Relevant bibliographies by topics / Insulin Analysis

Academic literature on the topic 'Insulin Analysis'

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Published: 10 December 2022
Last updated: 28 January 2023

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Journal articles on the topic "Insulin Analysis"

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Du, Xinli, Rihua Zhang, Yi Xue, Dong Li, Jinmei Cai, Suming Zhou, Zhengkai Huang, Rongbin Yu, and Yun Liu. "Insulin Glargine and Risk of Cancer: A Meta-Analysis." International Journal of Biological Markers 27, no. 3 (July 2012): 241–46. http://dx.doi.org/10.5301/jbm.2012.9349.

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Aims Recently, more and more attention has been drawn on the long-term effects of insulin glargine. Here we strived to estimate the association of cancer occurrence with the use of insulin glargine. Methods We searched all the publications regarding the association between cancer occurrence and the use of insulin glargine using the US National Library of Medicine's PubMed database. Data were independently extracted and analyzed using random or fixed effects meta-analysis depending upon the degree of heterogeneity. Results Seven cohort studies were included in the meta-analysis. Cancer occurrence had no significant difference in glargine-treated patients compared to patients treated with other insulins (RR=0.86, 95% CI=0.69–1.07, p=0.17, Pheterogeneity <0.00001). In our subgroup analysis, glargine, compared to other insulins, did not increase the risk of breast cancer (RR=1.14, 95% CI=0.65–2.02, p=0.65, Pheterogeneity=0.002), prostate cancer (RR=1.00, 95% CI=0.79–1.26, p=0.99, Pheterogeneity=0.78), pancreatic cancer (RR=0.57, 95% CI=0.14–2.35, p=0.44, Pheterogeneity=0.0002) and gastrointestinal cancer (RR=0.80, heterogeneity 95% CI=0.62–1.02, p=0.07, Pheterogeneity=0.86). Conclusions This meta-analysis of open-label studies does not support an increased cancer risk in patients treated with insulin glargine. The result provides confidence for the development of insulin glargine, but needs confirmation by further clinical studies.
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Whitmore, T. J., N. J. Trengove, D. F. Graham, and P. E. Hartmann. "Analysis of Insulin in Human Breast Milk in Mothers with Type 1 and Type 2 Diabetes Mellitus." International Journal of Endocrinology 2012 (2012): 1–9. http://dx.doi.org/10.1155/2012/296368.

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Despite the important role that insulin plays in the human body, very little is known about its presence in human milk. Levels rapidly decrease during the first few days of lactation and then, unlike other serum proteins of similar size, achieve comparable levels to those in serum. Despite this, current guides for medical treatment suggest that insulin does not pass into milk, raising the question of where the insulin in milk originates. Five mothers without diabetes, 4 mothers with type 1, and 5 mothers with type 2 diabetes collected milk samples over a 24-hour period. Samples were analysed for total and endogenous insulin content and for c-peptide content. All of the insulin present in the milk of type 1 mothers was artificial, and c-peptide levels were 100x lower than in serum. This demonstrates that insulin is transported into human milk at comparable concentration to serum, suggesting an active transport mechanism. The role of insulin in milk is yet to be determined; however, there are a number of potential implications for the infant of the presence of artificial insulins in milk.
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Kim, Christian C. K., Thomas G. Rosano, Erin E. Chambers, Manjunath P. Pai, and James Desemone. "Insulin Glargine and Insulin Aspart Overdose With Pharmacokinetic Analysis." AACE Clinical Case Reports 2, no. 2 (April 2016): e122-e128. http://dx.doi.org/10.4158/ep15689.cr.

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Couchman, Lewis, David R. Taylor, and Cajetan F. Moniz. "Analysis of insulin and insulin analogues by mass spectrometry." Annals of Clinical Biochemistry 53, no. 2 (March 2016): 302–3. http://dx.doi.org/10.1177/0004563215597011.

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Silva, Thales B. C., Paulo H. R. F. Almeida, Vania E. Araújo, Francisco de Assis Acurcio, Augusto A. Guerra Júnior, Brian Godman, and Juliana Alvares. "Effectiveness and safety of insulin glargine versus detemir analysis in patients with type 1 diabetes: systematic review and meta-analysis." Therapeutic Advances in Endocrinology and Metabolism 9, no. 8 (June 22, 2018): 241–54. http://dx.doi.org/10.1177/2042018818781414.

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Background: Diabetes mellitus type 1 (DM1) is an autoimmune disease characterized by metabolic destruction of pancreatic cells responsible for insulin production, with treatment based on replacing insulin. Long-acting insulin analogs are indicated for patients with DM1 who exhibit important oscillations of their daily glycemia, despite its higher cost. Our study objective was to evaluate the effectiveness and safety of two long-acting insulins, insulin glargine and detemir, in treating patients with DM1. Methods: We undertook a systematic review with meta-analysis of observational studies (cohort and registry) available in the databases and the gray literature, and a complementary search in the Diabetes Care journal. Outcomes assessed were: glycated hemoglobin concentration; fasting plasma or capillary glucose; occurrence of episodes of severe hypoglycemia and occurrence of nocturnal hypoglycemia. The assessment of methodological quality was performed using the Newcastle score. The meta-analyses were performed on software Review Manager® 5.2. Results: Out of 705 publications, 8 cohort studies were included. The quality of these studies was classified as high. In the meta-analysis, results regarding episodes of severe hypoglycemia ( p = 0.02) and fasting glucose ( p = 0.01) were in favor of detemir. The glycated hemoglobin ( p = 0.49; I2 = 89) showed high heterogeneity and no statistically significant difference between the two. The meta-analysis of total insulin dose favored glargine ( p = 0.006; I2 = 75). The rates of nocturnal hypoglycemia (NH) were evaluated only for one study and showed a significant reduction of NH after therapy with detemir, ( p < 0.0001). Conclusion: Although some outcomes were favorable to detemir insulin analog, it has not been possible to identify important differences of effectiveness and safety between the two analogs. These results can help in the current debate on the inclusion of long-acting analogs on the list of reimbursed medicines in Brazil, especially with the recent introduction of an insulin glargine biosimilar at a considerably lower price.
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Kinsella, Hannah M., Laura D. Hostnik, Hailey A. Snyder, Sarah E. Mazur, Ahmed M. Kamr, Teresa A. Burns, John C. Mossbarger, and Ramiro E. Toribio. "Comparison of insulin sensitivity between healthy neonatal foals and horses using minimal model analysis." PLOS ONE 17, no. 1 (January 14, 2022): e0262584. http://dx.doi.org/10.1371/journal.pone.0262584.

