Development of an Optical System for Strain Drop Measurement of Osteosarcoma Cells on Substrates with Different Stiffness.

Adherent cells perceive mechanical feedback from the underlying matrix and convert it into biochemical signals through a process known as mechanotransduction. The response to changes in the microenvironment relies on the cell's mechanical properties, including elasticity, which was recently identified as a biomarker for various diseases. Here, we propose the design, development, and characterization of a new system for the measurement of adherent cells' strain drop, a parameter correlated with cells' elasticity. To consider the interplay between adherent cells and the host extracellular matrix, cell stretching was combined with adhesion on substrates with different stiffnesses. The technique is based on the linear stretching of silicone chambers, high-speed image acquisition, and feedback for image centering. The system was characterized in terms of the strain homogeneity, impact of collagen coating, centering capability, and sensitivity. Subsequently, it was employed to measure the strain drop of two osteosarcoma cell lines, low-aggressive osteoblast-like SaOS-2 and high-aggressive 143B, cultured on two different substrates to recall the stiffness of the bone and lung extracellular matrices. Results demonstrated good substrate homogeneity, a negligible effect of the collagen coating, and an accurate image centering. Finally, the experimental results showed an average strain drop that was lower in the 143B cells in comparison with the SaOS-2 cells in all the tested conditions.

Determination of a Measurement Procedure for the Study of Cells' Dielectric Properties through Descriptive Statistic.

This paper presents a measurement procedure for analyzing the dielectric properties of cells using descriptive statistics. The study focuses on four cancer cell lines (MDA-MB-231 and MCF-7 breast cancer, SaOS-2, and 143B osteosarcoma) and DMEM culture medium, utilizing the Lorentzian fit model of the return-loss function. The measurements are performed using a circular patch resonator with a 40 mm diameter, powered by a miniVNA operating in the frequency range of 1 MHz to 3 GHz. Eight specimens are prepared for each group to ensure reliability, and the return loss is recorded ten times for each specimen. Various statistical parameters are calculated and evaluated, including the average value, standard deviation, coefficient of variation, and relative error between the average and the first values. The results demonstrate that one single acquisition highly represents the entire set of ten data points, especially for the resonant frequency, with an accuracy error lower than 0.05%. These findings have significant implications for the methodological approach to detecting cells' dielectric properties, as they substantially reduce time and preserve the specimens without compromising the accuracy of the experimental results.

Remodeled eX vivo muscle engineered tissue improves heart function after chronic myocardial ischemia.

The adult heart displays poor reparative capacities after injury. Cell transplantation and tissue engineering approaches have emerged as possible therapeutic options. Several stem cell populations have been largely used to treat the infarcted myocardium. Nevertheless, transplanted cells displayed limited ability to establish functional connections with the host cardiomyocytes. In this study, we provide a new experimental tool, named 3D eX vivo muscle engineered tissue (X-MET), to define the contribution of mechanical stimuli in triggering functional remodeling and to rescue cardiac ischemia. We revealed that mechanical stimuli trigger a functional remodeling of the 3D skeletal muscle system toward a cardiac muscle-like structure. This was supported by molecular and functional analyses, demonstrating that remodeled X-MET expresses relevant markers of functional cardiomyocytes, compared to unstimulated and to 2D- skeletal muscle culture system. Interestingly, transplanted remodeled X-MET preserved heart function in a murine model of chronic myocardial ischemia and increased survival of transplanted injured mice. X-MET implantation resulted in repression of pro-inflammatory cytokines, induction of anti-inflammatory cytokines, and reduction in collagen deposition. Altogether, our findings indicate that biomechanical stimulation induced a cardiac functional remodeling of X-MET, which showed promising seminal results as a therapeutic product for the development of novel strategies for regenerative medicine.

Machine Learning and Wearable Sensors for the Early Detection of Balance Disorders in Parkinson's Disease.

