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Molecules (Basel, Switzerland) Jun 2020Near-infrared (NIR) spectroscopy occupies a specific spot across the field of bioscience and related disciplines. Its characteristics and application potential differs... (Review)
Review
Near-infrared (NIR) spectroscopy occupies a specific spot across the field of bioscience and related disciplines. Its characteristics and application potential differs from infrared (IR) or Raman spectroscopy. This vibrational spectroscopy technique elucidates molecular information from the examined sample by measuring absorption bands resulting from overtones and combination excitations. Recent decades brought significant progress in the instrumentation (e.g., miniaturized spectrometers) and spectral analysis methods (e.g., spectral image processing and analysis, quantum chemical calculation of NIR spectra), which made notable impact on its applicability. This review aims to present NIR spectroscopy as a matured technique, yet with great potential for further advances in several directions throughout broadly understood bio-applications. Its practical value is critically assessed and compared with competing techniques. Attention is given to link the bio-application potential of NIR spectroscopy with its fundamental characteristics and principal features of NIR spectra.
Topics: Animals; Blood Chemical Analysis; Blood Glucose; Humans; Oxygen; Spectroscopy, Near-Infrared; Spectrum Analysis, Raman
PubMed: 32604876
DOI: 10.3390/molecules25122948 -
Nutricion Hospitalaria Apr 2015In Spain, nearly 14% of the population is diabetic, 95% corresponds to Type 2 Diabetes Mellitus patients. Poor glycemic control increases morbidity and mortality. There... (Meta-Analysis)
Meta-Analysis Review
INTRODUCTION
In Spain, nearly 14% of the population is diabetic, 95% corresponds to Type 2 Diabetes Mellitus patients. Poor glycemic control increases morbidity and mortality. There are three pillars in the treatment of type 2 diabetes: diet, medication and exercise. However, the potential for prescribing exercise training has not been fully exploited.
OBJECTIVE
To analyze the effect of different exercise modalities (AE, RT, Combo, HIIT) on glycemic control in patients with type 2 diabetes mellitus.
METHODS
The reserch was performed in 3 electronic databases (Pubmed, Scopus and Proquest), including publications from 2011 to the present, publications undertaking interventions with AE, RT, Combo or HIIT, and those that measured capillary glucose, CGMS or HbA1c.
RESULTS
Of the 386 articles found, 14 met the inclusion criteria. These items were classified according to exercise intervention modality (AE, RT, Combo, HIIT) and whether glycemic control was measured as a result of continued training or 24-48h post-workout.
CONCLUSIONS
EA, RT, Combo and HIIT show efficacy in glycemic control in both the continuous training and 24-48h post-training. To achieve certain benefits in glycemic control, prescribing a structured frequency, volume and intensity training is required. Combo is the modality that gets better results through continued training.
Topics: Blood Glucose; Diabetes Mellitus, Type 2; Exercise; Exercise Therapy; Humans
PubMed: 25795929
DOI: 10.3305/nh.2015.31.4.7907 -
Journal of Voice : Official Journal of... Sep 2022The possibility to estimate glucose value from voice would make a breakthrough in diabetes treatment: namely, remove the delay in the nonintrusive instantaneous blood... (Review)
Review
The possibility to estimate glucose value from voice would make a breakthrough in diabetes treatment: namely, remove the delay in the nonintrusive instantaneous blood glucose estimation, relieve medical budgets and significantly improve wellbeing of diabetics. In this review, different approaches have been described and systematized, in order to provide an objective snapshot of the state of the art. Since nonintrusive glucose estimation is notoriously difficult, we included a Transparence and Reproducibility Score aimed at revealing the biases in the primary research articles. The review is completed with the discussion on future research pathways.
Topics: Blood Glucose; Humans; Reproducibility of Results; Voice
PubMed: 33041176
DOI: 10.1016/j.jvoice.2020.08.034 -
International Heart Journal 2020
Topics: Biomarkers; Blood Glucose; Cardiovascular Diseases; Glucose Tolerance Test; Humans
PubMed: 32727998
DOI: 10.1536/ihj.20-417 -
Journal of Diabetes Science and... Sep 2016In general, patients with diabetes performing self-monitoring of blood glucose (SMBG) can strongly rely on the accuracy of measurement results. However, various factors... (Review)
Review
In general, patients with diabetes performing self-monitoring of blood glucose (SMBG) can strongly rely on the accuracy of measurement results. However, various factors such as application errors, extreme environmental conditions, extreme hematocrit values, or medication interferences may potentially falsify blood glucose readings. Incorrect blood glucose readings may lead to treatment errors, for example, incorrect insulin dosing. Therefore, the diabetes team as well as the patients should be well informed about limitations in blood glucose testing. The aim of this publication is to review the current knowledge on limitations and interferences in blood glucose testing with the perspective of their clinical relevance.
