Insufficient Insulin Production Research Articles

Proposed prototype for a quantitative biosensor for insulin detection using a liquid crystal

In this paper, we present a novel approach for label-free detection of human insulin using 5CB liquid crystal (LC). Monitoring insulin levels in the body is essential for treating diseases like diabetes, which are caused by insufficient production or inefficient use of insulin. Contemporary techniques of detection rely on glucose detection, and limited information is available on the direct detection of insulin. A simple method using a common room temperature LC 4-cyano-4-pentyl biphenyl (5CB) interacting with different concentrations of insulin, observed through a polarising optical microscope (POM), is reported in this paper. An analysis of observed textures revealed the detection of insulin as low as 25 μM concentration. The distinguishable features of the observed textures provide a scale for estimating insulin concentration. The effect of different pH and salt concentrations was studied in order to understand the optimal working condition of the proposed biosensor. Image J analysis demonstrated a positive correlation (R2 equal to 0.9553) between grey index (GI) values and human insulin concentrations. RGB analysis and interference studies confirmed the biosensor’s specificity in insulin detection. To correlate the textures to the happenings at the molecular level, docking studies were carried out. In addition to this, properties such as aromaticity, H-bond, hydrophobicity, interpolated charges, ionizability and solvent accessible area (SAS) in the minimum energy binding sites of 5CB-LC and human insulin chains were measured to know more about the interaction properties. This novel strategy shows promise for improving insulin detection sensitivity and accuracy, ultimately leading to better diabetes management and healthcare outcomes.

Weighted Bayesian Belief Network for diabetics: a predictive model.

Diabetes is an enduring metabolic condition identified by heightened blood sugar levels stemming from insufficient production of insulin or ineffective utilization of insulin within the body. India is commonly labeled as the "diabetes capital of the world" owing to the widespread prevalence of this condition. To the best of the authors' last knowledge updated on September 2021, approximately 77 million adults in India were reported to be affected by diabetes, reported by the International Diabetes Federation. Owing to the concealed early symptoms, numerous diabetic patients go undiagnosed, leading to delayed treatment. While Computational Intelligence approaches have been utilized to improve the prediction rate, a significant portion of these methods lacks interpretability, primarily due to their inherent black box nature. Rule extraction is frequently utilized to elucidate the opaque nature inherent in machine learning algorithms. Moreover, to resolve the black box nature, a method for extracting strong rules based on Weighted Bayesian Association Rule Mining is used so that the extracted rules to diagnose any disease such as diabetes can be very transparent and easily analyzed by the clinical experts, enhancing the interpretability. The WBBN model is constructed utilizing the UCI machine learning repository, demonstrating a performance accuracy of 95.8%.

Insulin Analogs and the Mode of Insulin Delivery: Recent Advances and Challenges.

Diabetes is a medical condition associated with impaired glucose regulation caused either due to insufficient insulin production or resistance to insulin (Type 2 diabetes, gestational diabetes) or the absence of insulin through the selective killing of beta cells in the pancreas (Type 1 diabetes). Irregular insulin production leads to various health complications. To prevent such complications, patients must adhere to medical recommendations before availing of any advanced insulin therapy(ies), considered productive for the treatment. Natural insulin, although highly effective in controlling blood glucose levels, patients are often at risk of developing hypoglycemia and many other complications. This has led to the development of insulin analogs, the modified variants of natural insulin having a minimal risk of causing hypoglycemia. Besides the development of analogs, the mode of insulin delivery is also considered critical in achieving better glycemic control in diabetic patients. Until recently, various exogenous insulin delivery methods were practiced, but effective glycemic control without any associated risk and ease of delivery remains a subject of paramount concern. It countered attenuation or delayed onset of diabetes-associated complications without a permanent cure, raising an unmet demand for insulin formulations and delivery methods that offer stability, biocompatibility, reproducibility, precision dosing, non-immunogenicity, and safety. The current practice utilizes non-physiological delivery methods with less invasive administration routes, offering glycemic stability and therapeutic effectiveness. This review focuses on the recent advances made and future perspectives envisioned about newer insulin therapies and delivery methods that tend to improve the management of diabetes by inculcating ideas to reduce the disease's severity and improve the quality of life.

