Relevant bibliographies by topics / Learning – Effect of glucose on

Academic literature on the topic 'Learning – Effect of glucose on'

Author: Grafiati
Published: 10 December 2022
Last updated: 28 January 2023

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Journal articles on the topic "Learning – Effect of glucose on"

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Hieu, Huynh Trung. "GLUCOSE CORRECTION IN HANDHELD DEVICES BY REDUCING THE EFFECT OF HEMATOCRIT." Vietnam Journal of Science and Technology 54, no. 3A (March 20, 2018): 91. http://dx.doi.org/10.15625/2525-2518/54/3a/11962.

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This study presents an approach for glucose correction in handheld devices by reducing the effects of hematocrit. The hematocrit values are estimated from the transduced current curves which are produced during the chemical reactions of glucose measurement process in the handheld devices. The hematocrit estimation is performed by applying the single-hidden layer feedforward neural network which is trained by the non-iterative learning algorithm. The experimental results show that the proposed approach can improve the accuracy of glucose measurement by using the handheld devices.
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Packard, Mark G., and Norman M. White. "Effect of posttraining injections of glucose on acquisition of two appetitive learning tasks." Psychobiology 18, no. 3 (September 1990): 282–86. http://dx.doi.org/10.3758/bf03327244.

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Li, Yiru, Chi Gao, Haonan Jing, Bozhao Fan, Qi Fan, Bingliang Hu, Xuebin Liu, Quan Wang, and Yutao Feng. "Low glucose concentration estimation based on reaction with 4,4'-biphenyl boronic acid using deep learning." Highlights in Science, Engineering and Technology 2 (June 22, 2022): 312–21. http://dx.doi.org/10.54097/hset.v2i.589.

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Minimally invasive blood glucose level estimation with Raman spectroscopy is an important research field and attracts great attention. However, glucose concentration in blood is low and is difficult to be accurately measured. In this paper, we creatively proposed applying the 4,4'-biphenyl boronic acid to react with different concentrations of glucose to obtain the complex—(C36H40O18B4) n. We performed a regression of the Raman spectral data of (C36H40O18B4) n and the glucose solution separately to compare their estimation results. We applied a deep learning network, ResNet, and compared it with regression models of conventional machine learning, uniformly using ten-fold cross-validation. The experimental results show that the generated (C36H40O18B4) n can effectively improve the estimation performance of glucose. The results showed, the ResNet model does not require explicit feature extraction and can achieve fast and accurate estimation. Its performance is significantly better than the traditional linear analysis method, and the R square can reach 0.93. The method in the article can effectively improve the estimation effect of low-concentration glucose.
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Joshi, Archita C., Chetna R. Patel, and Naresh D. Kantharia. "Effect of leptin on spatial learning, memory and blood glucose level in streptozotocin induced diabetes mellitus in male wistar albino rats." International Journal of Basic & Clinical Pharmacology 9, no. 1 (December 24, 2019): 24. http://dx.doi.org/10.18203/2319-2003.ijbcp20195594.

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Background: Diabetes mellitus is known to cause cognitive impairment that can be possibly attributed to deficient levels of leptin in diabetic animals. This study was undertaken to study the effect of administration of leptin on spatial learning, memory and blood glucose levels in diabetic rats.Methods: Rats were divided into three groups. The first group was the control group. Diabetes was induced in groups 2 and 3 by streptozotocin (STZ) injection (60 mg/kg) intraperitoneally. Group 2 received saline while group 3 received leptin (0.1 mg/kg) subcutaneously for 10 days from 4th day of STZ administration. Behavioural assessment was done in T maze after 21 days of the last injection of leptin. Blood glucose levels were also analysed.Results: The number of correct arm entries decreased while time spent being immobile and time spent to reach the correct arm increased in the diabetic group when compared to the control group and correct arm entries increased while time spent immobile and time spent to reach the correct arm decreased with leptin treatment when compared to the diabetic control rats. Blood glucose levels increased in the diabetic rats while leptin administration reduced blood glucose levels in the group 3.Conclusions: Our study suggests that leptin can improve learning and memory while also producing a slight reduction in the blood glucose levels in diabetic rats.
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Lim, Ilhan, Hye-Young Joung, A. Ram Yu, Insop Shim, and Jin Su Kim. "PET Evidence of the Effect of Donepezil on Cognitive Performance in an Animal Model of Chemobrain." BioMed Research International 2016 (2016): 1–7. http://dx.doi.org/10.1155/2016/6945415.

