Trained study coordinators delivered standardized lifestyle modification counseling to all patients throughout the entire trial. The lifestyle modification education materials were given to patients and selected sections were discussed at each monthly contact (five face-to-face sessions and two phone sessions). The curriculum was adapted from the NIDDK-sponsored “Take Charge of Your Health” and focused on encouraging patients to make healthier food choices and increase levels of physical activity. Pedometers and step-counting logs were given to all patients. Following baseline testing, patients were equally and randomly assigned to exenatide or matching placebo injection for three-months, followed by a three-month open label extension during which all patients received exenatide. The protocol was approved by the University of Minnesota and the Children’s Hospitals and Clinics of Minnesota institutional review boards. Consent and assent were obtained from parents and patients, respectively. An investigational new drug exemption was obtained from the FDA prior to study initiation and the study was registered on the clinicaltrials.gov website (NCT01237197).
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Amino Acid
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Exenatide
Exenatide
Exenatide is a synthetic peptide that mimics the glucagon-like peptide-1 (GLP-1) hormone, which regulates glucose homeostasis.
Approved for the treatment of type 2 diabetes, exenatide helps lower blood glucose levels by stimulating insulin secretion and suppressing glucagon release.
Resarch on exenatide protocols and prodcuts can be optimized using PubCompare.ai, an AI-driven platform that identifies the best experimental approaches from literature, preprints, and patents.
PubComapre.ai enhances the reproducibility and accuracy of exenatide research, helping researchers find the optimal protocols and products.
Approved for the treatment of type 2 diabetes, exenatide helps lower blood glucose levels by stimulating insulin secretion and suppressing glucagon release.
Resarch on exenatide protocols and prodcuts can be optimized using PubCompare.ai, an AI-driven platform that identifies the best experimental approaches from literature, preprints, and patents.
PubComapre.ai enhances the reproducibility and accuracy of exenatide research, helping researchers find the optimal protocols and products.
Most cited protocols related to «Exenatide»
Adolescent
Bariatric Surgery
Child
Conditioning, Psychology
Diabetes Mellitus
Diabetes Mellitus, Insulin-Dependent
Eating Disorders
Ethics Committees, Research
Exenatide
Face
Hereditary Diseases
Hospital Administration
Hypertriglyceridemia
Hypothyroidism
Infantile Neuroaxonal Dystrophy
Investigational New Drugs
Kidney
Mental Disorders
Obesity
Obesity, Morbid
Pancreatitis
Parent
Patients
Pharmaceutical Preparations
Pharmacotherapy
Placebos
Pregnancy
Safety
System, Endocrine
Biological Assay
Buffers
Exenatide
Hormones
Insulin
Insulin Secretion
Proteins
Radioimmunoprecipitation Assay
secretion
An NMA was performed in accordance with guidance from the National Institute for Health and Care Excellence (NICE), ISPOR and the Cochrane Institute [36 –40 ], to assess the relative efficacy of orally administered semaglutide compared with GLP-1 RAs for the treatment of T2D as an add-on to 1–2 OADs. In the analysis, the primary intervention of interest was orally administered semaglutide 14 mg QD and the primary comparators of interest were all licensed doses of injectable GLP-1 RAs—liraglutide, dulaglutide, exenatide twice-daily (BID), exenatide extended release, lixisenatide and subcutaneously administered semaglutide once-weekly (QW). Albiglutide was withdrawn from the market in 2018 [41 ] and therefore was not considered a relevant comparator in the NMA. GLP-1 RAs were often taken with other background antidiabetic medications in the trials. To reduce variability between populations across the different trials, the definition of the population receiving an add-on to 1–2 OADs was aligned as closely as possible to populations in the relevant PIONEER trials of orally administered semaglutide (the primary intervention of interest). The trial population in PIONEER 2 was patients inadequately controlled on metformin, and the trial populations in PIONEER 3, 4 and 7 were patients inadequately controlled on 1–2 OADs (metformin ± sulfonylureas) in PIONEER 3, metformin ± SGLT2i in PIONEER 4 and 1–2 OADs (metformin, sulfonylureas, SGLT2i or thiazolidinediones) in PIONEER 7.