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The equine neonate is considered to have impaired glucose tolerance due to delayed maturation of the pancreatic endocrine system. Few studies have investigated insulin sensitivity in newborn foals using dynamic testing methods. The objective of this study was to assess insulin sensitivity by comparing the insulin-modified frequently sampled intravenous glucose tolerance test (I-FSIGTT) between neonatal foals and adult horses. This study was performed on healthy neonatal foals (n = 12), 24 to 60 hours of age, and horses (n = 8), 3 to 14 years of age using dextrose (300 mg/kg IV) and insulin (0.02 IU/kg IV). Insulin sensitivity (SI), acute insulin response to glucose (AIRg), glucose effectiveness (Sg), and disposition index (DI) were calculated using minimal model analysis. Proxy measurements were calculated using fasting insulin and glucose concentrations. Nonparametric statistical methods were used for analysis and reported as median and interquartile range (IQR). SI was significantly higher in foals (18.3 L·min-1· μIU-1 [13.4–28.4]) compared to horses (0.9 L·min-1· μIU-1 [0.5–1.1]); (p < 0.0001). DI was higher in foals (12 × 103 [8 × 103−14 × 103]) compared to horses (4 × 102 [2 × 102−7 × 102]); (p < 0.0001). AIRg and Sg were not different between foals and horses. The modified insulin to glucose ratio (MIRG) was lower in foals (1.72 μIUinsulin2/10·L·mgglucose [1.43–2.68]) compared to horses (3.91 μIU insulin2/10·L·mgglucose [2.57–7.89]); (p = 0.009). The homeostasis model assessment of beta cell function (HOMA-BC%) was higher in horses (78.4% [43–116]) compared to foals (23.2% [17.8–42.2]); (p = 0.0096). Our results suggest that healthy neonatal foals are insulin sensitive in the first days of life, which contradicts current literature regarding the equine neonate. Newborn foals may be more insulin sensitive immediately after birth as an evolutionary adaptation to conserve energy during the transition to extrauterine life.
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Koechlin, Alice, Mathieu Boniol, Chris Robertson, Geremia Bolli, Julio Rosenstock, and Peter Boyle. "Meta-analysis of cancer risk among users of insulin glargine." Journal of Clinical Oncology 30, no. 15_suppl (May 20, 2012): 1510. http://dx.doi.org/10.1200/jco.2012.30.15_suppl.1510.

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1510 Background: The association between diabetes, its risk factors and treatments, and cancer risk and death is now high on the clinical and research agenda. Methods: All data regarding cancer risk and use of insulin glargine has been assembled and meta-analyses performed using state-of-the-art statistical methodology. Glargine is the most studied insulin in this regard. A random effects model was employed with tests for heterogeneity (I2) and publication bias. These meta-analyses are based on reports from epidemiological studies involving a total of 907,008 diabetic subjects and 2,597,602 person-years of observation. Results: Based on independent estimates from 14 studies, the Summary Relative Risk (SRR) for all forms of cancer was (SRR=0.90, 95% CI (0.82, 0.98)) and for breast cancer SRR=1.14 (95% CI (1.00, 1.29)). For new users of glargine, from 7 studies, the SRR for breast cancer was SRR=1.20 (95% CI (0.90, 1.58)). Based on independent estimates for 9 studies, for colorectal cancer the SRR was 0.73 (95% CI (0.59, 0.91)) and for prostate cancer SRR=1.16 (95% CI (1.03, 1.30)). Overall, the risk of developing cancer among users of insulin glargine is reduced compared to the risk of users of other insulins. Similarly, the risk of colorectal cancer is reduced among users of glargine. While above unity, the risks of breast cancer and prostate cancer are increased marginally. Potential limitations to this meta-analysis include that the comparison group was not the same in all studies but this could also be seen as a strength. This is not likely to invalidate the findings of this analysis nor would the fact that different adjustments were made in the individual studies. Conclusions: The current evidence gives no support to the hypothesis that insulin glargine is associated with an increased risk of cancer as compared to other insulins and should give reassurance to physicians and their patients. Given the short exposure time possible to glargine (less than 5 years maximum), it is not biologically plausible to have a causal link to common forms of cancer.
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Bajaj, M., T. L. Blundell, R. Horuk, J. E. Pitts, S. P. Wood, L. K. Gowan, C. Schwabe, A. Wollmer, J. Gliemann, and S. Gammeltoft. "Coypu insulin. Primary structure, conformation and biological properties of a hystricomorph rodent insulin." Biochemical Journal 238, no. 2 (September 1, 1986): 345–51. http://dx.doi.org/10.1042/bj2380345.