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A considerable number of patients with breast cancer complain of cognitive impairment after chemotherapy. In this study, we showed that donepezil enhanced memory function and increased brain glucose metabolism in a rat model of cognitive impairment after chemotherapy using behavioral analysis and positron emission tomography (PET). We found that chemotherapy affected spatial learning ability, reference memory, and working memory and that donepezil improved these cognitive impairments. According to PET analysis, chemotherapy reduced glucose metabolism in the medial prefrontal cortex and hippocampus, and donepezil increased glucose metabolism in the bilateral frontal lobe, parietal lobe, and hippocampus. Reduced glucose metabolism was more prominent after treatment with doxorubicin than cyclophosphamide. Our results demonstrated the neural mechanisms for cognitive impairment after chemotherapy and show that cognition was improved after donepezil intervention using both behavioral and imaging methods. Our results suggested that donepezil can be employed clinically for the treatment of cognitive deficits after chemotherapy.
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Wang, R. "Change of learning and memory ability and IGF-1 level in type 3 diabetes rats and effect of analog P165 of APP 5-mer peptide." European Psychiatry 26, S2 (March 2011): 503. http://dx.doi.org/10.1016/s0924-9338(11)72210-6.

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ObjectiveTo investigate the effect of Analog P165 of APP5-mer peptide on change of learning and memory ability in type 3 diabetes rats.MethodHealthy adult male rats were randomly divided into 3 groups: Control group; type 3 diabetes (T3DM) group; T3DM administrated P165 group. T3DM models were induced by intracerebroventricular injection of Streptozotocin (STZ, 3 mg/kg) bilaterally. P165 groups were treated with gastric P165 (355 μg/kg) Then, learning and memory ability was detected by Morris water maze test. Body weight and serum glucose were recorded. The rat serum Insulin, Gluocagon, insulin-like growth factor-1 (IFG-1) was detected by ELISA method.ResultsIn the Morris water maze test, compared with control group, the escape latency increased significantly (p < 0.05) in model group at the 3rd day. Compared with model group, the escape latency decreased significantly (p < 0.05) in the models administrated P165 group at the 3rd day. Although there was no significant difference, the escape latency decreased in P165 group at the 4th and 5th day. From the result of rats blood serum detection, the serum IGF-1 level decreased significantly in the model group (p < 0.01) than the control group. The serum IGF-1 level increased significantly in P165 treated group(p < 0.05).The body weight and the serum glucose, insulin, gluocagon had no significant difference among the groups in the period of experiment.ConclusionThere is learning and memory impairment in the T3DM rats. P165 can raise the rats blood serum IGF-1 level, ameliorate learning and memory ability but don’t influence the serum glucose.
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Qian, Dongni, and Hong Gao. "Efficacy Analysis of Team-Based Nursing Compliance in Young and Middle-Aged Diabetes Mellitus Patients Based on Random Forest Algorithm and Logistic Regression." Computational and Mathematical Methods in Medicine 2022 (July 29, 2022): 1–7. http://dx.doi.org/10.1155/2022/3882425.

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Objective. Long-term hyperglycemia in young and middle-aged diabetic patients can be complicated with diabetic ketoacidosis, stroke, myocardial infarction, infection, and other complications. The objective was to explore the application value of machine learning in predicting the recurrence risk of young and middle-aged diabetes patients with team-based nursing intervention. Methods. Clinical data of 80 patients with diabetes treated in the Department of Endocrinology from 2019 to 2020 were retrospectively collected. The data set was divided into 70% training set ( n =56) and 30% test set ( n =24). All the selected research cases were intervened by the team-based management mode involving family and clinical doctors and nurses. The degree of diabetes knowledge learning, the level of blood glucose changes, and the psychological state of the patients were evaluated. The random forest (RF) algorithm and logistic regression prediction model were constructed to predict the risk factors of diabetes recurrence. Results. There was no significant difference in the degree of diabetes knowledge learning, the level of blood glucose changes, and the psychological state between the training set and the test set ( P > 0.05 ). The FPG, HbA1c, and 2hPG of recurrence group patients were significantly higher than those of nonrecurrence group patients, and the difference was statistically significant ( P < 0.05 ). In descending order of importance based on the RF algorithm prediction model were glucose, BMI, age, insulin, pedigree function, skin thickness, and blood diastolic pressure. The accuracy of RF and logistic regression prediction models is 81.46% and 80.21%, respectively. Conclusion. The team-based nursing model has a good effect on the blood glucose control level of middle-aged and young diabetic patients. Age, BMI, and glucose values are risk factors for diabetes. The SF algorithm has a good effect on predicting the risk of diabetes, which is worthy of further clinical application.
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Ngo, Phuong D., Susan Wei, Anna Holubová, Jan Muzik, and Fred Godtliebsen. "Control of Blood Glucose for Type-1 Diabetes by Using Reinforcement Learning with Feedforward Algorithm." Computational and Mathematical Methods in Medicine 2018 (December 30, 2018): 1–8. http://dx.doi.org/10.1155/2018/4091497.