Purification of exopolysaccharide produced from Lactobacillus spp. using ionic-liquid as adjuvant in alcohol/salt-based aqueous two-phase system for its antidiabetic properties

Diabetes is a chronic, metabolic disease characterized by hyperglycemia resulting from either insufficient insulin production or impaired cellular response to insulin. Exopolysaccharides (EPS) produced by Lactobacillus spp. demonstrated promising therapeutic potential in terms of their anti-diabetic properties. Extraction and purification of EPS produced by Lactobacillus acidophilus and Limosilactobacillus reuteri were performed using ethanol precipitation, followed by alcohol/salt based aqueous two-phase system (ATPS). The purification process involved ethanol precipitation followed by an alcohol/salt-based ATPS. The study systematically investigated various purification parameters in ATPS, including ethanol concentration, type and concentration of ionic liquid, type and concentration of salt and pH of salt. Purified EPS contents from L. acidophilus (63.30 μg/mL) and L. reuteri (146.48 μg/mL) were obtained under optimum conditions of ATPS which consisted of 30 % (w/w) ethanol, 25 % (w/w) dipotassium hydrogen phosphate at pH 10 and 2 % (w/w) 1-butyl-3-methylimidazolium octyl sulfate. The extracted EPS content was determined using phenol sulphuric acid method. In α-amylase inhibition tests, the inhibitory rate was found to be 92.52 % (L. reuteri) and 90.64 % (L. acidophilus), while in α-glucosidase inhibition tests, the inhibitory rate was 73.58 % (L. reuteri) and 68.77 % (L. acidophilus), based on the optimized parameters selected in ATPS. These results suggest that the purified EPS derived from the postbiotics of Lactobacillus spp. hold promise as potential antidiabetic agents.

Diabetes Disease Detection Classification Using Light Gradient Boosting (LightGBM) With Hyperparameter Tuning

Diabetes is a condition caused by an imbalance between the need for insulin in the body and insufficient insulin production by the pancreas, causing an increase in blood sugar concentration. This study aims to find the best classification performance on diabetes datasets with the LightGBM method. The dataset used consists of 768 rows and 9 columns, with target values of 0 and 1. In this study, resampling is applied to overcome data imbalance using SMOTE and perform hyperparameter optimization. Model evaluation is performed using confusion matrix and various metrics such as accuracy, recall, precision and f1-score. This research conducted several tests. In hyperparameter optimization tests using GridSearchCV and RandomSearchCV, the LightGBM method showed good performance. In tests that apply data resampling, the LightGBM method achieves the highest accuracy, namely the LightGBM method with GridSearchCV optimization with the highest accuracy reaching 84%, while LightGBM with RandomSearchCV optimization reaches 82% accuracy.

Computational Modeling of the Interactions between DPP IV and Hemorphins.

Type 2 diabetes is a chronic metabolic disorder characterized by high blood glucose levels due to either insufficient insulin production or ineffective utilization of insulin by the body. The enzyme dipeptidyl peptidase IV (DPP IV) plays a crucial role in degrading incretins that stimulate insulin secretion. Therefore, the inhibition of DPP IV is an established approach for the treatment of diabetes. Hemorphins are a class of short endogenous bioactive peptides produced by the enzymatic degradation of hemoglobin chains. Numerous in vitro and in vivo physiological effects of hemorphins, including DPP IV inhibiting activity, have been documented in different systems and tissues. However, the underlying molecular binding behavior of these peptides with DPP IV remains unknown. Here, computational approaches such as protein-peptide molecular docking and extensive molecular dynamics (MD) simulations were employed to identify the binding pose and stability of peptides in the active site of DPP IV. Findings indicate that hemorphins lacking the hydrophobic residues LVV and VV at the N terminal region strongly bind to the conserved residues in the active site of DPP IV. Furthermore, interactions with these critical residues were sustained throughout the duration of multiple 500 ns MD simulations. Notably, hemorphin 7 showed higher binding affinity and sustained interactions by binding to S1 and S2 pockets of DPP IV.

