Healthcare Monitoring Research Articles

Computational modeling of thermal radiation and activation energy effects in Casson nanofluid flow with bioconvection and microorganisms over a disk

Ensuring sustained thermal propagation is a crucial role in many industrial and thermal systems since it facilitates the improvement of the efficiency of thermal engineering engines and machinery. Therefore, the usage of magnetized nanoparticles in a heat-carrying non-Newtonian fluid is a promising development for the enhancement of thermal power energy. This paper uniquely contributes by comprehensively analyzing heat and mass transfer characteristics in a Casson nanofluid subjected to bioconvection over a disk. The study also explores in detail the interaction of gyrotactic microorganisms, and activation energy in the system. Similarity transformations have been used to make the governing partial differential equations (PDEs) dimensionless, which has then transformed them into ordinary differential equations. The solution method has utilized the Shooting technique combined with the Bvp4c solver in MATLAB. Graphs have been drawn to explain the different parameters of the flow; also, other engineering quantities, like motile microbes density and Sherwood numbers, have been calculated and are represented graphically. Furthermore, the interplay of mixed convection, buoyancy ratio, bioconvection Rayleigh constant, and resistivity due to magnetization significantly influences the distribution of velocity in the Casson nanofluid. Remarkably, parameters that characterize the motile microorganism profile significantly attenuate said profile; therefore, they play a very important role in shaping the system dynamics. The application of bioconvection phenomena spans diverse fields, ranging from healthcare and environmental monitoring to agriculture and renewable energy, offering innovative solutions to address complex challenges.

Monitoring Suicidal Behaviour in Dutch Youth Mental Healthcare: A Modified Delphi Approach

The monitoring of suicide-related indicators in youth mental healthcare is important in improving healthcare related suicide prevention. The aim of this study is to develop item-s to be included in a yet-to-be-implemented suicide-related monitoring system in youth mental healthcare in the Netherlands. A modified Delphi approach was used to achieve consensus among healthcare professionals, peer specialists and parents on suicide-related indicators and their definitions. Participants in the Delphi rounds were able to identify nine suicide-related indicators that are relevant when monitoring the quality of care delivered to people with suicidality in youth mental healthcare. The next step is to implement the monitoring in youth mental healthcare and, ultimately, reduce suicidal behavior.

Clinical indicators to monitor health care in low back pain: a scoping review.

Clinical care indicators for low back pain can be used to monitor healthcare practices and consequently be used to evaluate success of strategies to improve care quality. The aim of this study was to identify the clinical care indicators that have been used to measure appropriateness of health care for patients with low back pain. We conducted a systematic search of five electronic databases and Google to identify clinical care indicators that have been used to measure any aspect of care for people with low back pain. Care indicators were narratively described according to their type (i.e. structure, process, or outcomes) and categorized by their purpose (e.g. to measure aspects related to assessment, imaging requests, treatment/prevention, and outcomes). A total of 3562 and 2180 records were retrieved from electronic databases and Google searches, respectively. We identified 280 indicators related to low back pain care from 40 documents and publications. Most quality indicators were process indicators (n = 213, 76%), followed by structure (n = 41, 15%) and outcome indicators (n = 26, 9%). The most common indicators were related to imaging requests (n = 41, 15%), referral to healthcare providers (n = 30, 11%), and shared decision-making (n = 21, 7%). Our review identified a range of clinical care indicators that have been used to measure the quality of health care for people with low back pain. Our findings will support a Delphi study to reach international consensus on what would be the most important and feasible indicators for a minimum dataset to be collected globally.

Transparent ionogel balancing rigidity and flexibility with prolonged stability for ultra-high sensitivity temperature sensing