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Background. Type-1 diabetes is a condition caused by the lack of insulin hormone, which leads to an excessive increase in blood glucose level. The glucose kinetics process is difficult to control due to its complex and nonlinear nature and with state variables that are difficult to measure. Methods. This paper proposes a method for automatically calculating the basal and bolus insulin doses for patients with type-1 diabetes using reinforcement learning with feedforward controller. The algorithm is designed to keep the blood glucose stable and directly compensate for the external events such as food intake. Its performance was assessed using simulation on a blood glucose model. The usage of the Kalman filter with the controller was demonstrated to estimate unmeasurable state variables. Results. Comparison simulations between the proposed controller with the optimal reinforcement learning and the proportional-integral-derivative controller show that the proposed methodology has the best performance in regulating the fluctuation of the blood glucose. The proposed controller also improved the blood glucose responses and prevented hypoglycemia condition. Simulation of the control system in different uncertain conditions provided insights on how the inaccuracies of carbohydrate counting and meal-time reporting affect the performance of the control system. Conclusion. The proposed controller is an effective tool for reducing postmeal blood glucose rise and for countering the effects of external known events such as meal intake and maintaining blood glucose at a healthy level under uncertainties.
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Alharbi, Ibrahim, Hindi Alharbi, Yasser Almogbel, Abdullah Alalwan, and Ahmad Alhowail. "Effect of Metformin on Doxorubicin-Induced Memory Dysfunction." Brain Sciences 10, no. 3 (March 7, 2020): 152. http://dx.doi.org/10.3390/brainsci10030152.

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Doxorubicin (DOX) is widely used to treat many types of cancer; however, it is associated with chemotherapy-related complications such as cognitive dysfunction, known as chemobrain. Chemobrain affects up to 75% of cancer survivors, and there are currently no available therapeutic options. This study aims to examine whether metformin (MET) can protect against the neurotoxicity caused by DOX treatment. Forty male rats were divided into four groups (10 rats/group): control, DOX, DOX + MET, and MET. Rats treated with DOX received five doses of 4 mg/kg DOX weekly (cumulative dose: 20 mg/kg). For the DOX-MET and MET groups, MET (3 mg/mL) was dissolved in drinking water. Behavioral and glucose tests were performed one day after treatment was completed. We found DOX (4 mg/kg/week, 5 weeks) caused learning and memory impairment in the Y-maze, novel object recognition, and elevated plus maze behavioral tests. MET did not rescue these DOX-induced memory impairments. Neither DOX nor MET nor MET + DOX altered glucose levels following the treatment. In summary, DOX treatment is associated with memory impairment in rats, but MET does not rescue this cognitive dysfunction.
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Wang, Pengwen, Caixin Su, Huili Feng, Xiaopei Chen, Yunfang Dong, Yingxue Rao, Ying Ren, et al. "Curcumin regulates insulin pathways and glucose metabolism in the brains of APPswe/PS1dE9 mice." International Journal of Immunopathology and Pharmacology 30, no. 1 (January 26, 2017): 25–43. http://dx.doi.org/10.1177/0394632016688025.

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Recent studies have shown the therapeutic potential of curcumin in Alzheimer’s disease (AD). In 2014, our lab found that curcumin reduced Aβ40, Aβ42 and Aβ-derived diffusible ligands in the mouse hippocampus, and improved learning and memory. However, the mechanisms underlying this biological effect are only partially known. There is considerable evidence in brain metabolism studies indicating that AD might be a brain-specific type of diabetes with progressive impairment of glucose utilisation and insulin signalling. We hypothesised that curcumin might target both the glucose metabolism and insulin signalling pathways. In this study, we monitored brain glucose metabolism in living APPswe/PS1dE9 double transgenic mice using a micro-positron emission tomography (PET) technique. The study showed an improvement in cerebral glucose uptake in AD mice. For a more in-depth study, we used immunohistochemical (IHC) staining and western blot techniques to examine key factors in both glucose metabolism and brain insulin signalling pathways. The results showed that curcumin ameliorated the defective insulin signalling pathway by upregulating insulin-like growth factor (IGF)-1R, IRS-2, PI3K, p-PI3K, Akt and p-Akt protein expression while downregulating IR and IRS-1. Our study found that curcumin improved spatial learning and memory, at least in part, by increasing glucose metabolism and ameliorating the impaired insulin signalling pathways in the brain.
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Dissertations / Theses on the topic "Learning – Effect of glucose on"

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Owen, Lauren. "The effect of glucose on cognition." Thesis, Lancaster University, 2008. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.538621.

