Relevant bibliographies by topics / Blood glucose

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Blood glucose'

Author: Grafiati
Published: 4 June 2021
Last updated: 31 July 2024

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Journal articles on the topic "Blood glucose"

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Rader, Sherry. "BLOOD GLUCOSE." Nursing 23, no. 8 (August 1993): 4. http://dx.doi.org/10.1097/00152193-199308000-00003.

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Shao, Jia-Liang, and Guo-Xin Hu. "Blood glucose, blood glucose fluctuation and hepatic fibrosis." World Chinese Journal of Digestology 18, no. 13 (2010): 1301. http://dx.doi.org/10.11569/wcjd.v18.i13.1301.

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Eerdekens, Gert-Jan, Steffen Rex, and Dieter Mesotten. "Accuracy of Blood Glucose Measurement and Blood Glucose Targets." Journal of Diabetes Science and Technology 14, no. 3 (February 11, 2020): 553–59. http://dx.doi.org/10.1177/1932296820905581.

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Background: To summarize new evidence regarding the methodological aspects of blood glucose control in the intensive care unit (ICU). Methods: We reviewed the literature on blood glucose control in the ICU up to August 2019 through Ovid Medline and Pubmed. Results: Since the publication of the Leuven studies, the benefits of glycemic control have been recognized. However, the methodology of blood glucose control, notably the blood glucose measurement accuracy and the insulin titration protocol, plays an important but underestimated role. This may partially explain the negative results of the large, pragmatic multicenter trials and made everyone realize that tight glycemic control with less-frequent glucose measurements on less accurate blood glucose meters is neither feasible nor advisable in daily practice. Blood gas analyzers remain the gold standard. New generation point-of-care blood glucose meters may be an alternative when using whole blood of critically ill patients in combination with a clinically validated insulin dosing algorithm. Conclusion: When implementing blood glucose management in an ICU one needs to take into account the interaction between aimed glycemic target and blood glucose measurement methodology.
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Litinskaia, Evgeniia L., Pavel A. Rudenko, Kirill V. Pozhar, and Nikolai A. Bazaev. "Validation of Short-Term Blood Glucose Prediction Algorithms." International Journal of Pharma Medicine and Biological Sciences 8, no. 2 (April 2019): 34–39. http://dx.doi.org/10.18178/ijpmbs.8.2.34-39.

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ET, GODAM, and IBIERE PEPPLE. "MAGNESIUM AND MELATONIN CO-ADMINISTRATION ATTENUATES BLOOD GLUCOSE." International Journal of Prevention Practice and Research 02, no. 05 (September 1, 2022): 01–04. http://dx.doi.org/10.55640/medscience-abcd616.

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The escalating prevalence of diabetes mellitus demands innovative approaches to manage and prevent its complications. Magnesium and melatonin have individually demonstrated potential in modulating various metabolic pathways, including glucose homeostasis. This study investigates the synergistic effects of magnesium and melatonin co-administration on blood glucose levels, aiming to provide novel insights into a potential therapeutic strategy for diabetes management.
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Seley, Jane J., and Laura Quigley. "Blood Glucose Testing." American Journal of Nursing 100, no. 8 (August 2000): 24A. http://dx.doi.org/10.2307/3522147.

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American Diabetes Association. "Postprandial Blood Glucose." Clinical Diabetes 19, no. 3 (July 1, 2001): 127–30. http://dx.doi.org/10.2337/diaclin.19.3.127.

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Davey, Sarah. "Blood glucose monitoring." Nursing Standard 28, no. 40 (June 4, 2014): 61. http://dx.doi.org/10.7748/ns.28.40.61.s48.

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Peck, Jackie. "Blood glucose monitoring." Nursing Standard 29, no. 14 (December 3, 2014): 61. http://dx.doi.org/10.7748/ns.29.14.61.s47.

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Bedlow, Julia. "Blood glucose monitoring." Nursing Standard 2, no. 34 (May 28, 1988): 24–25. http://dx.doi.org/10.7748/ns.2.34.24.s57.

