Search Results (60)

The study presents the potential of milkfish frame, a by-product of milkfish processing, as a source of dipeptidyl peptidase IV (DPP-IV) inhibitory and antioxidant peptides with potential applications in type 2 diabetes management. Proteomic analysis identified key proteins, including 65 kDa warm temperature acclimation protein 1 and myosin heavy chain. In silico prediction (BIOPEP-UWM) guided the selection of proteases for generating DPP-IV inhibitory peptides. Enzymatic hydrolysates were produced and evaluated for bioactivity. Among the treatments, pepsin hydrolysis (2% v/v, 8 h) yielded the highest peptide content (283.64 mg/g), soluble protein (86.46%), and DPP-IV inhibitory activity (68.47%). The resulting milkfish frame pepsin hydrolysate (MFH) was further enhanced through ultrafiltration and simulated gastrointestinal digestion, which improved the DPP-IV inhibitory and antioxidant capacities. Cytotoxicity assays confirmed that MFH (0–100 μg/mL) was non-toxic to FL83B hepatocytes after 24 h. Moreover, treating TNF-α-induced FL83B cells with 10 μg/mL MFHs improved cell viability, reducing the toxicity induced by TNF-α in cells. These findings show that MFHs exhibit promising antidiabetic potential and could serve as natural alternatives to synthetic drugs for type 2 diabetes management. This also demonstrates the valorization of fish processing by-products into functional food ingredients, advancing sustainable approaches in food innovation. Full article

This research introduces a hybrid framework that integrates stochastic modeling and machine learning for predicting postprandial glucose levels in individuals with Type 1 Diabetes (T1D). The primary aim is to enhance the accuracy of glucose predictions by merging a biophysical Glucose–Insulin–Meal (GIM) model with advanced machine learning techniques. This framework is tailored to utilize the Kaggle BRIST1D dataset, which comprises real-world data from continuous glucose monitoring (CGM), insulin administration, and meal intake records. The methodology employs the GIM model as a physiological prior to generate simulated glucose and insulin trajectories, which are then utilized as input features for the machine learning (ML) component. For this component, the study leverages the Light Gradient Boosting Machine (LightGBM) due to its efficiency and strong performance with tabular data, while Long Short-Term Memory (LSTM) networks are applied to capture temporal dependencies. Additionally, Bayesian regression is integrated to assess prediction uncertainty. A key advancement of this research is the transition from a deterministic GIM formulation to a stochastic differential equation (SDE) framework, which allows the model to represent the probabilistic range of physiological responses and improves uncertainty management when working with real-world data. The findings reveal that this hybrid methodology enhances both the precision and applicability of glucose predictions by integrating the physiological insights of Glucose Interaction Models (GIM) with the flexibility of data-driven machine learning techniques to accommodate real-world variability. This innovative framework facilitates the creation of robust, transparent, and personalized decision-support systems aimed at improving diabetes management. Full article

Ketones, which are key biomarkers of fat oxidation, are relevant for metabolic health maintenance and disease development, making continuous monitoring essential. In this study, we introduce a novel colorimetric sensor designed for potential continuous acetone detection in biological fluids. The sensor features a polydimethylsiloxane (PDMS) shell that encapsulates a sensitive and specific liquid-core acetone-sensing probe. The microsphere sensors were characterized by evaluating their size, PDMS shell thickness, colorimetric response, and sensitivity under realistic conditions, including 100% relative humidity (RH) and CO₂ interference. The microsphere size and sensor sensitivity can be controlled by modifying the fabrication parameters. Critically, the sensor showed high selectivity for acetone detection, with negligible interference from CO₂ concentrations up to 4%. In addition, the sensor displayed good reproducibility (CV < 5%) and stability under realistic storage conditions (over two weeks at 4 °C). Finally, the accuracy of the microsphere sensor was validated against a gold standard gas chromatography-mass spectrometry (GC-MS) method using simulated and real breath samples from healthy individuals and type 1 diabetes patients. The correlation between the microsphere sensor and GC-MS produced a linear fit with a slope of 0.948 and an adjusted R-squared value of 0.954. Therefore, the liquid-core microsphere-based sensor is a promising platform for acetone body fluid analysis. Full article

