Monitoring Of Glucose Levels Research Articles

DIGITAL INTERVENTIONS ROLE IN SELF-MANAGEMENT OF DIABETES MELLITUS (DM): A NARRATIVE REVIEW

Diabetes mellitus (DM) is a chronic illness that raises morbidity and death rates worldwide and substantially negatively influences patients quality of life. Health authorities now teach patients and their families good behavioral changes and self-management techniques to enhance clinical therapy and lower complications. Diabetes requires a complicated set of everyday habits to manage, such as regular blood glucose testing, eating a balanced diet, exercising, and keeping a healthy weight. Patients now have the tools they need to effectively manage their conditions thanks to the development of e-health and mobile health (mHealth). These technologies help optimize and monitor glucose levels and have been warmly received. A meta-analysis by Captieux M., et al. (2016) found that application users attitudes toward self-care had improved, and their HbA1c levels had significantly decreased. The efficacy of these technologies varies, though, and obstacles to their broad adoption include low levels of literacy, privacy and security concerns, economic constraints, and the requirement for standardization across various demographics and technical platforms. Notwithstanding these obstacles, digital approaches have demonstrated the potential to enhance diabetic self-management and clinical outcomes. These include telemedicine, mobile health applications, and e-health systems. Applications like BlueStar and OneTouch Reveal, as well as programs like My Diabetes Coach, have been shown to improve glucose management and health-related quality of life. However, more investigation and creation are required to improve the usability and accessibility of these digital health solutions. This narrative review examines the current state of digital health technologies for diabetes self-management and highlights their benefits and drawbacks. It emphasizes the importance of addressing current challenges to fully achieve the potential of digital interventions in controlling type 2 diabetes and type 1 diabetes.

Predictors of New Onset Dysglycemia Post COVID-19

Abstract Background: Hyperglycemia postcoronavirus disease 2019 (COVID-19) infection is multifactorial and warrants serial monitoring of blood glucose. Aims: The aim of the study was to assess the incidence of glucose intolerance and new-onset diabetes in patients who have recovered from COVID-19 and to correlate the risk factors for dysglycemia. Materials and Methods: Participants who were admitted for COVID-19 infection at St John’s Hospital, Bangalore, India, at least 6 months previously were enrolled in the study using telephonic interviews. Details regarding age, gender, weight, and history were obtained. Laboratory parameters such as fasting blood sugar, postprandial blood sugar, and glycated hemoglobin (HbA1c) were obtained. The comparison between the dysglycemia and normoglycemia groups was conducted using either independent samples t-test or Mann–Whitney U test and using the Chi-squared test or Fisher’s exact test, as appropriate. Multivariate logistic regression was employed to determine the adjusted effect of multiple variables on dysglycemia. Results: The overall mean age of the study group was 40 ± 13 years with 68 (52%) male patients. Of the 131 patients, 93 had mild, 32 had moderate, and 6 had severe COVID-19. Thirty-nine percent of patients who were previously infected with COVID-19 were found to have elevated fasting, postprandial, or HbA1c levels. Of these, 3.1% were diagnosed as having type 2 diabetes mellitus and 9.9% had prediabetes. On excluding those with an isolated HbA1c elevation and comparing prediabetes and diabetes with normoglycemia, age, overweight and obese range body mass index (BMI), and the presence of comorbidities showed significant association (P < 0.05). Age (odds ratio [OR] 1.06, 95% confidence interval [CI] 1.02–1.11), the presence of comorbidities (OR 4.84, 95% CI 1.49–15.5), and overweight and obese range BMI (OR 3.48, 95% CI 1.13–13.1) were associated with dysglycemia on multivariate logistic regression analysis. Conclusion: A high incidence of dysglycemia over 6 months post-COVID-19 infection, especially in those with higher BMI, comorbidities, and in those in the middle age group warrants serial long-term monitoring of glucose levels in view of the high background risk of diabetes in the Asian population.

