Junho 2019 vol. 2 num. 1 - Encontro Anual da Biofísica 2019

Artigo completo - Open Access.

Idioma principal | Segundo idioma

ALTERAÇÕES NOS PADRÕES DO ECOG DE RATOS INDUZIDOS AO DIABETES MELLITUS TIPO 2

ALTERAÇÕES NOS PADRÕES DO ECOG DE RATOS INDUZIDOS AO DIABETES MELLITUS TIPO 2

Silva, Eva Luana Almeida da ; Pessoa, Daniella Tavares ; Santos, Ardilles Juan Carlos Alves dos ; Aguiar, Leandro Álvaro de Alcantara ; Costa, Edbhergue Ventura Lola ; Nogueira, Romildo de Albuquerque ;

Artigo completo:

A glicose é principal fonte da energia para o cérebro dos mamíferos e o metabolismo desta molécula acaba fornecendo energia para funções cerebrais através da geração de ATP. Desregulação do metabolismo da glicose podem promover injúrias ao cérebro tanto através da hipoglicemia quanto da hiperglicemia, essas variações dos níveis glicêmicos plasmáticos podem ser apresentadas pelo individuo portador de diabetes. A glicose é a base para manutenção das células neuronais e não neuronais e também serve como precursora para síntese de neurotransmissores (TYCE e WONG, 1980; MERGENTHALER et al., 2013). Desregulação do metabolismo da glicose podem promover injúrias ao cérebro tanto através da hipoglicemia quanto da hiperglicemia (MERGENTHALER et al., 2013), essas variações dos níveis glicêmicos plasmáticos podem ser apresentadas pelo individuo portador de diabetes (HOFFMAN et al. 1989; CRYER, 2012).

Artigo completo:

A glicose é principal fonte da energia para o cérebro dos mamíferos e o metabolismo desta molécula acaba fornecendo energia para funções cerebrais através da geração de ATP. Desregulação do metabolismo da glicose podem promover injúrias ao cérebro tanto através da hipoglicemia quanto da hiperglicemia, essas variações dos níveis glicêmicos plasmáticos podem ser apresentadas pelo individuo portador de diabetes. A glicose é a base para manutenção das células neuronais e não neuronais e também serve como precursora para síntese de neurotransmissores (TYCE e WONG, 1980; MERGENTHALER et al., 2013). Desregulação do metabolismo da glicose podem promover injúrias ao cérebro tanto através da hipoglicemia quanto da hiperglicemia (MERGENTHALER et al., 2013), essas variações dos níveis glicêmicos plasmáticos podem ser apresentadas pelo individuo portador de diabetes (HOFFMAN et al. 1989; CRYER, 2012).

Download (PDF)