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Puthanveetil, Prasanth Nair. "Glucocorticoid and its effect on cardiac glucose utilization." Thesis, University of British Columbia, 2008. http://hdl.handle.net/2429/5038.

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Glycogen is an immediate source of glucose for cardiac tissue to maintain its metabolic homeostasis. However, its excess brings about cardiac structural and physiological impairments. Previously, we have demonstrated that in hearts from dexamethasone (DEX) treated animals, glycogen accumulation was enhanced. We examined the influence of DEX on glucose entry and glycogen synthase as a means of regulating the accumulation of this stored polysaccharide. Following DEX, cardiac tissue had limited contribution towards the development of whole body insulin resistance. Measurement of GLUT4 at the plasma membrane revealed an excess presence of this transporter protein at this location. Interestingly, this was accompanied by an increase in GLUT4 in the intracellular membrane fraction, an effect that was well correlated to an increased GLUT4 mR.NA. Both total and phosphorylated AMPK increased following DEX. Immunoprecipitation of AS 160 followed by Western blotting demonstrated no change in Akt phosphorylation at Ser473 and Thr308 in DEX treated hearts. However, there was a significant increase in AMPK phosphorylation at Thr172, which correlated well with AS 160 phosphorylation. In DEX hearts, there was a considerable reduction in the phosphorylation of glycogen synthase, whereas GSK-3-β phosphorylation was augmented. Our data suggest that AMPK mediated glucose entry, combined with activation of glycogen synthase and reduction in glucose oxidation (Qi, D., et al. Diabetes 53:1790, 2004), act together to promote glycogen storage. Our data suggest that in the presence of intact insulin signaling, AMPK mediated glucose entry, combined with activation of glycogen synthase and the previously reported reduction in glucose oxidation, act together to promote glycogen storage. Should these effects persist chronically, they may explain the increased morbidity and mortality observed with long term excesses in endogenous or exogenous glucocorticoids.
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Chen, Mimi Zhu. "The effect of bariatric surgery on glucose homeostasis." Thesis, University of Bristol, 2014. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.665171.

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Bariatric surgery is very effective at inducing weight loss and diabetes resolution in morbidly obese patients. Whether WL or increased incretin response is the crucial factor in normalising diabetes is still debatable. This thesis work prospectively investigated how bariatric surgery affected insulin action and beta-cell function in patients with morbid obesity and type 2 diabetes. Understanding these can help us to optimise diabetes treatments in patients with morbid obesity. I first discussed how obesity affects insulin sensitivity and beta-cell function, evidences that bariatric surgery is superior to conventional medical therapy at inducing weight loss and euglycaemia, and its associated mechanisms. I concluded that more robust data are needed to understand the effects of LAGB and RYGB surgery on glucose homeostasis, as this will have clinical implications for patients undergoing bariatric surgery (Chapter 1). I then described and justified the methods used for investigating insulin sensitivity and insulin secretion in the two studies (GLIPO and ISP) that make up this thesis (Chapter 2). I demonstrated that at 1 week post-op, improvements in glycaemia, insulin sensitivity and weight were the same in all patients, despite unilateral increase in incretin responses in the RYGB group. At 18 months I found that RYGB (n=32) had induced greater weight loss than LAGB (n=17). This resulted in better glycaemic control, further insulin sensitivity enhancement and marked improvements in insulin secretion and pancreatic secretory reserve in this group (Chapter 3&4). Finally, I demonstrated that marked weight loss after RYGB normalised insulin signalling (PI3K-Akt), but not glucose uptake in muscle. This suggested that major defects in the insulin signalling pathway still exist and may explain why not all patients can achieve diabetes remission after RYGB (Chapter 5). In conclusion, the degree of weight loss, not enhanced incretin response, is the major determinant of glycaemic improvement after bariatric surgery. This improvement is first brought about by improvements in insulin sensitivity followed by improvements in insulin secretion.
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Messier, Claude. "Effect of glucose on memory : examination of possible mechanisms." Thesis, McGill University, 1986. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=74362.