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Insulin from a hystricomorph rodent, coypu (Myocaster coypus), was isolated and purified to near homogeneity. Like the other insulins that have been characterized in this Suborder of Rodentia, coypu insulin also exhibits a very low (3%) biological potency, relative to pig insulin, on lipogenesis in isolated rat fat-cells. The receptor-binding affinity is significantly higher (5-8%) in rat fat-cells, in rat liver plasma membranes and in pig liver cells, indicating that the efficacy of coypu insulin on receptors is about 2-fold lower than that of pig insulin. The primary structures of the oxidized A- and B-chains were determined, and our sequence analysis confirms a previous report [Smith (1972) Diabetes 21, Suppl. 2, 457-460] that the C-terminus of the A-chain is extended by a single residue (i.e. aspartate-A22), in contrast with most other insulin sequences, which terminate at residue A21. In spite of a large number of amino acid substitutions (relative to mammalian insulins), computer-graphics model-building studies suggest a similar spatial arrangement for coypu insulin to that for pig insulin. The substitution of the zinc-co-ordinating site (B10-His----Gln) along with various substitutions on the intermolecular surfaces involved in the formation of higher aggregates are consistent with the observation that this insulin is predominantly ‘monomeric’ in nature. The c.d. spectrum of coypu insulin is relatively similar to those of casiragua insulin and of bovine insulin at low concentration.
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Mao, Xuhua, Hucheng Chen, Junmin Tang, Liangliang Wang, and Tingting Shu. "Hepcidin links gluco-toxicity to pancreatic beta cell dysfunction by inhibiting Pdx-1 expression." Endocrine Connections 6, no. 3 (April 2017): 121–28. http://dx.doi.org/10.1530/ec-16-0115.

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Objective Gluco-toxicity is a term used to convey the detrimental effect of hyperglycemia on β-cell function through impaired insulin synthesis. Although it is known that the expression and activity of several key insulin transcription regulators is inhibited, other molecular mechanisms that mediate gluco-toxicity are poorly defined. Our objective was to explore the role of hepcidin in β-cell gluco-toxicity. Design We first confirmed that high glucose levels inhibited hepcidin expression in the mouse insulinoma cell line, MIN6. The downregulation of hepcidin decreased Pdx-1 expression, which reduced insulin synthesis. Methods MIN6 cells were exposed to high glucose concentrations (33.3 mmol/L). Glucose-stimulated insulin secretion (GSIS) and serum hepcidin levels were measured by ELISA. The mRNA levels of insulin1, insulin2, Pdx-1 and hepcidin were measured by real-time polymerase chain reaction. Western blot analysis was used to detect the changes in PDX-1 expression. Transient overexpression with hepcidin was used to reverse the downregulation of Pdx-1 and insulin synthesis induced by gluco-toxicity. Results Exposure of MIN6 cells to high glucose significantly decreased GSIS and inhibited insulin synthesis as well as Pdx-1 transcriptional activity and expression at both the mRNA and protein levels. High glucose also decreased hepcidin expression and secretion. Hepcidin overexpression in MIN6 cells partially reversed the gluco-toxicity-induced downregulation of Pdx-1 and insulin expression and improved GSIS. The restoration of insulin synthesis by transfection of a hepcidin overexpression plasmid confirmed the role of hepcidin in mediating the gluco-toxic inhibition of insulin synthesis. Conclusions Our observations suggest that hepcidin is associated with gluco-toxicity-reduced pancreatic β-cell insulin synthesis in type 2 diabetes by inhibiting Pdx-1 expression.
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Savoy, L. A., R. M. L. Jones, S. Pochon, J. G. Davies, A. V. Muir, R. E. Offord, and K. Rose. "Identification by fast atom bombardment mass spectrometry of insulin fragments produced by insulin proteinase." Biochemical Journal 249, no. 1 (January 1, 1988): 215–22. http://dx.doi.org/10.1042/bj2490215.

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We describe the isolation by reversed-phase h.p.l.c. of a number of products of the degradation of insulin by insulin proteinase and their direct analysis by fast atom bombardment mass spectrometry (f.a.b.-m.s.). Various semisynthetically labelled insulins were used, including [[2H2]GlyA1]insulin and [18O]LysB29]insulin. The results obtained confirm and extend the results obtained by non-mass-spectrometric methods [Davies, Muir, Rose & Offord (1988) Biochem. J. 249, 209-214, and papers cited therein]. Cleavage sites were identified between positions A13-A14, A14-A15, B9-B10, B13-B14, B24-B25 and B25-B26. The advantages and disadvantages of the application of f.a.b.-m.s. to such studies are discussed.
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Dissertations / Theses on the topic "Insulin Analysis"

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Raab, R. Michael. "Genomic analysis of hepatic insulin resistance." Thesis, Massachusetts Institute of Technology, 2005. http://hdl.handle.net/1721.1/33762.

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Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Chemical Engineering, February 2006.
Includes bibliographical references (leaves 159-191).
Type II Diabetes mellitus is a genetically complex disease characterized by insulin resistance in peripheral tissues, which results in simultaneous hyperglycemia and hyperinsulinemia. Because of the prevalence of type II diabetes, many researchers are investigating the genetics of glucose homeostasis, however, traditional mapping techniques have not been successful in determining all of the genes that regulate glycemia. To complement these efforts, we used DNA microarrays to find differentially expressed genes and combinatorial siRNA screening to investigate the effects of hepatic gene transcription during periods of high and low glucose production. This strategy provides a new approach to studying the molecular mechanisms of disease pathogenesis. Our investigations focused on discovering new genes that influence hepatic metabolism and glucose production. Hepatocytes help maintain whole body glycemia by providing glucose and other substrates during non-feeding periods. DNA microarrays containing 17,000 unique gene probes were used to study hepatic gene transcription during normal, insulin resistant, and fasting states in C57/BL/6J mice. We analyzed this data set using a combination of statistical and multivariate techniques to determine 41 different, genes that are differentially expressed and highly discriminatory of the treatment groups.
(cont.) Hepatocytes perform many physiological roles, thus to investigate which genes from the microarray analysis affected hepatic metabolism, we developed combinatorial RNA-interference (RNAi) based gene silencing techniques. Using combinatorial siRNA screening, we silenced genes that were over-expressed within the microarray data set to study loss of function effects on hepatic metabolism, which was quantified by measuring intracellular metabolite concentrations in relevant metabolic pathways. Based upon the metabolite dependent clustering of experimental and control samples using Fisher Discriminant Analysis, four of the silenced genes had a significant effect on key metabolites involved in hepatic glucose output. Of these four genes, three were shown to influence hepatic glucose output in our primary cell model.
by R. Michael Raab.
Ph.D.
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2

James, Declan Jonathan. "An analysis of insulin- and non-insulin- stimulated glucose transport in rat skeletal muscle." Thesis, University of Glasgow, 2002. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.394962.