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Dissertations / Theses on the topic "Blood glucose"

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Rahaghi, Farbod N. "Human blood glucose dynamics." Diss., Connect to a 24 p. preview or request complete full text in PDF format. Access restricted to UC campuses, 2007. http://wwwlib.umi.com/cr/ucsd/fullcit?p3259621.

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Thesis (Ph. D.)--University of California, San Diego, 2007.
Title from first page of PDF file (viewed June 21, 2007). Available via ProQuest Digital Dissertations. Vita. Includes bibliographical references (p. 269-276).
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Eagles, O. D. "Non-invasive blood glucose monitoring." Thesis, Swansea University, 1995. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.636758.

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This Thesis covers the investigation into the feasibility of monitoring blood glucose non-invasively. The work carried out involved the development of an in-vitro instrument through a series of four stages, each stage of development being an improvement on the previous one. Using these instruments it was shown that by using an appropriate wavelength, glucose could be detected down to 156 mg/dL repeatedly in distilled water, saline and a non-opaque blood analogue. It was also demonstrated that this wavelength could be used to detect the difference between blood samples with different glucose levels. The instruments were also used to demonstrate that a appropriate wavelength could be used as a reference wavelength. In addition to the in-vitro instrument, a basic in-vivo instrument was developed so that physiological data could be taken from either a person's ear or little finger non-invasively. It was clearly demonstrated that the instrument could detect a physiological change in a person whilst the person carried out a 75 g oral glucose to tolerance test.
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Kumari, N. "Blood glucose levels and wellbeing." Thesis, University of Nottingham, 1986. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.374804.

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Parekh, Bhavin. "Volatile biomarkers of blood glucose." Thesis, University of Cambridge, 2011. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.609459.

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Owren, Marit. "Automatic Blood Glucose Control in Diabetes." Thesis, Norwegian University of Science and Technology, Department of Engineering Cybernetics, 2009. http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-8974.

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In this thesis, a closed-loop control algorithm for regulating the blood glucose concentration in type 1 diabetic patients is developed. Two control criteria are imposed on the system, namely: -Avoidance of hypoglycemia. (blood glucose concentrations should always be above $3 frac{mmol}{L}$) -Reduction in the average blood glucose concentration compared to what is achieved with manual control. (average blood glucose concentrations should preferably be less than $7.0 frac{mmol}{L}$). The developed control algorithm manages to fulfill both these control criteria. Hypoglycemia is avoided, and average blood glucose concentrations is reduced by $20%$ and $22%$ to a level of $7.0 frac{mmol}{L}$ and $6.9 frac{mmol}{L}$ in the two test subjects. However, further experiments should be carried out to test the robustness of the control algorithm, and a thorough investigation of safety issues for the user needs to performed. As a basis for the implementation of closed-loop blood glucose control, data from three diabetic patients is used to identify the parameters of a proposed mathematical model of the human insulin-glucose regulatory system. The identification process reveals that there is large variations between individual patient's parameter values, and the difference in insulin sensitivity is found to be specially high, both between and within patients.

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Abel, Evan Dale. "Insulin and blood pressure." Thesis, University of Oxford, 1990. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.257939.

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Kultala, Henrik, and Simon Persson. "Blood-glucose prediction : Comparing insulin treatment methods." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-280324.

