheme. Tests have been performed to evaluate the improvement for our detection task, based on sustained speech as well as combined with a continuous speech classifier, resulting in a 10% relative reduction in classification for the first and a 33% relative reduction for the fused scheme. Results encourage us to consider the existence of non-linear effects on OSA patients' voices, and to think about tools which could be used to improve short-time analysis.
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We present a novel approach for the detection of severe obstructive sleep apnea (OSA) based on patients' voices introducing nonlinear measures to describe sustained speech dynamics. Nonlinear features were combined with state-of-the-art speech recognition systems using statistical modeling techniques (Gaussian mixture models, GMMs) over cepstral parameterization (MFCC) for both continuous and sustained speech. Tests were performed on a database including speech records from both severe OSA and control speakers. A 10 % relative reduction in classification error was obtained for sustained speech when combining MFCC-GMM and nonlinear features, and 33 % when fusing nonlinear features with both sustained and continuous MFCC-GMM. Accuracy reached 88.5 % allowing the system to be used in OSA early detection. Tests showed that nonlinear features and MFCCs are lightly correlated on sustained speech, but uncorrelated on continuous speech. Results also suggest the existence of nonlinear effects in OSA patients' voices, which should be found in continuous speech.
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La presente Tesis analiza las posibilidades que ofrecen en la actualidad las tecnologías del habla para la detección de patologías clínicas asociadas a la vía aérea superior. El estudio del habla que tradicionalmente cubre tanto la producción como el proceso de transformación del mensaje y las señales involucradas, desde el emisor hasta alcanzar al receptor, ofrece una vía de estudio alternativa para estas patologías. El hecho de que la señal emitida no solo contiene este mensaje, sino también información acerca del locutor, ha motivado el desarrollo de sistemas orientados a la identificación y verificación de la identidad de los locutores. Estos trabajos han recibido recientemente un nuevo impulso, orientándose tanto hacia la caracterización de rasgos que son comunes a varios locutores, como a las diferencias existentes entre grabaciones de un mismo locutor. Los primeros resultan especialmente relevantes para esta Tesis dado que estos rasgos podrían evidenciar la presencia de características relacionadas con una cierta condición común a varios locutores, independiente de su identidad. Tal es el caso que se enfrenta en esta Tesis, donde los rasgos identificados se relacionarían con una de la patología particular y directamente vinculada con el sistema de físico de conformación del habla. El caso del Síndrome de Apneas Hipopneas durante el Sueno (SAHS) resulta paradigmático. Se trata de una patología con una elevada prevalencia mundo, que aumenta con la edad. Los pacientes de esta patología experimentan episodios de cese involuntario de la respiración durante el sueño, que se prolongan durante varios segundos y que se reproducen a lo largo de la noche impidiendo el correcto descanso. En el caso de la apnea obstructiva, estos episodios se deben a la imposibilidad de mantener un camino abierto a través de la vía aérea, de forma que el flujo de aire se ve interrumpido. En la actualidad, el diagnostico de estos pacientes se realiza a través de un estudio polisomnográfico, que se centra en el análisis de los episodios de apnea durante el sueño, requiriendo que el paciente permanezca en el hospital durante una noche. La complejidad y el elevado coste de estos procedimientos, unidos a las crecientes listas de espera, han evidenciado la necesidad de contar con técnicas rápidas de detección, que si bien podrían no obtener tasas tan elevadas, permitirían reorganizar las listas de espera en función del grado de severidad de la patología en cada paciente. Entre otros, los sistemas de diagnostico por imagen, así como la caracterización antropométrica de los pacientes, han evidenciado la existencia de patrones anatómicos que tendrían influencia directa sobre el habla. Los trabajos dedicados al estudio del SAHS en lo relativo a como esta afecta al habla han sido escasos y algunos de ellos incluso contradictorios. Sin embargo, desde finales de la década de 1980 se conoce la existencia de patrones específicos relativos a la articulación, la fonación y la resonancia. Sin embargo, su descripción resultaba difícilmente aprovechable a través de un sistema de reconocimiento automático, pero apuntaba la existencia de un nexo entre voz y SAHS. En los últimos anos las técnicas de procesado automático han permitido el desarrollo de sistemas automáticos que ya son capaces de identificar diferencias significativas en el habla de los pacientes del SAHS, y que los distinguen de los locutores sanos. Por contra, poco se conoce acerca de la conexión entre estos nuevos resultados, los sé que habían obtenido en el pasado y la patogénesis del SAHS. Esta Tesis continua la labor desarrollada en este ámbito considerando específicamente: el estudio de la forma en que el SAHS afecta el habla de los pacientes, la mejora en las tasas de clasificación automática y la combinación de la información obtenida con los predictores utilizados por los especialistas clínicos en sus evaluaciones preliminares. Las dos primeras tareas plantean problemas simbióticos, pero diferentes. Mientras el estudio de la conexión entre el SAHS y el habla requiere de modelos acotados que puedan ser interpretados con facilidad, los sistemas de reconocimiento se sirven de un elevado número de dimensiones para la caracterización y posterior identificación de patrones. Así, la primera tarea debe permitirnos avanzar en la