Estimation Of Blood Glucose Levels Research Articles

Accurate blood glucose level monitoring using microwave imaging

Painless and non-invasive detection techniques are needed to replace finger-prick blood collection for people with diabetes. A first-of-its-kind, noninvasive, and continuous blood glucose level (BGL) detection method based on microwave imaging is introduced in this paper. This method avoids the complex task of frequency choice for the design of electromagnetic sensors. A radar-based microwave imaging technology combined with an improved very-deep super-resolution (VDSR-BL) method is presented to obtain high-resolution (HR) microwave images. After image super-resolution reconstruction by VDSR-BL, the peak signal-to-noise ratio and structural similarity index of HR images reach 35.4461 dB and 0.9761, respectively. Then, an ensemble learning strategy based on support vector regression and random forest algorithms is proposed to identify HR microwave images for BGL estimation. The developed detection system has been verified on the medium under tests with different glucose solutions. The final detection results obtain a root mean squared error of 0.1394 mg ml−1 and a mean absolute relative difference of 8.02%, which show good accuracy with clinical acceptance. Meanwhile, we also conducted human trials. A high correlation coefficient (R) of 0.9254 was achieved between the results of microwave imaging and invasive BGL. Together, these results show that microwave imaging offers a promising new approach for noninvasive BGL monitoring.

Comparison between mathematical methods to estimate blood glucose levels from ECG signals

Diabetes mellitus, known as diabetes, is a chronic disease that affects the control of blood glucose concentration levels, it is a disease that mostly affects adults (type 2 diabetes), but it can also occur in children (type 1 or childhood diabetes), as well as in pregnant women (gestational diabetes). Diabetes is one of the diseases with the highest prevalence and high mortality worldwide. Diabetes has no cure, but continuous monitoring to maintain blood glucose levels in normal ranges reduces the possibility of suffering from gastrointestinal problems, vision loss, limb amputations (such as diabetic foot) and damage to vital organs such as the heart and kidneys, among other associated complications. This article compares the results in glucose estimation by using a linear, quadratic and cubic regression considering the electrical characteristics generated in the cardiac conduction (HR, HRV, T-wave peak, and QT interval) recorded on a single-lead electrocardiogram (VII), used as a non-invasive blood glucose estimation model. The best estimate was obtained using a cubic regression. The validation was performed using the Clarke grid having 77.78 % of data in the A zone and 22.22 % in the B zone and a Pearson correlation value of 0.94103 in the cubic regression.

Non-invasive blood glucose estimation method based on the phase delay between oxy- and deoxyhemoglobin using visible and near-infrared spectroscopy.

Many researchers have attempted to estimate blood glucose levels (BGLs) noninvasively using near-infrared (NIR) spectroscopy. However, the optical absorption change induced by blood glucose is weak in the NIR region and often masked by interference from other components such as water and hemoglobin. Instead of using direct optical absorption by glucose, this study proposes an index calculated from oxy- and deoxyhemoglobin signals that shows a good correlation with BGLs while using conventional visible and NIR spectroscopy. The metabolic index, which is based on tissue oxygen consumption, was derived through analytical methods and further verified and reproduced in a series of glucose challenge experiments. Blood glucose estimation units were prototyped by utilizing commercially available smart devices. Our experimental results showed that the phase delay between the oxy- and deoxyhemoglobin signals in near-infrared spectroscopy correlates with BGL measured by a conventional continuous glucose monitor. The proposed method was also confirmed to work well with visible spectroscopy systems based on smartphone cameras. The proposed method also demonstrated excellent repeatability in results from a total of 19 oral challenge tests. This study demonstrated the feasibility of non-invasive glucose monitoring using existing photoplethysmography sensors for pulse oximeters and smartwatches. Evaluating the proposed method in diabetic or unhealthy individuals may serve to further increase its practicality.

