Intelligent Wearable Sensors Interconnected with Advanced Wound Dressing Bandages for Contactless Chronic Skin Monitoring: Artificial Intelligence for Predicting Tissue Regeneration.

Toward the adoption of artificial intelligence-enabled wearable sensors interconnected with intelligent medical objects, this contactless multi-intelligent wearable technology provides a solution for healthcare to monitor hard-to-heal wounds and create optimal efficiencies for clinical professionals by minimizing the risk of disease infection. This article addressed a flexible artificial intelligence-guiding (FLEX-AI) wearable sensor that can be operated with a deep artificial neural network (deep ANN) algorithm for chronic wound monitoring via short-range communication toward a seamless, MXENE-attached, radio frequency-tuned, and wound dressing-integrated (SMART-WD) bandage. Based on a supervised training set of on-contact pH-responsive voltage output, the confusion matrix for healing-stage recognition from this deep ANN machine learning revealed an accuracy of 94.6% for the contactless measurement. The core analytical design of these smart bandages integrated wound dressing of poly(vinyl acrylic) gel@PANI/Cu2O NPs for instigating pH-responsive current during the wound healing process. Effectively, a chip-free bandage tag was fabricated with a capacitive Mxene/PTFE electret and adhesive acrylic inductance to match the resonance frequency generated by the intelligent wearable antenna. Under zero-current electrochemical potential, the wound dressing attained a slope of -76 mV/pH. With the higher activation voltage applied toward the wound dressing electrodes, cuprous ions intercalated more into the hybrid PVA gel/PANI shell, resulting in an exponential increase of the two-terminal current response. The healing phase diagram was classified into regimes of fast-curing, slow-curing, and no-curing for skin disease treatment with corticosteroids. Ultimately, the near-field sensing technology offers adequate information for guiding treatment decisions as well as drug effectiveness for wound care.

Lab-on-Eyeglasses to Monitor Kidneys and Strengthen Vulnerable Populations in Pandemics: Machine Learning in Predicting Serum Creatinine Using Tear Creatinine.

The serum creatinine level is commonly recognized as a measure of glomerular filtration rate (GFR) and is defined as an indicator of overall renal health. A typical procedure in determining kidney performance is venipuncture to obtain serum creatinine in the blood, which requires a skilled technician to perform on a laboratory basis and multiple clinical steps to acquire a meaningful result. Recently, wearable sensors have undergone immense development, especially for noninvasive health monitoring without a need for a blood sample. This article addresses a fiber-based sensing device selective for tear creatinine, which was fabricated using a copper-containing benzenedicarboxylate (BDC) metal-organic framework (MOF) bound with graphene oxide-Cu(II) and hybridized with Cu2O nanoparticles (NPs). Density functional theory (DFT) was employed to study the binding energies of creatinine toward the ternary hybrid materials that irreversibly occurred at pendant copper ions attached with the BDC segments. Electrochemical impedance spectroscopy (EIS) was utilized to probe the unique charge-transfer resistances of the derived sensing materials. The single-use modified sensor achieved 95.1% selectivity efficiency toward the determination of tear creatinine contents from 1.6 to 2400 µM of 10 repeated measurements in the presence of interfering species of dopamine, urea, and uric acid. The machine learning with the supervised training estimated 83.3% algorithm accuracy to distinguish among low, moderate, and high normal serum creatinine by evaluating tear creatinine. With only one step of collecting tears, this lab-on-eyeglasses with disposable hybrid textile electrodes selective for tear creatinine may be greatly beneficial for point-of-care (POC) kidney monitoring for vulnerable populations remotely, especially during pandemics.

Satellite-Based Sensor for Environmental Heat-Stress Sweat Creatinine Monitoring: The Remote Artificial Intelligence-Assisted Epidermal Wearable Sensing for Health Evaluation.

Wearable human sweat sensors have offered a great prospect in epidermal detection for self-monitoring and health evaluation. These on-body epidermal sensors can be integrated with the Internet of Things (IoT) as augmented diagnostics tools for telehealth applications, especially for noninvasive health monitoring without using blood contents. One of many great benefits in utilizing sweat as biofluid is the capability of instantaneously continuous diagnosis during normal day-to-day activities. Here, we revealed a textile-based sweat sensor selective for perspired creatinine that is prepared by coating poly(vinyl alcohol) (PVA)-Cu2+-poly(3,4-ethylenedioxythiophene) polystyrenesulfonate (PEDOT:PSS) and cuprous oxide nanoparticles on stretchable nylon, is equipped with heart rate monitoring and a satellite-communication device to locate wearers, and incorporates machine learning to predict the levels of environmental heat stress. Electrochemical impedance spectroscopy (EIS) was used to investigate different charge-transfer resistances of PVA and PEDOT:PSS with cuprous and cuprite ions induced by single-chain and ionic cross-linking. Furthermore, density function theory (DFT) studies predicted the catalytic binding of sweat creatinine with the sensing materials that occurred at thiophene rings. The hybrid sensor successfully achieved 96.3% selectivity efficacy toward the determination of creatinine contents from 0.4 to 960 µM in the presence of interfering species of glucose, urea, uric acid, and NaCl as well as retained 92.1% selectivity efficacy in the existence of unspecified human sweat interference. Ultimately, the hand-grip portable device can offer the great benefit of continuous health monitoring and provide the location of any wearer. This augmented telemedicine sensor may represent the first remote low-cost and artificial-intelligence-based sensing device selective for heat-stress sweat creatinine.