In the realm of modern flexible electronics, temperature sensors are pivotal, driving transformative advancements in electronic skins, healthcare monitoring systems, and intelligent firefighting technologies. However, impediments such as diminished temperature sensitivity, the dichotomy of rigidity versus flexibility, and compromised long-term stability hinder their full potential, adversely affecting sensor efficacy and reducing device longevity. In this research, we propose an innovative methodology for synthesizing P(HFA-co-SBMA)/[LI-BMIM] ionogels via a meticulously engineered ionic liquid system. This technique engenders temperature sensors with unparalleled sensitivity and adaptability, boasting tailor-made mechanical properties that achieve a harmonious balance between stiffness and malleability, alongside enhanced environmental tolerance. The fabricated ionogels, distinguished by their intrinsic conductivity, demonstrate superior mechanical performance, evidenced by a Young’s modulus of 44.74 MPa, high strength (7.79 MPa), and elevated toughness of 7.36 MJ/m3, coupled with notable transparency (∼90.7 %). These sensors display remarkable environmental endurance and remarkable temperature sensitivity (−9.81 % °C−1 and a B-value of 3894.6 K), achieving a high linear correlation coefficient (R2 = 0.997) across a broad operating spectrum. Leveraging density functional theory (DFT) analyses, we unravel the enhanced ion transport dynamics, validating the efficacy of our approach. By integrating 3D printing, mold casting, and in-situ polymerization techniques, we streamline the custom fabrication of miniaturized flexible ionogel sensors, enhancing the design, development, and production of sensors endowed with ideal characteristics.

Metal-Organic Framework-Based Surface-Enhanced Raman Scattering Sensing Platform for Trace Malondialdehyde Detection in Tears.

Disease biomarkers in tears are crucial for clinical diagnosis and health monitoring. However, the limited volume of tear samples, low concentration of tear biomarkers, and complex tear composition present challenges for precise testing. We introduce a spot-on testing platform of metal-organic framework (MOF)-based surface-enhanced Raman scattering (SERS) capillary column, which is capable of target molecules selective separation and enrichment for tear biomarkers in situ detection. It consists of Au nanostars for effective SERS signal and a porous MOF shell for separating impurities through molecular sieving effect. This platform allows for simultaneous collection and detection of tear, capturing the disease biomarker malondialdehyde in tears with a 9.38 × 10-9 mol/L limit of detection. Moreover, we designed a hand-held device based on this tubular SERS sensor, successfully diagnosing patients with dry eye disease. This functional capillary column enables noninvasive and rapid diagnosis of biomarkers in biofluids, providing potential for disease diagnosis and healthcare monitoring.

Tutorial on methods for estimation of optical absorption and scattering properties of tissue.

The estimation of tissue optical properties using diffuse optics has found a range of applications in disease detection, therapy monitoring, and general health care. Biomarkers derived from the estimated optical absorption and scattering coefficients can reflect the underlying progression of many biological processes in tissues. Complex light-tissue interactions make it challenging to disentangle the absorption and scattering coefficients, so dedicated measurement systems are required. We aim to help readers understand the measurement principles and practical considerations needed when choosing between different estimation methods based on diffuse optics. The estimation methods can be categorized as: steady state, time domain, time frequency domain (FD), spatial domain, and spatial FD. The experimental measurements are coupled with models of light-tissue interactions, which enable inverse solutions for the absorption and scattering coefficients from the measured tissue reflectance and/or transmittance. The estimation of tissue optical properties has been applied to characterize a variety of ex vivo and in vivo tissues, as well as tissue-mimicking phantoms. Choosing a specific estimation method for a certain application has to trade-off its advantages and limitations. Optical absorption and scattering property estimation is an increasingly important and accessible approach for medical diagnosis and health monitoring.

Achieving Secured Medical Network (SMN) through Stateless Mechanism and SkeyM in Medical-Internet of Things (M-IoT)

Medical Internet of Things (M-IoT) synchronizes medical devices in a network to provide smart healthcare monitoring to doctors and to provide an interactive model for patients. This embedded networked system gained lots of importance in the last few years. Now almost every hospital adopts M-IoT. Though it had a lot of challenges in the initial stages of implementation such as feasibility, accuracy, and autonomy, now it has bridged the flaws with the help of several researchers in this field. But still, M-IoT lags behind in security and privacy aspects due to which attackers can intrude on the network and exploit patients’ health data. By examining the various intensive security threats imposed on M-IoT a unique way of handling and transferring data over the network is proposed in this paper. From our research findings, this would resolve the data security issues in M-IoT and commence the next advancements.