segunda, al igual que la incorporación de los predictores utilizados por los especialistas clínicos. La Tesis aborda el estudio tanto del habla continua como del habla sostenida, con el fin de aprovechar las sinergias y diferencias existentes entre ambas. En el análisis del habla continua se tomo como punto de partida un esquema que ya fue evaluado con anterioridad, y sobre el cual se ha tratado la evaluación y optimización de la representación del habla, así como la caracterización de los patrones específicos asociados al SAHS. Ello ha evidenciado la conexión entre el SAHS y los elementos fundamentales de la señal de voz: los formantes. Los resultados obtenidos demuestran que el éxito de estos sistemas se debe, fundamentalmente, a la capacidad de estas representaciones para describir dichas componentes, obviando las dimensiones ruidosas o con poca capacidad discriminativa. El esquema resultante ofrece una tasa de error por debajo del 18%, sirviéndose de clasificadores notablemente menos complejos que los descritos en el estado del arte y de una única grabación de voz de corta duración. En relación a la conexión entre el SAHS y los patrones observados, fue necesario considerar las diferencias inter- e intra-grupo, centrándonos en la articulación característica del locutor, sustituyendo los complejos modelos de clasificación por el estudio de los promedios espectrales. El resultado apunta con claridad hacia ciertas regiones del eje de frecuencias, sugiriendo la existencia de un estrechamiento sistemático en la sección del tracto en la región de la orofaringe, ya prevista en la patogénesis de este síndrome. En cuanto al habla sostenida, se han reproducido los estudios realizados sobre el habla continua en grabaciones de la vocal /a/ sostenida. Los resultados son cualitativamente análogos a los anteriores, si bien en este caso las tasas de clasificación resultan ser más bajas. Con el objetivo de identificar el sentido de este resultado se reprodujo el estudio de los promedios espectrales y de la variabilidad inter e intra-grupo. Ambos estudios mostraron importantes diferencias con los anteriores que podrían explicar estos resultados. Sin embargo, el habla sostenida ofrece otras oportunidades al establecer un entorno controlado para el estudio de la fonación, que también había sido identificada como una fuente de información para la detección del SAHS. De su estudio se pudo observar que, en el conjunto de datos disponibles, no existen variaciones que pudieran asociarse fácilmente con la fonación. Únicamente aquellas dimensiones que describen la distribución de energía a lo largo del eje de frecuencia evidenciaron diferencias significativas, apuntando, una vez más, en la dirección de las resonancias espectrales. Analizados los resultados anteriores, la Tesis afronta la fusión de ambas fuentes de información en un único sistema de clasificación. Con ello es posible mejorar las tasas de clasificación, bajo la hipótesis de que la información presente en el habla continua y el habla sostenida es fundamentalmente distinta. Esta tarea se realizo a través de un sencillo esquema de fusión que obtuvo un 88.6% de aciertos en clasificación (tasa de error del 11.4%), lo que representa una mejora significativa respecto al estado del arte. Finalmente, la combinación de este clasificador con los predictores utilizados por los especialistas clínicos ofreció una tasa del 91.3% (tasa de error de 8.7%), que se encuentra dentro del margen ofrecido por esquemas más costosos e intrusivos, y que a diferencia del propuesto, no pueden ser utilizados en la evaluación previa de los pacientes. Con todo, la Tesis ofrece una visión clara sobre la relación entre el SAHS y el habla, evidenciando el grado de madurez alcanzado por la tecnología del habla en la caracterización y detección del SAHS, poniendo de manifiesto que su uso para la evaluación de los pacientes ya sería posible, y dejando la puerta abierta a futuras investigaciones que continúen el trabajo aquí iniciado. ABSTRACT This Thesis explores the potential of speech technologies for the detection of clinical disorders connected to the upper airway. The study of speech traditionally covers both the production process and post processing of the signals involved, from the speaker up to the listener, offering an alternative path to study these pathologies. The fact that utterances embed not just the encoded message but also information about the speaker, has motivated the development of automatic systems oriented to the identification and verificaton the speaker’s identity. These have recently been boosted and reoriented either towards the characterization of traits that are common to several speakers, or to the differences between records of the same speaker collected under different conditions. The first are particularly relevant to this Thesis as these patterns could reveal the presence of features that are related to a common condition shared among different speakers, regardless of their identity. Such is the case faced in this Thesis, where the traits identified would relate to a particular pathology, directly connected to the speech production system. The Obstructive Sleep Apnea syndrome (OSA) is a paradigmatic case for analysis. It is a disorder with high prevalence among adults and affecting a larger number of them as they grow older. Patients suffering from this disorder experience episodes of involuntary cessation of breath during sleep that may last a few seconds and reproduce throughout the night, preventing proper rest. In the case of obstructive apnea, these episodes are related to the collapse of the pharynx, which interrupts the air flow. Currently, OSA diagnosis is done through a polysomnographic study, which focuses on the analysis of apnea episodes during sleep, requiring the patient to stay at the hospital for the whole night. The complexity and high cost of the procedures involved, combined with the waiting lists, have