Comparison of HPLC with Tinia for hba1c measurement in diabetic and non-diabetic patient; a study from a tertiary care hospital of Baluchistan

Background: Diabetes mellitus, a disease with serious implications on human health ,is on continuous rise. It is the need of hour to find and establish the markers to measure its long term effects. Diabetes Control and Complication Trial has confirmed role of HbA1c in effective blood glucose level monitoring .New methods are being developed for measurement of HbA1c .Before using these methods, it is essential to analyze these methods for reliability .Therefore in our study we will compare and evaluate correlation and agreement between Turbidimetric Inhibition Immunoassay TINIA and High Performance Liquid Chromatography HPLC for HbA1c measurement. Methods: Three hundred and ninety-four samples were analyzed simultaneously on Roche Cobas 501 and D-10 HbA1c analyzer by Bio-Rad for HbA1c level estimation. The results were entered into SPSS version 26 for method validation. This is an observational study conducted at Department of Pathology CMH Quetta from January 2022 to August 2023. Results: Inter and intra run coefficient of variation (CV) was within the allowable limit of 2 %. Mean difference between results of Turbidimetric Inhibition Immunoassay TINIA and High-Performance Liquid Chromatography HPLC was very low with HPLC results slightly on higher side. For low control TINIA had between run CV of 0.3% (Low control) 0.8 % (High control) and within run CV of 0.3% (Low control), 0.6% (High control); while HPLC had between run CV of 0.6%(Low control) 1.8% (High control), and within run CV of 0.3% (Low control), and 0.7%. Linear regression analysis displayed high concordance and agreement between the results of both methods. Conclusion: Both methods have comparable results in HbA1c estimation.

Evaluation of Proteinuria in Diabetic Patients Attending Gitwe District Hospital

Background: Proteinuria is a serious condition in which body proteins leak into urine due to kidney dysfunction nearly always caused by elevated serum glucose due to prolonged diabetes. Proteinuria occurs and affects individuals with diabetes, approximately 25% of diabetic patients may have proteinuria. Aim: The aim of this study is to assess the levels of serum glucose and proteins in urine and their association in diabetic patients attending Gitwe District Hospital. Methodology: The study population mainly comprised of diabetic patients, 110 study participants were included. Blood was drawn from finger for testing serum glucose using glucometer and urine samples were collected from diabetic patients and samples were analyzed in parasitology service using urine chemistry strips for proteins in urine testing. Results: During data analysis, SPSS version 22 was used, the majority were female with 66.4% while 33.6% were male. The highest frequency of participants presented in this study were 27(24.6%) found in [43-52] years old. The mean age was 53 years ± 13.882 SD (ranged from 23 to 86 years). 53.7% had normal serum glucose level while 46.3% had high serum glucose level. However, 63.6% had no proteinuria while 20.0% had trace, 11.8% had mild and 4.6% had moderate proteinuria levels. Association between serum glucose and proteinuria was analyzed for statistical significance with Chi-square test and it was statistically significant as their p values were below 0.05(P-value of 0.000). Conclusion: The study found that high serum glucose level leads to the presence of proteins in the urine. This study recommends regular monitoring of serum glucose levels, as well as routine urine tests to detect proteinuria as essential components of diabetes management and other researchers to study the effects of proteinuria in diabetic patients. Keywords: Proteinuria, serum glucose, proteins, diabetic patients

Real-world evidence of effectiveness and safety of pasireotide in the treatment of acromegaly: a systematic review and meta-analysis.