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Kaizik, Stephan Martin. "Analysis of mouse models of insulin secretion disorders." Thesis, University of Oxford, 2010. http://ora.ox.ac.uk/objects/uuid:4d44b68a-a0a0-4c92-8809-00ddbfe3e636.

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Hernández, Adrian V., Mirella Guarnizo, Yony Miranda, Vinay Pasupuleti, Abhishek Deshpande, Socorro Paico, Hosten Lenti, et al. "Association between Insulin Resistance and Breast Carcinoma: A Systematic Review and Meta-Analysis." Public Library of Science (PLoS), 2014. http://hdl.handle.net/10757/320267.

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This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Objective: This study was undertaken to evaluate the association between components defining insulin resistance and breast cancer in women. Study Design: We conducted a systematic review of four databases (PubMed-Medline, EMBASE, Web of Science, and Scopus) for observational studies evaluating components defining insulin resistance in women with and without breast cancer. A meta-analysis of the association between insulin resistance components and breast cancer was performed using random effects models. Results: Twenty-two studies (n = 33,405) were selected. Fasting insulin levels were not different between women with and without breast cancer (standardized mean difference, SMD 20.03, 95%CI 20.32 to 0.27; p = 0.9). Similarly, non-fasting/ fasting C-peptide levels were not different between the two groups (mean difference, MD 0.07, 20.21 to 0.34; p = 0.6). Using individual odds ratios (ORs) adjusted at least for age, there was no higher risk of breast cancer when upper quartiles were compared with the lowest quartile (Q1) of fasting insulin levels (OR Q2 vs. Q1 0.96, 0.71 to 1.28; OR Q3 vs. Q1 1.22, 0.91 to 1.64; OR Q4 vs. Q1 0.98, 0.70 to 1.38). Likewise, there were no differences for quartiles of non-fasting/fasting C-peptide levels (OR Q2 vs. Q1 1.12, 0.91 to 1.37; OR Q3 vs. Q1 1.20, 0.91 to 1.59; OR Q4 vs. Q1 1.40, 1.03 to 1.92). Homeostatic model assessment (HOMAIR) levels in breast cancer patients were significantly higher than in people without breast cancer (MD 0.22, 0.13 to 0.31, p, 0.00001). Conclusions: Higher levels of fasting insulin or non-fasting/fasting C-peptide are not associated with breast cancer in women. HOMA-IR levels are slightly higher in women with breast cancer.
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Pretty, Christopher Grant. "Analysis, classification and management of insulin sensitivity variability in a glucose-insulin system model for critical illness." Thesis, University of Canterbury. Mechanical Engineering, 2012. http://hdl.handle.net/10092/6580.

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Hyperglycaemia in critical care is common and has been linked to increased mortality and morbidity. Tight control of blood glucose concentrations to more normal levels can significantly reduce the negative outcomes associated with hyperglycaemia. However, hypoglycaemia and glycaemic variability have also been independently shown to increase mortality in critically ill patients. Further complicating the matter, critically ill patients exhibit high inter- and intra patient metabolic variability and thus consistent, safe control of glycaemia has proved very difficult. Model-based and model-derived tight glycaemic control methods have shown significant ability to provide very tight control with little or no hypoglycaemia in the intensive care unit (ICU). The model-based control practised in the Christchurch Hospital ICU uses a physiological model that relies on a single, time-varying parameter, SI, to capture the patient-specific glycaemic response to insulin. As an identified parameter, SI is prone to also capturing other, unintended, dynamics that add variability on multiple timescales. The objective of this research was to enable enhanced glycaemic control by addressing this variability of the SI parameter through better modelling and implementation. An improved model of insulin secretion as a function of blood glucose concentration was developed using data collected from a recent study at the Christchurch Hospital ICU. Separate models were identified for non-diabetic patients and diagnosed, or suspected type II diabetic patients, with R2 = 0.61 and 0.69, respectively. The gradients of the functions identified were comparable to data published in a number of other studies on healthy and diabetic subjects. The transcapilliary diffusion (nI) and cellular clearance (nC) rate parameters were optimised using data from published microdialysis studies. Interactions between these key parameters determine maximum interstitial insulin concentrations available for glucose disposal, and thus directly influence SI. The optimal values of these parameters were determined to be nI = nC = 0.0060 1/min. Models of endogenous glucose production (EGP), as functions of blood glucose concentration and time, were assessed. These models proved unsatisfactory due to difficulties in identifying reliable functions with the available data set. Thus, it was determined that EGP should continue to be treated as a population constant, except during real-time glycaemic control, where the value may be adjusted temporarily to ensure valid SI values. The first 24 hours of ICU stay proved to be a period of significantly increased SI variability, both in terms of hour-to-hour changes and longer-term evolution of level. This behaviour was evident for the entire study cohort as a whole and was particularly pronounced during the first 12-18 hours. The subgroup of cardiovascular surgery patients, in which there was sufficient data for analysis, mirrored the results of the whole cohort, but was found to have even lower and more variable SI. Glucocorticoid steroids were also found to be associated with clinically significant reductions in overall level and increases in hour-to-hour variability of SI. To manage variability caused by factors external to the physiological model, the use of several stochastic models was proposed. Using different models for the early part of ICU stay and for different diagnostic subgroups as well as when patients were receiving certain drug therapies would permit control algorithms to reduce the impact of the SI variability on outcome glycaemia. The impact of measurement timing and BG concentration errors on the variability of SI was assessed. Results indicated that the impact of both sources of errors on SI level was unlikely to be clinically significant. The impact of BG sensor errors on hour-to-hour SI variability was more pronounced. Understanding the effect of sensor and timing errors on SI allows their impact to be reduced by using the 5-95 percentile forecast range of stochastic models during glycaemic control. The performance of the model incorporating the proposed insulin kinetic parameters and secretion enhancements was validated for clinical glycaemic control and virtual trial purposes. This validation was conducted by self- and cross validation on a cohort independent to that with which the model was developed. The use of multiple stochastic models to reduce the impact of this extrinsic variability during glycaemic control was validated using virtual trials.
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Reed, Peter Wayne. "Genetic analysis of Type 1 (insulin-dependent) diabetes mellitus." Thesis, University of Oxford, 1999. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.325788.