The development of orally bioavailable non-peptidomimetic glucagon-like peptide-1 receptor agonists (GLP-1RAs) offers a promising therapeutic avenue for the treatment of type 2 diabetes mellitus (T2DM) and obesity. An extensive in silico approach combining structure-based drug design and ligand-based strategies together with pharmacokinetic properties and drug-likeness predictions is implemented to identify novel non-peptidic GLP-1RAs from the COCONUT and Marine Natural Products (CMNPD) libraries. More than 700,000 compounds were screened by shape-based similarity filtering in combination with precision docking against the orthosteric site of the GLP-1 receptor (PDB ID: 6X1A). The docked candidates were further assessed with the molecular mechanics MM-GBSA tool to check the binding affinities; the final list of candidates was validated by running a 500 ns long MD simulation. Twenty final hits were identified, ten from each database. The hits contained compounds with reported antidiabetic effects but with no evidence of GLP-1 agonist activity, including hits 1, 6, 7, and 10. These findings proposed a novel mechanism for these hits through GLP-1 activity and positioned the other hits as potential promising scaffolds. Among the studied compounds—especially hits 1, 5, and 9—possessed strong and stable interactions with critical amino acid residues such as TRP-203, PHE-381, and GLN-221 at the active site of the 6X1A-substrate along with favorable pharmacokinetic profiles. Moreover, the RMSF and RMSD plots further suggested the possibility of stable interactions. Specifically, hit 9 possessed the best docking score with a ΔG_bind value of −102.78 kcal/mol, surpassing even the control compound in binding affinity. The ADMET profiling also showed desirable drug-likeness and pharmacokinetic characteristics for hit 9. The pipeline of computational integration underscores the potential of non-peptidic alternatives in natural product libraries to pursue GLP-1-mediated metabolic therapy into advanced preclinical validation. Full article

Diabetes mellitus (DM) is a multifactorial metabolic disorder characterized by chronic hyperglycemia and systemic metabolic dysregulation. Although ginsenosides, the primary bioactive components of Panax ginseng Meyer, exhibit regulatory effects on glucose and lipid metabolism, their precise mechanisms and key targets in DM remain incompletely understood. Unlike previous studies focusing solely on crude extracts or individual ginsenosides, this study integrates network pharmacology, molecular docking, and molecular dynamics (MD) simulations to systematically elucidate the multi-target mechanisms of ginsenosides, with experimental validation using the ginsenoside derivative AD-1. Network pharmacology identified 134 potential targets, with protein–protein interaction (PPI) analysis revealing 25 core targets (such as NFKB1, HDAC1, ESR1, and EP300). Molecular docking and MD simulations showed that ginsenosides have stable binding conformations with these targets and exhibit excellent dynamic stability. Notably, in vivo experiments using AD-1 in streptozotocin-induced type 1 diabetic mice confirmed its therapeutic efficacy, significantly downregulating key diabetic markers (e.g., NFKB1 and HDAC1) in pancreatic tissues—a finding unreported in prior studies. This study not only revealed the multitarget pharmacological mechanism of ginsenosides but also highlighted the therapeutic potential of AD-1. These findings provide a foundation for further mechanistic studies and suggest new strategies for the application of novel ginsenoside derivatives in diabetes therapy. Full article

In recent years, various types of sensors have been developed at both millimeter (mm) and micrometer (µm) scales for numerous biomedical applications. Each design has its own advantages and limitations. This study compares the electrical characteristics and sensitivity of millimeter- and micrometer-scale sensors, emphasizing the superior performance of millimeter-scale designs for detecting type-2 diabetes. Elevated glucose levels in type-2 diabetes alter the complex permittivity of red blood cells (RBCs), affecting their rheological and electrical properties, such as viscosity, volume, relative permittivity, dielectric loss, and AC conductivity. These alterations may manifest as a unique bio-impedance signature, offering a diagnostic topology for diabetes. In view of this, various concentrations (ranging from 10% to 100%) of 400 µL of normal and diabetic RBCs suspended in phosphate-buffered saline (PBS) solution are examined to record the changes in bio-impedance signatures across a spectrum of frequencies, ranging from 1 MHz to 10 MHz. In this study, simulations are performed using the finite element method (FEM) with COMSOL Multiphysics^® to analyze the electrical behavior of the sensors at both millimeter (mm) and micrometer (µm) scales. These simulations provide valuable insights into the performance parameters of the sensors, aiding in the selection of the most effective design by using this topology. Full article