Antihyperlipidemic and antioxidant effects of biogenic copper oxide nanoparticles in diabetic rats

Diabetes mellitus is often associated with metabolic disorders like hyperglycemia, hyperlipidemia, oxidative stress and obesity. Health problems related to these disorders are on a rise globally. Although nanotechnology based approaches have been explored for diabetes treatment, specific information on alleviation of the associated health problem is scanty. Here we report the beneficial effects of copper oxide nanoparticles (CuOnpls) for the control of hyperlipidemia and oxidative stress caused due to diabetes. Wistar rats were fasted overnight and type 2 diabetes was induced by intraperitoneal (i.p.) injection of freshly prepared nicotinamide followed by streptozotocin. Induction of diabetes was confirmed by estimation of blood glucose levels of the animals. Estimation of total cholesterol (TC), triglyceride (TG), low density lipoprotein (LDL) and high density lipoprotein-cholesterol (HDL-c) from the serum was carried out for ascertaining hyperlipidemia. Biogenic CuOnpls (spherical, 88.25 nm diameter) capped with bud extract of Syzygium aromaticum and α-tocopherol were administered in the animals per-oral route. Superoxide dismutase (SOD), catalase (CAT) and glutathione (GSH) levels were determined to evaluate antioxidant activity of CuOnpls. The nanoparticles were characterized for surface chemical groups by FTIR and HRLC-MS. The nanoparticles revealed novel surface groups responsible for site-specific delivery and beneficial effects. Diabetic rats showed enhanced serum BG, TC, TG and LDL levels and reduction in levels of HDL-c, SOD, CAT and GSH. Administration of CuOnpls caused significant reversal of the effects of diabetes on lipid profile and oxidative stress enzymes. The results pointed to the beneficial effects of CuOnpls for management of post-diabetes complications.

The effect of high ethanol concentration on E-nose response for diabetes detection in exhaled breath: Laboratory studies

The growing number of people with diabetes has paved the way for the creation of non-invasive devices to measure blood glucose levels and enable the detection of diabetes non-invasively, for example, through exhaled air. This paper presents the development of a breath detection system using multiple sensors (e-nose system) for the non-invasive estimation of blood glucose levels. The system employs commercially available ethanol, CO2, and acetone sensors, with synthetic breath mixtures used for testing, including four scenarios with high ethanol concentrations (0–570 ppm) as an influence factor. Results showed a mean absolute error of 0.245 ppm when analysing commonly observed acetone concentrations in diabetic breath using four sensors and the XGBoost Regressor. In mixtures with high ethanol concentrations and varying acetone concentrations (0–8.62 ppm), the CatBoost Regressor outperformed other machine learning algorithms with a mean absolute error of 0.568 ppm. This study emphasises the significant impact of ethanol on acetone detection and the need to consider ethanol levels in the developing of non-invasive devices for blood glucose prediction based on the exhaled acetone measurements. The research shows that a set of three gas sensors is optimal for estimating acetone concentrations in gas mixtures. The presented results constitute a preliminary step towards developing a non-invasive device for estimating blood glucose levels based on breath analysis, with the novelty of considering alcohol intake as a potential influencing factor.

EMD-Based Noninvasive Blood Glucose Estimation from PPG Signals Using Machine Learning Algorithms

Effective management of diabetes requires accurate monitoring of blood glucose levels. Traditional invasive methods for such monitoring can be cumbersome and uncomfortable for patients. In this study, we introduce a noninvasive approach to estimate blood glucose levels using photoplethysmography (PPG) signals. We have focused on blood glucose prediction using wrist PPG signals and explored various PPG waveform-based features, including AC to DC ratio (AC/DC) and intrinsic mode function (IMF)-based features derived from empirical mode decomposition (EMD). To the best of our knowledge, no studies have been found using EMD-based features to estimate blood glucose levels noninvasively. Additionally, feature importance-based selection has also been used to further improve the accuracy of the proposed model. Among the four machine learning algorithms considered in this study, CatBoost consistently outperformed XGBoost, LightGBM, and random forest across a wide number of features. The best performing model, CatBoost, achieved Pearson’s r of 0.96, MSE 0.08, R2 score 0.92, and MAE 8.01 when considering the top 50 features selected from both PPG waveform-based features and IMF-based features. The p-values for all models were <0.001, indicating statistically significant correlations. Overall, this study provides valuable insights into the feasibility and effectiveness of noninvasive blood glucose monitoring using advanced machine learning techniques.