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Pearson, James. "Analysis of the repertoire of insulin-reactive CD8+ T cells." Thesis, Cardiff University, 2014. http://orca.cf.ac.uk/68395/.

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Proinsulin is an auto-antigen in type 1 diabetes in the Non-obese Diabetic (NOD) mouse. The CD8+ T cell clone, G9C8, which utilises a T cell receptor (TCR) comprising TRAV8-1*01TRAJ9 and TRBV19*01TRBD1TRBJ2-3, recognises insulin B15-23 presented by H-2Kd, escapes negative selection and rapidly causes diabetes upon adoptive transfer into immunocompromised NODscid mice. To understand how these insulin B15-23 reactive CD8+ T cells develop, G9C8-derived single TCR TRAV8-1*01TRAJ9 chain transgenic NOD mice were generated. These mice were bred with different proinsulin-expressing NOD mice to generate proinsulin1 deficient, proinsulin2 deficient, proinsulin2 over-expressing and proinsulin1 and 2 deficient mice with a mutated proinsulin transgene, preventing G9C8 antigen recognition. Although proinsulin-specific CD8+ TCR repertoire changes in TRBV19 were seen in these mice, the proportion of insulin B15-23 reactive CD8+ T cells was unaffected by proinsulin expression. Interestingly, TCR clonotyping of these insulin B15-23 reactive T cells revealed minimal shared sequences between the strains, with mice exhibiting individual clonal expansions. However, by isolating TRBV19-expressing insulin B15-23-responsive T cells, shared sequences across the different proinsulin-expressing mice were identified, with a requirement of TRBJ2-3 for insulin recognition (used by the original G9C8 T cell clone). Furthermore, male proinsulin2 deficient TRAV8-1*01TRAJ9 mice developed diabetes, with a higher incidence seen upon antibiotic administration. Although the TCR repertoire was unaffected by antibiotics, the gut microbial diversity was greatly reduced in all the mice with age, independent of antibiotic use, with firmicutes bacteria comprising 90-97% of the microbiota. In summary, proinsulin expression and antibiotics modify diabetes susceptibility; however, insulin B15-23 reactive T cells develop independently of antigen expression and are not directly affected by antibiotics. This data suggests that iii non-antigen dependent mechanisms exist in controlling the development of auto-reactive T cells, but T cell activation and pathogenicity is influenced indirectly by a combination of antigen expression and gut microbiota.
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Thota, P., F. R. Perez-Lopez, Vicente A. Benítes-Zapata, V. Pasupuleti, and Adrian V. Hernandez. "Obesity-related insulin resistance in adolescents: a systematic review and meta-analysis of observational studies." Taylor and Francis Ltd, 2017. http://hdl.handle.net/10757/622281.

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El texto completo de este trabajo no está disponible en el Repositorio Académico UPC por restricciones de la casa editorial donde ha sido publicado.
Insulin resistance is common among obese adolescents; however, the extent of this problem is not clear. We conducted a systematic review of PubMed-Medline, CINAHL, The Web of Science, EMBASE and Scopus for observational studies evaluating components defining insulin resistance (insulin, C-peptide and homeostatic model assessment-insulin resistance [HOMA-IR]) in obese adolescents (12–18 years) versus non-obese adolescents. Our systematic review and meta-analysis followed the PRISMA guidelines. Data were combined using a random-effects model and summary statistics were calculated using the mean differences (MDs). 31 studies were included (n = 8655). In 26 studies, fasting insulin levels were higher in obese adolescents when compared to non-obese adolescents (MD = 64.11 pmol/L, 95%CI 49.48–78.75, p < 0.00001). In three studies, fasting C-peptide levels were higher in obese adolescents when compared to non-obese adolescents (MD = 0.29 nmol/L, 95%CI 0.22–0.36, p < 0.00001). In 24 studies, HOMA-IR values were higher in obese adolescents when compared to non-obese adolescents (MD = 2.22, 95%CI 1.78–2.67, p < 0.00001). Heterogeneity of effects among studies was moderate to high. Subgroup analyses showed similar results to the main analyses. Circulating insulin and C-peptide levels and HOMA-IR values were significantly higher in obese adolescents compared to those non-obese.
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9

Derewenda, Urszula. "X-ray analysis of dimeric and hexameric insulins." Thesis, University of York, 1990. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.276517.

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Lunt, Helen. "Analysis of a system used to detect islet cell stimulating antibodies." Thesis, University of Southampton, 1992. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.385080.

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Books on the topic "Insulin Analysis"

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Petrisor, Bradley Allan. Analysis of the insulin receptor-related gene promoter. Ottawa: National Library of Canada, 1994.

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Oelbaum, Raymond Stuart. An analysis of four candidate genes for non-insulin-dependent diabetes using restriction fragment length polymorphism markers. Manchester: University of Manchester, 1994.

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Seale, J. V. A quantitative immunohistochemical analysis of the distribution of insulin receptors in the rat brain for regions associated with memory compared to regions not associated with memory. London: University of Surrey Roehampton, 2001.

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Tyrone, Fernando, ed. Closed-loop control of blood glucose. Berlin: Springer, 2007.

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Leibowitz, Arnold H. Defining status: A comprehensive analysis of United States territorial relations. Dordrecht: Nijhoff, 1989.

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Leibowitz, Arnold H. Defining status: A comprehensive analysis of United States territorial relations. Dordrecht: Nijhoff, 1989.

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Malewezi, Ngunga Peter. Malewezi's response to Muluzi's "insults": An analysis from wisdom teaching point of view. Zomba, Malawi: Kachere Series, 2006.