Background/Objectives: Type 2 diabetes mellitus (T2DM) is a chronic condition marked by hyperglycemia, which can affect metabolic, vascular, and hematological parameters. A low-carbohydrate, high-fat (LCHF) diet has been shown to improve glycemic control and blood pressure regulation. Exercise in hypoxia (EH) enhances insulin sensitivity, erythropoiesis, and angiogenesis. The combination of LCHF and EH may offer a promising strategy for managing T2DM and hypertension (HTN), although evidence remains limited. This study aimed to assess the effects of an eight-week normobaric EH intervention at 3000 m simulated altitude combined with an LCHF diet on hematological and lipid profiles, inflammation, and blood pressure in older patients with T2DM and HTN. Methods: Forty-two diabetic patients with HTN were randomly assigned to three groups: (1) control group (control diet + exercise in normoxia), (2) EH group (control diet + EH), and (3) intervention group (EH+LCHF) Baseline and eight-week measurements included systolic, diastolic, and mean blood pressure (SBP, DBP, MAP), hematological and lipid profiles, and inflammation biomarkers. Results: Blood pressure decreased after the intervention (p < 0.001), with no significant differences between groups (SBP: p = 0.151; DBP: p = 0.124; MAP: p = 0.18). No differences were observed in lipid profile or C-reactive protein levels (p > 0.05). Mean corpuscular hemoglobin (MCH) increased in the EH group (p = 0.027), while it decreased in the EH+LCHF group (p = 0.046). Conclusions: Adding hypoxia or restricting carbohydrates did not provide additional benefits on blood pressure in T2DM patients with HTN. Further elucidation of the mechanisms underlying hematological adaptations is imperative. Trial registration number: NCT05094505. Full article

The etiology of type 1 diabetes mellitus (T1DM) is intricate, leading to its classification as an autoimmune metabolic disorder. T1DM often coexists with various visceral diseases. N-acetylcysteine (NAC) is widely acknowledged for its potent antioxidant properties. Studies have demonstrated that the combination of NAC and insulin can effectively alleviate iron-induced nephropathy in T1DM and mitigate oxidative stress injury in skeletal muscle associated with the condition. However, the potential impact of NAC alone on liver disease in individuals with T1DM remains uncertain. In this study, a beagle model was established to simulate T1DM, enabling investigation into the role of NAC in liver disease using RNA-seq biogenic analysis and subsequent validation through molecular biological methods. The findings revealed suppressed expression of CXCL12 chemokine in the livers of individuals with T1DM, while treatment with NAC induced specific activation of CXCL12 within the liver affected by T1DM. These results suggest that CXCL12 may serve as a regulatory factor involved in the therapeutic effects of NAC on liver disease associated with TIDM. This discovery holds significant implications for utilizing NAC as an adjunctive therapy for managing complicated liver diseases accompanying type 1 diabetes mellitus. Full article

Rehmannia glutinosa Libosch., which belongs to the Orobanchaceae family, is a perennial herb found in China, Japan, and Korea. In traditional medicine, it is used to cool the body, improve water metabolism in the kidney, and provide protection from metabolic diseases such as type 2 diabetes mellitus (T2DM) and obesity. In this study, three new compounds were isolated from the roots of R. glutinosa, along with eighteen known compounds. Structure elucidation was performed with spectroscopic analyses including nuclear magnetic resonance (NMR) and circular dichroism (CD) spectroscopy. As the AMP-activated protein kinase (AMPK) signaling pathway is reportedly related to metabolic diseases, AMPK activation studies were conducted using in silico simulations and in vitro assays. Among the isolated compounds, 1 showed a potential as an AMPK activator in both in silico simulations and in vitro experiments. Our findings expand the chemical profiles of the plant R. glutinosa and suggest that one newly found compound (1) activates AMPK. Full article

Background: Non-insulin-dependent diabetes mellitus, or type 2 diabetes, is one of the diseases of greatest concern worldwide, and research into natural compounds that are capable of regulating glycemia and insulin resistance is therefore gaining importance. In the preclinical stages, Caenorhabditis elegans is considered a promising in vivo model for research into this disease. Most studies have been carried out using daf-2 mutant strains and observing changes in their phenotype rather than directly measuring the effects within the worms. Methods: We evaluated the in vitro α-glucosidase inhibition of two oral formulations of Origanum vulgare before and after a simulated gastrointestinal digestion process. After confirming this activity, we developed a method to measure α-glucosidase inhibition in vivo in the C. elegans wild-type strain. Results: The crude extract showed a similar IC50 value to that of acarbose (positive control), before and after gastrointestinal digestion. Formulation 1 also showed no differences with the positive control after digestion (111.86 ± 1.26 vs. 110.10 ± 1.00 µL/mL; p = 0.282). However, formulation 2 showed a higher hypoglycemic activity (59.55 ± 0.85 µL/mL; p < 0.05). The IC50 values obtained in the in vivo assays showed results that correlated well with the in vitro results, so the proposed new method of direct quantification of the in vivo activity seems suitable for directly quantifying the effects of this inhibition without the need to measure changes in the phenotype. Conclusion: A novel, simple and reliable method has been developed to directly determine pharmacological activities in an in vivo model of wild-type C. elegans. This allows the hypoglycemic activity to be directly attributed to in vivo treatment without quantifying phenotypic changes in mutant strains that may arise from other effects, opening the door to a simple analysis of in vivo pharmacological activities. Full article