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Type 1 diabetes requires its patients to inject artificial insulin in their bodies to control their blood-glucose levels. This can to some extent be automated through the use of insulin pumps and continuous blood-glucose monitoring systems, enabling automatic insulin injections and automatic blood-glucose measurements. To inject an appropriate amount of insulin, a prediction of the future blood-glucose values has to be made, the accuracy of which dictates how autonomous such a system can be. In this paper, the performance of a machine learning model is examined, when using data from different insulin treatment methods. The two treatment methods compared are the closed-loop insulin pump system and the traditional insulin pump system. By training a convolutional recurrent neural network separately on the different datasets, the resulting models were compared on four different performance metrics; root-mean-square error, mean average relative difference, Matthews correlation coefficient for hypoglycemia, and Matthews correlation coefficient for hyperglycemia. While the results showed some indication of the closed-loop models being better, the differences were too small to be statistically significant. To get more conclusive results, a study involving more clinical patients would be needed.
Typ 1 diabetes kräver insjuknade att injicera artificiellt framställt insulin för att kontrollera blocksockernivån. Processen kan till viss del automatiseras med hjälp av insulinpumpar och kontinuerliga glukosmätningssytem, vilka möjliggör automatiska insulininjektioner och automatiska blodsockermätningar. För att kunna injicera rätt mängd insulin behöver framtida glukosvärden förutspås, och noggrannheten i uppskattningen av glukosvärdena avgör hur autonomt ett sådant system kan agera. I denna rapport undersöks prestandan för en maskininlärningsmodell när data från olika insulinbehandlingsmetoder används. De två behandlingsmetoderna som jämförs är ett “closed-loop” system och ett traditionellt insulinpumpsystem. Genom att träna ett “convolutional recurrent neural network” separat på de olika datamängderna jämfördes de resulterande modellerna inom fyra olika prestandamått; “root-mean-square error”, “mean average relative difference”, “Matthews correlation coefficient” för hypoglykemi och “Matthews correlation coefficient” för hyperglykemi. Resultaten visade en viss indikation på att modellerna som hade tränats på data från “closed-loop” systemet var bättre, men skillnaderna var för små för att vara statistiskt signifikanta. För att få mer avgörande resultat skulle det behövas en studie med fler kliniska patienter.
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Talebi, Fard Sahba. "Glucose monitoring measuring blood glucose using vertical cavity surface emitting lasers (VCSELs)." Thesis, University of British Columbia, 2008. http://hdl.handle.net/2429/1509.

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Diabetes Mellitus is a common chronic disease that is an ever-increasing public health issue. Continuous glucose monitoring has been shown to help diabetes mellitus patients stabilize their glucose levels, leading to improved patient health. Hence, a glucose sensor, capable of continuous real-time monitoring, has been a topic of research for three decades. Current methods of glucose monitoring, however, require taking blood samples several times a day, hence patient compliance is an issue. Optical methods are one of the painless and promising methods that can be used for blood glucose predictions. However, having accuracies lower than what is acceptable clinically has been a major concern. To improve on the accuracy of the predictions, the signal-to-noise ratio in the spectrum can be increased, for which the use of thermally tunable vertical cavity surface emitting lasers (VCSELs) as the light source to obtain blood absorption spectra, along with a multivariate technique (Partial Least Square (PLS) techniques) for analysis, is proposed. VCSELs are semiconductor lasers with small dimensions and low power consumption, which makes them suitable for implants. VCSELs provide higher signal-to-noise ratio as they have high power spectral density and operate within a small spectrum. In the current research, experiments were run for the preliminary investigations to demonstrate the feasibility of the proposed technique for glucose monitoring. This research involves preliminary investigations for developing a novel optical system for accurate measurement of glucose concentration. Experiments in aqueous glucose solutions were designed to demonstrate the feasibility of the proposed technique for glucose monitoring. In addition, multivariate techniques, such as PLS, were customized for various specific purposes of this project and its preliminary investigation. This research will lead to the development of a small, low power, implantable optical sensor for diabetes patients, which will be a major breakthrough in the area of treating diabetes patients, upon successful completion of this research and development of the device.
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Ekram, Fatemeh. "Blood glucose regulation in type II diabetic patients." Thesis, University of British Columbia, 2016. http://hdl.handle.net/2429/57070.

