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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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Hooshmandja, Manizhe, Aeen Mohammadi, Alireza Esteghamti, Khadije Aliabadi, and Mohammadreza Nili. "Effect of mobile learning (application) on self-care behaviors and blood glucose of type 2 diabetic patients." Journal of Diabetes & Metabolic Disorders 18, no. 2 (July 12, 2019): 307–13. http://dx.doi.org/10.1007/s40200-019-00414-1.
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Riby, Leigh M., Hazel McMurtrie, Jonathan Smallwood, Carrie Ballantyne, Andrew Meikle, and Emily Smith. "The facilitative effects of glucose ingestion on memory retrieval in younger and older adults: is task difficulty or task domain critical?" British Journal of Nutrition 95, no. 2 (February 2006): 414–20. http://dx.doi.org/10.1079/bjn20051649.
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The ingestion of a glucose-containing drink has been shown to improve cognitive performance, particularly memory functioning. However, it remains unclear as to the extent to which task domain and task difficulty moderate the glucose enhancement effect. The aim of this research was to determine whether boosts in performance are restricted to particular classes of memory (episodic v. semantic) or to tasks of considerable cognitive load. A repeated measures (25g glucose v saccharin), counterbalanced, double-blind design was used with younger and older adults. Participants performed a battery of episodic (e.g. paired associate learning) and semantic memory (e.g. category verification) tasks under low and high cognitive load. Electrophysiological measures (heart rate and galvanic skin response) of arousal and mental effort were also gathered. The results indicated that whilst glucose appeared to aid episodic remembering, cognitive load did not exaggerate the facilitative effect. For semantic memory, there was little evidence to suggest that glucose can boost semantic memory retrieval even when the load was manipulated. One exception was that glucose facilitated performance during the difficult category fluency task. Regardless, the present findings are consistent with the domain-specific account in which glucose acts primarily on the hippocampal region, which is known to support episodic memory. The possible contribution of the hippocampus in semantic memory processing is also discussed.
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Albers, David J., Matthew E. Levine, Andrew Stuart, Lena Mamykina, Bruce Gluckman, and George Hripcsak. "Mechanistic machine learning: how data assimilation leverages physiologic knowledge using Bayesian inference to forecast the future, infer the present, and phenotype." Journal of the American Medical Informatics Association 25, no. 10 (October 1, 2018): 1392–401. http://dx.doi.org/10.1093/jamia/ocy106.
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Abstract We introduce data assimilation as a computational method that uses machine learning to combine data with human knowledge in the form of mechanistic models in order to forecast future states, to impute missing data from the past by smoothing, and to infer measurable and unmeasurable quantities that represent clinically and scientifically important phenotypes. We demonstrate the advantages it affords in the context of type 2 diabetes by showing how data assimilation can be used to forecast future glucose values, to impute previously missing glucose values, and to infer type 2 diabetes phenotypes. At the heart of data assimilation is the mechanistic model, here an endocrine model. Such models can vary in complexity, contain testable hypotheses about important mechanics that govern the system (eg, nutrition’s effect on glucose), and, as such, constrain the model space, allowing for accurate estimation using very little data.
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Jaloli, Mehrad, William Lipscomb, and Marzia Cescon. "Incorporating the Effect of Behavioral States in Multi-Step Ahead Deep Learning Based Multivariate Predictors for Blood Glucose Forecasting in Type 1 Diabetes." BioMedInformatics 2, no. 4 (December 16, 2022): 715–26. http://dx.doi.org/10.3390/biomedinformatics2040048.
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Behavioral factors can affect the blood glucose (BG) levels in people with type 1 diabetes (T1D), therefore, their effects need to be incorporated in blood glucose management for these individuals. Accordingly, in this work, we study the effect of two behavioral states, physical activity (PA) and stress state (SS), on BG fluctuations in individuals with T1D. We provide two methods for quantifying biomarkers related to PA and SS using raw acceleration (ACC) and electrodermal activity (EDA) data collected with a wearable device. We evaluate the impact of PA and SS on BG fluctuation by adding the derived behavior-related biomarkers in two cutting-edge deep learning-based glucose predictive models, a long short-term memory (LSTM) and a convolutional neural network (CNN)-LSTM network, for prediction horizons (PHs) of 30, 60 and 90 min. Through an ablation study, we demonstrate that incorporating the estimated behavior-related biomarkers improves the BG predictive model’s performance obtaining mean absolute error (MAE) 9.13 ± 0.95, 17.75 ± 1.93 and 31.85 ± 2.88 in [mg/dL], root mean square error (RMSE), 12.35 ± 1.06, 24.71 ± 2.31 and 41.64 ± 4.12 in [mg/dL], and coefficient of determination (R2), 95.34 ± 3.34, 78.87 ± 4.35 and 60.11 ± 4.76 in [%], for the LSTM model; and MAE 9.37 ± 0.88, 17.87 ± 1.67 and 29.47 ± 2.13 in [mg/dL], RMSE 12.51 ± 1.40, 24.37 ± 2.49 and 39.52 ± 3.89 in [mg/dL], and R2 94.65 ± 3.90, 78.37 ± 4.11 and 61.12 ± 4.30 in [%], for the CNN-LSTM model, respectively, across all PHs. Additionally, we illustrate the generalizability of the proposed models by performing both population- and patient-wise.
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Mana, Lulu, Huili Feng, Yunfang Dong, Yahan Wang, Jing Shi, Jinzhou Tian, and Pengwen Wang. "Effect of Chinese herbal compound GAPT on the early brain glucose metabolism of APP/PS1 transgenic mice." International Journal of Immunopathology and Pharmacology 33 (January 2019): 205873841984148. http://dx.doi.org/10.1177/2058738419841482.
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A number of studies have shown that early-stage Alzheimer’s disease (AD) is associated with abnormal brain glucose metabolism before cognitive decline, which may be the key pathological change of asymptomatic AD. The pathogenesis of AD in traditional Chinese medicine is kidney deficiency and turbid phlegm. Based on this, GAPT (a mixture of herbal extracts) was made to invigorate kidney Yang and eliminate phlegm. Previous studies have shown that GAPT can improve and delay the memory decline, but the specific therapeutic target of AD in an early stage has not been studied. The aim of this study was to investigate the effect of GAPT on glucose metabolism in the early stage of AD. Eighty-eight 3-month-old male APP/PS1 transgenic mice were randomly divided into model group; donepezil group; and low, middle and high GAPT dosage groups. Twelve 3-month-old C57BL/6J mice were used as a control group. The Morris water maze test and the Step-Down Passive-Avoidance test were used to evaluate learning and memory ability. Cerebral extraction and the accumulation of glucose were scanned with a micro-positron-emission tomography (PET) imaging system. Immunohistochemistry, western blot analysis and polymerase chain reaction (PCR) were used to detect the expression of the PI3K/AKT-mTOR signalling pathway–related proteins and messenger RNAs (mRNAs) in hippocampus of APP/PS1 transgenic mice after 3 months of drug administration. GAPT can shorten the escape latency and error numbers compared to the model group. In micro-PET imaging analysis, GAPT can increase the glucose uptake average rate in the frontal lobe, temporal lobe, parietal lobe and hippocampus. The immunohistochemistry, western blot analysis and PCR results indicated that GAPT can increase the expression of PI3K, AKT, GLUT1 and GLUT3 in the hippocampus of APP/PS1 transgenic mice. In summary, GAPT can improve brain glucose metabolism damage in APP/PS1 transgenic mice, mainly by increasing brain glucose uptake, increasing glucose transport and improving the insulin signalling pathway.
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Kraft, Vanessa, Katja Schmitz, Annett Wilken-Schmitz, Gerd Geisslinger, Marco Sisignano, and Irmgard Tegeder. "Trehalose Reduces Nerve Injury Induced Nociception in Mice but Negatively Affects Alertness." Nutrients 13, no. 9 (August 25, 2021): 2953. http://dx.doi.org/10.3390/nu13092953.
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Trehalose, a sugar from fungi, mimics starvation due to a block of glucose transport and induces Transcription Factor EB- mediated autophagy, likely supported by the upregulation of progranulin. The pro-autophagy effects help to remove pathological proteins and thereby prevent neurodegenerative diseases such as Alzheimer’s disease. Enhancing autophagy also contributes to the resolution of neuropathic pain in mice. Therefore, we here assessed the effects of continuous trehalose administration via drinking water using the mouse Spared Nerve Injury model of neuropathic pain. Trehalose had no effect on drinking, feeding, voluntary wheel running, motor coordination, locomotion, and open field, elevated plus maze, and Barnes Maze behavior, showing that it was well tolerated. However, trehalose reduced nerve injury-evoked nociceptive mechanical and thermal hypersensitivity as compared to vehicle. Trehalose had no effect on calcium currents in primary somatosensory neurons, pointing to central mechanisms of the antinociceptive effects. In IntelliCages, trehalose-treated mice showed reduced activity, in particular, a low frequency of nosepokes, which was associated with a reduced proportion of correct trials and flat learning curves in place preference learning tasks. Mice failed to switch corner preferences and stuck to spontaneously preferred corners. The behavior in IntelliCages is suggestive of sedative effects as a “side effect” of a continuous protracted trehalose treatment, leading to impairment of learning flexibility. Hence, trehalose diet supplements might reduce chronic pain but likely at the expense of alertness.