A Healthcare System Employing Lightweight CNN for Disease Prediction with Artificial Intelligence

Introduction/Background This research introduces the EO-optimized Lightweight Automatic Modulation Classification Network (EO-LWAMCNet) model, employing AI and sensor data for forecasting chronic illnesses within the Internet of Things framework. A transformative tool in remote healthcare monitoring, it exemplifies AI's potential to revolutionize patient experiences and outcomes. This study unveils a novel Healthcare System integrating a Lightweight Convolutional Neural Network (CNN) for swift disease prediction through Artificial Intelligence. Leveraging the efficiency of lightweight CNN, the model holds promise for revolutionizing early diagnosis and enhancing overall patient care. By merging advanced AI techniques, this healthcare model holds the potential for revolutionizing early diagnosis and improving overall patient care. Materials and Methods The Lightweight Convolutional Neural Network (CNN) is implemented to analyze sensor data in real-time within an Internet of Things (IoT) framework. The methodology also involves the integration of the EO-LWAMCNet model into a cloud-based IoT ecosystem, demonstrating its potential for reshaping remote healthcare monitoring and expanding access to high-quality care beyond conventional medical settings. Results Utilizing the Chronic Liver Disease (CLD) and Brain Disease (BD) datasets, the algorithm achieved remarkable accuracy rates of 94.8% and 95%, respectively, showcasing the robustness of the model as a reliable clinical tool. Discussion These outcomes affirm the model's reliability as a robust clinical tool, particularly crucial for diseases benefiting from early detection. The potential transformative impact on healthcare is emphasized through the model's integration into a cloud-based IoT ecosystem, suggesting a paradigm shift in remote healthcare monitoring beyond traditional medical confines. Conclusion Our proposed model presents a cutting-edge solution with remarkable accuracy in forecasting chronic illnesses. The potential revolutionization of remote healthcare through the model's integration into a cloud-based IoT ecosystem underscores its innovative impact on enhancing patient experiences and healthcare outcomes.

Graphene oxide microstrip antenna-based sensor for oxygen gas sensing in medical applications

In this work, we report the development of a new graphene oxide (GO) sensor based on a microstrip antenna for detecting and measuring O2 concentration. GO was prepared from natural graphite powder using a modified Hummers procedure and then deposited upon a phenolic substrate to act as the sensing element of the designed device. Detection tests employing the sensor to evaluate oxygen gas (O2) in higher concentrations were performed, whereas it was observed that the two operating frequencies of the device was displaced to higher values as the concentration oxygen gas increased. The effect of relative humidity (RH) in the response sensor too was observed, whereas the 9.704 GHz mode demonstrating to be the most sensitive to O2 variation after sensitivity values have been corrected, presenting limit of detection (LOD) equal to 0.38%O2. Evaluating the results, it is possible to note that the device studied could be used as sensor in medical equipment operating at elevated oxygen concentrations, such as ventilators, O2 concentrators, and other medical equipment. Moreover, evaluated sensor would be a promisor candidate for healthcare monitoring applications for the reason that this device could act as both an O2 sensor and antenna, and thus transmitting their information to other devices.

Remote magnetically stimulated xanthan-biochar-Fe3O4-molecularly imprinted biopolymer hydrogel toward electrochemical enantioselection of l-tryptophan

Monitoring the levels of L-Tryptophan (L-Trp) in body fluids is crucial due to its significant role in metabolism and protein synthesis, which ultimately affects neurological health. Herein, we have developed a novel magneto-responsive electrochemical enantioselective sensor for the recognition of L-Trp based on oriented biochar derived from Loofah, Fe3O4 nanoparticles, and molecularly imprinted polydopamine (MIPDA) in xanthan hydrogel. The successful synthesis of these materials has been confirmed through physicochemical and electrochemical characterization. Various operational factors such as pH, response time, loading sample volume, and loading of active materials were optimized. As a result, the sensor exhibited an affordable linear range of 1.0–60.0 μM, with a desirable limit of detection of 0.44 μM. Furthermore, the proposed electrochemical sensor demonstrated good reproducibility and desirable selectivity for the determination of L-Trp, making it suitable for analyzing L-Trp levels in human plasma and serum samples. The development presented offers an appealing, easily accessible, and efficient strategy. It utilizes xanthan hydrogel to improve mass transfer and adhesion, biochar-stabilized Fe3O4 to facilitate magnetic orientation and accelerate mass transfer and sensitivity, and polydopamine MIP to enhance selectivity. This approach enables on-site evaluation of L-Trp levels, which holds significant value for healthcare monitoring and early detection of related conditions.