Predicting blood glucose level using salivary glucose and other associated factors: A machine learning model selection and evaluation study

IntroductionThere is a need for designing non-invasive methods to predict blood glucose levels to ensure timely diagnosis of Diabetes Mellitus. Needle anxiety and bleeding disorders preclude many from undertaking blood tests. ObjectivesThe primary objective of this study was to assess if biomarkers like saliva can be used to estimate blood glucose levels. The second objective was to develop and evaluate Machine Learning (ML) models to predict blood glucose levels based on salivary glucose and associated features. An insight into the patient's features, which was important for predicting blood glucose levels, was also required. MethodsA cross-sectional study was conducted, and blood and saliva samples, along with patient-related data, were collected from healthy and diabetic patients. ML techniques were applied to the data to develop a tool for predicting blood glucose levels using patient features. The prediction intervals were computed, clinical accuracy was assessed, and important features for the prediction were identified. ResultsThe Random Forest Regressor Model, with features identified using the wrapper method, was selected as the best, with an average RMSE of 43.28. The prediction intervals were computed for point estimate, MAE = 23.821, and coverage was 100 percent, the clinical accuracy was compared with that of glucometers and continuous monitoring systems. All predicted values are in Zones A and B of the Clarke error grid, and the bias was 6.41. The most important feature for predicting blood glucose level is salivary glucose level, followed by known risk factors like Family History, BMI, etc. The study found that salivary glucose levels are insufficient to classify blood glucose levels as high or normal. ConclusionThe study concluded that salivary glucose with associated patient features could be a potential non-invasive biomarker for predicting blood glucose levels in patients. The developed ML model could be deployed in a device that inputs patient features, analyzes salivary glucose, and can monitor blood glucose levels in a non-invasive manner. Further research is needed to validate the findings of this study and develop a proof of concept.

A novel multipurpose device for dataset creation and on-device immediate estimation of blood glucose level from reflection ppg

We propose a completely non-invasive and highly accurate portable blood glucose estimator, which is simple hardware that anyone, regardless of their prior experience or knowledge, can use to obtain an immediate reading of their blood sugar level. Glucose levels can be monitored in real time and displayed on the device thanks to its infrared LED light source, sensor with built-in amplification unit, processing unit with blood glucose regression model, power management unit for autonomous operation, and display. The device was initially used to create a dataset of photoplethysmography (PPG) signals collected from the fingertip of 50 subjects. The extracted signal features were correlated with the subject's glucose level, which was measured at the same time using a commercial glucometer, and several regression models were constructed. The models were evaluated using signals from up to 110 subjects across three datasets, and the most optimized model was implemented in the device to predict the subject's blood glucose level solely based on PPG in real-time. The device with the built-in model has been subjected to extensive testing to gauge its efficacy. The device's clinical accuracy is encouraging. The pricey strips and needles that must be purchased along with the hardware in the conventional method will no longer be necessary with this device.

On the role of perfusion index for estimating blood glucose levels with ultrasound-assisted and conventional finger photoplethysmography in the near-infrared wavelength range

Background and objectivesThe perfusion index (PI) has recently attracted considerable interest and started to be investigated as a cost-effective diagnostic tool due to its non-invasive character. However, its potential as a painless marker of blood glucose concentration remains to be explored. This study aimed to assess the usefulness of perfusion index (PI) in estimating blood glucose using conventional and ultrasound-assisted finger photoplethysmography (PPG) in the near-infrared (NIR) wavelength range. MethodA customized module combining a reflective PPG sensor with a peak emission wavelength of 940 nm and an ultrasonic sensor with an operating frequency of 40 kHz was developed. 4-min PPG recordings (two minutes with ultrasound; two minutes without ultrasound) were acquired from 53 volunteers, including 19 diabetic patients, after conventional blood sample collection via an invasive glucometer. We computed Pearson's coefficient (r), the mean absolute error (MAE), Bland-Altman plots, and Clarke error grids to explore the relationship between PI and glucose values and evaluate the performance of the proposed approach, with and without ultrasound. ResultsWhen ultrasonic waves were applied, PI showed a higher correlation with blood glucose levels (r = −0.90, p < 0.001), MAE significantly decreased (p = 0.0051), the bias was slightly closer to zero, the LOA interval became narrower, and predicted glucose levels deviating from reference values by more than 20 % decreased in number. ConclusionsThere is an association between PI and the blood glucose level, and 40 kHz ultrasound can significantly increase the accuracy in estimating the blood glucose level through PI.