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Prexler, Carolin, Marie Sophie Knape, Janina Erlewein-Schweizer, Wolfgang Roll, Katja Specht, Klaus Woertler, Wilko Weichert, et al. "Correlation of Transcriptomics and FDG-PET SUVmax Indicates Reciprocal Expression of Stemness-Related Transcription Factor and Neuropeptide Signaling Pathways in Glucose Metabolism of Ewing Sarcoma." Cancers 14, no. 23 (December 5, 2022): 5999. http://dx.doi.org/10.3390/cancers14235999.
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Background: In Ewing sarcoma (EwS), long-term treatment effects and poor survival rates for relapsed or metastatic cases require individualization of therapy and the discovery of new treatment methods. Tumor glucose metabolic activity varies significantly between patients, and FDG-PET signals have been proposed as prognostic factors. However, the biological basis for the generally elevated but variable glucose metabolism in EwS is not well understood. Methods: We retrospectively included 19 EwS samples (17 patients). Affymetrix gene expression was correlated with maximal standardized uptake value (SUVmax) using machine learning, linear regression modelling, and gene set enrichment analyses for functional annotation. Results: Expression of five genes correlated (MYBL2, ELOVL2, NETO2) or anticorrelated (FAXDC2, PLSCR4) significantly with SUVmax (adjusted p-value ≤ 0.05). Additionally, we identified 23 genes with large SUVmax effect size, which were significantly enriched for “neuropeptide Y receptor activity (GO:0004983)” (adjusted p-value = 0.0007). The expression of the members of this signaling pathway (NPY, NPY1R, NPY5R) anticorrelated with SUVmax. In contrast, three transcription factors associated with maintaining stemness displayed enrichment of their target genes with higher SUVmax: RNF2, E2F family, and TCF3. Conclusion: Our large-scale analysis examined comprehensively the correlations between transcriptomics and tumor glucose utilization. Based on our findings, we hypothesize that stemness may be associated with increased glucose uptake, whereas neuroectodermal differentiation may anticorrelate with glucose uptake.
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Jung, Eun-Yee, Mi-Sook Lee, Chang Joon Ahn, Seung-Hun Cho, Hyunsu Bae, and Insop Shim. "The Neuroprotective Effect of Gugijihwang-Tang on Trimethyltin-Induced Memory Dysfunction in the Rat." Evidence-Based Complementary and Alternative Medicine 2013 (2013): 1–6. http://dx.doi.org/10.1155/2013/542081.
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Gugijihwang-Tang (the herbal formula PM012), a decoction consisting of several herbs includingRehmanniae Radix Preparata, has been widely used as herbal treatment for dementia. In order to investigate the neuroprotective action of this prescription, we examined the effect of Gugijihwang-Tang on learning and memory using the Morris water maze and [F-18]FDG micro PET neuroimaging technique. After injection of trimethyltin (TMT, 8.0 mg/kg, i.p.), which is a potent toxicant that selectively kills cells in the central nervous system, rats were administered Gugijihwang-Tang (100 mg/kg, p.o.) daily for two weeks, followed by the Morris water maze tasks and [F-18]FDG micro PET neuroimaging. In Gugijihwang-Tang administered TMT-treated rats, they showed improved learning and memory abilities in water maze tasks and glucose metabolism, suggesting that Gugijihwang-Tang plays effectively positive role in the improvement of brain function including learning and memory after TMT-induced neurodegeneration. Taken together, our results suggested that the Gugijihwang-Tang should be useful for developing strategies protecting nervous system and improving brain function.
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Dai, Yun, Yuanzi Zhao, Masatoshi Tomi, Bo-Chul Shin, Shanthie Thamotharan, Andrey Mazarati, Raman Sankar, et al. "Sex-Specific Life Course Changes in the Neuro-Metabolic Phenotype of Glut3 Null Heterozygous Mice: Ketogenic Diet Ameliorates Electroencephalographic Seizures and Improves Sociability." Endocrinology 158, no. 4 (January 24, 2017): 936–49. http://dx.doi.org/10.1210/en.2016-1816.
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Abstract We tested the hypothesis that exposure of glut3+/− mice to a ketogenic diet ameliorates autism-like features, which include aberrant behavior and electrographic seizures. We first investigated the life course sex-specific changes in basal plasma–cerebrospinal fluid (CSF)–brain metabolic profile, brain glucose transport/uptake, glucose and monocarboxylate transporter proteins, and adenosine triphosphate (ATP) in the presence or absence of systemic insulin administration. Glut3+/− male but not female mice (5 months of age) displayed reduced CSF glucose/lactate concentrations with no change in brain Glut1, Mct2, glucose uptake or ATP. Exogenous insulin-induced hypoglycemia increased brain glucose uptake in glut3+/− males alone. Higher plasma-CSF ketones (β-hydroxybutyrate) and lower brain Glut3 in females vs males proved protective in the former while enhancing vulnerability in the latter. As a consequence, increased synaptic proteins (neuroligin4 and SAPAP1) with spontaneous excitatory postsynaptic activity subsequently reduced hippocampal glucose content and increased brain amyloid β1-40 deposition in an age-dependent manner in glut3+/− males but not females (4 to 24 months of age). We then explored the protective effect of a ketogenic diet on ultrasonic vocalization, sociability, spatial learning and memory, and electroencephalogram seizures in male mice (7 days to 6 to 8 months of age) alone. A ketogenic diet partially restored sociability without affecting perturbed vocalization, spatial learning and memory, and reduced seizure events. We conclude that (1) sex-specific and age-dependent perturbations underlie the phenotype of glut3+/− mice, and (2) a ketogenic diet ameliorates seizures caused by increased cortical excitation and improves sociability, but fails to rescue vocalization and cognitive deficits in glut3+/− male mice.
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V, Ajay, Andrew S. Dhas, Anish Sanchith, Chandan K R, and Vinay B V. "DIABETES PREDICTION USING MACHINE LEARNING." International Research Journal of Computer Science 9, no. 8 (August 12, 2022): 190–94. http://dx.doi.org/10.26562/irjcs.2022.v0908.007.
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Diabetes is a fatal chronic illness that adversely affects every system in the body. This disease affects many people, and its adverse effects cause a sizable number of sufferers to pass away each year. High blood glucose levels are a concern for diabetic patients. It is quite challenging to make a robust and accurate prediction of diabetes due to the tiny amount of labeled data and outliers (or incomplete data) in the datasets for diabetes. We are putting up a reliable framework for diabetes prediction in this literature. Untreated diabetes may result in hearing loss, renal damage, heart and blood vessel disease, poor wound healing, and a few skin problems. For the classification, early diagnosis, and prediction of diabetes, a machine learning (ML)-based strategy has been put forth.
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Zhu, Jinpiao, Zongze Zhang, Junke Jia, Lirong Wang, Qiuyue Yang, Yanlin Wang, and Chang Chen. "Sevoflurane Induces Learning and Memory Impairment in Young Mice Through a Reduction in Neuronal Glucose Transporter 3." Cellular and Molecular Neurobiology 40, no. 6 (December 28, 2019): 879–95. http://dx.doi.org/10.1007/s10571-019-00779-0.
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AbstractSevoflurane, which is widely used in paediatric anaesthesia, induces neural apoptosis in the developing brain and cognitive impairment in young mammals. Glucose hypometabolism is the key pathophysiological modulator of cognitive dysfunction. However, the effects and mechanism of sevoflurane on cerebral glucose metabolism after its use as an anaesthetic and its complete elimination are still unknown. We therefore investigated the influence of sevoflurane on neuronal glucose transporter isoform 3 (GLUT3) expression, glucose metabolism and apoptosis in vivo and in vitro and on neurocognitive function in young mice 24 h after the third exposure to sevoflurane. Postnatal day 14 (P14) mice and neural cells were exposed to 3% sevoflurane 2 h daily for three days. We found that sevoflurane anaesthesia decreased GLUT3 gene and protein expression in the hippocampus and temporal lobe, consistent with a decrease in glucose metabolism in the hippocampus and temporal lobe observed by [18F] fluorodeoxyglucose positron emission tomography (18F-FDG PET). Moreover, sevoflurane anaesthesia increased the number of TUNEL-positive cells and the levels of Bax, cleaved caspase 3 and cleaved PARP and reduced Bcl-2 levels in the hippocampus and temporal lobe. Young mice exposed to sevoflurane multiple times also showed learning and memory impairment. In addition, sevoflurane inhibited GLUT3 expression in primary hippocampal neurons and PC12 cells. GLUT3 overexpression in cultured neurons ameliorated the sevoflurane-induced decrease in glucose utilization and increase in the apoptosis rate. These data indicate that GLUT3 deficiency may contribute to sevoflurane-induced learning and memory deficits in young mice.
