RESUMEN
Knee osteoarthritis is a major cause of global disability and is a major cost for the healthcare system. Lower extremity loading is a determinant of knee osteoarthritis onset and progression; however, technology that assists rehabilitative clinicians in optimizing key metrics of lower extremity loading is significantly limited. The peak vertical component of the ground reaction force (vGRF) in the first 50% of stance is highly associated with biological and patient-reported outcomes linked to knee osteoarthritis symptoms. Monitoring and maintaining typical vGRF profiles may support healthy gait biomechanics and joint tissue loading to prevent the onset and progression of knee osteoarthritis. Yet, the optimal number of sensors and sensor placements for predicting accurate vGRF from accelerometry remains unknown. Our goals were to: 1) determine how many sensors and what sensor locations yielded the most accurate vGRF loading peak estimates during walking; and 2) characterize how prescribing different loading conditions affected vGRF loading peak estimates. We asked 20 young adult participants to wear 5 accelerometers on their waist, shanks, and feet and walk on a force-instrumented treadmill during control and targeted biofeedback conditions prompting 5% underloading and overloading vGRFs. We trained and tested machine learning models to estimate vGRF from the various sensor accelerometer inputs and identified which combinations were most accurate. We found that a neural network using one accelerometer at the waist yielded the most accurate loading peak vGRF estimates during walking, with average errors of 4.4% body weight. The waist-only configuration was able to distinguish between control and overloading conditions prescribed using biofeedback, matching measured vGRF outcomes. Including foot or shank acceleration signals in the model reduced accuracy, particularly for the overloading condition. Our results suggest that a system designed to monitor changes in walking vGRF or to deploy targeted biofeedback may only need a single accelerometer located at the waist for healthy participants.
RESUMEN
In this work, direct electron transfer (DET)-type extended gate field effect transistor (EGFET) enzymatic sensors were developed by employing DET-type or quasi-DET-type enzymes to detect glucose or lactate in both 100 mM potassium phosphate buffer and artificial sweat. The system employed either a DET-type glucose dehydrogenase or a quasi-DET-type lactate oxidase, the latter of which was a mutant enzyme with suppressed oxidase activity and modified with amine-reactive phenazine ethosulfate. These enzymes were immobilized on the extended gate electrodes. Changes in the measured transistor drain current (ID) resulting from changes to the working electrode junction potential (φ) were observed as glucose and lactate concentrations were varied. Calibration curves were generated for both absolute measured ID and ΔID (normalized to a blank solution containing no substrate) to account for variations in enzyme immobilization and conjugation to the mediator and variations in reference electrode potential. This work resulted in a limit of detection of 53.9 µM (based on ID) for glucose and 2.12 mM (based on ID) for lactate, respectively. The DET-type and Quasi-DET-type EGFET enzymatic sensor was then modeled using the case of the lactate sensor as an equivalent circuit to validate the principle of sensor operation being driven through OCP changes caused by the substrate-enzyme interaction. The model showed slight deviation from collected empirical data with 7.3% error for the slope and 8.6% error for the y-intercept.
Asunto(s)
Técnicas Biosensibles , Electrones , Técnicas Biosensibles/métodos , Glucosa/metabolismo , Glucosa 1-Deshidrogenasa/metabolismo , Ácido Láctico , Enzimas Inmovilizadas/metabolismo , ElectrodosRESUMEN
Glutamate and dopamine (DA) represent two key contributors to striatal functioning, a region of the brain that is essential to motor coordination and motivated behavior. While electroanalytical techniques can be utilized for rapid, spatially resolved detection of DA in the interferent-rich brain environment, glutamate, a nonelectroactive analyte, cannot be directly detected using electroanalytical techniques. However, it can be probed using enzyme-based sensors, which generate an electroactive reporter in the presence of glutamate. The vast majority of glutamate biosensors have relied on amperometric sensing, which is an inherently nonselective detection technique. This approach necessitates the use of complex and performance-limiting modifications to ensure the desired single-analyte specificity. Here, we present a novel glutamate microbiosensor fabricated on a carbon-fiber microelectrode substrate and coupled with fast-scan cyclic voltammetry (FSCV) to enable the simultaneous quantification of glutamate and DA at single recording sites in the brain, which is impossible when using typical amperometric approaches. The glutamate microbiosensors were characterized for sensitivity, stability, and selectivity by using a voltammetric waveform optimized for the simultaneous detection of both species. The applicability of these sensors for the investigation of neural circuits was validated in the rat ventral striatum. Electrically evoked glutamate and DA release were recorded at single-micrometer-scale locations before and after pharmacological manipulation of glutamatergic signaling. Our novel glutamate microbiosensor advances the state of the art by providing a powerful tool for probing coordination between these two species in a way that has previously not been possible.
Asunto(s)
Dopamina , Ácido Glutámico , Ratas , Animales , Ratas Sprague-Dawley , Fibra de Carbono , EncéfaloRESUMEN
Carbon nanotubes (CNTs) are known for their excellent conductive properties. Here, we present two novel methods, "sandwich" (sCNT) and dual deposition (DD CNT), for incorporating CNTs into electrospun polycaprolactone (PCL) and gelatin scaffolds to increase their conductance. Based on CNT percentage, the DD CNT scaffolds contain significantly higher quantities of CNTs than the sCNT scaffolds. The inclusion of CNTs increased the electrical conductance of scaffolds from 0.0 ± 0.00 kS (non-CNT) to 0.54 ± 0.10 kS (sCNT) and 5.22 ± 0.49 kS (DD CNT) when measured parallel to CNT arrays and to 0.25 ± 0.003 kS (sCNT) and 2.85 ± 1.12 (DD CNT) when measured orthogonally to CNT arrays. The inclusion of CNTs increased fiber diameter and pore size, promoting cellular migration into the scaffolds. CNT inclusion also decreased the degradation rate and increased hydrophobicity of scaffolds. Additionally, CNT inclusion increased Young's modulus and failure load of scaffolds, increasing their mechanical robustness. Murine fibroblasts were maintained on the scaffolds for 30 days, demonstrating high cytocompatibility. The increased conductivity and high cytocompatibility of the CNT-incorporated scaffolds make them appropriate candidates for future use in cardiac and neural tissue engineering.