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Previous research has shown that ingestion of sucrose or injection of glucose following a learning experience can improve an animal's memory for that experience. The present work was directed towards elucidating the mechanisms by which sucrose and glucose produce this effect. Memory was tested by determining the effects of post-training injections of various substances on a conditioned emotional response. Glucose itself exerted a dose-dependent bidirectional action on retention. This action was shown not to depend on particular blood glucose levels. Insulin did not improve retention at any of the doses tested. Fructose, a sugar that does not cross the blood-brain barrier produced a dose-response effect on retention similar to that of glucose suggesting that fructose and glucose may act through a common peripheral mechanism. The observation of a memory improvement following injections of either 2-deoxyglucose or 3-O-methylgucose, two non-metabolized glucose analogs, suggested that the effect of glucose on retention may be due to an action on glucose transport and not to any metabolic effects of glucose. Two peripheral organs were examined for their possible involvement in the memory-improving action of glucose. This action was shown not to be dependent on the adrenal medulla which has been implicated in the action of other mnemoactive treatments. Partial denervation of the liver produced a partial attenuation of the effect of glucose on retention. The results are discussed in terms of the action of reinforcers on endogenous physiological mechanisms that modulate memory consolidation.
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Quinn, C. E. "Vascular function in impaired glucose tolerance : Effect of pioglitazone." Thesis, Queen's University Belfast, 2008. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.501394.

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Inteeworn, Natalie. "The Effect of Hypothyroidism on Glucose Tolerance in Dogs." Thesis, Virginia Tech, 2008. http://hdl.handle.net/10919/32030.

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Background: Canine hypothyroidism is thought to cause abnormalities in glucose homeostasis, but the effect on glucose tolerance and insulin sensitivity has not been determined to date. Hypothesis/Objectives: The purpose of the study was to investigate whether hypothyroidism has an effect on glucose tolerance and insulin sensitivity in dogs. We hypothesized that hypothyroidism causes insulin resistance. Animals: Sixteen euthyroid bitches were randomly selected and allocated into two groups. In 8 dogs, hypothyroidism was induced by administration of 1 mCi/kg I-131. Experiments were performed on non-anesthetized, fasted dogs in anestrous approximately 12 months after hypothyroidism was induced. Methods: The insulin-modified frequently sampled intravenous glucose tolerance test (FSIGT) and minimal model analysis were used to determine basal insulin and glucose concentrations, acute insulin response to glucose (AIRg), insulin sensitivity (SI), glucose effectiveness (SG) and the disposition index (DI). Results: In the hypothyroid group, basal glucose concentrations were mildly decreased (P = 0.0079), whereas basal insulin was increased (P = 0.019). Insulin sensitivity was reduced in the hypothyroid group (P<0.001), whereas AIRg was higher (P=0.01). Other parameters were not different between groups. Conclusions/Clinical Importance: Hypothyroidism negatively affects glucose homeostasis by inducing insulin resistance. In hypothyroid dogs, the disposition index (insulin sensitivity x insulin secretion) remained unchanged due to a compensatory increase in insulin secretion, thereby maintaining glucose tolerance. In cases with impaired insulin secretion, such as canine diabetes mellitus, concurrent hypothyroidism can have important clinical implications in the successful management of the disease.
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Winnick, Jason Joseph. "Effect of aerobic exercise on peripheral glucose uptake and endogenous glucose production in type 2 diabetes mellitus." Columbus, Ohio : Ohio State University, 2006. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=osu1157551296.

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Winnick, Jason J. "Effect of aerobic exercise on peripheral glucose uptake and endogenous glucose production in type 2 diabetes mellitus." The Ohio State University, 2006. http://rave.ohiolink.edu/etdc/view?acc_num=osu1157551296.

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Pidduck, Clare. "Studies of the effect of glucose on insulin-secreting cells." Thesis, University of Leicester, 1987. http://hdl.handle.net/2381/35128.

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The long terra effects of glucose on the rate of (pro) insulin biosynthesis and the amount of preproinsulin mRNA in rat islets maintained in tissue culture were investigated. The rate of (pro)insulin synthesis was 35 times greater in islets cultured for 6 days in 8 mM glucose than it was in islets cultured in 4 mM glucose. The preproinsulin mRNA content at this time was 2 fold greater in islets incubated with 8 mM glucose compared to 4 mM glucose. The rate of (pro)insulin synthesis and the preproinsulin mRNA content of islets cultured at 8 mM glucose were maximal since no further significant increases were observed in islets cultured at 16 mM glucose for 6 days. These results indicate that the long term effects of glucose on the rate of (pro)insulin synthesis in rat islets of Langerhans is mediated both by transcriptional and translational events and that translational events exert the major controlling influence.
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Duwaihy, Mansour Mohammad. "Effect of dietary fat on glucose tolerance in the rat." Thesis, University of Surrey, 2000. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.326901.