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Kukula, Osman, and Caner Günaydın. "Atorvastatin reduces alloxan-induced impairment of aversive stimulus memory in mice." Asian Biomedicine 16, no. 2 (April 1, 2022): 71–78. http://dx.doi.org/10.2478/abm-2022-0009.
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Abstract Background An association between dysregulated glucose levels in patients with diabetes mellitus and detrimental effects on the central nervous system, particularly in Alzheimer disease, has been recognized. Atorvastatin treatment has improved memory and cognition in some patients with diabetes mellitus and Alzheimer disease. Objectives To determine possible neuroprotective effects of atorvastatin on memory and cognition by measuring changes in an adverse stimulus avoidance learning deficit induced by alloxan in a murine model of diabetes mellitus and impaired memory and cognition. Methods We administered 150 mg/kg and 100 mg/kg alloxan in saline (intraperitoneally, i.p.) at a 48 h interval to produce a model of diabetes mellitus in male BALB/c mice. An oral glucose tolerance test (OGTT) was used to assess blood glucose regulation. After demonstrating hyperglycemia in mice (n = 7 per group) we administered vehicle (saline, i.p.), atorvastatin (10 mg/kg, i.p.), or liraglutide (200 μg/kg, i.p.) for 28 d except for those in a negative control group, which were given saline instead of alloxan, and a group administered atorvastatin alone, which were given saline instead of alloxan followed by atorvastatin (10 mg/kg, i.p.) for 28 d. Locomotor activity was measured 24 h after the final drug treatments, and subsequently their learned behavioral response to an adverse electrical stimulus to their plantar paw surface in a dark compartment was measured using a passive avoidance apparatus (Ugo Basile) in a model of impaired memory and cognition associated with Alzheimer disease. To determine any deficit in their learned avoidance of the adverse stimulus, we measured the initial latency or time mice spent in an illuminated white compartment before entering the dark compartment in the learning trial, and on the day after learning to avoid the adverse stimulus, the retention period latency in the light compartment and time spent in the dark compartment. Results Atorvastatin alone produced no significant change in blood glucose levels (F 4,10 = 0.80, P = 0.55) within 2 h. Liraglutide decreased blood glucose levels after 0.5 h (F 4,10 = 11.7, P < 0.001). We found no significant change in locomotor activity in any group. In mice with alloxan-induced diabetes, atorvastatin significantly attenuated the decreased avoidance associated with the diabetes (F 4,30 = 38.0, P = 0.02) and liraglutide also significantly attenuated the decreased avoidance (F 4,30 = 38.0, P < 0.001). Atorvastatin alone had no significant effect on the adversive learned response compared with vehicle treatment (F 4,30 = 38.0, P > 0.05). Atorvastatin significantly decreased the time mice with alloxan-induced diabetes spent in the dark compartment compared with mice in the diabetes group without atorvastatin treatment (F 4,30 = 53.9, P = 0.046). Liraglutide also significantly reduced the time mice with alloxan-induced diabetes spent in the dark compartment compared with vehicle-treated mice with alloxan-induced diabetes (F 4,30 = 53.9, P < 0.001). Atorvastatin treatment alone had no significant effect on the time mice spent in dark compartment compared with the control group (F 4,30 = 53.9, P > 0.05). Conclusion Atorvastatin significantly attenuated the adverse stimulus avoidance learning deficit in the alloxan-induced murine model of diabetes suggesting decreased impairment of memory and cognition.
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Volchegorskiĭ, I. A., L. M. Rassokhina, and I. Iu Miroshnichenko. "Insulin-potentiating action of antioxidants in experimental diabetes mellitus." Problems of Endocrinology 56, no. 2 (April 15, 2010): 27–35. http://dx.doi.org/10.14341/probl201056227-35.
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The objective of this work was to study effects of alpha-lipoic acid (α-LA), 3-oxypyridine and succinic acid derivatives (emoxipine, reamberine, and mexidole) on the metabolic status, conditioned reflex learning, and motivated behaviour in rats with alloxan-induced diabetes receiving basal insulin therapy. The data obtained were compared with effects of the above antioxidants (AO) on insulin sensitivity and results of glucose tolerance test in intact animals. It was shown that administration of AO during two weeks corrected disturbances of carbohydrate and lipid metabolism, normalized the behaviour and learning in terms of the conditioned reflex in diabetic rats. These effects depended on the influence of antioxidative compounds on insulin sensitivity and tolerance of glucose loading. The best correction of diabetic manifestations was achieved using course doses of a-LA that maximally enhanced insulin sensitivity and of mexidole that markedly improved glucose tolerance. It is concluded that significant elevation of blood lipoperoxide levels in rats with alloxan-induced diabetes following administration of AO agents for 14 days gives no evidence of the direct relationship between their efficiency and antioxidative action.
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Hajizadeh, Iman, Mudassir Rashid, Kamuran Turksoy, Sediqeh Samadi, Jianyuan Feng, Mert Sevil, Nicole Hobbs, et al. "Incorporating Unannounced Meals and Exercise in Adaptive Learning of Personalized Models for Multivariable Artificial Pancreas Systems." Journal of Diabetes Science and Technology 12, no. 5 (July 31, 2018): 953–66. http://dx.doi.org/10.1177/1932296818789951.
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Background: Despite the recent advancements in the modeling of glycemic dynamics for type 1 diabetes mellitus, automatically considering unannounced meals and exercise without manual user inputs remains challenging. Method: An adaptive model identification technique that incorporates exercise information and estimates of the effects of unannounced meals obtained automatically without user input is proposed in this work. The effects of the unknown consumed carbohydrates are estimated using an individualized unscented Kalman filtering algorithm employing an augmented glucose-insulin dynamic model, and exercise information is acquired from noninvasive physiological measurements. The additional information on meals and exercise is incorporated with personalized estimates of plasma insulin concentration and glucose measurement data in an adaptive model identification algorithm. Results: The efficacy of the proposed personalized and adaptive modeling algorithm is demonstrated using clinical data involving closed-loop experiments of the artificial pancreas system, and the results demonstrate accurate glycemic modeling with the average root-mean-square error (mean absolute error) of 25.50 mg/dL (18.18 mg/dL) for six-step (30 minutes ahead) predictions. Conclusions: The approach presented is able to identify reliable time-varying individualized glucose-insulin models.
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Perkins, Bruce A., Jennifer L. Sherr, and Chantal Mathieu. "Type 1 diabetes glycemic management: Insulin therapy, glucose monitoring, and automation." Science 373, no. 6554 (July 29, 2021): 522–27. http://dx.doi.org/10.1126/science.abg4502.
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Despite innovations in insulin therapy since its discovery, most patients living with type 1 diabetes do not achieve sufficient glycemic control to prevent complications, and they experience hypoglycemia, weight gain, and major self-care burden. Promising pharmacological advances in insulin therapy include the refinement of extremely rapid insulin analogs, alternate insulin-delivery routes, liver-selective insulins, add-on drugs that enhance insulin effect, and glucose-responsive insulin molecules. The greatest future impact will come from combining these pharmacological solutions with existing automated insulin delivery methods that integrate insulin pumps and glucose sensors. These systems will use algorithms enhanced by machine learning, supplemented by technologies that include activity monitors and sensors for other key metabolites such as ketones. The future challenges facing clinicians and researchers will be those of access and broad clinical implementation.
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Ernawati, Nunung, Suharto Suharto, and Yulis Setiya Dewi. "Patients Empowerment Based on Experimential Learning to Behavior of Acute Compilation Prevention and Blood Glucose Levels of Patients DM." Jurnal NERS 10, no. 2 (October 15, 2015): 256. http://dx.doi.org/10.20473/jn.v10i22015.256-264.