The role of Diabetes mellitus comorbidity on Tuberculosis treatment outcomes in Nepal: A prospective cohort study

Aim: The Objective of this study was to assess the effect of Diabetes Mellitus (DM) on treatment outcomes of tuberculosis (TB) patients in the Central Development Region of Nepal.&#x0D; Methods: A prospective cohort study was conducted in central Nepal. The study population of n=408 was consecutively recruited from treatment centers of all 19 districts of central Nepal. The TB cases (n=306) and TB with DM (n=102) cases were followed up for the estimation of blood glucose level, HbA1c level, and sputum examination on 2, 5, and 6 months after TB treatment started. The Generalized Estimating Equation (GEE) was performed to identify the risk ratio among TB and TB with DM cases on treatment outcome.&#x0D; Results: Our study identified that the magnitude of treatment failure among the tuberculosis cases was 19.7% (95% CI: 17.44-21.95). The GEE analysis observed that factors associated with the treatment failure had uncontrolled DM (HbA1C ≥7 %) (adj.RR=5.24, 95% CI: 2.58-10.62, P value &lt;0.001), aged ≥ 45 (adj.RR= 6.13, 95% CI: 2.55-14.76, P value &lt;0.001), had inadequate financial status (adj.RR= 2.33, 95% CI: 1.07-5.06, P value 0.033) and had prior TB (adj.RR=2.33, 95% CI: 1.09-4.97, P value 0.028) respectively. &#x0D; Conclusion: The prevalence of worsening TB treatment among patients with TB and DM was significantly higher than those who had TB only. Poor glycaemic control, increasing age, inadequate financial status, and previous history of tuberculosis were strong predictors of worsening tuberculosis treatment outcomes.

Feature Transformation for Efficient Blood Glucose Prediction in Type 1 Diabetes Mellitus Patients.

Diabetes Mellitus, a metabolic disease, causes the body to lose control over blood glucose regulation. With recent advances in self-monitoring systems, a patient can access their personalized glycemic profile and may utilize it for efficient prediction of future blood glucose levels. An efficient diabetes management system demands the accurate estimation of blood glucose levels, which, apart from using an appropriate prediction algorithm, depends on discriminative data representation. In this research work, a transformation of event-based data into discriminative continuous features is proposed. Moreover, a multi-layered long short-term memory (LSTM)-based recurrent neural network is developed for the prediction of blood glucose levels in patients with type 1 diabetes. The proposed method is used to forecast the blood glucose level on a prediction horizon of 30 and 60 min. The results are evaluated for three patients using the Ohio T1DM dataset. The proposed scheme achieves the lowest RMSE score of 14.76 mg/dL and 25.48 mg/dL for prediction horizons of 30 min and 60 min, respectively. The suggested methodology can be utilized in closed-loop systems for precise insulin delivery to type 1 patients for better glycemic control.

Hardware implementation for estimation of fasting blood glucose from saliva

Worldwide, people are facing diabetes which is characterized by high Fast Blood Glucose Level (FBGL) over a prolonged period of time. The main drawbacks of existing methods for estimation of FBGL are their high cost and invasive nature requiring frequent blood with-drawls. Hence, there is an unmet need to find an alternate method that can estimate blood glucose level conventionally, non-invasively and affordably. In the proposed work, an alternative method for estimating blood glucose based on variation in the electrochemical parameters of saliva is discussed. The role of saliva as a diagnostic tool is assessed by correlating fasting blood serum glucose and salivary glucose in the patient. The proposed system consists of sensor module, processor module, and IOT module. A proof of concept prototype is developed with integrated sensor for all the relevant parameters. PIC Microcontroller is the controlling unit of the whole system. The various sensors for measuring heart rate, temperature, pH and humidity are used in the work. This non-invasive blood glucose monitoring method is efficient and painless for FBGL estimation from saliva.