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Books on the topic "Learning – Effect of glucose on"

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1947-, Barnett Ronald, ed. Learning to effect. London: Society for Research into Higher Education, 1992.

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Guyon, Isabelle, Alexander Statnikov, and Berna Bakir Batu, eds. Cause Effect Pairs in Machine Learning. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-21810-2.

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Stephen, Colagiuri, and Brand Miller Janette 1952-, eds. The pocket guide to the glucose revolution and losing weight. London: Coronet, 2000.

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Goh, Tracy T. The chronic effect of free fatty acids on glucose-stimulated insulin secretion in rats. Ottawa: National Library of Canada, 1999.

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Mallouh, Suzy Karen. The effect of external cues on route learning. Ottawa: National Library of Canada, 1994.

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Dorrestijn, Jannette. Signal transduction related to the metabolic action of insulin. [Leiden: University of Leiden, 1998.

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Rogerson, Parker. The effect of diauxic growth on the induction of quinoprotein glucose dehydrogenase in Agrobacterium tumefaciens. Sudbury, Ont: Laurentian University, 1998.

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Wong, Evelyn Yin-Yue. Effect of viscosity modification by fiber dose and heat treatment on postprandial blood glucose response. Ottawa: National Library of Canada = Bibliothèque nationale du Canada, 1999.

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Towell, Richard. The effect of explicit instruction in advanced language learning. Salford: University of Salford Department of Modern Languages, 1991.

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Fleming, Peter F. The effect of culture on the transfer of learning. Oxford: Oxford Brookes University, 1998.

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Book chapters on the topic "Learning – Effect of glucose on"

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Nahler, Gerhard. "learning effect." In Dictionary of Pharmaceutical Medicine, 105. Vienna: Springer Vienna, 2009. http://dx.doi.org/10.1007/978-3-211-89836-9_778.

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Spellacy, William N. "Oral Contraceptives Effect on Glucose Metabolism." In Clinical Perspectives in Obstetrics and Gynecology, 25–33. New York, NY: Springer New York, 1993. http://dx.doi.org/10.1007/978-1-4612-2730-4_3.

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Chatham, John C. "The Effect of Diabetes on Glucose Metabolism." In The Heart in Diabetes, 189–214. Boston, MA: Springer US, 1996. http://dx.doi.org/10.1007/978-1-4613-1269-7_9.

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Lee, Tanya, and Jean-Jacques Dugoua. "Nutritional Supplements and Their Effect on Glucose Control." In Advances in Experimental Medicine and Biology, 381–95. New York, NY: Springer New York, 2012. http://dx.doi.org/10.1007/978-1-4614-5441-0_27.

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Yamasaki, Kazuo. "Effect of Some Saponins on Glucose Transport System." In Advances in Experimental Medicine and Biology, 195–206. Boston, MA: Springer US, 1996. http://dx.doi.org/10.1007/978-1-4899-1367-8_18.

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Pernat, Nicole L. M., and Daniel M. Bernstein. "Revelation Effect." In Encyclopedia of the Sciences of Learning, 2863–65. Boston, MA: Springer US, 2012. http://dx.doi.org/10.1007/978-1-4419-1428-6_1080.

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Ginns, Paul. "Imagination Effect." In Encyclopedia of the Sciences of Learning, 1484–87. Boston, MA: Springer US, 2012. http://dx.doi.org/10.1007/978-1-4419-1428-6_140.

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Jin, Putai. "Redundancy Effect." In Encyclopedia of the Sciences of Learning, 2787–88. Boston, MA: Springer US, 2012. http://dx.doi.org/10.1007/978-1-4419-1428-6_200.

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Higgins, Erin Jones, and Brian H. Ross. "Typicality Effect on Learning." In Encyclopedia of the Sciences of Learning, 3357–59. Boston, MA: Springer US, 2012. http://dx.doi.org/10.1007/978-1-4419-1428-6_211.

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Low, Renae. "Modality Effect on Learning." In Encyclopedia of the Sciences of Learning, 2295–98. Boston, MA: Springer US, 2012. http://dx.doi.org/10.1007/978-1-4419-1428-6_256.