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Type II diabetes is the most pervasive diabetic disorder, characterized by insulin resistance, β-cell failure in secreting insulin and impaired regulatory effects of the liver on glucose concentration. Although in the initial steps of the disease, it can be controlled by lifestyle management, but most of the patients eventually require oral diabetic drugs and insulin therapy. The target for the blood glucose regulation is a certain range rather than a single value and even in this range, it is more desirable to keep the blood glucose close to the lower bound. Due to ethical issues and physiological restrictions, the number of experiments that can be performed on a real subject is limited. Mathematical modeling of glucose metabolism in the diabetic patient is a safe alternative to provide sufficient and reliable information on the medical status of the patient. In this thesis, dynamic model of type II diabetes has been expanded by incorporation of the pharmacokinetic-pharmacodynamic model of different types of insulin and oral drug to study the impact of several treatment regimens. The most efficient treatment has been then selected amongst all possible multiple daily injection regimens according to the patient's individualized response. In this thesis, the feedback control strategy is applied in this thesis to determine the proper insulin dosage continuously infused through insulin pump to regulate the blood glucose level. The logarithm of blood glucose concentration has been used as the controlled variable to reduce the nonlinearity of the glucose-insulin interactions. Also, the proportional-integral controller has been modified by scheduling gains calculated by a fuzzy inference system. Model predictive control strategy has been proposed in this research for the time that sufficient measurements of the blood glucose are available. Multiple linear models have been considered to address the nonlinearity of glucose homeostasis. On the other hand, the optimization objective function has been adjusted to better fulfill the objectives of the blood glucose regulation by considering asymmetric cost function and soft constraints. The optimization problem has been solved by the application of multi-parametric quadratic programming approach which reduces the on-line optimization problem to off-line function evaluation.
Applied Science, Faculty of
Chemical and Biological Engineering, Department of
Graduate
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Araujo, Cespedes Fabiola. "RF Sensing System for Continuous Blood Glucose Monitoring." Scholar Commons, 2017. http://scholarcommons.usf.edu/etd/6998.

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The purpose of this research was to design a blood glucose sensing system based on the induced shift in the resonant frequency of an antenna patch operating in the ISM band (5.725 – 5.875 GHz). The underlying concept is the fact that when a person has variations in their blood glucose levels, the permittivity of their blood varies accordingly. This research analyzed the feasibility of using an antenna patch as a blood glucose sensing device in three configurations: 1) as an implantable active sensor, 2) as an implantable passive antenna sensor, and 3) as a non-invasive sensor. In the first arrangement, the antenna is to be implanted inside the body as an active antenna, requiring that its power supply and internal circuitry to be implanted. In the second arrangement, the antenna is also implanted, but would not require a power supply or internal circuity since it would be passive. For the third arrangement, the non-invasive sensing approach, the antenna is placed facing the upper arm while mounted outside the body. In order to evaluate the best approach all the three approaches were simulated using the electromagnetic field tool simulator ANSYS EM15.0 HFSSTM, along with a human tissue model. The tissue model included physiological and electrical characteristics of the human abdomen for simulating the active and passive approaches, and the upper arm for the non-invasive approach. The electromagnetic boundaries were set with perfectly matched layers to eliminate any reflections which would cause a non-physical resonance in the results. Simulation of the active sensing configuration resulted in a resonant frequency shift from 5.76 to 5.78GHz (i.e., a 20 MHz shift) for a simulated blood permittivity variation of 62.0 to 63.6. This corresponds, theoretically, to an approximate glucose shift of 500 mg/dL. The passive configuration simulations did not yield conclusive variations in resonant frequency and this approach was abandoned early on in this research. Thirdly, the non-invasive approach resulted in a simulated shift of resonant frequency from 5.797 to 5.807 (i.e., a 10MHz shift) for simulated blood permittivity variation of 51.397 to 52.642 (an approximate variation of 2000 mg/dL in glucose). In the literature planar, continuous blood-rich layers are used to simulate RF sensing of glucose, which is not applicable when measuring glucose in actual human veins, which are tubular in geometry and of finite extent. Therefore the model employed assumed a 1.8 mm diameter blood vessel, buried under a fatty layer that was capped with skin. The above results, both simulated and verified experimentally, used this more realistic model which is further proof that a practical non-invasive blood glucose measurement system should be possible. The non-invasive approach was tested experimentally by using oil in gel phantoms to mimic the electrical properties of skin, fat, blood and muscle. A fat phantom was placed over a muscle phantom, with a strip of blood phantom within and a skin phantom was placed on top. The blood phantom had a 2000mg/dL variation of D-glucose in the phantom mixture which decreased the relative permittivity from 52.635 to 51.482 and resulted in a shift of resonant frequency from 5.855 to 5.842 (i.e., a 13MHz shift). This is consistent with the non-invasive simulated results thus validating our model of the non-invasive sensing approach. While this variation in blood glucose is non-physical (typical human glucose range can range in the extremes from 30 to 400 mg/dL, where healthy glucose levels vary from 70mg/dL to 180mg/dL) it was necessary to provide a high confidence fit between the simulated and experimental data. This is because the level of precision with which the physical phantoms could be fabricated with was insufficient to match the highly precise simulated data. Analysis on the effect of lateral displacement of the antenna from the blood vessel, its elevation above the skin and variations caused by different skin thickness, and blood vessel depth were evaluated. A calibration technique to correct physical misalignment by the user is proposed in which two additional antennas, located diagonally with respect to the sensing antenna, serve as reference point for placement over the upper arm in line of sight with the blood vessel. Once the non-invasive sensor approach was shown to be viable for continuous glucose monitoring, a sensor platform was designed whereby an RF generator was used to drive the antenna with a frequency sweep between 5.725 to 5.875GHz. A fraction of its output power was coupled to both the antenna and the system analysis circuitry through a directional coupler. The transmitted and received power were then processed with demodulating logarithmic amplifiers which convert the RF signal to a corresponding voltage for downstream processing. Both inputs were then fed into a microcontroller and the measured shift in resonant frequency, fO, converted to glucose concentration which was displayed on glucose meter display.
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Books on the topic "Blood glucose"