Combined Approach to Estimate Blood Glucose Level in Noninvasive Monitoring: Ultra-Wide Band Microwave and Cascaded General Regression Neural Network

As the increasing number of diabetic around the world and the deficiencies of invasive blood glucose detection such as prickly pain and no continuous monitoring. In this paper, an ultra-wide band microwave based technique combined with a cascaded general regression neural network (C-GRNN) was proposed for blood glucose level estimation to achieve noninvasive and intelligent monitoring. Different glucose solutions were tested by using the reported monitoring system, in which C-GRNN was used for intelligent detection and frequency choice. The proposed C-GRNN could reduce the prediction interval from 12.3858 mg/dl to 2.1375 mg/dl compared with conventional GRNN, and the root mean square error (RMSE) reached 2.20 mg/dl. These results showed good estimation performance. We also conducted a continuous-time blood glucose level (BGL) monitoring on volunteer using proposed noninvasive method. The final results showed well accuracy, where the RMSE and mean absolute percentage error (MAPE) were 13.3208 mg/dl and 9.74% respectively.

Low glucose concentration estimation based on reaction with 4,4'-biphenyl boronic acid using deep learning

Minimally invasive blood glucose level estimation with Raman spectroscopy is an important research field and attracts great attention. However, glucose concentration in blood is low and is difficult to be accurately measured. In this paper, we creatively proposed applying the 4,4'-biphenyl boronic acid to react with different concentrations of glucose to obtain the complex—(C36H40O18B4) n. We performed a regression of the Raman spectral data of (C36H40O18B4) n and the glucose solution separately to compare their estimation results. We applied a deep learning network, ResNet, and compared it with regression models of conventional machine learning, uniformly using ten-fold cross-validation. The experimental results show that the generated (C36H40O18B4) n can effectively improve the estimation performance of glucose. The results showed, the ResNet model does not require explicit feature extraction and can achieve fast and accurate estimation. Its performance is significantly better than the traditional linear analysis method, and the R square can reach 0.93. The method in the article can effectively improve the estimation effect of low-concentration glucose.

Intelligent estimation of blood glucose level using wristband PPG signal and physiological parameters

The inconvenience and risk associated with the regular use of invasive blood glucose measurements has led to tremendous research in this area. This paper proposes the design of a non-invasive blood glucose estimation system using novel Mel frequency cepstral coefficients features of wristband photoplethysmogram signal and physiological parameters. A dataset from 217 participants of a hospital in Cuenca Ecuador is used to validate the proposed model. The support vector regression (SVR) and extreme gradient boost regression (XGBR) techniques are used to estimate blood glucose levels (BGL). The XGBR technique achieves the least value for the standard error of prediction (SEP), 9.78 mg/dL. Further, 5 features are selected from the feature set based on the feature importance in XGBR. The XGBR model with the reduced feature set results in further reduction of SEP value (5.53 mg/dL) with a correlation coefficient of 0.99. Standard Clarke error grid analysis and Bland-Altman analysis shows that the predicted glucose values are in the clinically acceptable region. The results of the proposed model demonstrate the potential of wearable BGL monitoring technology.

Wearable Smart Contact Lenses for Continual Glucose Monitoring: A Review.

Diabetes mellitus is a chronic disease requiring a careful management to prevent its collateral complications, such as cardiovascular and Alzheimer's diseases, retinopathy, nephropathy, foot and hearing impairment, and neuropathy. Self-monitoring of blood glucose at point-of-care settings is an established practice for diabetic patients. However, current technologies for glucose monitoring are invasive, costly, and only provide single snapshots for a widely varying parameter. On the other hand, tears are a source of physiological information that mirror the health state of an individual by expressing different concentrations of metabolites, enzymes, vitamins, salts, and proteins. Therefore, the eyes may be exploited as a sensing site with substantial diagnostic potential. Contact lens sensors represent a viable route for targeting minimally-invasive monitoring of disease onset and progression. Particularly, glucose concentration in tears may be used as a surrogate to estimate blood glucose levels. Extensive research efforts recently have been devoted to develop smart contact lenses for continual glucose detection. The latest advances in the field are reviewed herein. Sensing technologies are described, compared, and the associated challenges are critically discussed.