Trials assessing a patient population that aligned with PIONEER trials 2, 3, 4 or 7 were considered for analysis and consequently, trials which included only patients inadequately controlled on two OADs were excluded. Similarly, studies which included less than 90% of patients inadequately controlled on metformin monotherapy, or on one OAD that was not metformin, were excluded from the analysis to reflect standard of care and align with international guidelines [7 ].
The PIONEER programme used two different estimands. The treatment policy estimand evaluated the treatment effect for all randomised patients regardless of trial product discontinuation and use of rescue medication (data analysed using multiple imputation), whereas the trial product estimand evaluated the treatment effect for all randomised patients under the assumption that all patients remained on trial product for the entire planned duration of the trial and did not use rescue medication (data analysed using a mixed model for repeated measures) [42 (link)]. To allow for accurate comparisons with trials reporting data without the use of rescue medication, the trial product estimand from the PIONEER trials was used for this NMA.
The identified studies were assessed for data on at least one outcome of interest, as well as their potential to form a connected network. A feasibility analysis for generating an evidence network for the 20 outcomes of interest was also conducted (supplementary information, Table S2). The NMA was considered feasible for the following efficacy outcomes: change from baseline in HbA1c; proportion of patients achieving HbA1c < 7% (53 mmol/mol) or ≤ 6.5% (48 mmol/mol); changes from baseline in body weight and blood pressure [i.e. systolic blood pressure (SBP) and diastolic blood pressure (DBP)], and safety outcomes including the proportion of patients experiencing any gastrointestinal (GI) adverse events (AEs) as specified in system organ class.
A normal likelihood, identity link model was used to perform all analyses of continuous outcomes. Where necessary, a shared parameter model was implemented to account for arm-level as well as trial-level data reported in the studies. A binomial likelihood (assuming a normal distribution) logit link model was used for the analysis of dichotomous outcomes. Both fixed effects and random effects models were run for each outcome, and the most suitable model was chosen on the basis of two criteria: the deviance information and the average posterior residual deviance.
The NMA models were implemented using WinBUGS software (MRC Biostatistics Unit, Cambridge, UK) [43 ] and employed a Bayesian framework with the use of uninformative prior distributions. Three Markov Monte Carlo chains were used, starting from different initial values of selected unknown parameters with a burn-in of 50,000 iterations. Convergence for all models was assessed by analysing history and density plots, and Brooks–Gelman–Rubin diagnostic plots [44 ]. In addition, autocorrelation plots were assessed to detect the presence of autocorrelation in the chains. Following this, model convergence inferences were made from data obtained by sampling for a further 10,000 iterations on the three chains.
Median treatment differences or odds ratios (ORs) and an associated 95% credible interval (CrI) are presented for the NMA results. For the continuous outcomes HbA1c (%), body weight (kg), SBP and DBP (mmHg), a treatment associated with a greater mean reduction from baseline is favoured. For efficacy dichotomous outcomes, a treatment associated with an increase in the OR (e.g. higher odds for achieving a HbA1c level < 7%) is favoured. For GI AEs, a treatment associated with a decrease in the OR is favoured.
In Bayesian statistics, it is considered that differences exist only where the CrI does not include 0.0 for treatment differences, or 1.0 for ORs. In some cases, orally administered semaglutide may be associated with a numerical reduction/increase against a comparator; however, it is assumed that there is no difference between treatments unless the CrI excludes 0.0 (for treatment differences), or 1.0 (for ORs).
The median ranks of each treatment are also provided in the supplementary information (Table S14). A treatment with a median rank of 1 is considered the best. If two drugs are both ranked as the second highest, they will both be given a lower median rank score (i.e. score 3). The surface under the cumulative ranking curve (SUCRA) is also presented in the supplementary information (Table S13). SUCRA values vary between 0% and 100%; a higher SUCRA value indicates an increased likelihood that a treatment is in the top rank or one of the top ranks [45 (link)]. This single numeric value can be a helpful simplification of information about the effect of each treatment, enabling easier interpretation of the many alternative results that are often calculated within an NMA network.
NMAs estimate treatment effects by combining evidence from clinical trials. This involves combining direct and indirect measures of effect, the findings of which may not always be aligned with each other. Therefore, it is important to examine consistency between the two ‘sources’ of evidence. Hence, where treatment loops were present in the network diagrams, these were statistically evaluated for inconsistency using Bucher’s method [39 ]. Additional informal checks were also performed by comparing the direct study data with the results of the NMA.