Pasireotide long-acting release (PAS-LAR) is a second-generation somatostatin receptor ligand (SRL) approved for acromegaly treatment. This meta-analysis aimed to evaluate the real-world effectiveness and safety of PAS-LAR in patients with acromegaly resistant to first-generation somatostatin receptor ligands (fgSRL). A systematic literature search was conducted in PubMed and Web of Science for real-world studies on PAS-LAR in acromegaly published between 2014 and 2023. Random-effects meta-analyses were performed on biochemical control rates, tumor shrinkage, and metabolic parameters. Twelve studies comprising 409 patients were included. The pooled rate of insulin-like growth factor 1 (IGF-1) control was 57.9% [95% CI: 48.4-66.8] and the percentage of patients with tumor shrinkage was 33.3% [95%CI: 19.7-50.4]. Significant reductions were observed in growth hormone standardized mean difference (SMD) 0.6 ng/mL [95% CI: 0.3 to 1.0] and IGF-1 levels SMD 0.9 ULN [95% CI: 0.4 to 1.4]. However, as expected, a worsening in glucose metabolism was noted as an increase in fasting glucose SMD - 0.8mg/dL [95% CI: -1.0 to -0.5, p < 0.01], glycated hemoglobin SMD - 0.5% [95% CI: -0.7 to -0.2]. and type 2 diabetes mellitus prevalence SMD - 11.5% (95% CI: -17.5 to -5.5). PAS-LAR demonstrated higher effectiveness in real-world settings, with over 60% of patients achieving IGF-1 control compared to the around 30% efficacy observed in clinical trials. These findings suggest that PAS-LAR is an effective option for acromegaly patients resistant to fgSRL, but careful monitoring of glucose levels is essential. The high heterogeneity observed across studies emphasizes the need for identifying PAS-LAR response biomarkers to set-up individualized treatment approaches for optimizing patient outcomes.

RSSDI Expert Consensus for Optimal Glucose Monitoring in Diabetes Mellitus in India and Recommendations for Clinical Practice

Effective management of diabetes warrants accurate and regular monitoring of glucose levels to prevent serious diabetes-associated complications and long-term health consequences. Hence, achieving target glucose levels and maintaining adequate glycemic control is essential in diabetes to prevent episodes of hypoglycemia. Multiple glucose monitoring tools have been developed over time, each with differing degrees of precision, convenience, and real-time input. The management of diabetes has been considerably improved by using these various glucose monitoring devices, both in terms of treatment outcomes and patient quality of life. However, in India, the lack of awareness, patient education, availability, and affordability of glucose monitoring devices poses a significant concern resulting in poor adherence to glucose monitoring and low medication adherence thereby increasing disease burden. Consequently, there is an urgent need for healthcare professionals and policymakers in India to collaborate and address the challenges associated with glucose monitoring in patients with diabetes, while also providing recommendations to overcome these concerns. This recommendation article offers a comprehensive evaluation of various glucose monitoring tools available for use by patients suffering from different types of diabetes, explores the challenges associated with glucose monitoring in India, and also presents expert panel recommendations to enhance diabetes management effectively.

Metabolic and Endocrine Correlates of Subclinical Hypothyroidism in Young Adults with First-Episode and Drug-Naive Major Depressive Disorder

BackgroundMajor Depressive Disorder (MDD) is often associated with Subclinical Hypothyroidism (SCH), but the clinical and biochemical characteristics in young, first-episode, drug-naive patients remain unclear. This study aims to examine the prevalence and clinical correlates of SCH in this population to enhance screening and management strategies. MethodA cross-sectional study included 917 young Chinese patients (aged 18-35) diagnosed with first-episode, drug-naive MDD. Comprehensive clinical assessments were conducted, involving demographic data, psychiatric evaluations using the Hamilton Depression Rating Scale (HAMD), Hamilton Anxiety Rating Scale (HAMA), and Positive and Negative Syndrome Scale (PANSS), alongside biochemical measurements such as thyroid stimulating hormone, thyroid peroxidase antibodies (TPOAb), fasting blood glucose (FBG), total cholesterol (TC), high-density lipoprotein cholesterol (HDLC), low-density lipoprotein cholesterol (LDLC), systolic blood pressure (SBP), and diastolic blood pressure (DBP). Binary logistic regression identified clinical correlates of SCH. ResultsThe prevalence of SCH among the study population was 58%. Logistic regression analysis identified significant predictors of SCH, including higher HAMD scores (OR = 1.26), TPOAb (OR = 1.003), FBG (OR = 2.28), TC (OR = 1.66), SBP (OR = 1.11), and DBP (OR = 1.07). In contrast, lower HDLC levels (OR = 0.28) were inversely associated with SCH. ConclusionThe high prevalence of SCH in young, first-episode, drug-naive MDD patients emphasizes the need for comprehensive metabolic and endocrine evaluations. Regular monitoring of thyroid function, glucose levels, blood pressure, and lipid profiles is crucial for early detection and intervention, potentially improving clinical outcomes in this vulnerable group.