2D Hybrid Perovskite Sensors for Environmental and Healthcare Monitoring.

Layered perovskites, a novel class of two-dimensional (2D) layered materials, exhibit versatile photophysical properties of great interest in photovoltaics and optoelectronics. However, their instability to environmental factors, particularly water, has limited their utility. In this study, we introduce an innovative solution to the problem by leveraging the unique properties of natural beeswax as a protective coating of 2D-fluorinated phenylethylammonium lead iodide perovskite. These photodetectors show outstanding figures of merit, such as a responsivity of >2200 A/W and a detectivity of 2.4 × 1018 Jones. The hydrophobic nature of beeswax endows the 2D perovskite sensors with an unprecedented resilience to prolonged immersion in contaminated water, and it increases the lifespan of devices to a period longer than one year. At the same time, the biocompatibility of the beeswax and its self-cleaning properties make it possible to use the very same turbidity sensors for healthcare in photoplethysmography and monitor the human heartbeat with clear systolic and diastolic signatures. Beeswax-enabled multipurpose optoelectronics paves the way to sustainable electronics by ultimately reducing the need for multiple components.

Low Driving Voltage Electroluminescence Device for Integrated Visual Strain Sensing.

As a pivotal component in human-machine interactions, display devices have undergone rapid development in modern life. Displays such as alternative current electroluminescence|alternative current electroluminescent (ACEL) devices with high flexibility and long operational lifetimes are essential for wearable electronics. However, ACEL devices are constrained by their inherent high driving voltage and complex fabrication processes. Our work presents an easy blade-coating method for fabricating flexible ACEL display devices based on an all-solution process. By dispersing BaTiO3 and ZnS/Cu powder into waterborne polyurethane, we successfully combined dielectric and fluorescence functionalities within a single layer, significantly reducing the device's driving voltage. Additionally, the ionic conducting hydrogel was chosen as a transparent electrode to achieve good electrical contact and strong interfacial adhesion through in situ polymerization. Owing to the unique method, our ACEL device exhibits high flexibility, low driving voltage (20-100 V), high brightness (300+ cd/m2 at 60 V), and environmental friendliness. Furthermore, by repurposing the hydrogel electrode, we integrated strain visualization capabilities within a single device, highlighting its potential for applications such as wearable healthcare monitoring.

Aberration-corrected hybrid metalens for longwave infrared thermal imaging

Abstract Wide-angle metalenses in the longwave infrared have shown great advantages over the traditional refractive doublets or triplets, due to light weight, CMOS compatibility, and low cost. However, previous endeavors have been plagued by challenges including a narrow waveband, large F-number, distortion, and spherical aberration. To address these problems, this study introduces two dispersive metasurfaces, placed near the front focal plane and upon the rear plane of a plano-convex lens, to correct optical aberrations. Utilizing this methodology, we propose and experimentally demonstrate an aberration-corrected hybrid metalens for thermal imaging in the 8–12 μm waveband, featuring an FOV of 24°, F-number of 1.2, and diameter of 12.2 mm. The developed hybrid metalens rigorously evaluated, exhibits Modulation Transfer Function (MTF) values exceeding 0.2 at 20 Lp/mm across the full FOV, and features an average transmission of 48.7 %, a relative focusing efficiencies of up to 42.1 %, polarization insensitivity and broadband imaging capacity. These results emphasize the potential applications of our system in diverse fields, such as camera lenses, autonomous driving, healthcare, and environmental monitoring.

Fiber‐Based Miniature Strain Sensor with Fast Response and Low Hysteresis

AbstractFlexible and stretchable strain sensors are in high demand in sports performance monitoring, structural health monitoring, and biomedical applications. However, existing stretchable soft sensors, primarily based on soft polymer materials, often suffer from drawbacks, including high hysteresis, low durability, and delayed response. To overcome these limitations, a stretchable miniature fiber sensor comprised of a stretchable core tightly coiled with parallel conductive wires is introduced. This fiber sensor is flexible and stretchable while exhibiting low hysteresis, a remarkable theoretical resolution of 0.015%, a response time of <30 milliseconds, and excellent stability after extensive cycling tests of over 16 000 cycles. To understand and predict the capacitive sensor response of the proposed sensor, an analytical expression is derived and proved to have good agreements with both experimental results and numerical simulation. The potential of the strain sensor as a wearable device is demonstrated by embedding it into belts, gloves, and knee protectors. Additionally, the sensor can extend its applications beyond wearable devices, as demonstrated by its integration into bladder and life safety rope monitoring systems. The sensor is envisioned to have applications in the field of sports performance evaluations, health care monitoring, and structural safety assessments.