Dynamic posturography combined with wearable sensors has high sensitivity in recognizing subclinical balance abnormalities in patients with Parkinson's disease (PD). However, this approach is burdened by a high analytical load for motion analysis, potentially limiting a routine application in clinical practice. In this study, we used machine learning to distinguish PD patients from controls, as well as patients under and not under dopaminergic therapy (i.e., ON and OFF states), based on kinematic measures recorded during dynamic posturography through portable sensors. We compared 52 different classifiers derived from Decision Tree, K-Nearest Neighbor, Support Vector Machine and Artificial Neural Network with different kernel functions to automatically analyze reactive postural responses to yaw perturbations recorded through IMUs in 20 PD patients and 15 healthy subjects. To identify the most efficient machine learning algorithm, we applied three threshold-based selection criteria (i.e., accuracy, recall and precision) and one evaluation criterion (i.e., goodness index). Twenty-one out of 52 classifiers passed the three selection criteria based on a threshold of 80%. Among these, only nine classifiers were considered "optimum" in distinguishing PD patients from healthy subjects according to a goodness index ≤ 0.25. The Fine K-Nearest Neighbor was the best-performing algorithm in the automatic classification of PD patients and healthy subjects, irrespective of therapeutic condition. By contrast, none of the classifiers passed the three threshold-based selection criteria in the comparison of patients in ON and OFF states. Overall, machine learning is a suitable solution for the early identification of balance disorders in PD through the automatic analysis of kinematic data from dynamic posturography.

The Development of an Innovative Embedded Sensor for the Optical Measurement of Ex-Vivo Engineered Muscle Tissue Contractility.

Tissue engineering is a multidisciplinary approach focused on the development of innovative bioartificial substitutes for damaged organs and tissues. For skeletal muscle, the measurement of contractile capability represents a crucial aspect for tissue replacement, drug screening and personalized medicine. To date, the measurement of engineered muscle tissues is rather invasive and not continuous. In this context, we proposed an innovative sensor for the continuous monitoring of engineered-muscle-tissue contractility through an embedded technique. The sensor is based on the calibrated deflection of one of the engineered tissue's supporting pins, whose movements are measured using a noninvasive optical method. The sensor was calibrated to return force values through the use of a step linear motor and a micro-force transducer. Experimental results showed that the embedded sensor did not alter the correct maturation of the engineered muscle tissue. Finally, as proof of concept, we demonstrated the ability of the sensor to capture alterations in the force contractility of the engineered muscle tissues subjected to serum deprivation.

A Novel Microwave Resonant Sensor for Measuring Cancer Cell Line Aggressiveness.

The measurement of biological tissues' dielectric properties plays a crucial role in determining the state of health, and recent studies have reported microwave biosensing to be an innovative method with great potential in this field. Research has been conducted from the tissue level to the cellular level but, to date, cellular adhesion has never been considered. In addition, conventional systems for diagnosing tumor aggressiveness, such as a biopsy, are rather expensive and invasive. Here, we propose a novel microwave approach for biosensing adherent cancer cells with different malignancy degrees. A circular patch resonator was designed adjusting its structure to a standard Petri dish and a network analyzer was employed. Then, the resonator was realized and used to test two groups of different cancer cell lines, based on various tumor types and aggressiveness: low- and high-aggressive osteosarcoma cell lines (SaOS-2 and 143B, respectively), and low- and high-aggressive breast cancer cell lines (MCF-7 and MDA-MB-231, respectively). The experimental results showed that the sensitivity of the sensor was high, in particular when measuring the resonant frequency. Finally, the sensor showed a good ability to distinguish low-metastatic and high-metastatic cells, paving the way to the development of more complex measurement systems for noninvasive tissue diagnosis.

Development of a Novel Technique for the Measurement of Neuromuscular Junction Functionality in Isotonic Conditions.