Glucose Disorders in Patients With Pheochromocytoma/Paraganglioma: Profile and Influence Effects in a Large Cohort With 705 Patients

ObjectiveSome patients with pheochromocytoma and paraganglioma (PPGL) suffer from glucose metabolic disorders. The aim was to investigate the relationship between glucose metabolic disorders and catecholamine levels in 705 patients with PPGL. MethodsA retrospective analysis was conducted on the clinical data of 705 patients diagnosed with PPGL at Peking Union Medical College Hospital from 2018 to 2023. Bar chart was utilized to depict the manifestations of glycemic disorder across different genders, age groups, body mass index, and catecholamine secretion types. Comparison of characteristics between patients with PPGL who had glycemic disorders and those who did not was conducted. Binary logistic regression analysis coupled with receiver operating characteristic curves was utilized to predict the occurrence of glycemic disorders. ResultsOf 705 patients, 492 patients were diagnosed with glucose abnormalities, while the remaining 213 had normal glucose metabolism. Compared to the normal glycemic group, the glycemic disorder group had significantly higher levels of HOMA-IR and TyG index, as well as higher high density lipoprotein, low-density lipoprotein, and total cholestrol. Univariate logistic regression identified age, norepinephrine, and epinephrine as independent risk factors for the occurrence of glucose metabolism disorders. After adjusting for confounding variables, age at diagnosis and norepinephrine levels both remained significant, confirming their roles as key risk factors. ConclusionsAlmost 70% of the patients manifested disturbances in glucose metabolism, with over one-third diagnosed with diabetes. These findings underscore the pivotal role catecholamines play in metabolic processes and emphasize the imperative need for close monitoring of blood glucose levels in patients with PPGL.

Impact of Cryptosporidium parvum infection on glucose transport and metabolism in calves: Physiological and molecular insight

The present study aimed to assess the impact of Cryptosporidium parvum infection on physiological parameters and glucose metabolism in calves. To achieve this goal, a study was conducted in Moscow, Russia, from 2022 to 2023, involving 20 calves, of which 10 were infected with C. parvum and 10 comprised the control group. Comprehensive methods were employed, including body weight measurement, fecal analysis, histopathological examinations, blood analysis, Using chamber experiments, and molecular analyses. The results of the study revealed a statistically significant decrease in body weight in the infected calf group (P < 0.052) and noticeable changes in fecal consistency changes were noticed on the 4th-day post-infection (P < 0.01). Histopathological examination identified acute catarrhal enteritis and villus atrophy in the small intestine of infected calves. Blood analysis demonstrated a statistically significant decrease in glucose levels in infected calves on the 6th and 7th days post-infection (P < 0.05). However, the results of the Ussing chamber experiments and studies of glucose metabolism genes did not reveal statistically significant differences between the groups. The findings suggest the potential applicability of monitoring body weight, fecal consistency, and glucose levels in the diagnosis and control of cryptosporidiosis in cattle, which could lead to a reduction in economic losses in agriculture.