Topics: Blood Glucose; Blood Glucose Self-Monitoring; Diabetes Mellitus; Female; Humans; Male
PubMed: 27044519
DOI: 10.1177/1932296816641433 -
Oncotarget Jul 2017The question of whether elevated blood glucose is a risk factor for liver cancer has been intensively studied, yet with inconsistent results. To explore the relationship... (Meta-Analysis)
Meta-Analysis Review
The question of whether elevated blood glucose is a risk factor for liver cancer has been intensively studied, yet with inconsistent results. To explore the relationship between blood glucose concentration and risk of liver cancer, we conduct a meta-analysis of prospective studies. Literature search was comprehensively performed using database of PubMed, EMBASE and the Cochrane Library through October 2016. Random-effect models were used to combine the effect estimations. Eight articles containing ten studies with a total of 1975 liver cancer cases were included. The pooled RRs demonstrated that elevated fasting blood glucose was associated with increased risk of liver cancer (combined RRs: 1.77; 95% CI: 1.46, 2.13) with mild heterogeneity (I2 = 30.40%, P = 0.17). In sensitivity analysis, the pooled result remained significant (combined RRs: 1.33; 95% CI: 1.12, 1.59; I2 = 33.90%, P = 0.16) when we restricted blood glucose categories in the range of nondiabetic subjects. We also detected a J-shaped non-linear dose-response relationship between blood glucose concentration and risk of liver cancer. There is evidence that elevated blood glucose increases risk of liver cancer across the range of prediabetes and diabetes. Considering the rapidly increasing prevalence of prediabetes and diabetes, controlling blood glucose may lower the risk of liver cancer.
Topics: Aged; Blood Glucose; Humans; Liver Neoplasms; Middle Aged; Prospective Studies; Risk Factors
PubMed: 28432278
DOI: 10.18632/oncotarget.16816 -
Journal of Healthcare Engineering 2022Frequent measurement of blood glucose concentration in diabetic patients is an important means for diabetes control. Blood glucose monitoring with noninvasive detection...
Frequent measurement of blood glucose concentration in diabetic patients is an important means for diabetes control. Blood glucose monitoring with noninvasive detection technology can not only avoid the pain of patients and eliminate the harm of some biological materials for measuring glucose in vivo but also improve the frequency of detection, so as to control blood glucose concentration more closely. Traditional blood glucose detection methods are invasive and have some limitations. In this study, the significance of noninvasive blood glucose testing was analyzed and was pointed out that noninvasive blood glucose testing can monitor the blood glucose concentration of patients and relieve the pain of patients. Then, this study analyzed the spectral detection methods of noninvasive blood glucose, including conservation of energy metabolism, near infrared spectroscopy, and other spectral detection methods. Finally, this study made a comprehensive analysis of the domestic and international clinical application of noninvasive glucose spectrum monitoring and summarized the clinical application status of noninvasive glucose spectrum monitoring.
Topics: Blood Glucose; Blood Glucose Self-Monitoring; Diabetes Mellitus; Glucose; Humans; Pain
PubMed: 35178236
DOI: 10.1155/2022/8325451 -
Oxidative Medicine and Cellular... 2020Glucagon, a hormone secreted by pancreatic alpha cells, contributes to the maintenance of normal blood glucose concentration by inducing hepatic glucose production in... (Review)
Review
Glucagon, a hormone secreted by pancreatic alpha cells, contributes to the maintenance of normal blood glucose concentration by inducing hepatic glucose production in response to declining blood glucose. However, glucagon hypersecretion contributes to the pathogenesis of type 2 diabetes. Moreover, diabetes is associated with relative glucagon undersecretion at low blood glucose and oversecretion at normal and high blood glucose. The mechanisms of such alpha cell dysfunctions are not well understood. This article reviews the genesis of alpha cell dysfunctions during the pathogenesis of type 2 diabetes and after the onset of type 1 and type 2 diabetes. It unravels a signaling pathway that contributes to glucose- or hydrogen peroxide-induced glucagon secretion, whose overstimulation contributes to glucagon dysregulation, partly through oxidative stress and reduced ATP synthesis. The signaling pathway involves phosphatidylinositol-3-kinase, protein kinase B, protein kinase C delta, non-receptor tyrosine kinase Src, and phospholipase C gamma-1. This knowledge will be useful in the design of new antidiabetic agents or regimens.