Introduction: The neuromuscular junction (NMJ) is a chemical synapse responsible for converting electrical pulses generated by the motor neuron into electrical activity in muscle fibers, and is severely impaired in various diseases, such as Amyotrophic Lateral Sclerosis (ALS). Here, we proposed a novel technique to measure, for the first time, NMJ functionality in isotonic conditions, which better reflect muscle physiological activity. Methods: We employed the in-situ testing technique, studied a proper placing of two pairs of wire electrodes for nerve and muscle stimulation, developed an extensive testing protocol, and proposed a novel parameter, the Isotonic Neurotransmission Failure (INF), to properly capture the impairments in neurotransmission during isotonic fatigue. We employed wild-type mice to assess the feasibility of the proposed technique, and the ALS model SOD1G93A mice to demonstrate the validity of the INF. Results: Results confirmed the measurement accuracy in term of average value and coefficient of variation of the parameters measured through nerve stimulation in comparison with the corresponding values obtained for membrane stimulation. The INF values computed for the SOD1G93A tibialis anterior muscles pointed out an impairment of ALS mice during the isotonic fatigue test, whereas, as expected, their resistance to fatigue was higher. Conclusions: In this work we devised a novel technique and a new parameter for a deep assessment of NMJ functionality in isotonic conditions, including fatigue, which is the most crucial condition for the neuronal signal transmission. This technique may be applied to other animal models, to unravel the mechanisms behind muscle-nerve impairments in other neurodegenerative pathologies.

Permittivity-Based Water Content Calibration Measurement in Wood-Based Cultural Heritage: A Preliminary Study.

In this work, the dielectric permittivity of four kinds of wood (Fir, Poplar, Oak, and Beech Tree), used in Italian Artworks and structures, was characterized at different humidity levels. Measurements were carried out using three different probes connected to a bench vector network analyzer: a standard WR90 X-band waveguide, a WR430 waveguide, and an open-ended coaxial probe. In particular, we investigated the dispersion model for the four wood species, showing how a log-fit model of the open-ended data presents a determination coefficient R2 > 0.990 in the 1−12 GHz frequency range. This result has proven helpful to fill the frequency gap between the measurements obtained at different water contents with the two waveguide probes showing an R2 > 0.93. Furthermore, correlating the log-fit vertical shift with the water content, it was possible to find a calibration curve with a linear characteristic. These experimental results will be helpful for on-site non-invasive water monitoring of wooden artworks or structures. Moreover, the final results show how the open-ended coaxial probe, with a measurement deviation lower than 7% from the waveguide measurements, may be used directly as a non-invasive sensor for on-site measurements.

Early balance impairment in Parkinson's Disease: Evidence from Robot-assisted axial rotations.

OBJECTIVE: Early postural instability (PI) is a red flag for the diagnosis of Parkinson's disease (PD). Several patients, however, fall within the first three years of disease, particularly when turning. We investigated whether PD patients, without clinically overt PI, manifest abnormal reactive postural responses to ecological perturbations resembling turning. METHODS: Fifteen healthy subjects and 20 patients without clinically overt PI, under and not under L-Dopa, underwent dynamic posturography during axial rotations around the longitudinal axis, provided by a robotic mechatronic platform. We measured reactive postural responses, including body displacement and reciprocal movements of the head, trunk, and pelvis, by using a network of three wearable inertial sensors. RESULTS: Patients showed higher body displacement of the head, trunk and pelvis, and lower joint movements at the lumbo-sacral junction than controls. Conversely, movements at the cranio-cervical junction were normal in PD. L-Dopa left reactive postural responses unchanged. CONCLUSIONS: Patients with PD without clinically overt PI manifest abnormal reactive postural responses to axial rotations, unresponsive to L-Dopa. The biomechanical model resulting from our experimental approach supports novel pathophysiological hypotheses of abnormal axial rotations in PD. SIGNIFICANCE: PD patients without clinically overt PI present subclinical balance impairment during axial rotations, unresponsive to L-Dopa.

Estimation of Human Center of Mass Position through the Inertial Sensors-Based Methods in Postural Tasks: An Accuracy Evaluation.