Exploration of micro-miniature venturi pump for continuous glucose monitoring through interstitial fluid (ISF) extraction

Interstitial Fluid (ISF) is a naturally occurring body fluid that surrounds cells and tissues and contains certain markers useful for continuously monitoring glucose levels in the blood without constantly extracting it. Miniaturizing can reduce its cost and timescale. In this study, for continuous glucose monitoring the numerical analysis of a Polymethyl methacrylate (PMMA) based Micro-miniature venturi pump was carried out to extract Interstitial Fluid (ISF). The numerical study was carried out using COMSOL Multiphysics software by employing the k-ε turbulence model to obtain output as a vacuum at the throat of the pump. The micro-miniature venturi pump was numerically analyzed for several key parameters, including the dimensions of the throat, the convergent angle, and the divergent angle. This study obtained a minimum pressure of 49.41 kPa numerically and 70 kPa experimentally when an input pressure of 252 kPa of compressed air was given to the system. Further influence of turbulence kinetic energy and turbulence dissipation rate on throat pressure output have been notified. Notably, the pressure output reaches its minimum value of 49.41 kPa when the turbulent kinetic energy and the turbulent dissipation rate reach maximum values of 5.0 × 109 m2/s3 and 7000 m2/s2 respectively. As per the previous research findings, for continuous glucose monitoring through ISF extraction, a pressure level of 95 kPa is deemed adequate hence the designed micro-miniature venturi pump is suitable for this application.

Coral-like Ti3C2Tx/PANI Binary Nanocomposite Wearable Enzyme Electrochemical Biosensor for Continuous Monitoring of Human Sweat Glucose

With the continuous advancement of contemporary medical technology, an increasing number of individuals are inclined towards self-monitoring their physiological health information, specifically focusing on monitoring blood glucose levels. However, as an emerging flexible sensing technique, continuous and non-invasive monitoring of glucose in sweat offers a promising alternative to conventional invasive blood tests for measuring blood glucose levels, reducing the risk of infection associated with blood testing. In this study, we fabricated a flexible and wearable electrochemical enzyme sensor based on a two-dimensional Ti3C2Tx MXene nanosheets and coral-like polyaniline (PANI) binary nanocomposite (denoted as Ti3C2Tx/PANI) for continuous, non-invasive, real-time monitoring of sweat glucose. The exceptional conductivity of Ti3C2Tx MXene nanosheets, in conjunction with the mutual doping effect facilitated by coral-like PANI, significantly enhances electrical conductivity and specific surface areas of Ti3C2Tx/PANI. Consequently, the fabricated sensor exhibits remarkable sensitivity (25.16 μA·mM−1·cm−2), a low detection limit of glucose (26 μM), and an extensive detection range (0.05 mM ~ 1.0 mM) in sweat. Due to the dense coral-like structure of Ti3C2Tx/PANI binary nanocomposite, a larger effective area is obtained to offer more active sites for enzyme immobilization and enhancing enzymatic catalytic activity. Moreover, the sensor demonstrates exceptional mechanical performance, enabling a 60° bend in practical applications, thus satisfying the rigorous demands of human sweat detection applications. The results obtained from continuous 60 min in vitro monitoring of sweat glucose levels demonstrate a robust correlation with the data of blood glucose levels collected by a commercial glucose meter. Furthermore, the fabricated Ti3C2Tx/PANI/GOx sensor demonstrated agreement with HPLC findings regarding the actual concentration of added glucose. This study presents an efficient and practical approach for the development of a highly reliable MXene glucose biosensor, enabling stable and long-term monitoring of glucose levels in human sweat.

Enhanced Diabetes Detection and Blood Glucose Prediction Using TinyML-Integrated E-Nose and Breath Analysis: A Novel Approach Combining Synthetic and Real-World Data.

Diabetes mellitus, a chronic condition affecting millions worldwide, necessitates continuous monitoring of blood glucose level (BGL). The increasing prevalence of diabetes has driven the development of non-invasive methods, such as electronic noses (e-noses), for analyzing exhaled breath and detecting biomarkers in volatile organic compounds (VOCs). Effective machine learning models require extensive patient data to ensure accurate BGL predictions, but previous studies have been limited by small sample sizes. This study addresses this limitation by employing conditional generative adversarial networks (CTGAN) to generate synthetic data from real-world tests involving 29 healthy and 29 diabetic participants, resulting in over 14,000 new synthetic samples. These data were used to validate machine learning models for diabetes detection and BGL prediction, integrated into a Tiny Machine Learning (TinyML) e-nose system for real-time analysis. The proposed models achieved an 86% accuracy in BGL identification using LightGBM (Light Gradient Boosting Machine) and a 94.14% accuracy in diabetes detection using Random Forest. These results demonstrate the efficacy of enhancing machine learning models with both real and synthetic data, particularly in non-invasive systems integrating e-noses with TinyML. This study signifies a major advancement in non-invasive diabetes monitoring, underscoring the transformative potential of TinyML-powered e-nose systems in healthcare applications.

