Impact of Treviamet® & Treviamet XR® on quality of life besides glycemic control in type 2 DM patients.

BACKGROUND: Maintaining the quality of life is the main objective of managing type 2 diabetes (T2DM) (QoL). Since it is a key factor in patient motivation and adherence, treatment-related QoL has always been considered when choosing glucose-lowering medicines. The objective of the study was to evaluate the quality of life besides glycemic control among type 2 diabetes mellitus patients receiving Treviamet® & Treviamet XR® (Sitagliptin with Metformin) in routine care. METHODS: It was a prospective, open-label, non-randomized clinical trial including T2DM patients uncontrolled on Metformin therapy. All patients received Treviamet® & Treviamet XR® for six months. Sequential changes in QoL, fasting plasma glucose, HbA1c, body weight, and blood pressure were monitored from baseline to 3 consecutive follow-up visits. The frequency of adverse events (AEs) was also noted throughout the study. RESULTS: A total of 504 patients were screened; 188 completed all three follow-ups. The mean QoL score significantly declined from 57.09% at baseline to 33.64% at the 3rd follow-up visit (p < 0.01). Moreover, a significant decline in mean HbA1c and FPG levels was observed from baseline to 3rd follow-up visit (p < 0.01). Minor adverse events were observed, including abdominal discomfort, nausea, flatulence, and indigestion. Gender, HbA1c, diarrhea, and abdominal discomfort were significant predictors of a patient's QoL, as revealed by the Linear Regression Model (R2 = 0.265, F(16, 99) = 2.231). CONCLUSION: Treviamet® & Treviamet XR® significantly improved glycemic control (HbA1c levels) and QoL in T2DM patients without serious adverse events. TRIAL REGISTRATION: ClinicalTrials.gov identifier (NCT05167513), Date of registration: December 22, 2021.

Deep learning based object detection and surrounding environment description for visually impaired people.

Object detection, one of the most significant contributions of computer vision and machine learning, plays an immense role in identifying and locating objects in an image or a video. We recognize distinct objects and precisely get their information through object detection, such as their size, shape, and location. This paper developed a low-cost assistive system of obstacle detection and the surrounding environment depiction to help blind people using deep learning techniques. TensorFlow object detection API and SSDLite MobileNetV2 have been used to create the proposed object detection model. The pre-trained SSDLite MobileNetV2 model is trained on the COCO dataset, with almost 328,000 images of 90 different objects. The gradient particle swarm optimization (PSO) technique has been used in this work to optimize the final layers and their corresponding hyperparameters of the MobileNetV2 model. Next, we used the Google text-to-speech module, PyAudio, playsound, and speech recognition to generate the audio feedback of the detected objects. A Raspberry Pi camera captures real-time video where real-time object detection is done frame by frame with Raspberry Pi 4B microcontroller. The proposed device is integrated into a head cap, which will help visually impaired people to detect obstacles in their path, as it is more efficient than a traditional white cane. Apart from this detection model, we trained a secondary computer vision model and named it the "ambiance mode." In this mode, the last three convolutional layers of SSDLite MobileNetV2 are trained through transfer learning on a weather dataset. The dataset comprises around 500 images from four classes: cloudy, rainy, foggy, and sunrise. In this mode, the proposed system will narrate the surrounding scene elaborately, almost like a human describing a landscape or a beautiful sunset to a visually impaired person. The performance of the object detection and ambiance description modes are tested and evaluated in a desktop computer and Raspberry Pi embedded system. Detection accuracy and mean average precision, frame rate, confusion matrix, and ROC curve measure the model's accuracy on both setups. This low-cost proposed system is believed to help visually impaired people in their day-to-day life.

Diabetes prediction using machine learning and explainable AI techniques.

Globally, diabetes affects 537 million people, making it the deadliest and the most common non-communicable disease. Many factors can cause a person to get affected by diabetes, like excessive body weight, abnormal cholesterol level, family history, physical inactivity, bad food habit etc. Increased urination is one of the most common symptoms of this disease. People with diabetes for a long time can get several complications like heart disorder, kidney disease, nerve damage, diabetic retinopathy etc. But its risk can be reduced if it is predicted early. In this paper, an automatic diabetes prediction system has been developed using a private dataset of female patients in Bangladesh and various machine learning techniques. The authors used the Pima Indian diabetes dataset and collected additional samples from 203 individuals from a local textile factory in Bangladesh. Feature selection algorithm mutual information has been applied in this work. A semi-supervised model with extreme gradient boosting has been utilized to predict the insulin features of the private dataset. SMOTE and ADASYN approaches have been employed to manage the class imbalance problem. The authors used machine learning classification methods, that is, decision tree, SVM, Random Forest, Logistic Regression, KNN, and various ensemble techniques, to determine which algorithm produces the best prediction results. After training on and testing all the classification models, the proposed system provided the best result in the XGBoost classifier with the ADASYN approach with 81% accuracy, 0.81 F1 coefficient and AUC of 0.84. Furthermore, the domain adaptation method has been implemented to demonstrate the versatility of the proposed system. The explainable AI approach with LIME and SHAP frameworks is implemented to understand how the model predicts the final results. Finally, a website framework and an Android smartphone application have been developed to input various features and predict diabetes instantaneously. The private dataset of female Bangladeshi patients and programming codes are available at the following link: https://github.com/tansin-nabil/Diabetes-Prediction-Using-Machine-Learning.