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Marc, Moseley, Portland Area Diabetes Program (Bellingham, Wash.), Northwest Portland Area Indian Health Board, and Indian Health Service Diabetes Program (U.S.), eds. Home blood sugar test. Bellingham, WA: Portland Area Diabetes Program, 1988.

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D, Cunningham David, and Stenken Julie A, eds. In vivo glucose sensing. Hoboken, N.J: Wiley, 2009.

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United States. Food and Drug Administration. Office of Women's Health. Your glucose meter. Silver Spring, Md.]: FDA, Office of Women's Health, 2010.

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Marc, Moseley, Portland Area Diabetes Program (Bellingham, Wash.), Northwest Portland Area Indian Health Board, and Indian Health Service Diabetes Program (U.S.), eds. Diabetes and oral pills. Bellingham, WA: Portland Area Diabetes Program, 1988.

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Burugapalli, Krishna. Nanomaterials in glucose sensing. New York, NY: ASME Press, 2013.

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Szablewski, Leszek, ed. Blood Glucose Levels. IntechOpen, 2020. http://dx.doi.org/10.5772/intechopen.73823.

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Warren, Helen. Blood Glucose Logbook. Independently Published, 2021.

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Blood Pressure and Blood Glucose Log. Independently Published, 2021.

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Morane, Lorine. Blood Glucose Log Book: A Simple Weekly Blood Glucose Monitoring Log Book Journal, Record Your Blood Glucose/Blood Sugar Levels. Independently Published, 2021.

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Hogen, Lorine. Blood Glucose Log Book: A Simple Weekly Blood Glucose Monitoring Log Book Journal, Record Your Blood Glucose/Blood Sugar Levels. Independently Published, 2021.

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Book chapters on the topic "Blood glucose"

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Carrillo, Adriana, and Carley Gomez-Meade. "Blood Glucose." In Encyclopedia of Behavioral Medicine, 265–66. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-39903-0_1187.