Metabolic management of a successful pregnancy and postpartum complications in fructose-1,6-bisphosphatase deficiency.

Fructose-1,6-bisphosphatase (FBPase) deficiency is a rare, inborn error of metabolism, that causes hypoglycemia and lactic acidosis in response to inadequate glucose intake and/or high intakes of fructose, sucrose, or sorbitol. Pregnancy in women with FBPase deficiency puts them at high risk for metabolic decompensation due to increased glucose demands from the growing fetus. Here we report a 31-year-old primipara who was treated starting at 14 weeks gestation with a diet high in complex carbohydrates and low in fructose, sucrose, and sorbitol and close monitoring of glucose levels throughout her pregnancy. She delivered a healthy 2860 g baby at 37 weeks via vaginal delivery with no complications or hypoglycemia. At 5 months postpartum and 5 months of life, the patient and baby are doing well, although the patient experienced an episode of hypoglycemia and lactic acidosis at 4 months postpartum due to the increased metabolic demands of breastfeeding. This report adds to the limited case reports that discuss outcomes and proposed interventions during pregnancy in individuals with FBPase deficiency.

Smart photonic crystal hydrogels for visual glucose monitoring in diabetic wound healing

Diabetes is a global chronic disease that seriously endangers human health and characterized by abnormally high blood glucose levels in the body. Diabetic wounds are common complications which associate with impaired healing process. Biomarkers monitoring of diabetic wounds is of great importance in the diabetes management. However, actual monitoring of biomarkers still largely relies on the complex process and additional sophisticated analytical instruments. In this work, we prepared hydrogels composed of different modules, which were designed to monitor different physiological indicators in diabetic wounds, including glucose levels, pH, and temperature. Glucose monitoring was achieved based on the combination of photonic crystal (PC) structure and glucose-responsive hydrogels. The obtained photonic crystal hydrogels (PCHs) allowed visual monitoring of glucose levels in physiological ranges by readout of intuitive structural color changes of PCHs during glucose-induced swelling and shrinkage. Interestingly, the glucose response of double network PCHs was completed in 15 min, which was twice as fast as single network PCHs, due to the higher volume fraction of glucose-responsive motifs. Moreover, pH sensing was achieved by incorporation of acid-base indicator dyes into hydrogels; and temperature monitoring was obtained by integration of thermochromic powders in hydrogels. These hydrogel modules effectively monitored the physiological levels and dynamic changes of three physiological biomarkers, both in vitro and in vivo during diabetic wound healing process. The multifunctional hydrogels with visual monitoring of biomarkers have great potential in wound-related monitoring and treatment.Graphical abstract

Multilayer patch functionalized microfibrillated cellulosic paper sensor for sweat glucose monitoring.

Electrochemical analysis of glucose monitoring without painful blood collection provides a new noninvasive route for monitoring glucose levels. Thus, in this study, biobased cellulosic papers (methylated and phosphorylated one) based glucose monitoring sensor is developed. To achieve high hydrophilicity, microfibrillated cellulose (MFC) were functionalized using hexokinase mediated phosphorylation (-OH to -[Formula: see text]). The instinctive increased surface charge density from 36.2 ± 3.4 to 118.4 ± 1.2 µmol/g and decrease contact angle (45°-22°) confirms the increased hydrophilicity of paper. Furthermore, functionalized phos-MFC paper increase the capillary flow of sweat, required low quantity (1µl) of sweat for accurate analysis of glucose level. Additionally, chemically induced methyl groups (-CH3) make the sensor more barrier to other chemicals. In addition, a multilayer patch design combined with sensor miniaturization was used to lead to an increase in the efficiency of the sweat collection and sensing processes. Besides, this paper sensor integrated with artificial transdermal drug delivery unit (agarose gel as skin) for monitoring glucose levels in sweat. The patch monitoring system increase the accuracy of sensing with fluctuation in sweat vol. (1-4µl), temperature (20-70 °C), and pH (4.0-7.0). In addition, temperature dependency artificial transdermal delivery (within agarose gel) of drug metformin agrees the measurement accuracy of sensor, called "switch system" without any error. As a result, the reported MFC paper based multi-patch disposable sensing system provides a novel closed-loop solution for the noninvasive sweat-based management of diabetes mellitus.