Palavras-chave: -,

Palavras-chave: -,

DOI: 10.5151/biofisica2019-07

Referências bibliográficas
  • [1] AGUIAR, L. A. A. et al. Long-term correlation of the electrocorticogram as a bioindicator of brain exposure to ionizing radiation. Brazilian Journal of Medical and Biological Research, n. AHEAD, p. 00-00, 2015.
  • [2] BACHMANN, Maie et al. Methods for classifying depression in single channel EEG using linear and nonlinear signal analysis. Computer methods and programs in biomedicine, v. 155, p. 11-17, 2018.
  • [3] BASCONES-MARTINEZ, Antonio et al. Periodontal disease and diabetes-Review of the Literature. Med Oral Patol Oral Cir Bucal, v. 16, n. 6, p. e722-9, 2011.
  • [4] BIESSELS, Geert Jan; LUCHSINGER, Jose A. (Ed.). Diabetes and the Brain. New York, NY, USA: Humana Press, 2009.
  • [5] BLUMER, Ian; CLEMENT, Maureen. Type 2 diabetes, hypoglycemia, and basal insulins: ongoing challenges. Clinical therapeutics, v. 39, n. 8, p. S1-S11, 2017.
  • [6] COORAY, Gerald et al. Effects of intensified metabolic control on CNS function in type 2 diabetes. Psychoneuroendocrinology, v. 36, n. 1, p. 77-86, 2011.
  • [7] CUEVAS, Heather. Assessment of Cognitive Function in Type 2 Diabetes. The Journal for Nurse Practitioners, v. 13, n. 7, p. e311-e315, 201
  • [8] CUI, Ying et al. Altered spontaneous brain activity in type 2 diabetes: a resting-state functional MRI study. Diabetes, v. 63, n. 2, p. 749-760, 2014.
  • [9] FRANCIONI, Fabiane Ferreira; SILVA, Denise Guerreiro Vieira da. The process of people with Diabetes Mellitus living helthful through a living group. Texto & Contexto-Enfermagem, v. 16, n. 1, p. 105-111, 2007.
  • [10] GUARIGUATA, Leonor et al. Global estimates of diabetes prevalence for 2013 and projections for 2035. Diabetes research and clinical practice, v. 103, n. 2, p. 137-149, 2014.
  • [11] Hoffman RG, Speelman DJ, Hinnen DA, Conley KL, Guthrie RA, HOFFMAN, Richard G. et al. Changes in cortical functioning with acute hypoglycemia and hyperglycemia in type I diabetes. Diabetes care, v. 12, n. 3, p. 193-197, 1989.
  • [12] JUHL, Claus B. et al. Automated detection of hypoglycemia-induced EEG changes recorded by subcutaneous electrodes in subjects with type 1 diabetes—the brain as a biosensor. Diabetes research and clinical practice, v. 88, n. 1, p. 22-28, 2010.
  • [13] KADIR, Ahmadul et al. Positron emission tomography imaging and clinical progression in relation to molecular pathology in the first Pittsburgh Compound B positron emission tomography patient with Alzheimer’s disease. Brain, v. 134, n. 1, p. 301-317, 2010.
  • [14] KANDIMALLA, Ramesh; THIRUMALA, Vani; REDDY, P. Hemachandra. Is Alzheimer's disease a type 3 diabetes? A critical appraisal. Biochimica et Biophysica Acta (BBA)-Molecular Basis of Disease, v. 1863, n. 5, p. 1078-1089, 2017.
  • [15] KRAMBERGER, Milica G. et al. Subclinical white matter lesions and medial temporal lobe atrophy are associated with EEG slowing in a memory clinic cohort. Clinical Neurophysiology, v. 128, n. 9, p. 1575-1582, 2017.
  • [16] LI, Wei et al. Type 2 diabetes mellitus and cerebrospinal fluid Alzheimer's disease biomarker amyloid β1-42 in Alzheimer's Disease Neuroimaging Initiative participants. Alzheimer's & Dementia: Diagnosis, Assessment & Disease Monitoring, v. 10, p. 94-98, 2018.
  • [17] MERGENTHALER, Philipp et al. Sugar for the brain: the role of glucose in physiological and pathological brain function. Trends in neurosciences, v. 36, n. 10, p. 587-597, 2013.
  • [18] MORETTI, Davide Vito et al. Cerebral PET glucose hypometabolism in subjects with mild cognitive impairment and higher EEG high-alpha/low-alpha frequency power ratio. Neurobiology of aging, v. 58, p. 213-224, 2017.
  • [19] NAKISA, Bahareh et al. Evolutionary computation algorithms for feature selection of EEG-based emotion recognition using mobile sensors. Expert Systems with Applications, v. 93, p. 143-155, 2018.
  • [20] NAYAK, Yogendra et al. Antidiabetic activity of benzopyrone analogues in nicotinamide-streptozotocin induced type 2 diabetes in rats. The Scientific World Journal, v. 2014, 2014.
  • [21] NOUWEN, Arie et al. Microstructural abnormalities in white and gray matter in obese adolescents with and without type 2 diabetes. NeuroImage: Clinical, v. 16, p. 43-51, 2017.
  • [22] PESSOA, Daniella et al. Analysis of electrocorticographic patterns in rats fed standard or hyperlipidic diets in a normal state or during status epilepticus. Nutritional neuroscience, v. 19, n. 5, p. 206-212, 2016.
  • [23] PESSOA, Daniella Tavares et al. Effect of diet with omega-3 in basal brain electrical activity and during status epilepticus in rats. Epilepsy research, v. 137, p. 33-38, 2017.
  • [24] RACHMIEL, M. et al. Hyperglycemia is associated with simultaneous alterations in electrical brain activity in youths with type 1 diabetes mellitus. Clinical Neurophysiology, v. 127, n. 2, p. 1188-1195, 2016.
  • [25] ROFEY, Dana L. et al. Brain volume and white matter in youth with type 2 diabetes compared to obese and normal weight, non-diabetic peers: A pilot study. International Journal of Developmental Neuroscience, v. 46, p. 88-91, 2015.
  • [26] SCARPA, Fabio et al. Hypoglycemia-induced EEG complexity changes in Type 1 diabetes assessed by fractal analysis algorithm. Biomedical Signal Processing and Control, v. 38, p. 168-173, 2017. SCHACTER, Daniel L. EEG theta waves and psychological phenomena: A review and analysis. Biological psychology, v. 5, n. 1, p. 47-82, 1977.
  • [27] SNOGDAL, Lena Sønder et al. Detection of hypoglycemia associated EEG changes during sleep in type 1 diabetes mellitus. Diabetes research and clinical practice, v. 98, n. 1, p. 91-97, 2012.
  • [28] TAPLIN, Craig E.; BARKER, Jennifer M. Autoantibodies in type 1 diabetes. Autoimmunity, v. 41, n. 1, p. 11-18, 2008.
  • [29] TYCE, Gertrude M.; WONG, Kar-Lit. Conversion of glucose to neurotransmitter amino acids in the brains of young and aging rats. Experimental gerontology, v. 15, n. 6, p. 527-532, 1980.
  • [30] VIEIRA, Marcelo NN; LIMA-FILHO, Ricardo AS; DE FELICE, Fernanda G. Connecting Alzheimer's disease to diabetes: underlying mechanisms and potential therapeutic targets. Neuropharmacology, v. 136, p. 160-171, 2018.
  • [31] WELCH, Peter. The use of fast Fourier transform for the estimation of power spectra: a method based on time averaging over short, modified periodograms. IEEE Transactions on audio and electroacoustics, v. 15, n. 2, p. 70-73, 1967.
  • [32] WHITING, David R. et al. IDF diabetes atlas: global estimates of the prevalence of diabetes for 2011 and 2030. Diabetes research and clinical practice, v. 94, n. 3, p. 311-321, 2011.
  • [33] WU, Guangyao et al. Brain gray matter changes in type 2 diabetes mellitus: a meta-analysis of whole-brain voxel-based morphometry study. Journal of Diabetes and its Complications, v. 31, n. 12, p. 1698-1703, 2017.
Como citar:

Silva, Eva Luana Almeida da; Pessoa, Daniella Tavares; Santos, Ardilles Juan Carlos Alves dos; Aguiar, Leandro Álvaro de Alcantara; Costa, Edbhergue Ventura Lola; Nogueira, Romildo de Albuquerque; "ALTERAÇÕES NOS PADRÕES DO ECOG DE RATOS INDUZIDOS AO DIABETES MELLITUS TIPO 2", p. 20-23 . In: Anais do Encontro Anual da Biofísica 2019. São Paulo: Blucher, 2019.
ISSN 2526--607-1, DOI 10.5151/biofisica2019-07

últimos 30 dias | último ano | desde a publicação


downloads


visualizações


indexações