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Wideman, Timothy H., Michael J. L. Sullivan, Shuji Inada, David McIntyre, Masayoshi Kumagai, Naoya Yahagi, J. Rick Turner, et al. "Blood Glucose." In Encyclopedia of Behavioral Medicine, 233–34. New York, NY: Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4419-1005-9_1187.

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Ujcic-Voortman, Joanne K. "Blood Glucose." In Encyclopedia of Immigrant Health, 288–89. New York, NY: Springer New York, 2012. http://dx.doi.org/10.1007/978-1-4419-5659-0_85.

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Devi, Rooma, Aman Chauhan, Simmi Kharb, and Chandra Shekhar Pundir. "Blood Glucose." In Clinical Biochemistry, 75–89. New York: Jenny Stanford Publishing, 2023. http://dx.doi.org/10.1201/9781003455660-6.

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Malik, Jamil A., Theresa A. Morgan, Falk Kiefer, Mustafa Al’Absi, Anna C. Phillips, Patricia Cristine Heyn, Katherine S. Hall, et al. "Low Blood Glucose." In Encyclopedia of Behavioral Medicine, 1179. New York, NY: Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4419-1005-9_100994.

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Smith, Joyce, and Rachel Roberts. "Blood Glucose Monitoring." In Vital Signs for Nurses, 187–204. Chichester, UK: John Wiley & Sons, Ltd, 2015. http://dx.doi.org/10.1002/9781119139119.ch11.

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Cherruault, Y. "Blood Glucose Regulation." In Mathematical Modelling in Biomedicine, 137–51. Dordrecht: Springer Netherlands, 1986. http://dx.doi.org/10.1007/978-94-009-5492-2_7.

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Uchino, H., and R. Kawamori. "Blood Glucose Control." In Contributions to Nephrology, 106–12. Basel: KARGER, 2001. http://dx.doi.org/10.1159/000060140.

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Gjedde, Albert, William R. Bauer, and Dean F. Wong. "Blood–Brain Glucose Transfer." In Neurokinetics, 177–210. Boston, MA: Springer US, 2010. http://dx.doi.org/10.1007/978-1-4419-7409-9_6.

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Valgimigli, Francesco, Fabrizio Mastrantonio, and Fausto Lucarelli. "Blood Glucose Monitoring Systems." In Security and Privacy for Implantable Medical Devices, 15–82. New York, NY: Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4614-1674-6_2.

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Conference papers on the topic "Blood glucose"

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Stahl, F., and R. Johansson. "Short-term diabetes blood glucose prediction based on blood glucose measurements." In 2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE, 2008. http://dx.doi.org/10.1109/iembs.2008.4649147.

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Frohner, Matthias, Markus Meyer, Klaus Donsa, Philipp Urbauer, Veronika David, and Stefan Sauermann. "Telemonitoring of Blood Glucose." In DSAI 2018: 8th International Conference on Software Development and Technologies for Enhancing Accessibility and Fighting Info-exclusion. New York, NY, USA: ACM, 2018. http://dx.doi.org/10.1145/3218585.3218678.

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Kiriakidis, Kiriakos, and Richard O'Brien. "Optimal Estimation of Blood Insulin From Blood Glucose." In ASME 2006 International Mechanical Engineering Congress and Exposition. ASMEDC, 2006. http://dx.doi.org/10.1115/imece2006-14776.

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Plasma insulin estimation from plasma glucose has been proposed in order to avoid hyperinsulinemia in the control of diabetes. This paper presents an estimator with error feedback based on measured and predicted plasma glucose designed to tolerate measurement noise as well as discretization error by means of the H∞ criterion. The proposed estimator is tested and evaluated using synthetic patient data.
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Qi Li and Jingqi Yuan. "Development of the Portable Blood Glucose Meter for Self-monitoring of Blood Glucose." In 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference. IEEE, 2005. http://dx.doi.org/10.1109/iembs.2005.1616050.

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Shukla, Rishabh, S. B. Somani, and V. V. Shete. "Wireless blood glucose monitoring system." In 2016 International Conference on Inventive Computation Technologies (ICICT). IEEE, 2016. http://dx.doi.org/10.1109/inventive.2016.7823277.