Feeding and Eating Disorders in Young People with Type 1 Diabetes: A Systematic Review

Eating disorders (ED) are a growing concern among young individuals with type 1 diabetes (T1D), with a prevalence significantly higher than that observed in the general adolescent population. Studies indicate that adolescents with diabetes are 2 to 3 times more likely to develop ED compared to their non-diabetic peers. This increased prevalence is partly attributed to several risk factors unique to this population. Adolescence itself is a major risk factor for ED, characterized by hormonal fluctuations, rising concerns about body image, and experimental eating behaviors. However, for young people with T1D, these risks are exacerbated by additional challenges. Daily diabetes management, which involves rigorous monitoring of blood glucose levels and precise control of dietary intake, can contribute to increased stress and excessive preoccupation with weight and body shape. Furthermore, social expectations and pressures related to glycemic control can reinforce problematic eating behaviors. ED in young diabetics can have severe consequences on diabetes management and overall health. Restrictive eating or purging behaviors can lead to glycemic imbalances, poor diabetes control, and increased risk of associated complications. This can also worsen psychological difficulties, such as anxiety and depression, thereby amplifying the cycle of deteriorating mental and physical health. Effective management of ED in young diabetics requires a multidisciplinary approach. It is crucial to integrate interventions that address not only nutritional and psychological aspects but also the specific challenges related to managing T1D. Early detection and treatment of ED can improve clinical outcomes and promote a better balance between diabetes management and the mental health of patients.

A small-period long-period fiber grating biosensor based on immobilization of concanavalin A on polydopamine nanospheres for simultaneous detection of D-glucose and temperature

The accurate and sensitive monitoring of glucose levels plays a crucial role in diagnosing diabetes in clinical settings. In this work, a novel small-period long-period fiber grating (SP-LPG) biosensor was first developed for D-glucose detection based on the immobilization of concanavalin A (Con A) on polydopamine (PDA) nanospheres. SP-LPG was fabricated by using femtosecond laser direct writing technology and the obtained grating region was only 2.4 mm. The recognition element of D-glucose, Con A, was attached to the surface of PDA nanospheres. Due to the high specific surface area of PDA nanospheres, a large number of binding sites were available for Con A. The as-prepared sensor showed selective and sensitive to D-glucose measurement in the range of 10 nM to 10 mM and a low detection limit of 1.95 nM (S/N = 3) was achieved. Moreover, the small grating period of SP-LPG allows for the observation of Bragg reflection, which can be utilized for temperature sensing. This property eliminates the issue of temperature cross-sensitivity present in refractive index sensing. And the sensor exhibited a thermal sensitivity of 10 pm/°C within the range of 30 ∼ 100 °C. The developed sensor offers a convenient and efficient platform of simultaneously measuring multiple parameters, including target molecules and temperature, for real-time medical monitoring applications.