The estimation of the body's center of mass (CoM) trajectory is typically obtained using force platforms, or optoelectronic systems (OS), bounding the assessment inside a laboratory setting. The use of magneto-inertial measurement units (MIMUs) allows for more ecological evaluations, and previous studies proposed methods based on either a single sensor or a sensors' network. In this study, we compared the accuracy of two methods based on MIMUs. Body CoM was estimated during six postural tasks performed by 15 healthy subjects, using data collected by a single sensor on the pelvis (Strapdown Integration Method, SDI), and seven sensors on the pelvis and lower limbs (Biomechanical Model, BM). The accuracy of the two methods was compared in terms of RMSE and estimation of posturographic parameters, using an OS as reference. The RMSE of the SDI was lower in tasks with little or no oscillations, while the BM outperformed in tasks with greater CoM displacement. Moreover, higher correlation coefficients were obtained between the posturographic parameters obtained with the BM and the OS. Our findings showed that the estimation of CoM displacement based on MIMU was reasonably accurate, and the use of the inertial sensors network methods should be preferred to estimate the kinematic parameters.

A wearable heart rate measurement device for children with autism spectrum disorder.

Autism spectrum disorder (ASD) is a neurodevelopmental condition characterized by early impairment in social and communication domains and autonomic nervous system unbalance. This study evaluated heart rate (HR) as a possible indicator of stress response in children with ASD as compared to children with language disorder (LD). Twenty-four patients [mean age = 42.62 months; SD = 8.14 months,12 with ASD (10 M/2F) and 12 with LD (8 M/4F)] underwent clinical [Leiter International Performance Scale-Revised, Autism Diagnostic Observation Schedule, second edition (ADOS-2)] and physiological evaluation (HR monitoring) during five interactive activities, while wearing an HR measurement device. IQ (ASD:IQ = 103.33 ± 12.85 vs. LD:IQ = 111.00 ± 8.88, p = 0.103) and fluid reasoning on the Leiter-R Scale were within the normal range in all subjects. Increased HR during the third activity (ADOS-2 bubble play) significantly correlated with autistic symptoms (r = 0.415; p = 0.044), while correlations between ADOS-2 total score and HR during the first activity (ADOS-2 free play; r = 0.368; p = 0.077), second activity (Leiter-R figure ground subscale; r = 0.373, p = 0.073), and fifth activity (ADOS-2 anticipation of a routine with objects; r = 0.368; p = 0.076) did not quite reach statistical significance. Applying a linear regression model, we found that the ADOS-2 total score significantly influenced HR variations (p = 0.023). HR monitoring may provide a better understanding of the stress-provoking situations for children with ASD. Furthermore, it could help clinicians detect the impact of the stressful condition on the autistic core and adress treatment strategy.

Muscle Synergies in Parkinson's Disease.

Over the last two decades, experimental studies in humans and other vertebrates have increasingly used muscle synergy analysis as a computational tool to examine the physiological basis of motor control. The theoretical background of muscle synergies is based on the potential ability of the motor system to coordinate muscles groups as a single unit, thus reducing high-dimensional data to low-dimensional elements. Muscle synergy analysis may represent a new framework to examine the pathophysiological basis of specific motor symptoms in Parkinson's disease (PD), including balance and gait disorders that are often unresponsive to treatment. The precise mechanisms contributing to these motor symptoms in PD remain largely unknown. A better understanding of the pathophysiology of balance and gait disorders in PD is necessary to develop new therapeutic strategies. This narrative review discusses muscle synergies in the evaluation of motor symptoms in PD. We first discuss the theoretical background and computational methods for muscle synergy extraction from physiological data. We then critically examine studies assessing muscle synergies in PD during different motor tasks including balance, gait and upper limb movements. Finally, we speculate about the prospects and challenges of muscle synergy analysis in order to promote future research protocols in PD.

Measuring Biomechanical Risk in Lifting Load Tasks Through Wearable System and Machine-Learning Approach.