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Litinskaia, E. L., and K. V. Pozhar. "Automated Blood Glucose Control System." In 2019 III International Conference on Control in Technical Systems (CTS). IEEE, 2019. http://dx.doi.org/10.1109/cts48763.2019.8973284.

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Ashton, H. S., H. A. MacKenzie, P. Rae, Y. C. Shen, S. Spiers, and J. Lindberg. "Blood glucose measurements by photoacoustics." In PHOTOACOUSTIC AND PHOTOTHERMAL PHENOMENA. ASCE, 1999. http://dx.doi.org/10.1063/1.58136.

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Anas, M. N., N. K. Nurun, A. N. Norali, and M. Normahira. "Non-invasive blood glucose measurement." In 2012 IEEE EMBS Conference on Biomedical Engineering and Sciences (IECBES 2012). IEEE, 2012. http://dx.doi.org/10.1109/iecbes.2012.6498114.

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MacKenzie, H. A., H. S. Ashton, Y. C. Shen, J. Lindberg, P. Rae, K. M. Quart, and S. Spiers. "Blood Glucose Measurements by Photoacoustics." In Biomedical Optical Spectroscopy and Diagnostics. Washington, D.C.: OSA, 1998. http://dx.doi.org/10.1364/bosd.1998.btuc1.

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Suthar, Kamlesh J., Muralidhar K. Ghantasala, and Derrick C. Mancini. "Simulation of Hydrogel Responsiveness to Blood Glucose." In ASME 2013 Conference on Smart Materials, Adaptive Structures and Intelligent Systems. American Society of Mechanical Engineers, 2013. http://dx.doi.org/10.1115/smasis2013-3167.

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This paper presents the results of our fully coupled, two-dimensional (2D) simulation of the swelling behavior of glucose-sensitive hydrogels at a constant glucose level with change in the surrounding pH. The model consists of a system of glucose-sensitive hydrogel and ionic fluid as a solvent. The hydrogel consists of two enzymes: glucose-oxidase and catalase, which are immobilized on the polymeric network. The surrounding solvent has certain level of glucose. The diffusion of glucose from a solvent and its reaction within the hydrogel are simulated using the Nernst-Planck equation. The local electrical charge is calculated by the Poisson’s equation, and deformation of the hydrogel is determined by the mechanical field equation. These equations are fully coupled and simulations are performed for varying pH and glucose concentrations. The glucose concentration was taken at 7.7mM (140mg/mL) and the pH is varied from 6.8 to 7.4. As glucose reacts with oxygen, gluconic acid is produced in the presence of glucose-oxidase. The formation of gluconic acid within the gel results in protonation and thereby causes the hydrogel expansion. The glucose level in the surrounding solution limits diffusion in the hydrogel. As the surrounding solution pH increases the available fixed charged for ionization increases, which results in an increase in maximum equilibrium swelling and gluconic acid as a product of the reaction. The gluconic acid production was found to be proportional to the change in pH. The gluconic acid decreases the internal pH of the hydrogel, which ultimately reduced the deformation of the gel.
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Reports on the topic "Blood glucose"

1

Lal, Shankar, and Ehtesham Khan. Perioperative Management of Diabetic Patients: Optimising Care with Insulin Pumps and CGM Devices. World Federation of Societies of Anaesthesiologists, June 2024. http://dx.doi.org/10.28923/atotw.524.

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This tutorial examines the perioperative management of diabetic patients using insulin pumps and CGM devices. It focuses on optimizing care through patient-centred strategies, preoperative evaluation, and education on glucose management to prevent complications and ensure stable blood glucose levels.
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Esmail, Jihan, and Ramasubbareddy Dhanireddy. Time to First Blood Glucose Determination and Administration of Intravenous Glucose at Birth in Extremely Low Birth Weight Infants. University of Tennessee Health Science Center, 2022. http://dx.doi.org/10.21007/com.lsp.2022.0010.