Assessment of Ferritin Level and Some Biochemical Variables in Iraqi Anemic Diabetic Mellitus in Both Gender

Diabetes mellitus (DM) is a prevalent metabolic disorder characterized by elevated blood sugar levels caused by diminished tissue sensitivity to insulin, insufficient insulin production, or a combination of both factors. This is a non-communicable disease that is also highly prevalent globally. The aim of the study is to evaluate ferritin level and other biochemical variables in type 2 diabetes mellitus patients and study the effect of biochemical variables between male and female (T2DM) . Method involves enrollment of 120 participant divided into two main groups (T2DM and control group) and every group has been subdivided based on gender to (30 male and 30 female) for each group. All the groups studied for determination of anthropometric and biochemical parameters to evaluate the relationship between the studied parameters and T2DM patients. Results: the result showed that (Age, body mass index (BMI), Fasting blood sugar (FBS), triglycerides (TC), Urea, systolic blood pressure (SBP), diastolic blood pressure (DBP) and Ferritin) were significantly difference in T2DM patients (male and female) as compared with control. While ( TG and Creatinine) were non-significantly differ in T2DM patients as compared with control. Furthermore, there was non-significant difference in the studied parameters between T2DM males and females. Conclusion: from the obtained result it could be concluded that patients with T2DM have a problem associated with ferritin level that effected by T2DM and decreased because of the impaired renal dysfunction and red blood cells decrease and that causes a decrease in the level of ferritin. Age and BMI levels have a direct effect and correlated with elevation of lipid continents and high level of FBS and blood pressure. Renal function is highly elevated because of the T2DM complication, and it is conceder as a risk factor for T2DM.

In vitro and in-silico Anti-diabetic Evaluation of the Combination of Annona squamosa Linn., Leaf Extract and Oleanolic Acid

Background:: Diabetes mellitus (DM) is a metabolic disorder caused by insufficient insulin production from pancreatic β-cells or insulin resistance; its prevalence rapidly increases worldwide. Increasing reports indicate that most plant bioactive agents exhibited alternative and safe effects in managing DM. Objective:: The study aims to evaluate the in vitro antioxidant and anti-diabetic efficacy of the combination of Annona squamosa Linn. (AS) leaf extract and Oleanolic acid (OA) using in vitro and in-silico approaches. Methods:: The leaf of AS was extracted by soxhlet extraction using n-hexane and methanol. The methanol extract of AS (MEAS) was subjected to GC-MS analysis. Quantification of total phenolic and flavonoid content and OA were carried out by HPLC and HPTLC analysis, respectively. In vitro antioxidant (DPPH, NO, and H2O2) and anti-diabetic (α-amylase and α-glucosidase) potentials of MEAS, OA, and a combination of MEAS and OA (MEAS + OA) were studied at different concentrations using ascorbic acid and acarbose as standard, respectively. An in-silico study determined their binding interactions with α-amylase (PDB ID-1B2Y) and α-glucosidase (PDB ID-3W37). Conclusion:: We found that the combination of MEAS + OA exhibited the highest in vitro antioxidant and anti-diabetic activities compared to MEAS and OA. It concluded that OA has a significant role in potentiating the anti-diabetic effect of A. squamosa. Results:: GC-MS analysis of MEAS revealed three major bioactives like bicyclo[7.2.0]undec-4- ene, 4,11,11-trimethyl-8-methylene-,[1R-(1R*,4Z,9S*)]-, germacrene D and undecane. The highest amount of phenolic (tannic acid and gallic acid) (150 μg/ml) and flavonoid (rutin and quercetin) (40 μg/ml) compounds were found in MEAS. OA was quantified as 356.74 ng/ml in MEAS by HPTLC. The significant inhibitory effects of MEAS, OA, and (MEAS + OA) on free radicals and α-amylase and α-glucosidase were observed concentration-dependent. However, MEAS + OA exhibited a greater percentage of inhibition than MEAS and OA alone. The in-silico analysis revealed highest docking-score of OA (-9.8 & -8.8), Germacrene D (-7.5 & -6.5) and Bicyclo[ 7.2.0]undec-4-ene, 4,11,11-trimethyl-8-methylene-,[1R-(1R*,4Z,9S*)]-, (-7.0 & -6.4) against IB2Y and 3W37 proteins, respectively.

Continuous six-month follow-up of patients with diabetes and hypertension: how to bring those people to the health unit?