This article does not contain any new studies with human or animal subjects performed by any of the authors.
Trials assessing a patient population that aligned with PIONEER trials 2, 3, 4 or 7 were considered for analysis and consequently, trials which included only patients inadequately controlled on two OADs were excluded. Similarly, studies which included less than 90% of patients inadequately controlled on metformin monotherapy, or on one OAD that was not metformin, were excluded from the analysis to reflect standard of care and align with international guidelines [7 ].
The PIONEER programme used two different estimands. The treatment policy estimand evaluated the treatment effect for all randomised patients regardless of trial product discontinuation and use of rescue medication (data analysed using multiple imputation), whereas the trial product estimand evaluated the treatment effect for all randomised patients under the assumption that all patients remained on trial product for the entire planned duration of the trial and did not use rescue medication (data analysed using a mixed model for repeated measures) [42 (link)]. To allow for accurate comparisons with trials reporting data without the use of rescue medication, the trial product estimand from the PIONEER trials was used for this NMA.
The identified studies were assessed for data on at least one outcome of interest, as well as their potential to form a connected network. A feasibility analysis for generating an evidence network for the 20 outcomes of interest was also conducted (supplementary information, Table S2). The NMA was considered feasible for the following efficacy outcomes: change from baseline in HbA1c; proportion of patients achieving HbA1c < 7% (53 mmol/mol) or ≤ 6.5% (48 mmol/mol); changes from baseline in body weight and blood pressure [i.e. systolic blood pressure (SBP) and diastolic blood pressure (DBP)], and safety outcomes including the proportion of patients experiencing any gastrointestinal (GI) adverse events (AEs) as specified in system organ class.
A normal likelihood, identity link model was used to perform all analyses of continuous outcomes. Where necessary, a shared parameter model was implemented to account for arm-level as well as trial-level data reported in the studies. A binomial likelihood (assuming a normal distribution) logit link model was used for the analysis of dichotomous outcomes. Both fixed effects and random effects models were run for each outcome, and the most suitable model was chosen on the basis of two criteria: the deviance information and the average posterior residual deviance.
The NMA models were implemented using WinBUGS software (MRC Biostatistics Unit, Cambridge, UK) [43 ] and employed a Bayesian framework with the use of uninformative prior distributions. Three Markov Monte Carlo chains were used, starting from different initial values of selected unknown parameters with a burn-in of 50,000 iterations. Convergence for all models was assessed by analysing history and density plots, and Brooks–Gelman–Rubin diagnostic plots [44 ]. In addition, autocorrelation plots were assessed to detect the presence of autocorrelation in the chains. Following this, model convergence inferences were made from data obtained by sampling for a further 10,000 iterations on the three chains.
Median treatment differences or odds ratios (ORs) and an associated 95% credible interval (CrI) are presented for the NMA results. For the continuous outcomes HbA1c (%), body weight (kg), SBP and DBP (mmHg), a treatment associated with a greater mean reduction from baseline is favoured. For efficacy dichotomous outcomes, a treatment associated with an increase in the OR (e.g. higher odds for achieving a HbA1c level < 7%) is favoured. For GI AEs, a treatment associated with a decrease in the OR is favoured.
In Bayesian statistics, it is considered that differences exist only where the CrI does not include 0.0 for treatment differences, or 1.0 for ORs. In some cases, orally administered semaglutide may be associated with a numerical reduction/increase against a comparator; however, it is assumed that there is no difference between treatments unless the CrI excludes 0.0 (for treatment differences), or 1.0 (for ORs).
The median ranks of each treatment are also provided in the supplementary information (Table S14). A treatment with a median rank of 1 is considered the best. If two drugs are both ranked as the second highest, they will both be given a lower median rank score (i.e. score 3). The surface under the cumulative ranking curve (SUCRA) is also presented in the supplementary information (Table S13). SUCRA values vary between 0% and 100%; a higher SUCRA value indicates an increased likelihood that a treatment is in the top rank or one of the top ranks [45 (link)]. This single numeric value can be a helpful simplification of information about the effect of each treatment, enabling easier interpretation of the many alternative results that are often calculated within an NMA network.