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Deriu, Morena. Nēsoi. L’immaginario insulare nell’Odissea. Venice: Fondazione Università Ca’ Foscari, 2020. http://dx.doi.org/10.30687/978-88-6969-470-7.

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The aim of this book is to shed new light on the connections between the islands of the Odyssey, setting aside the common perspectives which fully contrast Ithaka to the isles of Odysseus’s travels. Indeed, on a close reading, the idea of ‘otherness’ frequently associated to these isles can be perceived as the result of shared traits. The book first offers an introductory survey on the studies about islands and insularity (not only) in the Odyssey. Then, it analyses how and in which terms the Odyssean representations of the islands are elaborated by means of references to the characters’ senses and actions. These representations are frequently parts of archipelagos of memories, and all bear witness to the fact that fantastic and realistic traits are intermingled and can permeate each other on all the Odyssean islands. Thus, the isles of these travels can be perceived as marginal and mixed places which are also meaningfully part of the archipelago of thematic and formal relations which links all Odyssean islands. The second section of the book examines this archipelagic scenario by using the concepts of utopia and heterotopia. The section shows how the islands of the Odyssey and, especially, the islands the hero encountered on his travels should not be considered utopias in the strict sense of the word. It then goes on to show how M. Foucault’s heterotopia can help to highlight a series of insular aspects, which, otherwise, could pass unnoticed. These lands stand at the margins of the world of the Odyssey and are, at the same time, connected to all the other islands. As a result, they work like mirrors which reflect images of different and possible worlds. In particular, the Odyssean isles of women mirror different and possible relationships between Odysseus and the lady of the island and help to enlighten the place which the hero perceives as the perfect home among all the possible choices. Finally, a brief analysis of the prophecy about the hero’s future last adventure shows that there is no chance of Odysseus feeling at home on that ‘other’ place of this last journey.
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Rezendes, Victor S. Comments on Smith Barney's uranium enrichment analysis: Statement of Victor S. Rezendes, Director, Energy Issues Resources, Community, and Economic Development Division, before the Subcommittee on Energy and the Environment, Committee on Interior and Insular Affairs, House of Representatives and the Subcommitte on Energy and Power, Committee on Energy and Commerce, House of Representatives. [Washington, D.C.]: The Office, 1990.

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Spielman, Richard S. Genetic Analysis of Complex Traits: Insulin-Dependent Diabetes Mellitus. John Wiley & Sons Inc, 2000.

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Book chapters on the topic "Insulin Analysis"

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Yalow, Rosalyn S., and Solomon A. Berson. "Immunoassay of Plasma Insulin." In Methods of Biochemical Analysis, 69–96. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2006. http://dx.doi.org/10.1002/9780470110300.ch2.

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Baratta, G., F. Barcellona, G. Lucidi, and A. M. Bersani. "Modeling Glucose-Insulin Behavior in Ill Patients (DM Type2)." In Medical Data Analysis, 71–78. Berlin, Heidelberg: Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/3-540-36104-9_8.

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Kim, Hyunbae, Deqiang Zhang, Zhenfeng Song, Xin Tong, and Kezhong Zhang. "Analysis of Insulin Resistance in Nonalcoholic Steatohepatitis." In Methods in Molecular Biology, 233–41. New York, NY: Springer US, 2022. http://dx.doi.org/10.1007/978-1-0716-2128-8_18.

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Radomski, Dariusz, and Jagoda Głowacka. "Sensitivity Analysis of the Insulin-Glucose Mathematical Model." In Advances in Intelligent Systems and Computing, 455–68. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-91211-0_40.

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Caves, Leo S. D., Dzung T. Nguyen, and Roderick E. Hubbard. "Conformational Variability of Insulin: a Molecular Dynamics Analysis." In Molecular Dynamics, 27–68. London: Macmillan Education UK, 1991. http://dx.doi.org/10.1007/978-1-349-11044-5_2.

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Hauser, František, and Michal Anděl. "Estimation of Drug Need from the Health Projection — The Case of Insulin." In Systems Analysis and Simulation II, 313–16. New York, NY: Springer US, 1988. http://dx.doi.org/10.1007/978-1-4613-8936-1_66.

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Rangel-Vázquez, Norma-Aurea, and Ana-Karen Frías-González. "Molecular Analysis of Insulin Through Controlled Adsorption in Hydrogels Based on Chitosan." In Structural Analysis using Computational Chemistry, 53–77. New York: River Publishers, 2022. http://dx.doi.org/10.1201/9781003339632-3.

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Fatica, Erica M., Nicholas E. Larkey, and Ravinder J. Singh. "Quantification of Insulin Analogs by Liquid Chromatography–High-Resolution Mass Spectrometry." In Clinical Applications of Mass Spectrometry in Biomolecular Analysis, 217–26. New York, NY: Springer US, 2022. http://dx.doi.org/10.1007/978-1-0716-2565-1_20.

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Radziuk, J., and T. Morishima. "New Methods for the Analysis of Insulin Kinetics in Vivo: Insulin Secretion, Degradation, Systemic Dynamics and Hepatic Extraction." In Advances in Experimental Medicine and Biology, 247–76. Boston, MA: Springer US, 1985. http://dx.doi.org/10.1007/978-1-4757-1850-8_14.

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Burattini, Roberto, Fabrizio Casagrande, Francesco Di Nardo, Massimo Boemi, and Pierpaolo Morosini. "Insulin Sensitivity and Plasma Glucose Appearance Profile by Oral Minimal Model in Normotensive and Normoglycemic Humans." In Biological and Medical Data Analysis, 128–36. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11946465_12.

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Conference papers on the topic "Insulin Analysis"

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Islami, Dian Dini, Didik Gunawan Tamtomo, and Hanung Prasetya. "The Effect of Insulin Provision on the Risk Reduction of Type 2 Diabetes Mellitus: Meta-Analysis." In The 7th International Conference on Public Health 2020. Masters Program in Public Health, Universitas Sebelas Maret, 2020. http://dx.doi.org/10.26911/the7thicph.05.49.