Affecting almost 20% of the population, Systemic Arterial Hypertension is a chronic disease characterized by high blood pressure levels, influenced by genetic, epigenetic, social and environmental factors. Diabetes Mellitus (DM) is characterized by insufficient production and/or malabsorption of insulin. Both are directly related, especially when without good control, with poor outcomes in several other pathologies. This is a descriptive study, of the experience report type, developed at the Basic Health Unit Metódio de Araújo Leitão, in the Municipality of Patos-PB, in the second half of 2022, using the problematizing method proposed by Arco de Charles Maguerez as a guide. After applying the method, it was seen that there was a deficit in the unit in the performance indicators proposed by the Ministry of Health, regarding the monitoring of hypertensive and diabetic patients, and this was the key point of application of the work, in order to bring these patients for follow-up in the unit. Focusing mainly on our initial problem, related to hypertensive and diabetic patients, we saw a jump in the period in question with the application, leaving a follow-up percentage of 30% for hypertensive and 34% for diabetics in the month of June 6/2022, with a peak in the month of August 8/2022, when we reached 53% of hypertensives and 56% of hypertensives, maintaining good levels until the end of the year. Thus, the Arch of Maguerez proved to be, as a method, resolving issues such as the one we applied, mainly due to its steps of easy application and systematization. As well as we saw that the active search is the main way to bring these patients to age for follow-up, as well as the need for participation of the entire team.

Detection of type-Ⅱ diabetes using graphene-based biosensors

Diabetes is a global pandemic that increases the risk of various health complications, including heart attacks, renal failure, blindness, stroke, and peripheral neuropathy. Type-2 diabetes (T2D) results from an imbalance in lipid and glucose metabolism due to hostility to insulin action and insufficient insulin production response. Valine amino acid has been identified as a potential biomarker for T2D, but there have been no rigorous studies on its interaction with branch chain amino acids. In this study, we investigated the potential of graphene/modified graphene as a valine biosensor using density functional theory to examine the electronic properties and adsorption behaviour of graphene, Si-doped graphene (SiG), and P-doped graphene (PG). The adsorption of valine over the substrates was physisorption in nature, and the adsorption energies were in the order of SiG > G > PG. Density of states (DOS) and partial DOS calculations confirmed the molecule’s adsorption over the monolayers and indicated variations in the electronic properties. We also performed recovery time calculations to examine the reusability of the nano-surfaces as potential biosensors. Ultrafast recovery times were predicted for all three systems, with SiG showing the best results. Our study suggests that SiG could be used as a biosensor for valine, providing a real-time and efficient diagnostic tool for T2D.

Mechanistic Insight of Innovative Biomarkers for Screening of Type II Diabetes Mellitus

Abstract: Diabetes Mellitus (DM) is a compounded, persistent illness symbolized by an increased range of glucose levels in the blood caused by cellular resistance to insulin action, insufficient insulin production by pancreatic -cells, or both. Type 1 Diabetes Mellitus (T1DM), the extremely widespread form of DM, is recorded for almost 85-90% of worldwide cases. T2DM is mostly common in middle-aged and older people, and its causes are multifaceted. The use of efficient and profitable solutions for DM screening is critical to ensure pre-identification and minimising patients' risk of acquiring the life-compromising illness. Identification of innovative biomarkers with test methods of DM is therefore critical in order to establish vigorous, non-invasive, pain-free, highly sensitive, and precise procedures for screening. The purpose of this review article is to mention and review all the necessary biomarkers that play a vital role in disease diagnosis and to highlight the present-day findings of the latest clinically validated and traditional biomarkers and procedures for determining them, which provide cost-efficient options for T2DM screening with early detection. It is concluded that various biomarkers, both conventional and innovative, go hand in hand to diagnose the DM of any type.