evidenced the need for screening techniques, which perhaps would not achieve outstanding performance rates but would allow clinicians to reorganize these lists ranking patients according to the severity of their condition. Among others, imaging diagnosis and anthropometric characterization of patients have evidenced the existence of anatomical patterns related to OSA that have direct influence on speech. Contributions devoted to the study of how this disorder affects scpeech are scarce and somehow contradictory. However, since the late 1980s the existence of specific patterns related to articulation, phonation and resonance is known. By that time these descriptions were virtually useless when coming to the development of an automatic system, but pointed out the existence of a link between speech and OSA. In recent years automatic processing techniques have evolved and are now able to identify significant differences in the speech of OSAS patients when compared to records from healthy subjects. Nevertheless, little is known about the connection between these new results with those published in the past and the pathogenesis of the OSA syndrome. This Thesis is aimed to progress beyond the previous research done in this area by addressing: the study of how OSA affects patients’ speech, the enhancement of automatic OSA classification based on speech analysis, and its integration with the information embedded in the predictors generally used by clinicians in preliminary patients’ examination. The first two tasks, though may appear symbiotic at first, are quite different. While studying the connection between speech and OSA requires simple narrow models that can be easily interpreted, classification requires larger models including a large number dimensions for the characterization and posterior identification of the observed patterns. Anyhow, it is clear that any progress made in the first task should allow us to improve our performance on the second one, and that the incorporation of the predictors used by clinicians shall contribute in this same direction. The Thesis considers both continuous and sustained speech analysis, to exploit the synergies and differences between them. On continuous speech analysis, a conventional speech processing scheme, designed and evaluated before this Thesis, was taken as a baseline. Over this initial system several alternative representations of the speech information were proposed, optimized and tested to select those more suitable for the characterization of OSA-specific patterns. Evidences were found on the existence of a connection between OSA and the fundamental constituents of the speech: the formants. Experimental results proved that the success of the proposed solution is well explained by the ability of speech representations to describe these specific OSA-related components, ignoring the noisy ones as well those presenting low discrimination capabilities. The resulting scheme obtained a 18% error rate, on a classification scheme significantly less complex than those described in the literature and operating on a single speech record. Regarding the connection between OSA and the observed patterns, it was necessary to consider inter-and intra-group differences for this analysis, and to focus on the articulation, replacing the complex classification models by the long-term average spectra. Results clearly point to certain regions on the frequency axis, suggesting the existence of a systematic narrowing in the vocal tract section at the oropharynx. This was already described in the pathogenesis of this syndrome. Regarding sustained speech, similar experiments as those conducted on continuous speech were reproduced on sustained phonations of vowel / a /. Results were qualitatively similar to the previous ones, though in this case perfomance rates were found to be noticeably lower. Trying to derive further knowledge from this result, experiments on the long-term average spectra and intraand inter-group variability ratios were also reproduced on sustained speech records. Results on both experiments showed significant differences from the previous ones obtained from continuous speech which could explain the differences observed on peformance. However, sustained speech also provided the opportunity to study phonation within the controlled framework it provides. This was also identified in the literature as a source of information for the detection of OSA. In this study it was found that, for the available dataset, no sistematic differences related to phonation could be found between the two groups of speakers. Only those dimensions which relate energy distribution along the frequency axis provided significant differences, pointing once again towards the direction of resonant components. Once classification schemes on both continuous and sustained speech were developed, the Thesis addressed their combination into a single classification system. Under the assumption that the information in continuous and sustained speech is fundamentally different, it should be possible to successfully merge the two of them. This was tested through a simple fusion scheme which obtained a 88.6% correct classification (11.4% error rate), which represents a significant improvement over the state of the art. Finally, the combination of this classifier with the variables used by clinicians obtained a 91.3% accuracy (8.7% error rate). This is within the range of alternative, but costly and intrusive schemes, which unlike the one proposed can not be used in the preliminary assessment of patients’ condition. In the end, this Thesis has shed new light on the underlying connection between OSA and speech, and evidenced the degree of maturity reached by speech technology on OSA characterization and detection, leaving the door open for future research which shall continue in the multiple directions that have been pointed out and left as future work.
