Monografias, Dissertações e Teses
URI permanente desta comunidadehttps://repositorioacademico.universidadebrasil.edu.br/handle/123456789/1
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Resultados da Pesquisa
Item Efeitos agudos da terapia de fotobiomodulação sobre a glicemia e a concentração de glicogênio muscular de ratos(Universidade Brasil, 2020) Castro, Kenia Mendes Rodrigues; Ferraresi, Cleber; Soto, Carlos Alberto TellezPhotobiomodulation (PBM) has many effects on the energetic metabolism of musculoskeletal tissue, such as increased glycogen synthesis. However, these effects can systemic, such as glycemic control. The primary objective was to evaluate the effects of photobiomodulation (PBM) by LEDs (light-emitting diodes) on glycemic levels and muscle glycogen concentration in non-diabetic rats. The secondary objectives were to evaluate the acute effects of 4 doses of PBM (dose-response: sham, 10 J/cm2, 30 J/cm2, 60 J/cm2) on glycemic levels in rats over 6 hours (time-response: pre irradiation,1 hour, 3 hours, 6 hours) after irradiation; and to evaluate the acute effects of dose-response of PBM on muscle glycogen levels in rats after 24 hours of irradiation.Finally, to correlate glycemic modulations with muscle glycogen concentrations after applying 4 different doses of PBM. Twenty-four Wistar rats were randomly and equally allocated to four groups: sham (placebo therapy), PBM 10J/cm2, PBM 30J/cm2, and FBM 60 J/cm2. The animals fasted for 6 hours. Feeding was interrupted immediately before PBM. Evaluations of glucose level were performed at pre-irradiation times (immediately before PBM), 1h, 3h and 6h. Muscle glycogen synthesis was measured 24 hours after PBM. PBM used an arrangement of 69 LEDs (light-emitting diodes) with 35 reds (630 ± 10 nm) and 34 infrared (850 ± 20 nm); 114 mW/cm2 for 90 s (10J/cm2), 270s (30J/cm2), 540s (60J/cm2) applied to the back, gluteus, and hind limbs of the animals. The 10J/cm2 group showed lower glycemic variability over 6 hours (5.92 mg/dL) compared to the sham (13.03 mg/dL), 30J/cm2 (7.77 mg/dL) and PBM 60 J/cm2 (9.07 mg/dL) groups. The PBM groups had the highest increase in muscle glycogen (10 J/cm2 > 60 J/cm2 > 30 J/cm2 > sham), characterizing a three-phase dose-response to PBM. There was a strong negative correlation between glycemic variability over 6h and muscle glycogen concentration for 10J/cm2 (r= -0.94; p<0.001) followed by 30 J/cm2 (r= -0.84; p<0.001) and 60J/cm2 (r= -0.73; p<0.006). The results suggest that PBM can play a very important role in the control of glycemic levels, and its possible mechanism of action is the induction of greater muscle glycogen synthesis independent of physical exercise.Item Modelo preditivo do nível glicêmico por monitoramento em tempo real em indivíduos portadores de diabetes mellitus tipo II(Universidade Brasil, 2022) Mourão, Marcelo Henrique de Vasconcelos; Amaral, Marcelo Magri; Santos, Laurita dosThis research deals with a prediction of the glycemic levels of people with Diabetes Mellitus II, collected through a continuous glycemic monitoring system, based on the architecture of LSTM neural networks. Diabetes, one of the non-communicable chronic diseases, is characterized by hyperglycemia in the bloodstream generated by insulin resistance. The control of this disease can occur through carbohydrate counting according to the glycemic level, which according to the anthropometric evaluation is quantified by the physician. However, this approach is not always well accepted by diabetics, who end up adhering to medication for their control. Despite this, some diabetics end up using continuous blood glucose monitoring sensors, which favored verifying whether the glycemic data collected every 15 minutes could be predicted. The glycemia of 20 patients was measured over a period of 14 days using real-time monitoring. During this period, eating habits were recorded to count ingested carbohydrates, using the carbohydrate counting app created by SBD. Using an artificial intelligence model (LSTM) a blood glucose prediction model was created. With this model, it was verified that the predicted values followed the real glycemic movement, anticipating 5 hours with glycemic data of 12 continuous hours, that is, 20 predicted observations and 48 observations collected by the glycemia sensor for each individual. A general predictive model was performed with 20 volunteers and two personalized ones. The glycemic data of the collected diabetics had a positive performance, as the predicted values followed the glycemic movement, with a glycemic peak of 170 mg/dL at 9 am and 180 mg/dL at 1 pm, converging with the data obtained from the blood count. of carbohydrates, physical and anthropometric evaluation, observed with the peaks of glycemia, lifestyles of the volunteers and the total carbohydrates consumed daily. The glycemic data of non-diabetics had a positive performance, given that the predicted data followed the actual glycemic movement. This model, therefore, can predict several applications directly in rehabilitation, contributing as one of the important instruments for improving the patient's quality of life.