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LI, Wenhui, Xiaoming HU, xuhong WANG, Lei XU, Guobin LIU, and Weijing FAN. Telemedicine for blood glucose in Diabetes Mellitus: an Overview of Systematic Reviews. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, June 2021. http://dx.doi.org/10.37766/inplasy2021.6.0024.

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4

Mei, Manxue, Min Jiang, Zunjiang Li, Wei Zhu, and Jianping Song. Meditation Programs for Adults with Type 2 Diabetes Mellitus: Protocol for a Systematic Review and Meta-analysis. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, October 2021. http://dx.doi.org/10.37766/inplasy2021.10.0008.

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Review question / Objective: Would meditation programs affect fasting blood glucose levels and HbA(1c) of patients with type 2 diabetes mellitus? Would meditation programs intervention be of benefit for remission of depression and anxiety level? Would meditation programs improve quality of life of individuals with type 2 diabetes？ Do meditation programs affect body mass index (BMI), serum lipid levels and level of blood pressure? Which type of meditation programs is better for type 2 diabetes patients? Are there any differences of efficacy among different meditation programs? To provide valid evidence for the effect of meditation programs for type 2 diabetes by synthesizing and comparing outcomes from clinical trials. Main outcome(s): The outcomes include fasting blood glucose levels and HbA(1c).
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Gao, Hui, Chen Gong, Shi-chun Shen, Jia-ying Zhao, Dou-dou Xu, Fang-biao Tao, Yang Wang, and Xiao-chen Fan. A systematic review on the associations between prenatal phthalate exposure and childhood glycolipid metabolism and blood pressure: evidence from epidemiological studies. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, June 2022. http://dx.doi.org/10.37766/inplasy2022.6.0111.

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Review question / Objective: The present systematic review was performed to obtain a summary of epidemiological evidence on the relationships of in utero exposure to phthalates with childhood glycolipid metabolism and blood pressure. Condition being studied: Childhood cardiovascular risk factors including blood pressure, lipid profile (e.g., triglycerides, total cholesterol, HDL−C, LDL−C) and glucose metabolism (e.g., insulin, insulin resistance, insulin sensitivity, glucose) were the interested outcomes. Eligibility criteria: In brief, epidemiological studies including cohort study, case-control study and cross-sectional survey were screened. Studies regarding relationships between human exposure to organophosphate esters and neurotoxicity were possible eligible for the present systematic review. The adverse neurodevelopmental outcomes included development of cognition, behavior, motor, brain change, emotion, etc. Studies that did not meet the above criteria were not included in this systematic review.
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Gomes, Tara, David Juurlink, Baiju Shah, Michael Paterson, and Muhammad Mamdani. Self-monitoring of blood glucose: Patterns, Costs and Potential Cost Reduction Associated with Reduced Testing. ODPRN, December 2009. http://dx.doi.org/10.31027/odprn.2009.01.

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Lane, S. M., and J. J. Mastrotaro. Development Of A Prototype Sensor For Continuous Blood Glucose Monitoring Final Report CRADA No. TC-1271-96. Office of Scientific and Technical Information (OSTI), November 2017. http://dx.doi.org/10.2172/1408980.

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Lane, S. Development Of A Prototype Sensor For Continuous Blood Glucose Monitoring Final Report CRADA No. TC-1271-96. Office of Scientific and Technical Information (OSTI), May 2001. http://dx.doi.org/10.2172/790111.

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9

Lane, Stephen M., and John J. Mastrototaro. Development of Chemically Amplified Optical Sensors for Continuous Blood Glucose Monitoring Final Report CRADA No. TSB-1162-95. Office of Scientific and Technical Information (OSTI), January 2018. http://dx.doi.org/10.2172/1418925.

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Lane, S. Development of Chemically Amplified Optical Sensors for Continuous Blood Glucose Monitoring Final Report CRADA No. TSB-1162-95. Office of Scientific and Technical Information (OSTI), August 2000. http://dx.doi.org/10.2172/773222.

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