Factors Affecting Adherence to Medications among Type2 Diabetic Patients Attending Primary Health Care Center in Cairo

Abstract Background Poor adherence to medications is a major public health problem. Non-adherence results in disease progression, reduced functional abilities, a lower quality of life, waste of medication and increase use of medical resources such as nursing homes, hospital visits and hospital admissions. Objective To measure the prevalence and factors affecting adherence to medications among T2DM patients attending primary health care center of Saraya El Quobba. To measure the agreement between MTA (Measure Treatment Adherence) and ARMS (Adherence to Refills and Medications Scale) questionnaires as a tool of measurement treatment adherence. Methods A Cross sectional study was carried out in family medicine center of Saraya El Qubba in Cairo on 130 participants who had been diagnosed T2DM at least3 months before. Patients with T1DM, Patients with known history of psychiatric illness or cognitive impairment, Patients with communication problems such as aphasia, severe hearing or visual impairment were excluded. Selection of participants was done through Systematic Random Sampling. Data about adherence was collected using structured questionnaires MTA (Measurement Treatment Adherence) and ARMS (Adherence to Refills and Medications Scale) questionnaires. Results 62.6% of our sample were females, 38.9% belonged to age group of 60-70 years, 71.8% were married, 29% had intermediate education, 61.8% were unemployed. The level of adherence among patients attending primary health centre of Saraya El Quobba was 65.6%. Many factors were found to affect medication adherence as regular monitoring of blood glucose level (76.7%), regular follow up visit (75.9%), presence of family support (20.6), monthly income (84.2%) and educational level. There was a statistically significant strong negative correlation between the 2 measurements scores (r=-0.8644, p &amp;lt; 0.001). Conclusion The level of adherence was high among the participants in the current study. Many factors were found to affect medication adherence as regular monitoring of blood glucose level, regular follow up visit, presence of family support, monthly income, both high and low educational level. Agreement between to questionnaires was found.

Study on the performance of MoS2/Au composites for non-enzymatic electrochemical glucose detection

Abstract Glucose concentration is considered an indicator for the diagnosis of diabetes, highlighting the importance of accurate glucose detection. Non-enzymatic electrochemical sensors have been extensively studied for glucose detection applications, with nanocomposites composed of molybdenum disulfide (MoS2) and gold nanoparticles (Au NPs) demonstrating high catalytic activity. In this study, a nanocomposite material composed of MoS2 and Au NPs (MoS2/Au) was synthesized and employed to construct a non-enzymatic electrochemical glucose biosensor. The detection limit of this sensor was explored, reaching as low as 1 mM. Additionally, compared to MoS2, the MoS2/Au nanocomposite exhibited a higher linear correlation coefficient and sensitivity, with a linear range of 1–25 mM and a sensitivity of 417.556 μA mM−1 cm−2. The sensor demonstrated excellent performance within the range of human blood glucose concentrations, showing potential for real-time monitoring and precise measurement of glucose levels. Furthermore, it exhibited good stability and reproducibility. These findings indicate the potential applications of MoS2/Au in biosensors and immunoassays.

Sensitivity and accuracy of single-mode fiber optic sensor for non-invasive glucose measurement

Abstract Routine glucose level monitoring is essential for diabetic patients. Standard measurement methods based on finger pricking are inconvenient and susceptible to infection risk. Therefore, simple and non-invasive measurements are urgently needed. Compared to other approaches, glucose level measurement using optical methods offers many advantages, such as set-up simplicity, high sensitivity, and high accuracy. We optimize the sensing performance of glucose level sensors based on a single-mode fiber optic. The fiber optic serves as a medium for light propagation from a diode laser to a photodiode and, at the same time, serves as an active sensing medium. In the sensing area, the fiber optic cladding is partly (i.e., 1 – 5 cm) stripped to allow direct contact, which leads to evanescent wave absorption by glucose molecules. The length of fiber optic stripping and bending radius, which determine the sensitivity and accuracy of the sensor, varied. Bent fiber optic sensors consistently show a higher sensitivity and accuracy than straight ones. Maximum sensitivity of 2.35 mV/(mg/dL) was attained for a 3 cm stripping length and 4.5 cm bending radius. The lowest sensitivity of the sensor, 0.453 mV/(mg/dL), was acquired at the stripping length of 5 cm for the straight fiber optic sensor. The highest accuracy of the developed sensor is 99% for bent fiber optic sensors.