NMAs estimate treatment effects by combining evidence from clinical trials. This involves combining direct and indirect measures of effect, the findings of which may not always be aligned with each other. Therefore, it is important to examine consistency between the two ‘sources’ of evidence. Hence, where treatment loops were present in the network diagrams, these were statistically evaluated for inconsistency using Bucher’s method [39 ]. Additional informal checks were also performed by comparing the direct study data with the results of the NMA.
This article does not contain any new studies with human or animal subjects performed by any of the authors.
A-A-1 antibiotic
albiglutide
Animals
Antidiabetics
Blood Pressure
Body Weight
Diagnosis
dulaglutide
Exenatide
Glucagon-Like Peptide 1
Homo sapiens
Liraglutide
lixisenatide
Metformin
MLL protein, human
Patients
Pharmaceutical Preparations
Pressure, Diastolic
Safety
semaglutide
Sulfonylurea Compounds
Systolic Pressure
Thiazolidinediones
We generated a separate mouse model of GLP-1r deficiency using a Cre-lox system, as described (Supplementary Fig. 1 ). Briefly, we generated mice with loxP sites flanking exons 6 and 7 of the GLP-1r gene and crossed them with mice that express Cre recombinase driven by the cytomegalovirus minimal (CMV) promoter. In CMV-Cre mice, deletion of loxP-flanked genes occurs in all tissues, including germ cells (14 (link)). Thus, mice that are homozygous for GLP-1r flox and hemizygous for CMV-Cre (GLP-1r flΔCMV) lack the GLP-1r in all tissues. Littermates that were hemizygous for CMV-Cre and WT for the GLP-1r (CMV-Cre) were used as controls. Gene expressions of GLP-1r exons 6 and 7 were determined by quantitative PCR and normalized to expression of L32. Experimental procedures, including surgery, mixed-meal tolerance test, and the anorexic response to exendin-4, were determined as described for experiment 2. Group sizes were as follows: 7 in CMV-Cre sham group, 7 in CMV-Cre VSG group, 11 in GLP-1r flΔCMV sham group, and 17 in GLP-1r flΔCMV VSG group. The mixed-meal tolerance test was performed 5 weeks after surgery, and the food intake response to exendin-4 was conducted 10 weeks later.
Anorexia
Cre recombinase
Cytomegalovirus
Eating
Exenatide
Exons
Gene Deletion
Gene Expression
Genes
Germ Cells
Hemizygote
Homozygote
Immune Tolerance
Mice, Laboratory
Operative Surgical Procedures
Tissues
The total diabetic population in Finland during 1997–2007 was obtained from the FinDM II database, which includes individuals with diabetes identified from 1) the register of individuals eligible for elevated reimbursement of medication for chronic conditions including diabetes (years 1964–2007), 2) the prescription register including all reimbursed medicines purchased (years 1994–2007), 3) the national hospital discharge registers including 3a) all inpatient care (years 1969–2007) and 3b) outpatient hospital visits (years 1998–2007), 4) the causes-of-death register (1971–2007), and 5) the medical birth register (1987–2007). Finnish personal identity codes allowed deterministic record linkage.
Individuals were considered to have diabetes since the first registration of diabetes in any of these registers. Women with gestational diabetes mellitus only were excluded. Individuals with diabetes were divided into subgroups depending on the hypoglycemic medication they used. Individuals with prescription data on insulin purchases for each possible year and no purchases for medication that stimulates insulin secretion (sulfonylureas, sitagliptin, vildagliptin, repaglinide, nateglinide, or exenatide) were considered to have insulin-treated diabetes (ITDM). The others were defined as having non–insulin-treated diabetes (NITDM). The data included 50,027 individuals with ITDM and 346,290 individuals with NITDM. individuals with ITDM were divided into two groups based on age at the first registration of diabetes: the Y-ITDM group included individuals aged <40 years (n = 33,805) and the O-ITDM group included individuals aged ≥40 years (n = 16,222) at registration. The Y-ITDM group contained individuals with type 1 diabetes, the NITDM group included individuals with type 2 diabetes, and the O-ITDM group was a mixed group consisting of individuals with late-onset type 1 diabetes and individuals with type 2 diabetes treated with insulin.