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ABSTRACT Background: Insulin is the pivotal hormone regulating cellular energy supply and macronutrient balance, directing anabolic processes of the fed state. Insulin is essential for the intra-cellular transport of glucose into insulin-dependent tissues such as muscle and adipose tissue. The purpose of this study was to examine the effect of insulin provision on the risk reduction of type 2 diabetes mellitus. Subjects and Method: This was meta-analysis and systematic review. The study was conducted by collecting articles from PubMed, Google Scholar, and Springer Link databases, from 2010-2020. Keywords used “effect insulin” OR “giving insulin” AND “diabetes mellitus” OR “diabetes” AND “cross sectional” AND “adjusted odd ratio”. The inclusion criteria were full text, using English or Indonesia language, and using crosssectional study design. The articles were selected by PRISMA flow chart. The quantitative data were analyzed by RevMan 5.3. Results: A meta-analysis from 5 studies in Ethiopia, Northeast Ethiopia, Taiwan, African American, and South Korea, reported that insulin provision reduced the risk of diabetes mellitus (aOR= 1.89; 95% CI= 1.82 to 3.57; p= 0.05) with I2= 84%. Conclusion: Insulin provision reduced the risk of diabetes mellitus. Keywords: insulin, type 2 diabetes mellitus Correspondence: Dian Dini Islami. Masters Program in Public Health, Universitas Sebelas Maret. Jl. Ir. Sutami 36A, Surakarta 57126, Central Java. Email: dian.dinii94@gmail.com. Mobile: 085729483960. DOI: https://doi.org/10.26911/the7thicph.05.49
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Jinjie, Hou, and Gu Chunqiu. "Research Hotspots Analysis of Insulin by PubMed." In 2015 7th International Conference on Information Technology in Medicine and Education (ITME). IEEE, 2015. http://dx.doi.org/10.1109/itme.2015.153.

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Velásquez, J., S. Wong, L. Encalada, H. Herrera, and E. Severeyn. "Lipid-anthropometric index optimization for insulin sensitivity estimation." In 11th International Symposium on Medical Information Processing and Analysis (SIPAIM 2015), edited by Eduardo Romero, Natasha Lepore, Juan D. García-Arteaga, and Jorge Brieva. SPIE, 2015. http://dx.doi.org/10.1117/12.2209328.

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Castro, Carolina, Cintia Dias, Hamid Sohrabi, Tejal Shah, Pratishtha Chatterjee, Heidi Hillebrandit, Stephanie Fuller, Manohar Garg, and Ralph Martins. "Medium-chain Fatty Acids for the Prevention or Treatment of Alzheimer’s Disease: A Systematic Review and Meta-analysis." In 2022 AOCS Annual Meeting & Expo. American Oil Chemists' Society (AOCS), 2022. http://dx.doi.org/10.21748/gemb5906.

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In preclinical AD, the brain gradually becomes insulin resistant causing a cellular energy deficit, which leads to the accumulation of free radicals increasing inflammation and damaging neurons, thus ketone bodies offer an alternative energy source having the potential to supply the insulin-resistant brain with energy and may improve cognition.
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"CONTINUOUS ANALYSIS OF REPOLARIZATION CHARACTERISTICS DURING INSULIN INDUCED HYPOGLYCEMIA." In International Conference on Bio-inspired Systems and Signal Processing. SciTePress - Science and and Technology Publications, 2011. http://dx.doi.org/10.5220/0003122201070111.

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Rojas, Ruben, Winston Garcia-Gabin, and B. Wayne Bequette. "Multivariate statistical analysis to detect insulin infusion set failure." In 2011 American Control Conference. IEEE, 2011. http://dx.doi.org/10.1109/acc.2011.5990951.

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Zhao, Qing, Zhi-hong Liu, Zhi-hui Zhao, Qin Luo, and Xiu-ping Ma. "Correlation Analysis Of Obstructive Sleep Apnea And Insulin Resistance." In American Thoracic Society 2012 International Conference, May 18-23, 2012 • San Francisco, California. American Thoracic Society, 2012. http://dx.doi.org/10.1164/ajrccm-conference.2012.185.1_meetingabstracts.a2163.

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Huchem, Zamen Issea, and Kareem Hamed Ghali. "The role of CXCL9 and IFIH1 in insulin dependent diabetes mellitus." In INTERNATIONAL CONFERENCE OF NUMERICAL ANALYSIS AND APPLIED MATHEMATICS ICNAAM 2019. AIP Publishing, 2020. http://dx.doi.org/10.1063/5.0027463.

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Kiriakidis, Kiriakos, and Richard O’Brien. "Estimation of Plasma Insulin Using Nonlinear Filtering." In ASME 2007 International Mechanical Engineering Congress and Exposition. ASMEDC, 2007. http://dx.doi.org/10.1115/imece2007-42175.

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The glucose-insulin dynamics as captured by the standard (Bergman) model are both nonlinear and time-varying. To develop an insulin estimator (or filter), the authors use an aggregate model expansion of the nonlinear dynamics while treating the time-varying component of the model as an exogenous input. The aggregate model allows for the design of a particular nonlinear filter (or observer) that uses a weighted summation of constant feedback gains and admits a straightforward implementation. Furthermore, the aggregate modeling approach enables the stability analysis of the estimation error equation through linear matrix inequalities. The aggregate model insulin filter is compared with an existing insulin filter through numerical simulation.
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Velásquez, J., E. Severeyn, H. Herrera, L. Encalada, and S. Wong. "Anthropometric index for insulin sensitivity assessment in older adults from Ecuadorian highlands." In 12th International Symposium on Medical Information Processing and Analysis, edited by Eduardo Romero, Natasha Lepore, Jorge Brieva, and Ignacio Larrabide. SPIE, 2017. http://dx.doi.org/10.1117/12.2257207.