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Introduction: Acute complications are common in patients with Diabetes Mellitus (DM). To improve the behavior prevention of complications and control blood sugar levels, patients need to be equipped with knowledge about the disease process to built a positive attitude and good behavior. The aim of this study was to analyze the effect of patient empowerment based on experiential learning behavior on the prevention of complications and blood sugar levels. Methods: This study used a quasi-experimental design with pre-post test approach using control groups. Samples were 46 diabetic patients who take control in poly RS Mardi Waluyo Blitar taken by consecutive sampling. Data were collected using a questionnaire and checklist recall. Data were analyzed using paired t test, wilcoxon signed rank test and mann whitney. Results: The patient empowerment-based experiential learning have a significant impact on the behavior of prevention of complications. Discussion: Methods of experiential learning can be applied to improve the self-care of patients, especially those who have experienced an acute complications, so the patient is easier to implement behavioral prevention of complications and control blood sugar levels.Keywords: patient empowerment, experiential learning, behavioral prevention, blood glucose
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Ono, T., K. Nakamura, M. Fukuda, and T. Kobayashi. "Catecholamine and acetylcholine sensitivity of rat lateral hypothalamic neurons related to learning." Journal of Neurophysiology 67, no. 2 (February 1, 1992): 265–79. http://dx.doi.org/10.1152/jn.1992.67.2.265.
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1. Unit activity in the rat lateral hypothalamus (LHA) was recorded during discrimination learning of cue tone (CTS) or cue light (CL) stimulation that predicted reward by glucose or intracranial self stimulation (ICSS), or aversion by weak electric shock or tail pinch. Roles of the catecholaminergic and cholinergic systems in the LHA were investigated by electrophoretic application of dopamine (DA), norepinephrine (NE), acetylcholine (ACh), and their antagonists [spiperone (SPP), phenoxybenzamine (PBZ), phentolamine, propranolol, and atropine (Atr)]. 2. Activity of 264 LHA neurons was recorded. Of these, 234 (89%) responded during CTS learning in one or more phases. Of 121 neurons tested by both rewarding and aversive stimuli, 86 (71%) discriminated reward and aversion and their respective CTSs. 3. Effects of DA on 138, NE on 134, and ACh on 73 neurons were tested. Among these, 67 were tested with all three. DA inhibited 40 and excited 14. NE inhibited 74 and excited 10. ACh excited 35 and inhibited 3. DA-sensitive neurons responded to both NE (P less than 0.001) and ACh (P less than 0.05) more often than DA-insensitive neurons. In most cases, the effect of DA was similar to the effect of NE, and opposite to the effect of ACh. The inhibitory effect of DA was blocked by SPP, a D2 antagonist, and the excitatory effect of ACh was blocked by Atr. The inhibitory effect of NE was blocked by the beta-antagonist, propranolol, and enhanced by the alpha-antagonist, phentolamine. 4. DA-sensitive neurons responded to both rewarding and aversive stimuli and respective CTS+ and CTS- more often than DA-insensitive neurons (P less than 0.01). The effect of DA was usually similar to the effect of rewarding stimuli and their predicting CTS+ and was opposite to the effect of aversive stimuli and their predicting CTS-. 5. The proportion of NE-sensitive neurons that responded to rewarding and aversive stimuli was the same as the proportion of NE-insensitive neurons that responded to the same stimuli. NE-sensitive neurons responded to CTS+ and CTS- more often than NE-insensitive neurons (P less than 0.01). The effect of NE was usually similar to the effect of rewarding stimuli and predicting CTS+, and opposite to the effect of aversive stimuli and predicting CTS-. 6. ACh-sensitive neurons responded to aversive stimuli and predicting CTS- more often than ACh-insensitive neurons (P less than 0.01), but the response ratio of ACh-sensitive neurons to rewarding stimuli was similar to that of ACh-sensitive neurons.(ABSTRACT TRUNCATED AT 400 WORDS)
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Cassano, Velia, Antonio Leo, Martina Tallarico, Valentina Nesci, Antonio Cimellaro, Teresa Vanessa Fiorentino, Rita Citraro, et al. "Metabolic and Cognitive Effects of Ranolazine in Type 2 Diabetes Mellitus: Data from an in vivo Model." Nutrients 12, no. 2 (January 31, 2020): 382. http://dx.doi.org/10.3390/nu12020382.
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Type 2 diabetes mellitus (T2DM) is a risk factor for cognitive impairment. Ranolazine, an anti-ischemic drug used in the treatment of angina pectoris, has been shown to possess hypoglycemic properties in pre-clinical and clinical studies. The aim of this study was to evaluate the effects of ranolazine on glucose metabolism and cognitive function in a T2DM model of Wistar rats. Diabetes was induced by a high fat diet (HFD) and streptozotocin (STZ). The control group received a normal caloric diet (NCD) and sodium citrate buffer. Metformin, an effective hypoglycemic drug, was employed as a positive control. Animals were divided into the following groups: HFD/STZ + Ranolazine, HFD/STZ + Metformin, HFD/STZ + Vehicle, NCD + Vehicle, NCD + Ranolazine, and NCD + Metformin. Rats received ranolazine (20 mg/kg), metformin (300 mg/kg), or water, for 8 weeks. At the end of the treatments, all animals underwent to an intraperitoneal glucose tolerance test (IPGTT) and behavioral tests, including passive avoidance, novel object recognition, forced swimming, and elevate plus maze tests. Interleukin-6 plasma levels in the six treatment groups were assessed by Elisa assay. Body mass composition was estimated by nuclear magnetic resonance (NMR). Glucose responsiveness significantly improved in the HFD/STZ + Ranolazine (p < 0.0001) and HFD/STZ + Metformin (p = 0.003) groups. There was a moderate effect on blood glucose levels in the NCD + Ranolazine and NCD + Metformin groups. Lean body mass was significantly increased in the HFD/STZ + Ranolazine and HFD/STZ + Metformin animals, compared to HFD/STZ + Vehicle animals. Ranolazine improved learning and long-term memory in HFD/STZ + Ranolazine compared to HFD/STZ + Vehicle (p < 0.001) and ameliorated the pro-inflammatory profile of diabetic mice. These results support the hypothesis of a protective effect of ranolazine against cognitive decline caused by T2DM.
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Gao, Liangcai, Xinyi Wang, Zejie Lin, Ningning Song, Xinnan Liu, Xinxin Chi, and Tiange Shao. "Antidiabetic and Neuroprotective Effect of the N-Butanol Extract of Fragaria nilgerrensis Schlecht. in STZ-Induced Diabetic Mice." Evidence-Based Complementary and Alternative Medicine 2018 (September 4, 2018): 1–12. http://dx.doi.org/10.1155/2018/6938370.
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Diabetes has been associated with neurodegenerative disorders that are accompanied by memory loss and cognitive impairments, but there is no effective treatment for it at present. Fragaria nilgerrensis Schlecht. (FNS), a well-known Chinese materia medica, has been traditionally used for the folkloric treatment of diabetes and other diseases. However, its effects are poorly documented. Here, we investigated the antidiabetic and neuroprotective effect of FNS in diabetic mice. Thin layer chromatography (TLC) and high performance liquid chromatography (HPLC) evaluations of N-butanol extract of Fragaria nilgerrensis Schlecht. (N-FNS) showed the presence of flavonoid and its structure is similar to scutellarin. For the first time, we show the potential neuroprotective and antidiabetic effects of FNS. After 4 weeks of FNS intervention, a significant decrease in blood glucose, increase in body weight, and amelioration in glucose tolerance were observed in FNS treated diabetic mice. In the acute study, FNS enhanced motor activity in the open field task and significantly prevented spatial-learning deficits in Morris water maze tests. Besides, synapse ultrastructure of the hippocampus showed that the mitochondrial morphology was basically restored and all the synaptic structural parameters were gradually normalized after treatment with FNS. Importantly, we found that the activities of SOD and CAT in liver and hippocampus of diabetic mice significantly increased after FNS administration. In vitro, FNS and scutellarin showed high DPPH radical scavenging activity. The study suggests that FNS exerted significant antidiabetic and neuroprotective effects which may be attributed to its antioxidant property.
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McCarthy, Elizabeth, Jillian Dunn, Kaylee Augustine, and Victoria P. Connaughton. "Prolonged Hyperglycemia Causes Visual and Cognitive Deficits in Danio rerio." International Journal of Molecular Sciences 23, no. 17 (September 5, 2022): 10167. http://dx.doi.org/10.3390/ijms231710167.
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The present study induced prolonged hyperglycemia (a hallmark symptom of Type 2 diabetes [T2DM]) in Danio rerio (zebrafish) for eight or twelve weeks. The goal of this research was to study cognitive decline as well as vision loss in hyperglycemic zebrafish. Fish were submerged in glucose for eight or twelve weeks, after which they were assessed with both a cognitive assay (three-chamber choice) and a visual assay (optomotor response (OMR)). Zebrafish were also studied during recovery from hyperglycemia. Here, fish were removed from the hyperglycemic environment for 4 weeks after either 4 or 8 weeks in glucose, and cognition and vision was again assessed. The 8- and 12-week cognitive results revealed that water-treated fish showed evidence of learning while glucose- and mannitol-treated fish did not within the three-day testing period. OMR results identified an osmotic effect with glucose-treated fish having significantly fewer positive rotations than water-treated fish but comparable rotations to mannitol-treated fish. The 8- and 12-week recovery results showed that 4 weeks was not enough time to fully recovery from the hyperglycemic insult sustained.