Elderly People Health Care Monitoring System Using Internet of Things (IOT) For Exploratory Data Analysis

The majority of senior persons are suffering a loss of physical condition their health is vital, the prevalence of illnesses has increased in the current age compared to previous generations, making health management in hospital sector. One of the concepts that can harness the advantages of the Internet of Things to better the older lifestyle is health monitoring for active and aided functioning. This approach aims to create a wireless communication system designed to remotely monitor patients. Each patient is wirelessly monitored based on Body temperature is measured via a temperature sensor, which gives an accuracy of around some certain value. The sensor carries a heart rate monitor with a pulse oximetry sensor. There are two light-emitting in it; one produces red light and the other infrared light. For tracking the amount of oxygen in the blood, two infrared LEDs are needed, but only one is needed to calculate the heart rate. The ESP 8266 (Node MCU) controller gathers the data and sends it to the specific application and Communication will be sent again to the consumers based on the data examination. The outcome results manage each patient's data separately and are used in hospitals, old age homes, and physically challenged people. Every sensor of data, such as temperature, heart rate, and abnormal heartbeat, will be managed in the database processed with GPS coordinates and data analyses using Think Speak Web Application is for informing about personal health data.

Electrochemical Detection of Nitrofurantoin using Green Synthesized Silver-doped Palladium Nanocluster-Modified Sensor

Aim: This study presents a novel green synthesis approach for successfully fabricating silver-doped palladium nanoclusters (Ag-Pd NCs) using the aqueous leaf extract of Strobilanthes kunthiana as a reducing and stabilizing agent. Background: The environmentally benign method offers a sustainable alternative to conventional chemical synthesis, circumventing hazardous chemicals and minimizing the generation of toxic byproducts. Objectives: The successful green synthesis of Ag-Pd NCs using Strobilanthes kunthiana leaf extract and their application as an efficient electrochemical sensing platform for determining nitrofurantoin (NFT). Methods:: The synthesized Ag-Pd NCs were extensively characterized by using diverse analytical techniques, including UV-Vis spectroscopy, X-ray diffraction (XRD), field-emission scanning electron microscopy (FE-SEM), energy-dispersive X-ray spectroscopy (EDS) and cyclic voltammetry (CV). Results: As-synthesized Ag-Pd NCs were employed as a sensing platform for electrochemical detection of NFT, an important antibiotic widely used in clinical applications. The electrochemical method demonstrated a remarkable sensitivity of about 1.56 μA μM−1 cm−2, the lowest detection limit (LOD) of 3.2 μM and a linear range of determination from 5 to 210 μM. This new electrochemical sensor exhibited excellent stability and reproducibility, making it suitable for practical applications in real-world samples. Conclusions: The green synthesis of Ag-Pd NCs using Strobilanthes kunthiana leaf extract and their application as an efficient electrochemical sensing platform for detecting NFT was demonstrated. The combination of green synthesis and advanced electrochemical sensing underscores the potential of these nanomaterials in developing environmentally friendly sensors for pharmaceutical analysis and clinical diagnostics. The findings presented herein will contribute to the growing field of green nanotechnology and sustainable sensor development for advanced healthcare and environmental monitoring.

Secure and portable health monitoring system for cyber physical systems in internet of things

The incorporation of Internet of Things (IoT) technology into healthcare has introduced vulnerabilities within medical devices, thereby posing noteworthy risks to both patient safety and the inclusive integrity of healthcare systems. This contribution addresses the security concern of health care monitoring system. The STM32BL-475E IoT node along with its sensors provides a greener way to monitor health remotely with data privacy, along with this many such applications can be containerized to enable multiuser application in virtual set up. This paper proposes a methodology of dynamic key generation highlighting the potential for transformative healthcare advancements. The proposed work provides an improved avalanche effect of 51.6%, minimal RAM utilization of 3.49% and a randomness of 2^128 times which is very unpredictable to detect. This paper aims to integrate these technologies to create a resilient health monitoring framework with greater data indemnity. The integration of artificial intelligence with the proposed design is reserved as future work.