Impact of Non-Surgical Periodontal Therapy on HbA1c Levels of Type 2 Diabetes at Khyber College of Dentistry, Peshawar: A Quasi-Experimental Study

Background: Diabetes mellitus (DM) constitutes a spectrum of persistent metabolic disorders originating from irregular glucose metabolism due to insufficient insulin production, compromised insulin function, or a combination of the two, leading to elevated blood sugar levels. This study assessed the influence of non-surgical periodontal therapy on glycated hemoglobin (HbA1c) levels in individuals with Type 2 Diabetes. Methods: This quasi-experimental study was conducted at Khyber College of Dentistry, Peshawar, with 40 participants diagnosed with Type 2 diabetes for more than 3 years and suffering from chronic periodontitis. Patients with Type 1 diabetes, pregnant women, and those with other medical complications were excluded. HbA1c levels were assessed at baseline, and non-surgical periodontal therapy was administered. Patients were educated and scheduled for a follow-up after 3 months when HbA1c levels were reassessed. A paired t-test was employed to compare HbA1c levels pre-and post-intervention. Results: In this study, males were 29 (58%) whereas 21 (42%) females. The mean value of HbA1c was less post-treatment with non-surgical periodontal therapy (6.928±0.866%) than pretreatment (9.048±0.73%) and results were statistically significant (p<0.001). After receiving therapy, the HbA1c level was significantly reduced in both genders (p<0.001). Irrespective of smoking status, the reduction of HbA1c level was statistically significant (p=0.01). Conclusion: Non-surgical periodontal treatment significantly reduces HbA1c levels, indicating a potentially positive impact on glycemic control in individuals with diabetes, which has enhanced the dentist’s role as a part of the health team, and timely interventions can significantly improve prognosis.

TOWARDS UNDERSTANDING NATURAL ALPHA-GLUCOSIDASE INHIBITORS: A COMPUTATIONAL STUDY

Objective: Diabetes mellitus is a metabolic disorder affecting hundreds of millions of people around the world. It is characterized by hyperglycemia caused by impaired glucose homeostasis that results from insufficient insulin production or insulin resistance. There are clinically available α-glucosidase inhibitor drugs that are used to decrease postprandial blood glucose level. However, these drugs have side effects that necessitated the discovery of new α-glucosidase inhibitors with less side effects and high potency. The interest in the use of natural products to deal with diabetes has been increasing. Therefore, the potential of natural α-glucosidase inhibitors to inhibit the enzyme was investigated through computational methods. Material and Method: The binding potential of selected natural α-glucosidase inhibitors was investigated through molecular docking. Thereafter, the stability of the complexes with the highest binding potential were assessed through molecular dynamics (MD) simulation. Result and Discussion: The molecular docking demonstrated that compound 2 had better binding potential than the standard drug, acarbose. Compound 7 had comparable binding potential to the standard drug. Furthermore, all the tested compounds exhibited a reasonable binding potential towards the enzyme but were weaker than the standard drug. The MD simulation demonstrated that compounds 2 and 7 gave complexes with similar stability to the standard drug. The overall computational results revealed that the natural inhibitors investigated had the ability to bind to the enzyme and formed stable complexes. Therefore, these compounds could be potential α-glucosidase inhibitors for clinical use. For this reason, further in vitro investigations on compounds with the highest binding potential is recommended.

Does vitamin D supplementation benefit patients with type 1 diabetes mellitus who are vitamin D deficient? A study was performed at the Sudan Childhood Diabetes Center from 2019 to 2022.

Type 1 diabetes mellitus is a chronic autoimmune disease caused by insufficient production of insulin. Many studies have linked type 1 diabetes mellitus to vitamin D3 deficiency. We investigated the prevalence of vitamin D deficiency in Sudanese children and adolescents with type 1 diabetes mellitus and assessed the impact of vitamin D deficiency treatment on their glycemic control. In 2019-2022, we conducted a quasi-experimental study on 115 children with type 1 diabetes mellitus (1-19 years old) at the Sudan Childhood Diabetes Center. Vitamin D supplements were given orally to deficient patients for 3 months. The concentrations of hemoglobin A1c, fasting blood glucose, insulin dosage, and vitamin D (25-hydroxyvitamin D (25(OH)D)) were measured before and after vitamin D3 administration. One-way ANOVA and paired sample t-tests were used to evaluate the effect of supplementation. Only 27% of type 1 diabetes mellitus children were deficient in vitamin D, whereas 31.1% were inadequate and 40.9% were sufficient. The administration of vitamin D supplements slightly improved hemoglobin A1c levels in 67.7% of the patients, but the difference was not significant (mean 10.8 ± 2.1% before, 10.1 ± 2.5% after, p0.05 = 0.199). However, there was a significant decrease in the fasting blood glucose level (mean: 174.978.5-136.759.1 ng/ml; p0.05 = 0.049). Vitamin D levels were significantly increased after treatment (mean = 49.6 ng/mL; t-test = -11.6, 95% CI 40.8-(-28.6); p0.05 = 0.000). After vitamin D3 supplementation, 25.8% of individuals changed their insulin dosage; however, there was no significant variation in insulin needs. The prevalence of vitamin D deficiency in children and adolescents with type 1 diabetes mellitus in Sudan is relatively high; incorporating vitamin D supplements in their treatment plan may improve their glycemic control.

