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This paper aims to concentrate on recent innovations in flexible wearable sensor technology tailored for monitoring vital signals within the contexts of wearable sensors and systems developed specifically for monitoring health and fitness metrics.

In recent decades, wearable sensors for monitoring vital signals in sports and health have advanced greatly. Vital signals include electrocardiogram, electroencephalogram, electromyography, inertial data, body motions, cardiac rate and bodily fluids like blood and sweating, making them a good choice for sensing devices.

This report reviewed reputable journal articles on wearable sensors for vital signal monitoring, focusing on multimode and integrated multi-dimensional capabilities like structure, accuracy and nature of the devices, which may offer a more versatile and comprehensive solution.

The paper provides essential information on the present obstacles and challenges in this domain and provide a glimpse into the future directions of wearable sensors for the detection of these crucial signals. Importantly, it is evident that the integration of modern fabricating techniques, stretchable electronic devices, the Internet of Things and the application of artificial intelligence algorithms has significantly improved the capacity to efficiently monitor and leverage these signals for human health monitoring, including disease prediction.

This review provides an overview of recent advances in electrochemical sensors for analyte detection in saliva, highlighting their potential applications in diagnostics and health care. The purpose of this paper is to summarize the current state of the field, identify challenges and limitations and discuss future prospects for the development of saliva-based electrochemical sensors.

The paper reviews relevant literature and research articles to examine the latest developments in electrochemical sensing technologies for saliva analysis. It explores the use of various electrode materials, including carbon nanomaterial, metal nanoparticles and conducting polymers, as well as the integration of microfluidics, lab-on-a-chip (LOC) devices and wearable/implantable technologies. The design and fabrication methodologies used in these sensors are discussed, along with sample preparation techniques and biorecognition elements for enhancing sensor performance.

Electrochemical sensors for salivary analyte detection have demonstrated excellent potential for noninvasive, rapid and cost-effective diagnostics. Recent advancements have resulted in improved sensor selectivity, stability, sensitivity and compatibility with complex saliva samples. Integration with microfluidics and LOC technologies has shown promise in enhancing sensor efficiency and accuracy. In addition, wearable and implantable sensors enable continuous, real-time monitoring of salivary analytes, opening new avenues for personalized health care and disease management.

This review presents an up-to-date overview of electrochemical sensors for analyte detection in saliva, offering insights into their design, fabrication and performance. It highlights the originality and value of integrating electrochemical sensing with microfluidics, wearable/implantable technologies and point-of-care testing platforms. The review also identifies challenges and limitations, such as interference from other saliva components and the need for improved stability and reproducibility. Future prospects include the development of novel microfluidic devices, advanced materials and user-friendly diagnostic devices to unlock the full potential of saliva-based electrochemical sensing in clinical practice.

Previous studies have reported that dietary fibers such as polydextrose and maltodextrin can reduce food intake; however, the studies on the differences of this effect are insufficient. The purpose of this paper is to compare the effects of dietary fibers maltodextrin and polydextrose on alterations of short-term satiety, energy intake and postprandial blood glucose in healthy females.

This study was designed as a randomized, crossover and double blind research. For this purpose, 21 healthy females consumed a milkshake containing 0 g (control), 15 g polydextrose (PDX) and 15 g maltodextrin (MDX), and an ad libitum lunch meal was served 150 min later. Subjective appetite scores (hunger, satiety, prospective food consumption and desire to eat) were measured using a visual analog scale. Appetite scores and blood glucose were measured before preload and once per 15 min after milkshake consumption.

Visual analog scale scores showed that PDX had an improved effect on satiety and hunger feelings. Compared to the control, dietary fiber increased the Area Under Curve (AUC) scores of satiety (p < 0.001) and decreased the AUC scores of hunger (p < 0.001), prospective food consumption (p < 0.001) and desire to eat (p < 0.001). Energy intake during ad libitum meal was significantly lower in PDX (Control: 862 (54.3) Kcal versus PDX: 679 (35.4) Kcal and MDX: 780 (49.3) Kcal. Moreover, the blood glucose levels were significantly lower in MDX.

This study conducted with healthy females demonstrated that PDX was more effective in inducing satiety during subsequent food intake, and that postprandial blood glucose were within more healthy levels in MDX.

A bio-sensor has been developed in this study for the purpose of point-of-care diagnostics. Point-of-care-diagnostic is a type of diagnosis where the diagnostic centre, i.e. the diagnosis kit is made available at the location of the patient when the patient needs immediate action. In this process of diagnosis a compact, portable, integrated kit must be available which can diagnose the disease of the patient by testing various analytes.