Computer vision-based approach to detect fatigue driving and face mask for edge computing device.

The fatality of road accidents in this era is alarming. According to WHO, approximately 1.30 million people die each year in road accidents. Road accidents result in significant socioeconomic losses for people, their families, and the country. The integration of modern technologies into automobiles can help to reduce the number of people killed or injured in road accidents. Most of the study and police reports claim that fatigued driving is one of the deadliest factors behind many road accidents. This paper presents a complete embedded system to detect fatigue driving using deep learning, computer vision, and heart rate monitoring with Nvidia Jetson Nano developer kit, Arduino Uno, and AD8232 heart rate module. The proposed system can monitor the driver's real-time situations, then analyze the situation to detect any fatigue conditions and act accordingly. The onboard camera module constantly monitors the driver. The frames are retrieved and analyzed by the core system that uses deep learning and computer vision techniques to verify the situation with Nvidia Jetson Nano. The driver's states are identified using eye and mouth localization approaches from 68 distinct facial landmarks. Experimentally driven threshold data is employed to classify the states. The onboard heart rate module constantly measures the heart rates and detects any fluctuation in BPM related to the drowsiness. This system uses a convolutional neural network-based deep learning framework to include additional face mask detection to cope with the current pandemic situation. The heart rate module works parallelly where the other modules work in a conditional sequential manner to ensure uninterrupted detection. It will detect any sign of drowsiness in real-time and generate the alarm. The system successfully passed the initial lab tests and some actual situation experiments with 97.44% accuracy in fatigue detection and 97.90% accuracy in face mask identification. The automatic device was able to analyze different situations of drivers (different distances of driver from the camera, various lighting conditions, wearing eyeglasses, oblique projection) more precisely and generate an alarm before the accident happened.

Numerical study of POD-Galerkin-DEIM reduced order modeling of cardiac monodomain formulation.

The three-dimensional cardiac monodomain model with inhomogeneous and anisotropic conductivity characterizes a complicated system that contains spatial and temporal approximation coefficients along with a nonlinear ionic current term. These complexities make its numerical modeling computationally challenging, and therefore, the formation of an efficient computational approximation is important for studying cardiac propagation. In this paper, a reduced order modeling approach has been developed for the simplified cardiac monodomain model, which yields a significant reduction of the full order dynamics of the cardiac tissue, reducing the required computational resources. Additionally, the discrete empirical interpolation technique has been implemented to accurately estimate the nonlinearity of the ionic current of the cardiac monodomain scheme. The proper orthogonal decomposition technique has been utilized, which transforms a given dataset called 'snapshots' to a new coordinate system. The snapshots are computed first from the original system, and they encapsulate all the information observed over both time and parameter variations. Next, the proper orthogonal decomposition provides a reduced order basis for projecting the original solution onto a low-dimensional orthonormal subspace. Finally, a reduced set of unknowns of the forward problem is obtained for which the solution involves significant computational savings compared to that for the original system of unknowns. The efficiency of the model order reduction technique for finite difference solution of cardiac electrophysiology is examined concerning simulation time, error potential, activation time, maximum temporal derivative, and conduction velocity. Numerical results for the monodomain show that its solution time can be reduced by a significant factor, with only 0.474 mV RMS error between the full order and reduced dimensions solution.

BRIGHT Guidelines on Self-Monitoring of Blood Glucose.

Pakistan, a developing country with limited resources, is having huge burden of diabetes and its complications. The local health care providers face limitations due to the related cost while emphasizing on self monitoring of blood glucose. The lack of health care infrastructure, non-affordability of the patients and non-existence of national guidelines are the most significant obstacles. Having realized these issues we decided to initiate a project of self monitoring of blood glucose, "BRIGHT (Better Recommendations, Implementation and Guideline development for Health care providers and their Training). After extensive literature search, the project team, approached and communicated with "Advisory Board for the Care of Diabetes (ABCD) of Pakistan" for their expert opinion and suggestions. The board members belong to the faculty of main teaching hospitals of the four provinces of Pakistan thus ensuring national representation. The endorsement of these guidelines has paved the way for their uniform implementation all over the country. Development of these Guidelines is the first part of BRIGHT project. In the next phase, we have started training of health care providers. Five mega programs have been conducted in this regard in the major cities. So far a patient's log book has also been designed and distributed. Like all other guidelines, this is a living document which will be revised and updated from time to time in the light of new information which becomes available.