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Conference papers on the topic "Learning – Effect of glucose on"

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Sanjaya, Rahmat Eko, Bahrul Ulum, Yesi Maysita, Kartika Dwi Asni Putri, Lailatul Fithri, Andre Pratama, Ali Rohman, Sofijan Hadi, Cahyo Budiman, and Ni Nyoman Tri Puspaningsih. "The effect of glucose on local recombinant β-xylosidase and cellulase cocktails production." In THE FIRST INTERNATIONAL CONFERENCE ON NEUROSCIENCE AND LEARNING TECHNOLOGY (ICONSATIN 2021). AIP Publishing, 2023. http://dx.doi.org/10.1063/5.0111376.

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Oroojeni Mohammad Javad, Mahsa, Stephen Agboola, Kamal Jethwani, Ibrahim Zeid, and Sagar Kamarthi. "Reinforcement Learning Algorithm for Blood Glucose Control in Diabetic Patients." In ASME 2015 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2015. http://dx.doi.org/10.1115/imece2015-53420.

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In this paper a reinforcement learning algorithm is applied to regulating the blood glucose level of Type I diabetic patients using insulin pump. In this approach the agent learns from its exploration and experiences to selects its actions. In the current reinforcement learning algorithm, body weight, A1C level, and physical activity define the state of a diabetic patient. For the agent, insulin dose levels constitute the actions. There are five alternative actions for the agent: (1) raising the insulin infusion rate during 24 hours, (2) keeping it the same, (3) decreasing insulin infusion rate, (4) adjusting basal rate two times during 24 hours, and (5) adjusting basal rate three times during 24 hours. As a result of a patient’s treatment, after each time step t, the reinforcement learning agent receives a numerical reward depending on the response of the patient’s health condition. At each stage the reward is calculated as a function of the deviation of the A1C from its target value. Since reinforcement learning algorithm can select actions that improve patient condition by taking into account delayed effects it has tremendous potential to control blood glucose level in diabetic patients. This research will utilize ten years of clinical data obtained from a hospital.
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Bhattacharjya, Debarun, Dharmashankar Subramanian, and Tian Gao. "State Variable Effects in Graphical Event Models." In Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}. California: International Joint Conferences on Artificial Intelligence Organization, 2020. http://dx.doi.org/10.24963/ijcai.2020/592.

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Many real-world domains involve co-evolving relationships between events, such as meals and exercise, and time-varying random variables, such as a patient's blood glucose levels. In this paper, we propose a general framework for modeling joint temporal dynamics involving continuous time transitions of discrete state variables and irregular arrivals of events over the timeline. We show how conditional Markov processes (as represented by continuous time Bayesian networks) and multivariate point processes (as represented by graphical event models) are among various processes that are covered by the framework. We introduce and compare two simple and interpretable yet practical joint models within the framework with relevant baselines on simulated and real-world datasets, using a graph search algorithm for learning. The experiments highlight the importance of jointly modeling event arrivals and state variable transitions to better fit joint temporal datasets, and the framework opens up possibilities for models involving even more complex dynamics whenever suitable.
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Yang, Jianchen, Jack Virostko, David A. Hormuth, and Thomas E. Yankeelov. "Abstract PO-091: Modeling the effects of glucose accessibility on tumor cell growth: A comparison of mechanism-based and machine learning models." In Abstracts: AACR Virtual Special Conference on Artificial Intelligence, Diagnosis, and Imaging; January 13-14, 2021. American Association for Cancer Research, 2021. http://dx.doi.org/10.1158/1557-3265.adi21-po-091.

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Han, Longfei, Dingwen Zhang, Dong Huang, Xiaojun Chang, Jun Ren, Senlin Luo, and Junwei Han. "Self-paced Mixture of Regressions." In Twenty-Sixth International Joint Conference on Artificial Intelligence. California: International Joint Conferences on Artificial Intelligence Organization, 2017. http://dx.doi.org/10.24963/ijcai.2017/252.

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Mixture of regressions (MoR) is the well-established and effective approach to model discontinuous and heterogeneous data in regression problems. Existing MoR approaches assume smooth joint distribution for its good anlaytic properties. However, such assumption makes existing MoR very sensitive to intra-component outliers (the noisy training data residing in certain components) and the inter-component imbalance (the different amounts of training data in different components). In this paper, we make the earliest effort on Self-paced Learning (SPL) in MoR, i.e., Self-paced mixture of regressions (SPMoR) model. We propose a novel self-paced regularizer based on the Exclusive LASSO, which improves inter-component balance of training data. As a robust learning regime, SPL pursues confidence sample reasoning. To demonstrate the effectiveness of SPMoR, we conducted experiments on both the sythetic examples and real-world applications to age estimation and glucose estimation. The results show that SPMoR outperforms the state-of-the-arts methods.
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Sabah, Al-ithawi. "Measurement of blood glucose level by Faraday effect." In 4TH ELECTRONIC AND GREEN MATERIALS INTERNATIONAL CONFERENCE 2018 (EGM 2018). Author(s), 2018. http://dx.doi.org/10.1063/1.5080822.