Background: This study explored the potential of MCM-48 mesoporous silica matrices as a drug delivery system for metformin hydrochloride, aimed at improving the therapeutic management of type 2 diabetes mellitus. The objectives included the synthesis and characterization of MCM-48, assessment of its drug loading capacity, analysis of drug release profiles under simulated physiological conditions, and the development of a multifractal dynamics-based theoretical framework to model and interpret the release kinetics. Methods: MCM-48 was synthesized using a sol–gel method and characterized by SEM-EDX, TEM, and nitrogen adsorption techniques. Drug loading was performed via adsorption at pH 12 using metformin hydrochloride solutions of 1 mg/mL (P-1) and 3 mg/mL (P-2). In vitro dissolution studies were conducted to evaluate the release profiles in simulated gastric and intestinal fluids. A multifractal dynamics model was developed to interpret the release kinetics. Results: SEM-EDX confirmed the uniform distribution of silicon and oxygen, while TEM images revealed a highly ordered cubic mesoporous structure. Nitrogen adsorption analyses showed a high specific surface area of 1325.96 m²/g for unloaded MCM-48, which decreased with drug loading, confirming efficient incorporation of metformin hydrochloride. The loading capacities were 59.788 mg/g (P-1) and 160.978 mg/g (P-2), with efficiencies of 99.65% and 89.43%, respectively. In vitro dissolution studies showed a biphasic release profile: an initial rapid release in gastric conditions followed by sustained release in intestinal fluids, achieving cumulative releases of 92.63% (P-1) and 82.64% (P-2) after 14 hours. The multifractal dynamics-based theoretical release curves closely matched the experimental data. Conclusions: MCM-48 mesoporous silica effectively enhanced metformin delivery, offering a controlled release profile well-suited for type 2 diabetes management. The multifractal theoretical framework provided valuable insights into drug release dynamics, contributing to the advancement of innovative drug delivery systems. Full article

Thiazolidinediones (TZDs) including rosiglitazone and pioglitazone function as peroxisome proliferator-activated receptor gamma (PPARγ) full agonists, which have been known as a class to be among the most effective drugs for the treatment of type 2 diabetes mellitus (T2DM). However, side effects of TZDs such as fluid retention and weight gain are associated with their full agonistic activities toward PPARγ induced by the AF-2 helix-involved “locked” mechanism. Thereby, this study aimed to obtain novel PPARγ partial agonists without direct interaction with the AF-2 helix. Through performing virtual screening of the Targetmol L6000 Natural Product Library and utilizing molecular dynamics (MD) simulation, as well as molecular mechanics Poisson–Boltzmann surface area (MM-PBSA) analysis, four compounds including tubuloside b, podophyllotoxone, endomorphin 1 and paliperidone were identified as potential PPARγ partial agonists. An in vitro TR-FRET competitive binding assay showed podophyllotoxone displayed the optimal binding affinity toward PPARγ among the screened compounds, exhibiting IC₅₀ and ki values of 27.43 µM and 9.86 µM, respectively. Further cell-based transcription assays were conducted and demonstrated podophyllotoxone’s weak agonistic activity against PPARγ compared to that of the PPARγ full agonist rosiglitazone. These results collectively demonstrated that podophyllotoxone could serve as a PPARγ partial agonist and might provide a novel candidate for the treatment of various diseases such as T2DM. Full article