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Reports on the topic "Insulin Analysis"

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Shi, Jinping, Feng L, Liting X, Jing L, and Xing L. Meta analysis of efficacy and safety of insulin aspart and biosynthetic human insulin in the treatment of gestational diabetes mellitus. Xi'an International Medical Center Hospital, July 2021. http://dx.doi.org/10.37766/inplasy2021.7.0047.

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Silva, Rodrigo Ribeiro e., Mateus de Miranda Gauza, Julia Opolski Nunes da Silva Opolski, and Maria Eduarda Schramm Guisso. Once-Weekly Insulin Icodec vs Once-Daily Insulin Glargine U100 for Type 2 Diabetes: A Meta-analysis of Phase 2 Randomized Controlled Trials. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, May 2022. http://dx.doi.org/10.37766/inplasy2022.5.0102.

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Review question / Objective: To compare Once-Weekly Insulin Icodec and Once-Daily Insulin Glargine U100 in patients with Type 2 Diabetes Mellitus using oral hypoglycemic drugs in need of insulin therapy. Condition being studied: Patients with Diabetes Mellitus Type 2 using oral hypoglycemic drugs in need for basal insulin. Eligibility criteria: Inclusion in this meta-analysis was restricted to studies that met all the following criteria: (1) randomized trials; (2) comparing the use once weekly insulin icodec to once daily insulin glargine; (3) enrolling patients with type 1 or type 2 diabetes mellitus; (4) evaluating any of the desired outcomes; (4) articles in written on english language. We excluded studies with (1) no control group; (2) overlapping studies population; clinical trial register entry only; (3) non-human studies and (4) studies reported only as abstracts.
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Long, Cong, XUke Han, Yunjiao Yang, Tongyi Li, Qian Zhou, and Qiu Chen. Efficacy of Intranasal Insulin in Improving Cognition in Mild Cognitive Impairment or Dementia: A Systematic Review and Meta-analysis. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, August 2022. http://dx.doi.org/10.37766/inplasy2022.8.0054.

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Review question / Objective: How does the efficacy of Intranasal Insulin in improving Cognition in Mild Cognitive Impairment or Dementia. Condition being studied: Insulin regulates many aspects of brain function related to mild cognitive impairment (MCI) or dementia, which can be delivered to the brain center via intranasal (IN) devices. Some small, single-site studies indicated that intranasal insulin can enhance memory in patients with MCI or dementia. The pathophysiology of Alzheimer disease (AD) and diabetes mellitus (DM) overlap, making insulin an attractive therapy for people suffering from MCI or dementia. The goal of the study is to evaluate the effectiveness of IN insulin on cognition in patients with MCI or dementia.
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Chen, Jiankun, Yingming Gu, Lihong Yin, Minyi He, Na Liu, Yue Lu, Changcai Xie, Jiqiang Li, and Yu Chen. Network meta-analysis of curative efficacy of different acupuncture methods on obesity combined with insulin resistance. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, August 2022. http://dx.doi.org/10.37766/inplasy2022.8.0075.

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Review question / Objective: Population：Patients diagnosed as obesity with insulin resistance. Obesity reference: Consensus of experts on the Prevention and treatment of adult obesity in China in 2011 and Consensus of Chinese experts on medical nutrition therapy for overweight/obesity in 2016 were developed by the Obesity Group of Chinese Society of Endocrinology(CSE); BMI≥28. IR reference: According to the Expert opinions on insulin resistance evaluation published by Chinese Diabetes Society, HOMA-IR≥2.68 is regarded as the standard for the diagnosis of IR. Regardless of age, gender and course of disease. Patients diagnosed as obesity with insulin resistance. Intervention：Any kind of acupuncture, moxibustion, acupuncture+moxibustion, warm acupuncture, electropuncture, auricular point, acupoint application and acupoint catgut embedding. Comparison：Other acupuncture treatments, Drug therapy or blank control. Outcome：Primary outcomes: ①Fasting blood-glucose (FBG); ②Fasting serum insulin (FINS); ③Homeostasis model assessment-IR (HOMA-IR); ④Body Mass Index (BMI). Secondary outcomes: ①Waistline; ②Waist-hip ratio;③Triglyceride (TG); ④Total cholesterol (TC); ⑤High-density lipoprotein (HDL); ⑥Low-density lipoprotein (LDL). Study: Randomized controlled trials (RCTs) of different acupuncture methods in the treatment on obesity with insulin resistance, blind method and language are not limited. Randomized controlled trials (RCTs).
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Gao, Hui, Dan Chen, and Miao Zang. Association between phthalate exposure and insulin resistance: A systematic review and meta-analysis. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, April 2021. http://dx.doi.org/10.37766/inplasy2021.4.0026.

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Cai, Wangyu, and Jian Xu. Insulin resistance in women with recurrent miscarriage: a systematic review and meta-analysis. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, November 2021. http://dx.doi.org/10.37766/inplasy2021.11.0055.

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Wang, WT, Y. Sun, and YB Sun. Mortality risk with insulin use in patients with COVID‐19 and diabetes: A meta‐analysis. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, January 2021. http://dx.doi.org/10.37766/inplasy2021.1.0052.

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Zhao, Junyu, Qianping Zhang, Yupeng Yang, Jinming Yao, Lin Liao, and Jianjun Dong. High Prevalence of Thyroid Carcinoma in Patients with Insulin Resistance: A Meta-analysis of Case-control Studies. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, August 2021. http://dx.doi.org/10.37766/inplasy2021.8.0043.

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Miao, Chenyun, Qingge Guo, Xiaojie Fang, Yun Chen, Ying Zhao, and Qin Zhang. Effects of Probiotics and Synbiotics Supplementation on Insulin Resistance in Women with Polycystic Ovary Syndrome: A Meta-analysis. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, May 2021. http://dx.doi.org/10.37766/inplasy2021.5.0112.

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HE, QINGQING, and CHANGQIANG LI. The effect of isotretinoin in the treatment of acne vulgaris on insulin resistance: a systematic review and meta-analysis. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, September 2021. http://dx.doi.org/10.37766/inplasy2021.9.0065.

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