Topics: Blood Glucose; Glucagon; Humans
PubMed: 32774668
DOI: 10.1155/2020/3089139 -
Journal of Medical Internet Research May 2019Diabetes mellitus is a chronic metabolic disorder that results in abnormal blood glucose (BG) regulations. The BG level is preferably maintained close to normality... (Review)
Review
BACKGROUND
Diabetes mellitus is a chronic metabolic disorder that results in abnormal blood glucose (BG) regulations. The BG level is preferably maintained close to normality through self-management practices, which involves actively tracking BG levels and taking proper actions including adjusting diet and insulin medications. BG anomalies could be defined as any undesirable reading because of either a precisely known reason (normal cause variation) or an unknown reason (special cause variation) to the patient. Recently, machine-learning applications have been widely introduced within diabetes research in general and BG anomaly detection in particular. However, irrespective of their expanding and increasing popularity, there is a lack of up-to-date reviews that materialize the current trends in modeling options and strategies for BG anomaly classification and detection in people with diabetes.
OBJECTIVE
This review aimed to identify, assess, and analyze the state-of-the-art machine-learning strategies and their hybrid systems focusing on BG anomaly classification and detection including glycemic variability (GV), hyperglycemia, and hypoglycemia in type 1 diabetes within the context of personalized decision support systems and BG alarm events applications, which are important constituents for optimal diabetes self-management.
METHODS
A rigorous literature search was conducted between September 1 and October 1, 2017, and October 15 and November 5, 2018, through various Web-based databases. Peer-reviewed journals and articles were considered. Information from the selected literature was extracted based on predefined categories, which were based on previous research and further elaborated through brainstorming.
RESULTS
The initial results were vetted using the title, abstract, and keywords and retrieved 496 papers. After a thorough assessment and screening, 47 articles remained, which were critically analyzed. The interrater agreement was measured using a Cohen kappa test, and disagreements were resolved through discussion. The state-of-the-art classes of machine learning have been developed and tested up to the task and achieved promising performance including artificial neural network, support vector machine, decision tree, genetic algorithm, Gaussian process regression, Bayesian neural network, deep belief network, and others.
CONCLUSIONS
Despite the complexity of BG dynamics, there are many attempts to capture hypoglycemia and hyperglycemia incidences and the extent of an individual's GV using different approaches. Recently, the advancement of diabetes technologies and continuous accumulation of self-collected health data have paved the way for popularity of machine learning in these tasks. According to the review, most of the identified studies used a theoretical threshold, which suffers from inter- and intrapatient variation. Therefore, future studies should consider the difference among patients and also track its temporal change over time. Moreover, studies should also give more emphasis on the types of inputs used and their associated time lag. Generally, we foresee that these developments might encourage researchers to further develop and test these systems on a large-scale basis.
Topics: Algorithms; Blood Glucose; Diabetes Mellitus, Type 1; Female; Humans; Machine Learning; Male
PubMed: 31042157
DOI: 10.2196/11030 -
Sensors (Basel, Switzerland) Jan 2017Diabetes has become a leading cause of death worldwide. Although there is no cure for diabetes, blood glucose monitoring combined with appropriate medication can enhance... (Review)
Review
Diabetes has become a leading cause of death worldwide. Although there is no cure for diabetes, blood glucose monitoring combined with appropriate medication can enhance treatment efficiency, alleviate the symptoms, as well as diminish the complications. For point-of-care purposes, continuous glucose monitoring (CGM) devices are considered to be the best candidates for diabetes therapy. This review focuses on current growth areas of CGM technologies, specifically focusing on subcutaneous implantable electrochemical glucose sensors. The superiority of CGM systems is introduced firstly, and then the strategies for fabrication of minimally-invasive and non-invasive CGM biosensors are discussed, respectively. Finally, we briefly outline the current status and future perspective for CGM systems.
Topics: Blood Glucose; Diabetes Mellitus; Humans; Monitoring, Physiologic; Point-of-Care Systems
PubMed: 28106820
DOI: 10.3390/s17010182