In silico molecular design phytoconstituents of Syzygium cumini (Indian Plum) as an antidiabetic scaffold

Insufficient insulin production results in hyperglycemia, a metabolic disorder known as diabetes mellitus (DM). The medical establishment still struggles to treat DM effectively and without side effects. As a result, there is an increase in demand for natural remedies with antidiabetic properties and few negative effects. The plant Syzygium cumini, which is used in traditional herbal medicine, is thought to have a variety of pharmacological properties. It also contains various additional chemical components in addition to terpenoids, tannins, anthocyanins, flavonoids, and other phenolic compounds. It has been hypothesized that S. cumini has antidiabetic characteristics because of the plant\'s high polyphenol and flavonoid content. The main aim of the current study was In silico test of the antidiabetic effects of various polyphenol-rich compounds such as anthocyanin (1), delphinidin (2), malvidin (3), petunidin (4), ellagic acid (5), gallic acid (6), myricetin (7), kaempherol (8), quercetin (9), betulinic acid (10), caffeic acid (11) and ferulic acid (12) from S. cumini against many targets related with DM to investigate the antidiabetic effect and prediction of the binding mode using molecular docking studies. These phytochemicals were chosen using computational techniques, and their ability to inhibit α-amylase was tested. These phytoconstituents have shown a lower α-amylase inhibitory activity capacity than voglibose. These phytochemicals additionally showed high binding energies, hydrogen bonds, and protein-ligand interactions. Additionally, these phytoconstituents\' ADMET characteristics met the safety requirements. As a result, S. cumini might be effective in the treatment of DM. The possible bioactive compounds discovered in this study could be used to develop diabetes-led medications. Serum levels of triglycerides and total cholesterol were also markedly decreased. The findings suggest that S. cumini may have pharmacological promise as a natural antidiabetic drug. The design and development of powerful, multi-functional candidate medications with few side effects for DM treatments can make use of this information.

Effect of Ethanol Extract of Sala (Cynometra ramiflora Linn.) leaves for reducing Blood Glucose Levels in Male Wistar Rats Induced by Alloxan

Diabetes is a chronic illness brought on by either insufficient insulin production by the pancreas or inefficient insulin utilisation by the body. There is currently no proven diabetes treatment. The development of current therapies is solely focused on preserving and enhancing patients' quality of life. In addition to utilising oral hypoglycemic medications or insulin injections to control blood sugar, several medicinal plants have also undergone pharmacological testing and can be used experimentally. There were 5groups, namely negative control group (0.5% Na CMC), positive control (glibenclamide 0.9mg/kg), ethanol extract of Sala leaves at doses of 250, 500, and 1000mg/kgBW. Each group was induced by alloxan 150mg/kg intraperitoneal body weight before fasting blood glucose levels 200mg/dL. Each group's blood glucose levels were taken and checked as pre alloxan, post alloxan, 7 and 10 days after treatment. The results showed that the ethanol extract of Sala leaves with doses of 250, 500, and 1000mg/kg for 7 and 10 days after treatment showed glucose-lowering activity (p<0.05) on all alloxan induction. The ethanolic extract of the leaves of the Sala plant (Cynometra ramiflora Linn.) with doses of 250, 500, and 1000mg/kgBW for 7 and 10 days showed significant glucose-lowering activity (p<0 0.05) in alloxan-induced mice.