The incidence of the first major amputation among patients with diabetes was compared with the incidence among nondiabetic individuals in Finland (total population >5 million). Data obtained from the hospital discharge register for amputations were identified using procedure codes (the classification of the Finnish Hospital League for 1987–1996 [9573, 9574, and 9575] and the Nordic Classification on Surgical Procedures since 1996 [NFQ10, NFQ20, NGQ10, NGQ20, and NHQ10]) (13 ,14 ). Record linkage between the diabetes register and the amputation with an encrypted personal identity code was used to identify the first major amputation for each person. The follow-up started in 1987. In reporting the first major amputation in 1997–2007, as a rule for the first major amputation, a 10-year major amputation free period was used; only 0.8% of major amputations were detected beyond this time range. Amputations performed before the year of the first diabetes registration were considered as not being diabetes-related.
Age- and sex-adjusted incidences were calculated by using indirect standardization. For the nondiabetic group, follow-up times were calculated by subtracting the number of the prevalent population with diabetes from the corresponding population figures in Finland. Age-adjusted relative risks between the groups were derived by means of indirect standardization. Mean ages at amputation were compared between the groups using parametric t tests and nonparametric Mann-Whitney tests. The average duration from the registration of diabetes to the first major amputation was adjusted for age at diabetes registration and compared among groups with a regression model. Cumulative mortalities were calculated using the product limit estimator and compared using log-rank tests. The analyses were performed with the Survo MM (http://www.survo.fi ) and SAS 9.1.3 (http://www.sas.com ) software packages.
Individuals were considered to have diabetes since the first registration of diabetes in any of these registers. Women with gestational diabetes mellitus only were excluded. Individuals with diabetes were divided into subgroups depending on the hypoglycemic medication they used. Individuals with prescription data on insulin purchases for each possible year and no purchases for medication that stimulates insulin secretion (sulfonylureas, sitagliptin, vildagliptin, repaglinide, nateglinide, or exenatide) were considered to have insulin-treated diabetes (ITDM). The others were defined as having non–insulin-treated diabetes (NITDM). The data included 50,027 individuals with ITDM and 346,290 individuals with NITDM. individuals with ITDM were divided into two groups based on age at the first registration of diabetes: the Y-ITDM group included individuals aged <40 years (n = 33,805) and the O-ITDM group included individuals aged ≥40 years (n = 16,222) at registration. The Y-ITDM group contained individuals with type 1 diabetes, the NITDM group included individuals with type 2 diabetes, and the O-ITDM group was a mixed group consisting of individuals with late-onset type 1 diabetes and individuals with type 2 diabetes treated with insulin.
The incidence of the first major amputation among patients with diabetes was compared with the incidence among nondiabetic individuals in Finland (total population >5 million). Data obtained from the hospital discharge register for amputations were identified using procedure codes (the classification of the Finnish Hospital League for 1987–1996 [9573, 9574, and 9575] and the Nordic Classification on Surgical Procedures since 1996 [NFQ10, NFQ20, NGQ10, NGQ20, and NHQ10]) (13 ,14 ). Record linkage between the diabetes register and the amputation with an encrypted personal identity code was used to identify the first major amputation for each person. The follow-up started in 1987. In reporting the first major amputation in 1997–2007, as a rule for the first major amputation, a 10-year major amputation free period was used; only 0.8% of major amputations were detected beyond this time range. Amputations performed before the year of the first diabetes registration were considered as not being diabetes-related.
Age- and sex-adjusted incidences were calculated by using indirect standardization. For the nondiabetic group, follow-up times were calculated by subtracting the number of the prevalent population with diabetes from the corresponding population figures in Finland. Age-adjusted relative risks between the groups were derived by means of indirect standardization. Mean ages at amputation were compared between the groups using parametric t tests and nonparametric Mann-Whitney tests. The average duration from the registration of diabetes to the first major amputation was adjusted for age at diabetes registration and compared among groups with a regression model. Cumulative mortalities were calculated using the product limit estimator and compared using log-rank tests. The analyses were performed with the Survo MM (
Age Groups
Amputation
Chronic Condition
Diabetes Mellitus
Diabetes Mellitus, Insulin-Dependent
Diabetes Mellitus, Non-Insulin-Dependent
Exenatide
Gestational Diabetes
Hospitalization
Hypoglycemic Agents
Insulin
Insulin Secretion
Nateglinide
Operative Surgical Procedures
Outpatients
Patient Discharge
Patients
Pharmaceutical Preparations
repaglinide
Sitagliptin
Sulfonylurea Compounds
Vildagliptin
Woman
Most recents protocols related to «Exenatide»
Example 14
CD1 mice were fasted for overnight and then administrated with certain amount of peptides through either i.v. or s.c. route. After 6 hours, mice were orally or intraperitoneally administrated with bolus dose of glucose solution at 2 g/kg body mass at concentration of 100 mg/mL and their tail blood glucose levels were measured before (0 min) and after glucose challenge for 2 to 3 hours.