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Martinsson, John, Alexander Schliep, Björn Eliasson, and Olof Mogren. "Blood Glucose Prediction with Variance Estimation Using Recurrent Neural Networks." Journal of Healthcare Informatics Research 4, no. 1 (December 1, 2019): 1–18. http://dx.doi.org/10.1007/s41666-019-00059-y.
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AbstractMany factors affect blood glucose levels in type 1 diabetics, several of which vary largely both in magnitude and delay of the effect. Modern rapid-acting insulins generally have a peak time after 60–90 min, while carbohydrate intake can affect blood glucose levels more rapidly for high glycemic index foods, or slower for other carbohydrate sources. It is important to have good estimates of the development of glucose levels in the near future both for diabetic patients managing their insulin distribution manually, as well as for closed-loop systems making decisions about the distribution. Modern continuous glucose monitoring systems provide excellent sources of data to train machine learning models to predict future glucose levels. In this paper, we present an approach for predicting blood glucose levels for diabetics up to 1 h into the future. The approach is based on recurrent neural networks trained in an end-to-end fashion, requiring nothing but the glucose level history for the patient. Our approach obtains results that are comparable to the state of the art on the Ohio T1DM dataset for blood glucose level prediction. In addition to predicting the future glucose value, our model provides an estimate of its certainty, helping users to interpret the predicted levels. This is realized by training the recurrent neural network to parameterize a univariate Gaussian distribution over the output. The approach needs no feature engineering or data preprocessing and is computationally inexpensive. We evaluate our method using the standard root-mean-squared error (RMSE) metric, along with a blood glucose-specific metric called the surveillance error grid (SEG). We further study the properties of the distribution that is learned by the model, using experiments that determine the nature of the certainty estimate that the model is able to capture.
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Xiao, Yao, Xifeng Wang, Siyi Wang, Jun Li, Xueyu Xu, Min Wang, Gang Li, and Wei Shen. "Celastrol Attenuates Learning and Memory Deficits in an Alzheimer’s Disease Rat Model." BioMed Research International 2021 (July 24, 2021): 1–11. http://dx.doi.org/10.1155/2021/5574207.
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Alzheimer’s disease (AD) is a chronic progressive neurodegenerative disorder that is associated with learning, memory, and cognitive deficits. Neuroinflammation and synapse loss are involved in the pathology of AD. Diverse measures have been applied to treat AD, but currently, there is no effective treatment. Celastrol (CEL) is a pentacyclic triterpene isolated from Tripterygium wilfordii Hook F that has been shown to enhance cell viability and inhibit amyloid-β production induced by lipopolysaccharides in vitro. In the present study, the protective effect of CEL on Aβ25-35-induced rat model of AD was assessed. Our results showed that CEL administration at a dose of 2 mg/kg/day improved spatial memory in the Morris water maze. Further biochemical analysis showed that CEL treatment of intrahippocampal Aβ25-35-microinjected rats attenuated hippocampal NF-κB activity; inhibited proinflammatory markers, namely, IL-1β, IL-6, and TNF-α; and upregulated anti-inflammatory factors, such as IL-4 and IL-10. Furthermore, CEL upregulated hippocampal neurexin-1β, neuroligin-1, CA1, and PSD95 expression levels, which may improve synaptic function. Simultaneously, CEL also increased glucose metabolism in Aβ25-35-microinjected rats. In conclusion, CEL could exert protective effects against learning and memory decline induced by intrahippocampal Aβ25-35 through anti-inflammation, promote synaptic development, and maintain hippocampal energy metabolism.
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Liu, Chunna, Chunhua Shao, Qi Du, Chaoran He, Xinyuan Sun, Anqi Lou, Zhijie Ma, and Junxian Yu. "Mechanism and effects of fructose diphosphate on anti-hypoxia fatigue and learning memory ability." Canadian Journal of Physiology and Pharmacology 98, no. 10 (October 2020): 733–40. http://dx.doi.org/10.1139/cjpp-2019-0690.
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This study aims to investigate the mechanisms through which fructose diphosphate (FDP) causes anti-hypoxia and anti-fatigue effects and improves learning and memory. Mice were divided into three groups: low-dose FDP (FDP-L), high-dose FDP (FDP-H), and a control group. Acute toxic hypoxia induced by carbon monoxide, sodium nitrite, and potassium cyanide and acute cerebral ischemic hypoxia were used to investigate the anti-hypoxia ability of FDP. The tests of rod-rotating, mouse tail suspension, and swimming endurance were used to explore the anti-fatigue effects of FDP. The Morris water maze experiment was used to determine the impact of FDP on learning and memory ability. Poisoning-induced hypoxic tests showed that mouse survival time was significantly prolonged in the FDP-L and FDP-H groups compared with the control group (p < 0.05). In the exhaustive swimming test, FDP significantly shortened struggling time and prolonged the time of mass-loaded swimming; the rod-rotating test showed that endurance time was significantly prolonged by using FDP (p < 0.05). FDP significantly decreased lactate and urea nitrogen levels and increased hepatic and muscle glycogen and glucose transporter-4 and Na+-K+-ATPase (p < 0.05). To conclude, FDP enhances hypoxia tolerance and fatigue resistance and improves learning and memory ability through regulating glucose and energy metabolism.
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Yan, Shu-Rong, Khalid A. Alattas, Mohsen Bakouri, Abdullah K. Alanazi, Ardashir Mohammadzadeh, Saleh Mobayen, Anton Zhilenkov, and Wei Guo. "Generalized Type-2 Fuzzy Control for Type-I Diabetes: Analytical Robust System." Mathematics 10, no. 5 (February 23, 2022): 690. http://dx.doi.org/10.3390/math10050690.
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The insulin injection rate in type-I diabetic patients is a complex control problem. The mathematical dynamics for the insulin/glucose metabolism can be different for various patients who undertake different activities, have different lifestyles, and have other illnesses. In this study, a robust regulation system on the basis of generalized type-2 (GT2) fuzzy-logic systems (FLSs) is designed for the regulation of the blood glucose level. Unlike previous studies, the dynamics of glucose–insulin are unknown under high levels of uncertainty. The insulin-glucose metabolism has been identified online by GT2-FLSs, considering the stability criteria. The learning scheme was designed based on the Lyapunov approach. In other words, the GT2-FLSs are learned using adaptation rules that are concluded from the stability theorem. The effect of the dynamic estimation error and other perturbations, such as patient activeness, were eliminated through the designed adaptive fuzzy compensator. The adaptation laws for control parameters, GT2-FLS rule parameters, and the designed compensator were obtained by using the Lyapunov stability theorem. The feasibility and accuracy of the designed control scheme was examined on a modified Bergman model of some patients under different conditions. The simulation results confirm that the suggested controller has excellent performance under various conditions.
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Mainardi, Marco, Salvatore Fusco, and Claudio Grassi. "Modulation of Hippocampal Neural Plasticity by Glucose-Related Signaling." Neural Plasticity 2015 (2015): 1–10. http://dx.doi.org/10.1155/2015/657928.
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Hormones and peptides involved in glucose homeostasis are emerging as important modulators of neural plasticity. In this regard, increasing evidence shows that molecules such as insulin, insulin-like growth factor-I, glucagon-like peptide-1, and ghrelin impact on the function of the hippocampus, which is a key area for learning and memory. Indeed, all these factors affect fundamental hippocampal properties including synaptic plasticity (i.e., synapse potentiation and depression), structural plasticity (i.e., dynamics of dendritic spines), and adult neurogenesis, thus leading to modifications in cognitive performance. Here, we review the main mechanisms underlying the effects of glucose metabolism on hippocampal physiology. In particular, we discuss the role of these signals in the modulation of cognitive functions and their potential implications in dysmetabolism-related cognitive decline.
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Stough, Con, Andrew Scholey, Gertrude Gentile-Rapinett, Jeroen Schimdt, Antinette Goh, Keith Wesnes, Marni Kras, Klem Kwek, and David Camfield. "An evaluation of the cognitive effects of malt extract and sucrose in school-aged Malaysian children." Bioactive Compounds in Health and Disease 3, no. 10 (October 16, 2020): 179. http://dx.doi.org/10.31989/bchd.v3i10.754.