Salivary Creatinine Detection Using a Cu(I)/Cu(II) Catalyst Layer of a Supercapacitive Hybrid Sensor: A Wireless IoT Device To Monitor Kidney Diseases for Remote Medical Mobility.

The stress-free electrochemical-based sensor equipped with the Internet of Things (IoT) device for salivary creatinine determination was fabricated for point-of-care (POC) diagnosis of advanced kidney disorders. Beneficial and real-time data readout for preventive diagnosis and clinical evaluation of chronic kidney diseases (CKD) at different stages and renal dysfunction can be acquired by noninvasive monitoring of the creatinine amounts in saliva. The direct determination and real-time response of salivary creatinine can be attained using the supercapacitor-based sensor of cuprous oxide nanoparticles entrapped by the synergistically cross-linked poly(acrylic acid) (PAA) gel-Cu2+ and Nafion perfluorinated membrane fabricated on a screen-printed carbon electrode (SPCE). Here, we demonstrated that the degree of renal illness could be evaluated using salivary creatinine detection via a catalytic mechanism as Cu2+ ions bound irreversibly with C═N functional groups of creatinine. Besides, the computer simulation was performed to study the interaction between 5 functional groups of creatinine toward acrylic gel-Cu2+. The linear increment between the obtained anodic currents and creatinine concentrations varying from 1 to 2000 µM was accomplished with a selectivity efficiency of 97.2%. Nyquist plots obtained by electrochemical impedance spectroscopy (EIS) validated that the increment of impedance changes strongly dependent on the amount of detected creatinine both in artificial and in human saliva. The porosity features were observed in this interconnected nanocomposite and correlated with Nafion doping. Successively, the friendly portable device was invented and integrated saliva sampling with miniaturized, low-cost IoT electronics of world-location mapping, representing the first remote medical sensor focusing on salivary creatinine sensing.

Evidence of Cu(I) Coupling with Creatinine Using Cuprous Nanoparticles Encapsulated with Polyacrylic Acid Gel-Cu(II) in Facilitating the Determination of Advanced Kidney Dysfunctions.

An electrochemical-based sensor created for creatinine detection has been developed for early point-of-care (POC) of diagnosis of renal illnesses. Useful information for the preventive diagnosis and clinical treatments of congenital disorders of creatinine mechanism, advanced liver and kidney diseases, and renal dysfunction can be obtained by the noninvasive evaluation of the creatinine levels in urine. The direct detection of creatinine can be achieved using the modified nanocomposite of cuprous nanoparticles encapsulated by polyacrylic acid (PAA) gel-Cu(II) fabricating on a screen-printed carbon electrode. Here, we report that the degree of kidney dysfunction failure can be determined by an amount of Cu(I) bound with the creatinine through the adsorptive mechanism on the modified electrode. Under cyclic voltammetry scans, the amount of creatinine was measured from the adsorptive signals of the redox peak current identifying the Cu(I)-creatinine complex with a natural logarithm of the creatinine concentration ranging from 200 µM to 100 mM. For this detection range, the theoretical calculation was postulated to describe experimental behaviors of the adsorptive mechanism as creatinine diffused to adsorb on the composite-modified electrode to reduce oxidized copper nanoparticles and transformed to Cu(II)-creatinine complexes. Interestingly, there was evidence that anodic peak potentials had been reduced in magnitudes and shifted negatively by natural logarithm during the formation of the Cu(I)-creatinine complex. For practical usage in POC technology, the creatinine detection in interference was carried out using differential pulse voltammetry to solely determine faradaic currents of creatinine-copper formation. With the interference of urea, glucose, ascorbic acid, glycine, and uric acid in artificial urine, the sensor showed promising results of the interference-free determination with 99.4% sensitivity efficiency, whereas for human urine interference, this sensor showed 85% sensitivity efficiency in detecting creatinine. This shows that this composite-modified sensor (PAA gel-Cu(II)/Cu2O NPs) has great potential for use in the next-generation devices for creatinine sensing to determine the progression in kidney dysfunctions.