Using a fully experimental methodology, a blood glucose sensor is made by conducting carbon interdigitated electrode (IDE) on a flexible substrate. IDEs are used to increase the effective capacitance of the structure, as well as the effective electroactive area of the sensor. Interdigitated structure permits two-electrode sticks with “each other” and “infuse” together. As a consequence, the distance between electrodes can be tuned to a much smaller value than traditional thin-film architectures. Narrowing the distance between electrodes allows for fast ion diffusion that offers better rate capability and efficiency in power density. The fabricated device exhibits a remarkable value of sensitivity in the order of 2.741 µA mM-1 cm⁻².

A highly sensitive, portable and inexpensive blood glucose sensor has been developed in this context.

This research study can be a scope for future research in the field of bio-sensors.

This paper aims to discuss the general principles behind the microwave sensing and demonstrates the potential of cavity microwave resonator device in real-time monitoring for: environmental monitoring with the focus on wastewater pollution, a system for oil/gas/water content evaluation in a dynamic pipeline, a system for real-time determination of bacteria concentration and a method for non-invasive glucose determination.

Microwave sensing is a rapidly developing technology which has been successfully used for various industrial applications including water level measurements, material moisture content, in construction industry for non-invasive evaluation of structures and even in the healthcare industry for non-invasive real-time monitoring of glucose in diabetic patients. Novel microwave cavities designed and tested for specific applications are presented.

The paper provides experimental results of testing the novel microwave sensing systems in a range of industrial and healthcare applications and discusses the potential of these systems for real-time monitoring of processes and parameters.

The concept of real-time microwave sensing was successfully tested, but further experiments are required to account for possible interference mechanisms before it can be used commercially on a large-scale.

It is suggested that a novel approach to wastewater monitoring, namely using specially designed microwave cavity sensors, could lead to a successful development of an advanced platform capable of providing for a real-time detection of water content with superior sensitivity. Also, a system for real-time multiphase fluid composition monitoring is reported, which is essential for sustainable oil industry operation.

The paper illustrated the potential of microwave sensing as a real-time monitoring platform for a broad spectrum of commercial applications, with a focus on system developed by the authors, namely, for the monitoring of a multiphase fluid flow in a dynamic oil pipeline, for real-time monitoring of nutrients concentration in wastewater and for healthcare industry, in particular for real-time non-invasive determination of the glucose levels and bacteria concentration.

The aim of this study was to evaluate the individual effect of sumac (S), Turkish coffee (C) and yerba mate tea (Y) on the postprandial glycemic response to Lebanese mankoucheh, a common breakfast item in the Lebanese culture, and to determine the glycemic index (GI) of this food. S, C and Y are typical constituents of Lebanese meals. They may influence the postprandial glycemic response to carbohydrate-rich foods, but this has not been studied to date.

Twelve healthy normoglycemic adults consumed on separate days the following test meals: mankoucheh without S (M) with water (control meal); M prepared with single or double doses of S (S1 and S2) with water; M with 60 or 120 mL of unsweetened C; or M with 100 or 200 mL of unsweetened Y. Meals were prepared according to standardized recipes containing 50 g of available carbohydrates. Capillary blood glucose measures were taken at fast and six times after meal ingestion over a two hour period. The GI of mankoucheh was determined using a standard protocol.

The glycemic responses, evaluated at each time following meal ingestion, did not differ significantly among the seven meals, and neither did the incremental area under the glycemic response curves. The GI of mankoucheh was 61 ± 6, with no significant difference between M, M with S1 and M with S2.

This study contributes to better characterize the glycemic properties of S, C, Y and mankoucheh in conditions that closely resemble how these dietary items are used and consumed by some cultural groups.

The purpose of this paper is to examine relationships of self-care behavior, illness representation and diabetes knowledge with A1C (level of glycemic control) in 124 incarcerated persons.

Using a cross-sectional design, summary scores and items from the self-care inventory revised, brief illness perception questionnaire and the spoken knowledge for low literacy in diabetes were evaluated using linear regression to assess their relationship to A1C.

Metabolic control was suboptimal for the majority of inmates with diabetes. The final regression model was statistically significant (F (3, 120)=9.51, p=0.001, R²=19.2 percent). Higher log10 HbA1C (A1C) was associated with lower personal control beliefs (B=−0.007, t (122)=−2.42, p=<0.02), higher self-report of diabetes understanding (B=0.009, t (122)=3.12, p=0.00) and using insulin (B=0.062, t (122)=2.45, p=0.02).