Exemplary data for mTA4 and mTA37 (see Table 6) are shown in
Blood Glucose
Exenatide
Glucose
Glucose Tolerance Test
Human Body
Mice, House
Oral Glucose Tolerance Test
Peptides
Suby's G solution
Tail
PREVAIL was a 20-week, open-label, parallel-arm trial wherein patients aged 30–80 years with T2DM of ≤ 7 years duration were randomized (1:1:1) to 8-weeks treatment with insulin glargine, glargine + thrice-daily lispro, or glargine + twice-daily exenatide, followed by 12-weeks washout. The study protocol and primary outcome have been described in detail previously [9 (link)]. The study was approved by the Mount Sinai Hospital Research Ethics Board and registered at ClinicalTrials.Gov (NCT02194595). All participants provided written informed consent. While the primary and metabolic outcomes have been reported recently [9 (link)], the current report presents the pre-specified ancillary outcome of endothelial function and associated vascular measures.
Blood Vessel
Endothelium
Exenatide
Insulin Glargine
Insulin Lispro
Patients
The Exendin-4 producing S. boulardii strains were incubated in 2 ml DELFT medium supplemented with 20 mg/L uracil in a 24-deep well plate (Axygen®, VWR) with a sandwich cover (Enzyscreen). The cultures had an initial OD600 of 0.05 and were performed with continuous shaking at 250 RPM at 37°C. All cultures were harvested after 24 and 48 h. Cell cultures were spun down at 10,000 g for 10 min at 4°C. Exendin-4 was quantified with Exendin-4 EIA (EK-070-94, Phoenix). The signals were detected by OD450 using a microplate reader Synergy™ H1 (BioTek).
Cell Culture Techniques
Exenatide
Strains
Uracil
Male SD rats (190–250 g) were fasted for 12 h and randomly divided into four groups (n = five/group). The subcutaneous exenatide group (s.c.) was administered a dose of 10 ug/kg and sampled after 0, 5, 15, and 30 min; and 1, 2, 4, 6, 8, 10, 12, and 24 h of administration. Exenatide solution, RM-ELNCs and FA-RM-ELNCs were administered orally at a dose of 100 ug/kg and sampled at 0, 30 min, 1, 2, 4, 6, 8, 10, 12, and 24 h after dosing. All blood samples were obtained through the orbital vein of the rat and placed in centrifuge tubes containing sodium heparin and peptidase. Blood samples were centrifuged and the supernatant was stored in a refrigerator at −80 °C for further testing (Jain et al., 2012 (link)).
The exenatide levels in the plasma were measured using the exenatide ELISA kit. The blood concentration-time curve of exenatide was drawn and the relative bioavailability (BR) of the exenatide-loaded nanoparticles was calculated. The BR was calculated using the following formula:
where AUC is the area under the curve, oral is oral administration and s.c. is a subcutaneous injection.
The exenatide levels in the plasma were measured using the exenatide ELISA kit. The blood concentration-time curve of exenatide was drawn and the relative bioavailability (BR) of the exenatide-loaded nanoparticles was calculated. The BR was calculated using the following formula:
where AUC is the area under the curve, oral is oral administration and s.c. is a subcutaneous injection.
Administration, Oral
BLOOD
Enzyme-Linked Immunosorbent Assay
Exenatide
Heparin Sodium
Males
Peptide Hydrolases
Plasma
Subcutaneous Injections
Veins
The db/db diabetic mice (6 weeks old) were randomly divided into five groups (n = 5/group). Subcutaneous administration group was exenatide solution (10 ug/kg). The oral administration groups were saline, exenatide solution, RM-ELNC, and FA-RM-ELNC (100 ug/kg). Blood samples were obtained from the tail vein of mice at 0, 1, 2, 4, 6, 8, 10, 12, and 24 h after administration and blood glucose values were measured (Agrawal et al., 2014 (link)).