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The effect of carbohydrates and sucrose on cognitive performance in children has been the subject of several trials. Some studies have found both positive and negative effects of glucose and carbohydrates on cognitive function in school children. These studies are important in terms of designing functional foods that can assist children in learning throughout the day, and in particular, within a school-educational context. In this study we conducted a 4-way repeated measures within subject crossover randomized controlled trial in which we compared an acute administration of malt, sucrose, milk and water in 58 Malaysian children aged 10-12 years on performance on a battery of cognitive and mood measures before and after exercise. Results indicated that there was a beneficial effect of malt on attention suggesting the importance of carbohydrates to alleviate attentional changes due to exercise in children.Keywords: Malt, Sucrose, Attention, Children
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Oikonomou, Evangelos K., Marc A. Suchard, Darren K. McGuire, and Rohan Khera. "Phenomapping-Derived Tool to Individualize the Effect of Canagliflozin on Cardiovascular Risk in Type 2 Diabetes." Diabetes Care 45, no. 4 (February 4, 2022): 965–74. http://dx.doi.org/10.2337/dc21-1765.
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OBJECTIVE Sodium–glucose cotransporter 2 (SGLT2) inhibitors have well-documented cardioprotective effects but are underused, partly because of high cost. We aimed to develop a machine learning–based decision support tool to individualize the atherosclerotic cardiovascular disease (ASCVD) benefit of canagliflozin in type 2 diabetes. RESEARCH DESIGN AND METHODS We constructed a topological representation of the Canagliflozin Cardiovascular Assessment Study (CANVAS) using 75 baseline variables collected from 4,327 patients with type 2 diabetes randomly assigned 1:1:1 to one of two canagliflozin doses (n = 2,886) or placebo (n = 1,441). Within each patient’s 5% neighborhood, we calculated age- and sex-adjusted risk estimates for major adverse cardiovascular events (MACEs). An extreme gradient boosting algorithm was trained to predict the personalized ASCVD effect of canagliflozin using features most predictive of topological benefit. For validation, this algorithm was applied to the CANVAS-Renal (CANVAS-R) trial, comprising 5,808 patients with type 2 diabetes randomly assigned 1:1 to canagliflozin or placebo. RESULTS In CANVAS (mean age 60.9 ± 8.1 years; 33.9% women), 1,605 (37.1%) patients had a neighborhood hazard ratio (HR) more protective than the effect estimate of 0.86 reported for MACEs in the original trial. A 15-variable tool, INSIGHT, trained to predict the personalized ASCVD effects of canagliflozin in CANVAS, was tested in CANVAS-R (mean age 62.4 ± 8.4 years; 2,164 [37.3%] women), where it identified patient phenotypes with greater ASCVD canagliflozin effects (adjusted HR 0.60 [95% CI 0.41–0.89] vs. 0.99 [95% CI 0.76–1.29]; Pinteraction = 0.04). CONCLUSIONS We present an evidence-based, machine learning–guided algorithm to personalize the prescription of SGLT2 inhibitors for patients with type 2 diabetes for ASCVD effects.
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Bormann, Daniel, Tamara Stojanovic, Ana Cicvaric, Gabor J. Schuld, Maureen Cabatic, Hendrik Jan Ankersmit, and Francisco J. Monje. "miRNA-132/212 Gene-Deletion Aggravates the Effect of Oxygen-Glucose Deprivation on Synaptic Functions in the Female Mouse Hippocampus." Cells 10, no. 7 (July 6, 2021): 1709. http://dx.doi.org/10.3390/cells10071709.
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Cerebral ischemia and its sequelae, which include memory impairment, constitute a leading cause of disability worldwide. Micro-RNAs (miRNA) are evolutionarily conserved short-length/noncoding RNA molecules recently implicated in adaptive/maladaptive neuronal responses to ischemia. Previous research independently implicated the miRNA-132/212 cluster in cholinergic signaling and synaptic transmission, and in adaptive/protective mechanisms of neuronal responses to hypoxia. However, the putative role of miRNA-132/212 in the response of synaptic transmission to ischemia remained unexplored. Using hippocampal slices from female miRNA-132/212 double-knockout mice in an established electrophysiological model of ischemia, we here describe that miRNA-132/212 gene-deletion aggravated the deleterious effect of repeated oxygen-glucose deprivation insults on synaptic transmission in the dentate gyrus, a brain region crucial for learning and memory functions. We also examined the effect of miRNA-132/212 gene-deletion on the expression of key mediators in cholinergic signaling that are implicated in both adaptive responses to ischemia and hippocampal neural signaling. miRNA-132/212 gene-deletion significantly altered hippocampal AChE and mAChR-M1, but not α7-nAChR or MeCP2 expression. The effects of miRNA-132/212 gene-deletion on hippocampal synaptic transmission and levels of cholinergic-signaling elements suggest the existence of a miRNA-132/212-dependent adaptive mechanism safeguarding the functional integrity of synaptic functions in the acute phase of cerebral ischemia.
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Myers, Kevin P., Megan Y. Summers, Elizabeth Geyer-Roberts, and Lindsey A. Schier. "The Role of Post-Ingestive Feedback in the Development of an Enhanced Appetite for the Orosensory Properties of Glucose over Fructose in Rats." Nutrients 12, no. 3 (March 18, 2020): 807. http://dx.doi.org/10.3390/nu12030807.
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The simple sugars glucose and fructose share a common “sweet” taste quality mediated by the T1R2+T1R3 taste receptor. However, when given the opportunity to consume each sugar, rats learn to affectively discriminate between glucose and fructose on the basis of cephalic chemosensory cues. It has been proposed that glucose has a unique sensory property that becomes more hedonically positive through learning about the relatively more rewarding post-ingestive effects that are associated with glucose as compared to fructose. We tested this theory using intragastric (IG) infusions to manipulate the post-ingestive consequences of glucose and fructose consumption. Food-deprived rats with IG catheters repeatedly consumed multiple concentrations of glucose and fructose in separate sessions. For rats in the “Matched” group, each sugar was accompanied by IG infusion of the same sugar. For the “Mismatched” group, glucose consumption was accompanied by IG fructose, and vice versa. This condition gave rats orosensory experience with each sugar but precluded the differential post-ingestive consequences. Following training, avidity for each sugar was assessed in brief access and licking microstructure tests. The Matched group displayed more positive evaluation of glucose relative to fructose than the Mismatched group. A second experiment used a different concentration range and compared responses of the Matched and Mismatched groups to a control group kept naïve to the orosensory properties of sugar. Consistent with results from the first experiment, the Matched group, but not the Mismatched or Control group, displayed elevated licking responses to glucose. These experiments yield additional evidence that glucose and fructose have discriminable sensory properties and directly demonstrate that their different post-ingestive effects are responsible for the experience-dependent changes in the motivation for glucose versus fructose.
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Okaichi, Yoko, and Hiroshige Okaichi. "Effects of Glucose on Scopolamine-Induced Learning Deficits in Rats Performing the Morris Water Maze Task." Neurobiology of Learning and Memory 74, no. 1 (July 2000): 65–79. http://dx.doi.org/10.1006/nlme.1999.3940.
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PYCH, J., M. KIM, and P. GOLD. "Effects of injections of glucose into the dorsal striatum on learning of place and response mazes." Behavioural Brain Research 167, no. 2 (February 28, 2006): 373–78. http://dx.doi.org/10.1016/j.bbr.2005.10.005.
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Wood, Thomas, Christopher Kelly, Megan Roberts, and Bryan Walsh. "An interpretable machine learning model of biological age." F1000Research 8 (January 4, 2019): 17. http://dx.doi.org/10.12688/f1000research.17555.1.
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Background: Assessments of biological (rather than chronological) age derived from patient biochemical data have been shown to strongly predict both all-cause and disease-specific mortality. However, these population-based approaches have yet to be translated to the individual. As well as using biological age as a research tool, by being able to better answer the question “why did we get this result?”, clinicians may be able to apply personalised interventions that could improve the long-term health of individual patients. Methods: Here, the boosted decision tree algorithm XGBoost was used to predict biological age using 39 commonly-available blood test results from the US National Health and Nutrition Examination Survey (NHANES) database. Results: Interrogation of the algorithm produced a description of how each marker contributed to the final output in a single individual. Additive explanation plots were then used to determine biomarker ranges associated with a lower biological age. Importantly, a number of markers that are modifiable with lifestyle changes were found to have a significant effect on biological age, including fasting blood glucose, lipids, and markers of red blood cell production. Conclusions: The combination of individualised outputs with target ranges could provide the ability to personalise interventions or recommendations based on an individual’s biochemistry and resulting predicted age. This would allow for the investigation of interventions designed to improve health and longevity in a targeted manner, many of which could be rooted in targeted lifestyle modifications.
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Kamath, Sowmya, Karthik Kappaganthu, Stefanie Painter, and Anmol Madan. "Improving Outcomes Through Personalized Recommendations in a Remote Diabetes Monitoring Program: Observational Study." JMIR Formative Research 6, no. 3 (March 21, 2022): e33329. http://dx.doi.org/10.2196/33329.