Administration of Costus igneus Nak leaf extract improves diabetic-induced impairment in hepatorenal functions in male albino rats

ObjectivesThe present study was designed to evaluate the therapeutic efficacy Costus igneus Nak leaf extract against the diabetes-induced hepatorenal injury in rats. MethodsDiabetes was induced in male albino rats by administering single injection of streptozotocin (55 mg/kg) for one day. The diabetic condition was established by estimation of blood glucose level; animals with blood glucose levels above 250 mg were considered as diabetic. Following the confirmation, animals were randomly divided into seven experimental groups, viz group I served as the control, group II-CI-300mg/Kg (per se), group III-Diabetic, group IV-diabetic+CI (100mg/kg), group V-diabetic+CI (200mg/kg), group VI-Diabetic+CI (300 mg/kg) and group VII-diabetic+glibenclamide (0.5mg/kg). Treatment regimens were started 24 h after the confirmation of the successful induction of diabetes and continued for four consecutive weeks at three doses (100–300 mg/kg). After the completion of experimental period animals were sacrificed, blood was collected through retro-orbital puncture for separation of serum for assessment of liver (ALT, AST, ALP, albumin, globulin and bilirubin) and kidney-specific biomarkers (creatinine, urea, and uric acid, Na+ and K+), and inflammatory cytokines (TNF-α, IL-6, IL-1β, TGF-β1, IL-10. Liver and kidneys were harvested for histopathology and biochemical screening. Hepatorenal protective activity was evaluated by estimating different biochemical variables and histopathology by hematoxylin and eosin staining of the liver and kidney tissues. Results and ConclusionsThree different daily doses of the plant extract showed recovery in all the biochemical parameters such as lipid peroxidation, reduced glutathione, superoxide dismutase, catalase, glucose-6-phosphate dehydrogenase, glutathione reductase, glutathione peroxidase, and glutathione-S-transferase; ALT, AST, and ALP; creatinine, urea, and uric acid; total cellular protein content in tissues, glycogen content, and serum protein content (albumin and globulin); and inflammatory cytokines. However, significantly higher recovery was observed in the 300 mg/kg dose treated group of animals. Considerations of the serological, biochemical and histopathological studies, it may be concluded that that the C. igneus Nak leaf extract abolished various deleterious effects of type 1 diabetes induced hepatorenal functions.

Randomized blind study to evaluate the sensitivity and specificity of glucose level estimation from gingival crevicular blood using glucometer

Context: To facilitate a reliable rapid chairside blood glucose estimation to detect blood glucose levels in patients reporting for bleeding from the gums. Aim: This study aimed to assess the blood glucose level in gingival crevicular blood (GCB) using a glucometer and compare the values obtained from venous blood from the same patient. Settings and Design: A blind study of the cross-sectional population of patients visiting a tertiary health-care center. The sample size was estimated based on the prevalence of diabetes mellitus in rural population, using the following formula: n = (Zα/2)2 × pq/e2. Subjects and Methods: The study involved collecting GCB and simultaneous collection of intravenous blood from every alternate patient with active bleeding on probing, till 110 samples were recorded. GCB was then analyzed with a handheld glucometer, and the intravenous blood was analyzed using Hexokinase method of estimation. Statistical Analysis Used: For statistical analysis, the data were gathered and coded in SPSS (IBM version 23). The frequency, percentages, mean, and standard deviation were among the descriptive statistics. Sensitivity and specificity were calculated from the contingency table. The Pearson correlation test and independent t-test were among the inferential statistics. At a 95% confidence level, the significance level was established at 0.05. Results: Of 110 participants, 57 were male, and 53 were female. The sensitivity showed 89.2%, specificity was 91.8%, and positive predictive value stood at 84.6%, negative predictive value was 94.4%, and accuracy was 90.91%. When mean venous and GCB glucose levels were compared, it was 151.1727 ± 87.65695 mg/dL and 147.7455 ± 79.44064 mg/dL, respectively (P = 0.762, > 0.05). Conclusions: Gingival blood glucose level estimation using a glucometer is an easy way to perform a minimally invasive procedure; it could support early diabetes identification in cases of atypical diabetic patients and enable physicians to improve the frequency of diabetes screening in dentistry offices. Despite not being a diabetic diagnostic test, such screening will play a crucial role as a clinical chairside test.