Administration, Oral
BLOOD
Blood Glucose
Exenatide
Mice, House
Saline Solution
Tail
Veins
Top products related to «Exenatide»
Sourced in United States, Japan, United Kingdom, Germany, Israel
Exendin-4 is a peptide that serves as a key component in various laboratory equipment and assays. It functions as a glucagon-like peptide-1 (GLP-1) receptor agonist, which is used in research and development applications.
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Nicotinamide is a form of vitamin B3 that serves as a precursor for the coenzyme nicotinamide adenine dinucleotide (NAD) in biological systems. NAD is essential for various metabolic processes within cells, including energy production and DNA repair.
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Fetal Bovine Serum (FBS) is a cell culture supplement derived from the blood of bovine fetuses. FBS provides a source of proteins, growth factors, and other components that support the growth and maintenance of various cell types in in vitro cell culture applications.
Sourced in Switzerland
Exendin-4 is a synthetic peptide that functions as a glucagon-like peptide-1 (GLP-1) receptor agonist. It is a laboratory tool used in research applications.
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Exendin-4 is a synthetic peptide that functions as an agonist of the glucagon-like peptide-1 (GLP-1) receptor. It is used in research and development applications.
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Liraglutide is a glucagon-like peptide-1 (GLP-1) receptor agonist. It is a synthetic version of the naturally occurring GLP-1 hormone, which plays a role in regulating blood glucose levels.
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DMSO is a versatile organic solvent commonly used in laboratory settings. It has a high boiling point, low viscosity, and the ability to dissolve a wide range of polar and non-polar compounds. DMSO's core function is as a solvent, allowing for the effective dissolution and handling of various chemical substances during research and experimentation.
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The N2 supplement is a laboratory-grade nitrogen enrichment solution used to support the growth and development of cell cultures. It provides an additional source of nitrogen to cell culture media, which is essential for cellular metabolism and protein synthesis.
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B27 supplement is a serum-free and animal component-free cell culture supplement developed by Thermo Fisher Scientific. It is designed to promote the growth and survival of diverse cell types, including neurons, embryonic stem cells, and other sensitive cell lines. The core function of B27 supplement is to provide a defined, optimized combination of vitamins, antioxidants, and other essential components to support cell culture applications.
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Activin A is a growth factor protein that plays a role in various biological processes. It is a member of the transforming growth factor-beta (TGF-β) superfamily. Activin A functions in the regulation of cell growth, differentiation, and other cellular activities.
More about "Exenatide"
Exenatide, a synthetic peptide, mimics the glucagon-like peptide-1 (GLP-1) hormone, which regulates glucose homeostasis.
Approved for the treatment of type 2 diabetes, exenatide helps lower blood glucose levels by stimulating insulin secretion and suppressing glucagon release.
Exendin-4, a related peptide, shares similar mechanisms of action.
Exenatide research can be optimized using PubCompare.ai, an AI-driven platform that identifies the best experimental approaches from literature, preprints, and patents.
PubCompare.ai enhances the reproducibility and accuracy of exenatide research, helping researchers find the optimal protocols and products.
This includes the use of nicotinamide, fetal bovine serum (FBS), liraglutide (another GLP-1 agonist), DMSO, N2 supplement, B27 supplement, and Activin A, which are commonly used in exenatide-related studies.
By leveraging the power of data-driven research optimization, scientists can experience improved outcomes and greater insights in their exenatide-focused investigations.
Approved for the treatment of type 2 diabetes, exenatide helps lower blood glucose levels by stimulating insulin secretion and suppressing glucagon release.
Exendin-4, a related peptide, shares similar mechanisms of action.
Exenatide research can be optimized using PubCompare.ai, an AI-driven platform that identifies the best experimental approaches from literature, preprints, and patents.
PubCompare.ai enhances the reproducibility and accuracy of exenatide research, helping researchers find the optimal protocols and products.
This includes the use of nicotinamide, fetal bovine serum (FBS), liraglutide (another GLP-1 agonist), DMSO, N2 supplement, B27 supplement, and Activin A, which are commonly used in exenatide-related studies.
By leveraging the power of data-driven research optimization, scientists can experience improved outcomes and greater insights in their exenatide-focused investigations.