Sedentary behavior (SB) is an essential risk factor for obesity, cardiovascular disease, and type 2 diabetes. Though certain levels of physical activity (PA) may attenuate the detrimental effects of SB, the inflammatory and cardiometabolic responses involved are still not fully understood. The focus of this secondary outcome analysis was to describe how light-intensity PA snacks (LIPASs, alternate sitting and standing, walking or standing continuously) compared with uninterrupted prolonged sitting affect inflammatory and cardiometabolic risk markers. Seventeen young adults with overweight and obesity participated in this study (eight females, 23.4 ± 3.3 years, body mass index (BMI) 29.7 ± 3.8 kg/m², glycated hemoglobin A1C (HbA_1c) 5.4 ± 0.3%, body fat 31.8 ± 8.2%). Participants were randomly assigned to the following conditions which were tested during an 8 h simulated workday: uninterrupted prolonged sitting (SIT), alternate sitting and standing (SIT-STAND, 2.5 h total standing time), continuous standing (STAND), and continuous walking (1.6 km/h; WALK). Each condition also included a standardized non-relativized breakfast and lunch. Venous blood samples were obtained in a fasted state at baseline (T₀), 1 h after lunch (T₁) and 8 h after baseline (T₂). Inflammatory and cardiometabolic risk markers included interleukin-6 (IL-6), c-reactive protein (CRP), total cholesterol (TC), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), triglycerides (TGs), visceral fat area (VFA), triglyceride-glucose (TyG) index, two lipid ratio measures, TG/HDL-C and TC/HDL-C, albumin, amylase (pancreatic), total protein, uric acid, and urea. We found significant changes in a broad range of certain inflammatory and cardiometabolic risk markers during the intervention phase for IL-6 (p = 0.014), TG (p = 0.012), TC (p = 0.017), HDL-C (p = 0.020), LDL-C (p = 0.021), albumin (p = 0.003), total protein (p = 0.021), and uric acid (p = 0.040) in favor of light-intensity walking compared with uninterrupted prolonged sitting, alternate sitting and standing, and continuous standing. We found no significant changes in CRP (p = 0.529), creatinine (p = 0.199), TyG (p = 0.331), and the lipid ratios TG/HDL-C (p = 0.793) and TC/HDL-C (p = 0.221) in response to the PA snack. During a simulated 8 h work environment replacement and interruption of prolonged sitting with light-intensity walking, significant positive effects on certain inflammatory and cardiometabolic risk markers were found in young adults with overweight and obesity. Full article

The paper “Using Absorption Models for Insulin and Carbohydrates and Deep Leaning to Improve Glucose Level Predictions” (Sensors 2021, 21, 5273) proposes a novel approach to predicting blood glucose levels for people with type 1 diabetes mellitus (T1DM). By building exponential models from raw carbohydrate and insulin data to simulate the absorption in the body, the authors reported a reduction in their model’s root-mean-square error (RMSE) from $15.5$ mg/dL (raw) to $9.2$ mg/dL (exponential) when predicting blood glucose levels one hour into the future. In this comment, we demonstrate that the experimental techniques used in that paper are flawed, which invalidates its results and conclusions. Specifically, after reviewing the authors’ code, we found that the model validation scheme was malformed, namely, the training and test data from the same time intervals were mixed. This means that the reported RMSE numbers in the referenced paper did not accurately measure the predictive capabilities of the approaches that were presented. We repaired the measurement technique by appropriately isolating the training and test data, and we discovered that their models actually performed dramatically worse than was reported in the paper. In fact, the models presented in the that paper do not appear to perform any better than a naive model that predicts future glucose levels to be the same as the current ones. Full article

This paper reports on the hardware implementation of a digital insulin-glucose regulator for type 2 diabetic patients by using a Field Programmable Gate Array board. For a real time-control of the patient insulin concentration, the insulin-regulator needs to measure only his blood glucose concentration. With respect to other reported solutions using general-purpose programmable hardware’s, the proposed insulin-glucose regulator allows to design a software-free, fully-hardware architecture of the system here described in detail. A prototype has been developed so to validate its functionality in the following two operating modes: (i) in the open loop condition for which only the insulin-glucose regulator is operating; (ii) in the closed loop condition for which the insulin-glucose regulator acting as an artificial pancreas is connected to a population of one hundred virtual patients individuated by employing a comprehensive theoretical model recognized by the U.S. Food and Drug Administration for the pre-clinical validation of glucose control strategies. These virtual patients present the same trend of the variation of the glucose concentration achieving different maximum and minimum values of glucose concentrations when eating a meal. The paper presents and discusses the experimental results by comparing them with those ones obtained by implementing the theoretical model through numerical simulations performed in SIMULINK. Relative errors lower than ±1% have been achieved by performing this comparison so demonstrating a very high accuracy of the proposed insulin-glucose regulator digital system. The implemented hardware solution of the digital controller can process the input data related to the glucose concentration of each virtual patient in about 1.1 μs with an estimated power consumption of about 36 mW. These achievements open the way for further investigations on digital architectures for glucose regulators to be integrated in VLSI as System-on-Chips and/or Lab-on-Chips for portable, wearable, and implantable solutions in real biomedical applications. Full article