Exploring User Experience and Usability of mHealth applications for people with diabetes: An Evaluation Study Using UEQ and HE4EH Checklist

Diabetes affects 10% of adults aged 20 to 79 globally, caused by insufficient insulin production. Mobile apps are effective in managing this chronic condition. This study aimed to investigate the experience of using mobile applications for healthcare patients with diabetes in their daily activities. Ten participants diagnosed with diabetes were invited to evaluate five Android mobile applications using a predefined protocol. We used the User Experience Questionnaire (UEQ) to assess the usability and user experience of the apps. In addition, the Heuristic Evaluations for mHealth (HE4EH) checklist was used by three domain experts to assess the apps. The results showed that one of the five apps had issues with the stimulus and novelty aspects of user experience. Among all the apps, the novelty had the lowest Likert scale value. While most apps had creative designs and attractive interfaces, the heuristic assessment revealed many violations and concerns about instruction and suitability due to a lack of crucial information for monitoring user routines.

Genetic association of PTPN22 polymorphisms with Type 1 diabetes in Pakistani children

ObjectiveType 1 diabetes, a multigenic autoimmune disorder, is caused by the destruction of pancreatic beta-cells leads to insufficient insulin production and hyperglycemia, resulting in early morbidities and mortality. This study was designed to explore the genetic association of PTPN22 gene polymorphisms with T1D susceptibility among Pakistani children. MethodsBlood samples of T1D patients were obtained from the Department of Diabetes and Endocrinology of Children Hospital & University of Child Health Sciences, Lahore. Genotyping of rs2476601, rs1310182, and rs1217414of the PTPN22 gene was performed by Tetra ARMS-PCR assay. Statistically, binary logistic regression was applied to determine variation in genotype distribution and association of PTPN22 gene polymorphism with T1D. ResultsGenetic analysis showed that the A allele of rs2476601 (OR = 0.53, 95 % CI = 0.31–0.90; P < 0.02) and T allele of rs1310182 was found to be risk allele for T1D development (OR = 0.51, 95 % CI = 0.36–0.76; P < 0.01) while the A allele of rs1217414 was a protective allele against T1D (OR = 1.19, 95 % CI = 0.80–1.77; P = 0.36). Genetic models revealed that GG genotypes of rs2476601 (OR = 2.01, 95 % CI = 1.13–3.58; P < 0.01), and AA genotypes of rs1310182 in the dominant model (OR = 1.83, 95 % CI = 1.03–3.24; P < 0.03) showed significant risk association with T1D. ConclusionFrom the results, it is concluded that PTPN22gene has a strong genetic association with SNP rs2476601andrs1310182with T1D in Pakistani children.

Nanomaterials Drug Delivery System in Herbal Formulation For Antidiabetic Activity: A Review

Diabetes, a chronic metabolic disorder, results in high blood glucose levels due to insufficient insulin production or ineffective insulin utilization. Management entails lifestyle changes, exercise, and medication adherence. Monitoring blood sugar levels, healthy eating, and medication are crucial for controlling diabetes and preventing complications. The illness, currently incurable, necessitates management strategies for regulation. Medical treatments are expensive and require long-term adherence, leading many, especially those from low-income nations, to resort to herbal remedies. However, phytocompounds, though promising, often suffer from limited bioavailability due to poor solubility, permeability, or rapid elimination. Plant nanomedicines offer a promising avenue to address these challenges and alleviate the financial strain on disadvantaged populations. Encapsulated treatments using plant extracts or antidiabetic chemicals at the nanoscale have shown promising results. Our study aims to provide a thorough examination of lipid- and inorganic-based nanoparticulate delivery systems combined with plant extracts or phytocompounds for diabetes management Our analysis will highlight both the advantages and limitations of these systems for future clinical application. Examined studies revealed that nanoparticulate formulations displayed strong antidiabetic effects at lower doses compared to individual plant extracts or phytocompounds. Additionally, nanoparticulate systems have effectively addressed the issue of low bioavailability in herbal medications, showing promise for enhanced therapeutic outcomes.

Diabetes mellitus: A lifestyle disorder

Diabetes mellitus is a chronic metabolic disorder characterized by elevated blood glucose levels, resulting from either insufficient insulin production or the body's inability to effectively utilize insulin. This condition has become a global health concern, with an increasing prevalence worldwide. This review article aims to provide an overview of diabetes mellitus, its diagnostic criteria, and recent updates in management, complications, and a discussion on its implications for public health.