A new framework to enhance healthcare monitoring using patient-centric predictive analysis

In the contemporary healthcare landscape, various intelligent automated approaches are revolutionizing healthcare tasks. Learning concepts are pivotal for activities like comprehending acquired data and monitoring patient behavior. Among patient-centric concerns, addressing data heterogeneity, extraction, and prediction challenges is crucial. To enhance patient monitoring using care indicators like cost and length of stay at healthcare centers, many researchers found a model for automated tools, but do not have the artificial intelligence (AI) based models as of now. Therefore, this research study will propose an AI and internet of things (IoT) integrated automated approach with smart sensors called the “PatientE” framework with heterogeneity and patient data. Employing certain rules for data extraction to form a distinct representation, our model integrates pre-treatment information and employs a modified combined random forest, long-short term memory (LSTM), and bidirectional long-short term memory (BiLSTM) algorithm for predictive post-treatment monitoring. This framework, synergizing AI, IoT, and advanced neural networks, facilitates real-time health monitoring, especially focusing on breast cancer patients. Embracing pre-treatment, in-treatment, and post-treatment phases, our model aims for accurate diagnosis, improved cost-efficiency, and extended stays. The evaluation underscores scalability, reliability enhancement, and validates the framework's efficacy in transforming healthcare practices.

Semi-liquid metal-based highly permeable and adhesive electronic skin inspired by spider web

Soft and stretchable electronics have garnered significant attention in various fields, such as wearable electronics, electronic skins, and soft robotics. However, current wearable electronics made from materials like conductive elastomers, hydrogels, and liquid metals face limitations, including low permeability, poor adhesion, inadequate conductivity, and limited stretchability. These issues hinder their effectiveness in long-term healthcare monitoring and exercise monitoring. To address these challenges, we introduce a novel design of web-droplet-like electronics featuring a semi-liquid metal coating for wearable applications. This innovative design offers high permeability, excellent stretchability, strong adhesion, and good conductivity for the electronic skin. The unique structure, inspired by the architecture of a spider web, significantly enhances air permeability compared to commercial breathable patches. Furthermore, the distribution of polyborosiloxane mimics the adhesive properties of spider web mucus, while the use of semi-liquid metals in this design results in remarkable conductivity (9 × 106 S/m) and tensile performance (up to 850% strain). This advanced electronic skin technology enables long-term monitoring of various physiological parameters and supports machine learning recognition functions with unparalleled advantages. This web-droplet structure design strategy holds great promise for commercial applications in medical health monitoring and disease diagnosis.

Bioinspired multifunctional MXene-decorated textile for thermal management, durable self-cleaning, bio-protection and wearable strain sensor

Textile-based wearable electronic devices have gained significant attention due to their promising applications in personal thermal management, human healthcare monitoring, and human motion detection. Herein, a multifunctional MXene-decorated textile has been fabricated as a promising wearable electronic textile for thermal management, durable self-cleaning, bio-protection and wearable strain sensor. In this study, Ti3C2Tx MXene nanosheets, which are followed by surface engineering with mussel-inspired polydopamine coatings and hydrophobic octadecyl trimethoxysilane, are chosen as active materials for constructing multifunctional MXene-decorated textile. Thus, the multifunctional MXene-decorated textile (Cotton@ODMX) is fabricated by spray-coating the hydrophobic MXene-based composites (ODMX) onto the surface of the cotton textile. Such a Cotton@ODMX epitomizes a highly innovative electronic textile that not only retains the flexibility of the original textile, but also possesses remarkable superhydrophobic properties, outstanding photothermal conversion performance, photothermal self-healing capabilities, environmental stability and exceptional photothermal sterilization capabilities. More importantly, Cotton@ODMX is demonstrated to serve as a wearable strain sensor to monitor fundamental human movements, thus highlighting its impressive sensing capabilities. This work offers valuable insights for the development of multifunctional, flexible and wearable electronic devices with promising potential in personal thermal management, human healthcare monitoring, and human motion detection.