A Study on Maternal and Perinatal Outcome in Gestational Diabetes Mellitus

Background and Aim: Gestational diabetes is initially diagnosed as glucose intolerance in pregnancy which might result in adverse maternal and perinatal outcomes. The present study aimed at different maternal and perinatal outcomes associated with pregnancy complicated by gestational diabetes. Methodology: This cross-sectional study was carried out on 148 women diagnosed with gestational diabetes in the department of Diabetes and Endocrinoogy and Obstetrics/ Gynecology, Hayatabad Medical Complex, Peshawar and Mian Rashid Hussain Shaheed Memorial Hospital Pabbi respectively for duration of six months from April 2021 to September 2021. Maternal and perinatal data were taken from patients' medical records in the hospital database. The data included age, gestational age, parity, mode of delivery, BMI, infant birth weight, antenatal complications and both maternal and neonatal morbidity and mortality. All the women with multiple pregnancies and who presented with the breech in the labor room were excluded. Estimation of platelet count and renal function test were performed in order to evaluate the maternal and fetal outcomes in addition to estimation of blood glucose levels and routine investigations. Results: Of the total 148 diagnosed with gestational diabetes, the majority of women 97 (65.5%) had an age between 20 and 30 years followed by 30 to 40 years 42 (28.4%). A higher incidence of pregnancy induced hypertension 18 (12.2%), cesarean section 87 (58.8%), labor induction 58 (39.2%), macrosomia in 4 (2.7%), and preterm delivery in 5 (3.4%) was reported despite early diagnosis and treatment of gestational diabetes. The prevalence of perinatal morbidity was 43 (29.1%) whereas the mortality was 5 (3.4%). Neonatal hypoglycemia in 49 (33.1%), meconium aspiration syndrome 12 (8.1%), hyperbilirubinemia 19 (12.8%), and requirement of neonatal unit admission 37 (25%) were common causes of perinatal morbidity. Neonatal and intrauterine deaths were reported in 3 (2%) and 2 (1.4%) respectively. Conclusion: The prevalence of maternal and perinatal morbidity and mortality increased in women with gestational diabetes. A 75 g oral glucose tolerance test was used for screening of gestational diabetes in pregnant women during 24 to 28 weeks gestation which assisted in earlier diagnosis and timely intervention, in turn, reducing complications. Pregnancy outcomes might be improved with the proper management of gestational diabetes. Keywords: Maternal outcome, Perinatal outcomes, Gestational diabetes

Evaluation of in vitro antioxidant activity of okra mucilage and its antidiabetic and antihyperlipidemic effect in alloxan-induced diabetic mice.

Okra (Abelmoschus esculentus) is a traditionally important vegetable herb used to treat numerous illnesses, including diabetes mellitus, in many rural parts of Bangladesh and the South Asian subcontinent. However, the scientific evidence for the aforementioned properties has not been adequately validated. Hence, the aim of this study was to explore the antidiabetic, antilipidemic activity of okra mucilage powder, and to compare its effectiveness with the isolated peel–seed of okra after mucilage extraction in Swiss albino mice. After extraction, both mucilage and peel–seed were made into powder. In addition, crude protein, mineral contents, and in vitro antioxidant activity of mucilage and peel–seed powder were assessed. After acute toxicity test, methanolic extracts of both powders were administered to alloxan‐induced diabetic mice for 3 weeks. Blood glucose levels were assessed weekly. Finally, blood samples were collected on day 21 to estimate blood glucose level, total protein (TP), and lipid profile levels. Okra mucilage powder showed less amount of protein, calcium, magnesium, phosphorus, potassium, sodium, and iron compared with peel–seed powder. In terms of antioxidant activity, the IC50 value and total phenolic content were found higher in okra mucilage powder in contrast to peel–seed powder. However, total flavonoid content was higher in peel–seed powder than mucilage powder. Three‐week administration of mucilage and peel–seed suspensions at a dose of 150 mg/kg and 200 mg/kg significantly (p < .05) reversed the abnormal changes of bodyweights, water consumption, feed consumption, and fasting blood glucose levels of diabetic subjects. Cholesterol, triglycerides, low‐density lipoproteins, high‐density lipoproteins, and total protein were found to be significantly (p < .05) improved after mucilage and peel–seed treatment. Thus, Okra can be recommended as a potential source of antidiabetic drug candidate for the effective management of diabetes mellitus and its complications.