Resumo:
Automatic systems based on speech signal analysis for the early dete ction of obstructive sleep apnea (OSA) have achieved fairly high performance rates in recent years. However, a satisfactory explanation of these results has not been available. This presentation aims at explaining via an examination of the long-term spectra of OSA patients and normal control speakers these systems’ ability to discover OSA speakers on the base of all-purpose cepstral coefficients. An in terpretation of the long- term spectra in terms of the underlying tract settings suggests that the speech of OSA patients is characterized by a pharyngeal narrowing that may be captured by acoustic cues of the spectral contour of windowed speech frames. A novel interpretation of long-term spectra in terms of the first principal component of the temporal sequence of short-term amplitude-spectra is also discussed.
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The aim of automatic pathological voice detection systems is to serve as tools, to medical specialists, for a more objective, less invasive and improved diagnosis of diseases. In this respect, the gold standard for those system include the usage of a optimized representation of the spectral envelope, either based on cepstral coefficients from the mel-scaled Fourier spectral envelope (Mel-Frequency Cepstral Coefficients) or from an all-pole estimation (Linear Prediction Coding Cepstral Coefficients) forcharacterization, and Gaussian Mixture Models for posterior classification. However, the study of recently proposed GMM-based classifiers as well as Nuisance mitigation techniques, such as those employed in speaker recognition, has not been widely considered inpathology detection labours. The present work aims at testing whether or not the employment of such speaker recognition tools might contribute to improve system performance in pathology detection systems, specifically in the automatic detection of Obstructive Sleep Apnea. The testing procedure employs an Obstructive Sleep Apnea database, in conjunction with GMM-based classifiers looking for a better performance. The results show that an improved performance might be obtained by using such approach.
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Acknowledgements This study received no specific funding. The study involved the analysis of data collected routinely as part of the national AAA screening programme in Scotland.
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The ability to tolerate a low-O2 environment varies widely among species in the animal kingdom. Some animals, such as Drosophila melanogaster, can tolerate anoxia for prolonged periods without apparent tissue injury. To determine the genetic basis of the cellular responses to low O2, we performed a genetic screen in Drosophila to identify loci that are responsible for anoxia resistance. Four X-linked, anoxia-sensitive mutants belonging to three complementation groups were isolated after screening more than 10,000 mutagenized flies. The identified recessive and dominant mutations showed marked delay in recovery from O2 deprivation. In addition, electrophysiologic studies demonstrated that polysynaptic transmission in the central nervous system of the mutant flies was abnormally long during recovery from anoxia. These studies show that anoxic tolerance can be genetically dissected.
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We have shown previously that interleukin-4 (IL-4) protects TS1αβ cells from apoptosis, but very little is known about the mechanism by which IL-4 exerts this effect. We found that Akt activity, which is dependent on phosphatidylinositol 3 kinase, is reduced in IL-4-deprived TS1αβ cells. Overexpression of wild-type Akt or a constitutively active Akt mutant protects cells from IL-4 deprivation-induced apoptosis. Readdition of IL-4 before the commitment point is able to restore Akt activity. We also show expression and c-Jun N-terminal kinase 2 activation after IL-4 deprivation. Overexpression of the constitutively activated Akt mutant in IL-4-deprived cells correlates with inhibition of c-Jun N-terminal kinase 2 activity. Finally, TS1αβ survival is independent of Bcl-2, Bcl-x, or Bax.
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The extracellular glutamate concentration ([glu]o) rises during cerebral ischemia, reaching levels capable of inducing delayed neuronal death. The mechanisms underlying this glutamate accumulation remain controversial. We used N-methyl-d-aspartate receptors on CA3 pyramidal neurons as a real-time, on-site, glutamate sensor to identify the source of glutamate release in an in vitro model of ischemia. Using glutamate and l-trans-pyrrolidine-2,4-dicarboxylic acid (tPDC) as substrates and dl-threo-β-benzyloxyaspartate (TBOA) as an inhibitor of glutamate transporters, we demonstrate that energy deprivation decreases net glutamate uptake within 2–3 min and later promotes reverse glutamate transport. This process accounts for up to 50% of the glutamate accumulation during energy deprivation. Enhanced action potential-independent vesicular release also contributes to the increase in [glu]o, by ≈50%, but only once glutamate uptake is inhibited. These results indicate that a significant rise in [glu]o already occurs during the first minutes of energy deprivation and is the consequence of reduced uptake and increased vesicular and nonvesicular release of glutamate.