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Background Diabetes management is complex, and program personalization has been identified to enhance engagement and clinical outcomes in diabetes management programs. However, 50% of individuals living with diabetes are unable to achieve glycemic control, presenting a gap in the delivery of self-management education and behavior change. Machine learning and recommender systems, which have been used within the health care setting, could be a feasible application for diabetes management programs to provide a personalized user experience and improve user engagement and outcomes. Objective This study aims to evaluate machine learning models using member-level engagements to predict improvement in estimated A1c and develop personalized action recommendations within a remote diabetes monitoring program to improve clinical outcomes. Methods A retrospective study of Livongo for Diabetes member engagement data was analyzed within five action categories (interacting with a coach, reading education content, self-monitoring blood glucose level, tracking physical activity, and monitoring nutrition) to build a member-level model to predict if a specific type and level of engagement could lead to improved estimated A1c for members with type 2 diabetes. Engagement and improvement in estimated A1c can be correlated; therefore, the doubly robust learning method was used to model the heterogeneous treatment effect of action engagement on improvements in estimated A1c. Results The treatment effect was successfully computed within the five action categories on estimated A1c reduction for each member. Results show interaction with coaches and self-monitoring blood glucose levels were the actions that resulted in the highest average decrease in estimated A1c (1.7% and 1.4%, respectively) and were the most recommended actions for 54% of the population. However, these were found to not be the optimal interventions for all members; 46% of members were predicted to have better outcomes with one of the other three interventions. Members who engaged with their recommended actions had on average a 0.8% larger reduction in estimated A1c than those who did not engage in recommended actions within the first 3 months of the program. Conclusions Personalized action recommendations using heterogeneous treatment effects to compute the impact of member actions can reduce estimated A1c and be a valuable tool for diabetes management programs in encouraging members toward actions to improve clinical outcomes.
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Nevzorova, V. A., T. A. Brodskaya, K. I. Shakhgeldyan, B. I. Geltser, V. V. Kosterin, and L. G. Priseko. "Machine learning for predicting 5-year mortality risks: data from the ESSE-RF study in Primorsky Krai." Cardiovascular Therapy and Prevention 21, no. 1 (January 28, 2022): 2908. http://dx.doi.org/10.15829/1728-8800-2022-2908.
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Aim. To develop and perform comparative assessment of the accuracy of models for predicting 5-year mortality risks according to the Epidemiology of Cardiovascular Diseases and their Risk Factors in Regions of Russian Federation (ESSE-RF) study in Primorsky Krai.Material and methods. The study included 2131 people (1257 women and 874 men) aged 23-67 years with a median of 47 years (95% confidence interval [46; 48]). The study protocol included measurement of blood pressure (BP), heart rate (HR), waist circumference, hip circumference, and waist-to-hip ratio (WHR). The following blood biochemical parameters: total cholesterol (TC), low and high density lipoprotein cholesterol, triglycerides, apolipoproteins AI and B, lipoprotein(a), N-terminal pro-brain natriuretic peptide (NT-proNBP), D-dimer, fibrinogen, C-reactive protein (CRP), glucose, creatinine, uric acid. The study endpoint was 5-year all-cause death (2013-2018). The group of deceased patients during this period consisted of 42 (2%) people, while those continued the study — 2089 (98%). The χ2, Fisher and MannWhitney tests, univariate logistic regression (LR) were used for data processing and analysis. To build predictive models, we used following machine learning (ML) methods: multivariate LR, Weibull regression, and stochastic gradient boosting.Results. The prognostic models developed on the ML basis, using parameters of age, sex, smoking, systolic blood pressure (SBP) and TC level in their structure, had higher quality metrics than Systematic COronary Risk Evaluation (SCORE) system. The inclusion of CRP, glucose, NT-proNBP, and heart rate into the predictors increased the accuracy of all models with the maximum rise in quality metrics in the multivariate LR model. Predictive potential of other factors (WHR, lipid profile, fibrinogen, D-dimer, etc.) was low and did not improve the prediction quality. An analysis of the influence degree of individual predictors on the mortality rate indicated the prevailing contribution of five factors as follows: age, levels of TC, NT-proNBP, CRP, and glucose. A less noticeable effect was associated with the level of HR, SBP and smoking, while the contribution of sex was minimal.Conclusion. The use of modern ML methods increases the accuracy of predictive models and provides a higher efficiency of risk stratification, especially among individuals with a low and moderate death risk from cardiovascular diseases.
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Ye, Minsook, Bong Hee Han, Jin Su Kim, Kyungsoo Kim, and Insop Shim. "Neuroprotective Effect of Bean Phosphatidylserine on TMT-Induced Memory Deficits in a Rat Model." International Journal of Molecular Sciences 21, no. 14 (July 11, 2020): 4901. http://dx.doi.org/10.3390/ijms21144901.
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Background: Trimethyltin (TMT) is a potent neurotoxin affecting various regions of the central nervous system, including the neocortex, the cerebellum, and the hippocampus. Phosphatidylserine (PS) is a membrane phospholipid, which is vital to brain cells. We analyzed the neuroprotective effects of soybean-derived phosphatidylserine (Bean-PS) on cognitive function, changes in the central cholinergic systems, and neural activity in TMT-induced memory deficits in a rat model. Methods: The rats were randomly divided into an untreated normal group, a TMT group (injected with TMT + vehicle), and a group injected with TMT + Bean-PS. The rats were treated with 10% hexane (TMT group) or TMT + Bean-PS (50 mg·kg−1, oral administration (p.o.)) daily for 21 days, following a single injection of TMT (8.0 mg/kg, intraperitoneally (i.p.)). The cognitive function of Bean-PS was assessed using the Morris water maze (MWM) test and a passive avoidance task (PAT). The expression of acetylcholine transferase (ChAT) and acetylcholinesterase (AchE) in the hippocampus was assessed via immunohistochemistry. A positron emission tomography (PET) scan was used to measure the glucose uptake in the rat brain. Results: Treatment with Bean-PS enhanced memory function in the Morris water maze (MWM) test. Consistent with the behavioral results, treatment with Bean-PS diminished the damage to cholinergic cells in the hippocampus, in contrast to those of the TMT group. The TMT+Bean-PS group showed elevated glucose uptake in the frontal lobe of the rat brain. Conclusion: These results demonstrate that Bean-PS protects against TMT-induced learning and memory impairment. As such, Bean-PS represents a potential treatment for neurodegenerative disorders, such as Alzheimer’s disease.
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Etchie, Laju, Devaka Fernando, Vakkat Muraleedharan, and Ashok Poduval. "A Rare Cause of Severe Hypoglycaemia." Physician 6, no. 3 (May 8, 2021): 1–5. http://dx.doi.org/10.38192/1.6.3.12.
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A 67-year old woman presented with an unwitnessed fall and decreased oral intake. She had a learning disability, hypertension, epilepsy, asthma, chronic iron deficiency anaemia, mild lymphopenia, osteoporosis and treated uterine cancer. After clinical review, she was treated for Hospital-acquired pneumonia (following a recent hospital admission) with possible aspiration. She was noted to have hyponatraemia secondary to dehydration. She was commenced on intravenous Levofloxacin and Metronidazole along with supportive care, based on antibiotic guidance due to her known allergy to penicillin.On day 3 of admission, she was found unresponsive with a capillary blood glucose of 0.6 mmol/L, which improved with 10% glucose infusion. The low blood glucose was attributed to poor oral intake. However, her serial blood sugar results demonstrated persistent hypoglycaemia for 72h needing further intravenous glucose infusions. A medication review was undertaken and Levofloxacin was discontinued. After 24hrs of discontinuation, the hypoglycaemic episode resolved. A short synacthen test showed a normal cortisol response. There were no further episodes of hypoglycaemia. Conclusion As her persistent hypoglycaemia resolved on discontinuation of Levofloxacin, a diagnosis of fluoroquinolone induced hypoglycaemia was reported to MHRA. Fluoroquinolones are thought to induce hypoglycaemia by increasing the insulin release via blockade of adenosine triphosphate-sensitive K+channels in the β cells of the pancreas. This effect may not be clinically evident in all patients because of physiologic mechanisms that regulate blood glucose levels. Health professionals should be aware of the potential risk of severe hypoglycaemia with the use of Fluoroquinolones which are a first or second-line treatment for common infective processes. Fluoroquinolones should be stopped immediately and switch to a non-Fluoroquinolones antibiotic if possible. In elderly patients with compromised oral intake or in those with other comorbidities, regular blood glucose monitoring should be carried out to avoid life-threatening hypoglycaemic episodes.
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Berikov, Vladimir B., Olga A. Kutnenko, Julia F. Semenova, and Vadim V. Klimontov. "Machine Learning Models for Nocturnal Hypoglycemia Prediction in Hospitalized Patients with Type 1 Diabetes." Journal of Personalized Medicine 12, no. 8 (July 31, 2022): 1262. http://dx.doi.org/10.3390/jpm12081262.