Similar to findings with community dwelling participants, enhancing diabetes personal control beliefs among inmates may lead to lower A1C.

Highly structured environments with limited options for self-care, personal choices and readily available health care may give some incarcerated persons with diabetes no motivation to improve diabetes control even if they have an understanding of what to do.

While there is abundant research in the community describing how these factors influence A1C levels, research of this nature with incarcerated persons with diabetes is limited. Findings will inform diabetes programming during incarceration to better prepare inmates for reentry.

The purpose of this paper is to report data on social support elements and health status differences across three age groups of Kuwaiti elderly.

Examined for this study were 1,427 adults. Social support elements considered include total social support scale, frequency of contact (FOC) with relatives and friends, strength of these contacts, religiosity, number of children and number of children living with the elderly. This study evaluates health status in terms of systolic and diastolic blood pressure, glucose levels and self-reported somatic symptoms, satisfaction with current health and health over the last year.

The data show that there are important social support elements in all of the age groups. Social support, FOC, strength of relationships, number of children living with the elderly and religiosity are shown to influence the health and well-being of elders. Social support elements are shown to be higher in the oldest age group.

Elderly studies in Kuwait are rare. This is one of first studies done in Kuwait which show that there are differences in social support, FOC and strength of relation among all of the age groups.

The primary objective of this study was to recognize critical indicators in predicting blood glucose (BG) through data-driven methods and to compare the prediction performance of four tree-based ensemble models, i.e. bagging with tree regressors (bagging-decision tree [Bagging-DT]), AdaBoost with tree regressors (Adaboost-DT), random forest (RF) and gradient boosting decision tree (GBDT).

This study proposed a majority voting feature selection method by combining lasso regression with the Akaike information criterion (AIC) (LR-AIC), lasso regression with the Bayesian information criterion (BIC) (LR-BIC) and RF to select indicators with excellent predictive performance from initial 38 indicators in 5,642 samples. The selected features were deployed to build the tree-based ensemble models. The 10-fold cross-validation (CV) method was used to evaluate the performance of each ensemble model.

The results of feature selection indicated that age, corpuscular hemoglobin concentration (CHC), red blood cell volume distribution width (RBCVDW), red blood cell volume and leucocyte count are five most important clinical/physical indicators in BG prediction. Furthermore, this study also found that the GBDT ensemble model combined with the proposed majority voting feature selection method is better than other three models with respect to prediction performance and stability.

This study proposed a novel BG prediction framework for better predictive analytics in health care.

This study incorporated medical background and machine learning technology to reduce diabetes morbidity and formulate precise medical schemes.

The majority voting feature selection method combined with the GBDT ensemble model provides an effective decision-making tool for predicting BG and detecting diabetes risk in advance.

Many foods naturally contain dietary Cr, but lost during processing and cooking. Type 2 diabetes mellitus (T2DM) has been associated with poor glycemic control and low Cr status. The objectives of the current study were to evaluate the dietary Cr intake and its relationship with diabetes risk factors in moderately obese subjects with T2DM.

Thirty‐six subjects (age: 26–65 years) were recruited through local advertisements. Subjects were taking stable doses of oral antidiabetic medication(s) excluding concomitant insulin. Subjects had HbA1c ≥7 per cent, persistent impaired glucose control (2 hour glucose >200 mg dL⁻¹) and at least a one‐year history of T2DM. Demographic characteristics, blood pressure, body mass index (BMI), family and medical history were recorded. Three‐day dietary intakes were collected and evaluated for Cr and nutrient content using Nutritionist V software. Plasma glucose, circulating insulin and lipid profile were analyzed. Homeostatic model assessment insulin resistance (IR), beta cell function (BCF) and derived ratios were calculated. Morning void urinary chromium levels were also measured.

It was observed that mean dietary Cr intake of adults (30 mcg) was below the suggested recommended daily intake (RDI) of 120 mcg day⁻¹. These estimates correspond to approximately 16.4 μg Cr per 1000 Kcals. A significant correlation was observed between dietary Cr and fasting insulin (p<0.05), total‐C (p<0.05), LDL‐C (p<0.01), triglycerides (p<0.05), BCF (p<0.05), TG/HDL‐C ratio (p<0.01), HOMA BCF (p<0.05) and with atherogenic index in plasma (p<0.05).

In this study, the consumption of chromium is less than the RDI. Overall the results suggest that the US diets are inadequate in the essential metal chromium. Further studies are required to explore the relationship of Cr absorption with dietary Cr intake and risk factors in T2DM.