A COMPREHENSIVE STUDY ON RELATIONSHIP OF DIABETES MELLITUS (TYPE 2) AND OBESITY IN PAKISTAN

Introduction: Type 2 diabetes mellitus is one of the most common public health issues worldwide and its incidence is on the rise, particularly in middle-income and low-income countries. Objectives of the study: The main objective of the study is to analyze the relationship of diabetes mellitus (type 2) and obesity in Pakistan. Methodology of the study: The cross sectional study was conducted at Allied Hospital Faisalabad during March 2018 till August 2018 The data was collected from 200 diabetic patients who visited the OPD of the hospital. The data was collected through a questionnaire. We assess the nutritional and economic health of patients by asking some survey questions. From the large pool of data we select health status, diet quality, lifestyle, food culture, food security, and demographic information of the selected patients. The economic and health status describe the level of awareness regarding disease. Results: The data were collected from 200 diabetic patients. We also collect the basic characteristics of patients and compared these values with normal values. So we can find that diseases person have more blood pressure value as compared to normal. The demographical conditions of the patients explains the co-efficient and standard error values. The level of confidence interval is 90 and 95 in this table for the significant value. Conclusion: It is concluded that T2DM patients require reinforcement of DM education including dietary management through stakeholders (health-care providers, health facilities, etc.) to encourage them to understand the disease management better, for more appropriate self-care and better quality of life

Analysis of Human Gait Using Hybrid EEG-fNIRS-Based BCI System: A Review

Human gait is a complex activity that requires high coordination between the central nervous system, the limb, and the musculoskeletal system. More research is needed to understand the latter coordination\u27s complexity in designing better and more effective rehabilitation strategies for gait disorders. Electroencephalogram (EEG) and functional near-infrared spectroscopy (fNIRS) are among the most used technologies for monitoring brain activities due to portability, non-invasiveness, and relatively low cost compared to others. Fusing EEG and fNIRS is a well-known and established methodology proven to enhance brain–computer interface (BCI) performance in terms of classification accuracy, number of control commands, and response time. Although there has been significant research exploring hybrid BCI (hBCI) involving both EEG and fNIRS for different types of tasks and human activities, human gait remains still underinvestigated. In this article, we aim to shed light on the recent development in the analysis of human gait using a hybrid EEG-fNIRS-based BCI system. The current review has followed guidelines of preferred reporting items for systematic reviews and meta-Analyses (PRISMA) during the data collection and selection phase. In this review, we put a particular focus on the commonly used signal processing and machine learning algorithms, as well as survey the potential applications of gait analysis. We distill some of the critical findings of this survey as follows. First, hardware specifications and experimental paradigms should be carefully considered because of their direct impact on the quality of gait assessment. Second, since both modalities, EEG and fNIRS, are sensitive to motion artifacts, instrumental, and physiological noises, there is a quest for more robust and sophisticated signal processing algorithms. Third, hybrid temporal and spatial features, obtained by virtue of fusing EEG and fNIRS and associated with cortical activation, can help better identify the correlation between brain activation and gait. In conclusion, hBCI (EEG + fNIRS) system is not yet much explored for the lower limb due to its complexity compared to the higher limb. Existing BCI systems for gait monitoring tend to only focus on one modality. We foresee a vast potential in adopting hBCI in gait analysis. Imminent technical breakthroughs are expected using hybrid EEG-fNIRS-based BCI for gait to control assistive devices and Monitor neuro-plasticity in neuro-rehabilitation. However, although those hybrid systems perform well in a controlled experimental environment when it comes to adopting them as a certified medical device in real-life clinical applications, there is still a long way to go

Feasibility study of multi-wavelength optical probe to analyze magnesium implant degradation effects

Near-infrared spectroscopy (NIRS) is a rapidly developing and promising technology with potential for spectrographic analysis. Understanding NIRS measurements on the implant-tissue interface for hydrogen gas formation as part of degradation is essential for interpreting the biodegradable Magnesium (Mg) based implants. This paper introduces novel NIR optical probe that can assess the state of Mg implant's degradation when in contact with biological tissues. A tissuemimicking phantom (TMP) to mimic biological tissue's optical properties helps investigate changes in reflectance spectra due to bubble formation at the implant-tissue interface. Spectra taken from different TMP samples containing biodegradable Mg and non-degradable Titanium (Ti) disk are suitable for evaluating the implant's interaction. The results show that the reflection in TMP for samples containing Mg disks, confirms the presence of hydrogen bubbles at the surface of implants. Multi-distance optical probe with depth selectivity of 3mm and 4mm has shown to be an effective tool to monitor bubble effect on different sample

In Vitro Monitoring of Magnesium-based Implants Degradation by Surface Analysis and Optical Spectroscopy

Magnesium (Mg)-based degradable alloys have attracted substantial attention for tissue engineering applications due to their biodegradability and potential for avoiding secondary removal surgeries. However, insufficient data in the existing literature regarding Mg’s corrosion and gas formation after implantation have delayed its wide clinical application. Since the surface properties of degradable materials constantly change after contact with body fluid, monitoring the behavior of Mg in phantoms or buffer solutions could provide some information about its physicochemical surface changes over time. Through surface analysis and spectroscopic analysis, we aimed to investigate the structural and functional properties of degradable disks. Since bubble formation may lead to inflammation and change pH, monitoring components related to acidosis near the cells is essential. To study the bubble formation in cell culture media, we used a newly developed Mg alloy (based on Mg, Zinc and Calcium), pure Mg and commercially available grade 2 Titanium (Ti) disks in Dulbecco's Modified Eagle Medium (DMEM) solution to observe their behavior over ten days of immersion. Using surface analysis, and the information from near-infrared spectroscopy (NIRS), we concluded conditions associated with the medical risks of Mg alloy disintegration. NIRS is used to investigate the degradation behaviour of Mg-based disks in the cell culture media, which is correlated with the surface analysis where possible

A Multimodal Deep Log-Based User Experience (UX) Platform for UX Evaluation

The user experience (UX) is an emerging field in user research and design, and the development of UX evaluation methods presents a challenge for both researchers and practitioners. Different UX evaluation methods have been developed to extract accurate UX data. Among UX evaluation methods, the mixed-method approach of triangulation has gained importance. It provides more accurate and precise information about the user while interacting with the product. However, this approach requires skilled UX researchers and developers to integrate multiple devices, synchronize them, analyze the data, and ultimately produce an informed decision. In this paper, a method and system for measuring the overall UX over time using a triangulation method are proposed. The proposed platform incorporates observational and physiological measurements in addition to traditional ones. The platform reduces the subjective bias and validates the user’s perceptions, which are measured by different sensors through objectification of the subjective nature of the user in the UX assessment. The platform additionally offers plug-and-play support for different devices and powerful analytics for obtaining insight on the UX in terms of multiple participants