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Nocturnal hypoglycemia (NH) is a dangerous complication of insulin therapy that often goes undetected. In this study, we aimed to generate machine learning (ML)-based models for short-term NH prediction in hospitalized patients with type 1 diabetes (T1D). The models were trained on continuous glucose monitoring (CGM) data obtained from 406 adult patients admitted to a tertiary referral hospital. Eight CGM-derived metrics of glycemic control and glucose variability were included in the models. Combinations of CGM and clinical data (23 parameters) were also assessed. Random Forest (RF), Logistic Linear Regression with Lasso regularization, and Artificial Neuron Networks algorithms were applied. In our models, RF provided the best prediction accuracy with 15 min and 30 min prediction horizons. The addition of clinical parameters slightly improved the prediction accuracy of most models, whereas oversampling and undersampling procedures did not have significant effects. The areas under the curve of the best models based on CGM and clinical data with 15 min and 30 min prediction horizons were 0.97 and 0.942, respectively. Basal insulin dose, diabetes duration, proteinuria, and HbA1c were the most important clinical predictors of NH assessed by RF. In conclusion, ML is a promising approach to personalized prediction of NH in hospitalized patients with T1D.
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HARRIS, IRINA M., MICHAEL J. FULHAM, and LAURIE A. MILLER. "The effects of mesial temporal and cerebellar hypometabolism on learning and memory." Journal of the International Neuropsychological Society 7, no. 3 (March 2001): 353–62. http://dx.doi.org/10.1017/s1355617701733097.
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The effects of mesial temporal (MT) and cerebellar hypometabolism were studied using measures of verbal, visual and motor skill learning. Twelve patients with refractory temporal lobe epilepsy who showed asymmetrical mesial temporal lobe hypometabolism on [18F] fluoro-2-deoxy-glucose positron emission tomography (FDG-PET) were given tests involving 4 consecutive learning trials and a 30-min delayed recall trial. Delayed recognition was also assessed for the words and designs, and skill transfer was evaluated for mirror drawing. Compared to 9 normal control participants, patients with more marked MT hypometabolism on the left had impaired delayed recall of words and patients with more marked MT hypometabolism on the right showed impaired learning of novel designs, but normal retention over delay. Patients were not impaired in their mirror-drawing performance. The findings for MT hypometabolism correspond well to those obtained in other studies where patients have been classified on the basis of side of hippocampal atrophy or temporal lobe excision. (JINS, 2001, 7, 353–362.)
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Deshmukh, Harshal, Maria Papageorgiou, and Thozhukat Sathyapalan. "RF18 | PSAT125 Estimating the relative influence (RI) of genetic risk of 11 glycemic traits on osteoporosis and fractures in 409633 participants in the UK Biobank using Machine learning." Journal of the Endocrine Society 6, Supplement_1 (November 1, 2022): A226. http://dx.doi.org/10.1210/jendso/bvac150.464.
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Abstract Introduction We used a machine-learning algorithm (gradient boosting) to test the association of genetic risk for 11 glycemic traits with osteoporosis and fractures in the UK Biobank population. Methods The study was performed with 409,633 caucasian participants in the UKBIobank. We identified 4626 SNPs associated with 11 glycemic traits from the NGHRI catalogue for GWAS studies. Weighted genetic risk scores (wGRS) were calculated using the effect estimates from the GWAS studies. We used a gradient-boosting machine-learning (GBM) model to identify the relative influence (RI) of baseline variables and wGRS of the glycemic traits on osteoporosis and all-cause fractures in the UK Biobank population. We split the data into training (2/3) and testing set (1/3) and calculated the discriminatory power of the models using the area under the curve (AUC) with the testing model. Results The study consisted of 409,633 individuals (53% females) with a median age of 58 (51-63) years and a median BMI of 26.7 (24.1-29.8) kg/m2. The study population had 41954 (10.2%) all-cause fractures and 4995 (1.2%) participants with osteoporosis. In the GBM model, top wGRS associated with all-cause fractures were wGRS for Type 1 diabetes (RI=4.49) and fasting glucose (4.17). In contrast, the top wGRS associated with osteoporosis were wGRS for acute insulin response to glucose (RI=6.74) and Type 1 diabetes (RI=5.62). Both models showed low to moderate discriminatory power with the area under the AUC of 0.57(CI: 0.56-0.57) for fractures and 0.75(CI: 0.74-0.76) for osteoporosis. Conclusion We showed a differential effect of wGRS for various glycemic traits on the risk of fractures and osteoporosis in the UK Biobank population. However, the machine-learning model with wGRS for glycemic traits demonstrated limited capacity to predict fractures and osteoporosis in the general population. Presentation: Saturday, June 11, 2022 1:00 p.m. - 3:00 p.m., Sunday, June 12, 2022 1:00 p.m. - 1:05 p.m.
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Ladak, Zeenat, Elizabeth Garcia, Jenny Yoon, Takaaki Landry, Edward A. Armstrong, Jerome Y. Yager, and Sujata Persad. "Sulforaphane (SFA) protects neuronal cells from oxygen & glucose deprivation (OGD)." PLOS ONE 16, no. 3 (March 18, 2021): e0248777. http://dx.doi.org/10.1371/journal.pone.0248777.
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Background Perinatal brain injury results in neurodevelopmental disabilities (neuroDDs) that include cerebral palsy, autism, attention deficit disorder, epilepsy, learning disabilities and others. Commonly, injury occurs when placental circulation, that is responsible for transporting nutrients and oxygen to the fetus, is compromised. Placental insufficiency (PI) is a reduced supply of blood and oxygen to the fetus and results in a hypoxic-ischemic (HI) environment. A significant HI state in-utero leads to perinatal compromise, characterized by fetal growth restriction and brain injury. Given that over 80% of perinatal brain injuries that result in neuroDDs occur during gestation, prior to birth, preventive approaches are needed to reduce or eliminate the potential for injury and subsequent neuroDDs. Sulforaphane (SFA) derived from cruciferous vegetables such as broccoli sprouts (BrSps) is a phase-II enzyme inducer that acts via cytoplasmic Nrf2 to enhance the production of anti-oxidants in the brain through the glutathione pathway. We have previously shown a profound in vivo neuro-protective effect of BrSps/SFA as a dietary supplement in pregnant rat models of both PI and fetal inflammation. Strong evidence also points to a role for SFA as treatment for various cancers. Paradoxically, then SFA has the ability to enhance cell survival, and with conditions of cancer, enhance cell death. Given our findings of the benefit of SFA/Broccoli Sprouts as a dietary supplement during pregnancy, with improvement to the fetus, it is important to determine the beneficial and toxic dosing range of SFA. We therefore explored, in vitro, the dosing range of SFA for neuronal and glial protection and toxicity in normal and oxygen/glucose deprived (OGD) cell cultures. Methods OGD simulates, in vitro, the condition experienced by the fetal brain due to PI. We developed a cell culture model of primary cortical neuronal, astrocyte and combined brain cell co-cultures from newborn rodent brains. The cultures were exposed to an OGD environment for various durations of time to determine the LD50 (duration of OGD required for 50% cell death). Using the LD50 as the time point, we evaluated the efficacy of varying doses of SFA for neuroprotective and neurotoxicity effects. Control cultures were exposed to normal media without OGD, and cytotoxicity of varying doses of SFA was also evaluated. Immunofluorescence (IF) and Western blot analysis of cell specific markers were used for culture characterization, and quantification of LD50. Efficacy and toxicity effect of SFA was assessed by IF/high content microscopy and by AlamarBlue viability assay, respectively. Results We determined the LD50 to be 2 hours for neurons, 8 hours for astrocytes, and 10 hours for co-cultures. The protective effect of SFA was noticeable at 2.5 μM and 5 μM for neurons, although it was not significant. There was a significant protective effect of SFA at 2.5 μM (p<0.05) for astrocytes and co-cultures. Significant toxicity ranges were also confirmed in OGD cultures as ≥ 100 μM (p<0.05) for astrocytes, ≥ 50 μM (p<0.01) for co-cultures, but not toxic in neurons; and toxic in control cultures as ≥ 100 μM (p<0.01) for neurons, and ≥ 50 μM (p<0.01) for astrocytes and co-cultures. One Way ANOVA and Dunnett’s Multiple Comparison Test were used for statistical analysis. Conclusions Our results indicate that cell death shows a trend to reduction in neuronal and astrocyte cultures, and is significantly reduced in co-cultures treated with low doses of SFA exposed to OGD. Doses of SFA that were 10 times higher were toxic, not only under conditions of OGD, but in normal control cultures as well. The findings suggest that: 1. SFA shows promise as a preventative agent for fetal ischemic brain injury, and 2. Because the fetus is a rapidly growing organism with profound cell multiplication, dosing parameters must be established to insure safety within efficacious ranges. This study will influence the development of innovative therapies for the prevention of childhood neuroDD.