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Ke, H., T. Yang, and G. Li. "Effect of Blood Glucose Tolerance on Pulmonary Function." In American Thoracic Society 2020 International Conference, May 15-20, 2020 - Philadelphia, PA. American Thoracic Society, 2020. http://dx.doi.org/10.1164/ajrccm-conference.2020.201.1_meetingabstracts.a3247.

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Daniel Szöllosi, Zoltan Kovács, Evelin Várvölgyi, and Andras Fekete. "The Effect of Glucose on Electronic Taste Analyzer." In 2013 Kansas City, Missouri, July 21 - July 24, 2013. St. Joseph, MI: American Society of Agricultural and Biological Engineers, 2013. http://dx.doi.org/10.13031/aim.20131619422.

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Liu, Yuwei, Wing-Kuen Ling, Chi-Kong Li, and Sam Ho. "Extreme Learning Approach for Blood Glucose Estimation." In 2018 11th International Symposium on Communication Systems, Networks and Digital Signal Processing (CSNDSP). IEEE, 2018. http://dx.doi.org/10.1109/csndsp.2018.8471836.

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Krishnan, S. Hari, P. Vinupritha, and D. Kathirvelu. "Non-Invasive Glucose Monitoring using Machine Learning." In 2020 International Conference on Communication and Signal Processing (ICCSP). IEEE, 2020. http://dx.doi.org/10.1109/iccsp48568.2020.9182434.

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Reports on the topic "Learning – Effect of glucose on"

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Lahti, Janet. The effect of glucose on the food intake of goldthioglucose injected mice. Portland State University Library, January 2000. http://dx.doi.org/10.15760/etd.1571.

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Donahue, Katrina, Laura Young, John Buse, Mark Weaver, Maihan Vu, C. Madeline Mitchell, Tamara Blakeney, Kimberlea Grimm, Jennifer Rees, and Franklin Niblock. Effect of Glucose Monitoring on Patient and Provider Outcomes in Non-Insulin Treated Diabetes. Patient-Centered Outcomes Research Institute (PCORI), March 2018. http://dx.doi.org/10.25302/3.2018.ce.12114980.

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Peden, Martha. The effect of attending behavior training upon learning. Portland State University Library, January 2000. http://dx.doi.org/10.15760/etd.2120.

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Fletcher, J. D. Technology, the Columbus Effect, and the Third Revolution in Learning. Fort Belvoir, VA: Defense Technical Information Center, March 2001. http://dx.doi.org/10.21236/ada406772.

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Caskey, Owen, and David Meier. The Effect of Guided Imagery and Internal Visualization on Learning. Fort Belvoir, VA: Defense Technical Information Center, January 1987. http://dx.doi.org/10.21236/ada179854.

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Han, Yiran, Zeyuan Lu, Meng Meng, Heran Wang, Pan Ting, Gao Tianjiao, and Mingjun Liu. Effect of Acupuncture on Glucose and Lipid Metabolism in Obese Type 2 Diabetes: A Systematic Review and Meta-Analysis. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, March 2021. http://dx.doi.org/10.37766/inplasy2021.3.0087.

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Whitehill, Betty V., and John A. Ellis. The Effect of Qualitative Explanations and Pictures on Learning, Retention, and. Fort Belvoir, VA: Defense Technical Information Center, October 1995. http://dx.doi.org/10.21236/ada300093.

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Bianchi, Nicola, Yi Lu, and Hong Song. The Effect of Computer-Assisted Learning on Students' Long-Term Development. Cambridge, MA: National Bureau of Economic Research, December 2020. http://dx.doi.org/10.3386/w28180.

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GAO, YONGQI, Kim Geok Soh, Noor Syamilah Zakaria, Roziah Mohd Rasdi, XIN JEAN LIM, Kim Lam Soh, and Guo Wei. Effect of Content Knowledge on Students’ Learning Achievement: A Systematic Review. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, July 2022. http://dx.doi.org/10.37766/inplasy2022.7.0072.

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Han, Yiran, Zeyuan Lu, Meng Meng, Heran Wang, Pan Ting, Tianjiao Gao, and Mingjun Liu. Effect of Electroacupuncture on Glucose and Lipid Metabolism in Type 2 Diabetes: A protocol for Systematic Review and Meta-Analysis. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, August 2021. http://dx.doi.org/10.37766/inplasy2021.8.0008.

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