Ergonomics evaluation through measurements of biomechanical parameters in real time has a great potential in reducing non-fatal occupational injuries, such as work-related musculoskeletal disorders. Assuming a correct posture guarantees the avoidance of high stress on the back and on the lower extremities, while an incorrect posture increases spinal stress. Here, we propose a solution for the recognition of postural patterns through wearable sensors and machine-learning algorithms fed with kinematic data. Twenty-six healthy subjects equipped with eight wireless inertial measurement units (IMUs) performed manual material handling tasks, such as lifting and releasing small loads, with two postural patterns: correctly and incorrectly. Measurements of kinematic parameters, such as the range of motion of lower limb and lumbosacral joints, along with the displacement of the trunk with respect to the pelvis, were estimated from IMU measurements through a biomechanical model. Statistical differences were found for all kinematic parameters between the correct and the incorrect postures (p < 0.01). Moreover, with the weight increase of load in the lifting task, changes in hip and trunk kinematics were observed (p < 0.01). To automatically identify the two postures, a supervised machine-learning algorithm, a support vector machine, was trained, and an accuracy of 99.4% (specificity of 100%) was reached by using the measurements of all kinematic parameters as features. Meanwhile, an accuracy of 76.9% (specificity of 76.9%) was reached by using the measurements of kinematic parameters related to the trunk body segment.

Using an ankle robotic device for motor performance and motor learning evaluation.

In this paper we performed the evaluation of ankle motor performance and motor learning during a goal-directed task, executed using the pediAnklebot robot. The protocol consisted of 3 phases (Familiarization, Adaptation, and Wash Out) repeated one time for each movement direction (plantarflexion, dorsiflexion, inversion, and eversion). During Familiarization and Wash out subjects performed goal-directed movements in unperturbed environment, whereas during Adaptation phase, a curl viscous force field was applied and it was randomly removed 10 times out of 200. Ankle motor performance was evaluated by means of a set of indices grouped into: accuracy, smoothness, temporal, and stopping indices. Learning Index was calculated to study the motor learning during the adaptation phase, which was subdivided into 5 temporal intervals (target sets). The outcomes related to the ankle motor performance highlighted that the best performance in terms of accuracy and smoothness of the trajectories was obtained in dorsiflexion movements in the sagittal plane, and in inversion rotations in the frontal plane. Differences between movement directions revealed an anisotropic behavior of the ankle joint. Results of the Learning index showed a capability of the subjects to rapidly adapt to a perturbed force field depending on the magnitude of the perceived field.

Reactive Postural Responses to Continuous Yaw Perturbations in Healthy Humans: The Effect of Aging.

Maintaining balance stability while turning in a quasi-static stance and/or in dynamic motion requires proper recovery mechanisms to manage sudden center-of-mass displacement. Furthermore, falls during turning are among the main concerns of community-dwelling elderly population. This study investigates the effect of aging on reactive postural responses to continuous yaw perturbations on a cohort of 10 young adults (mean age 28 ± 3 years old) and 10 older adults (mean age 61 ± 4 years old). Subjects underwent external continuous yaw perturbations provided by the RotoBit1D platform. Different conditions of visual feedback (eyes opened and eyes closed) and perturbation intensity, i.e., sinusoidal rotations on the horizontal plane at different frequencies (0.2 Hz and 0.3 Hz), were applied. Kinematics of axial body segments was gathered using three inertial measurement units. In order to measure reactive postural responses, we measured body-absolute and joint absolute rotations, center-of-mass displacement, body sway, and inter-joint coordination. Older adults showed significant reduction in horizontal rotations of body segments and joints, as well as in center-of-mass displacement. Furthermore, older adults manifested a greater variability in reactive postural responses than younger adults. The abnormal reactive postural responses observed in older adults might contribute to the well-known age-related difficulty in dealing with balance control during turning.

Detection of the Strains Induced in Murine Tibias by Ex Vivo Uniaxial Loading with Different Sensors.

In this paper, the characterization of the main techniques and transducers employed to measure local and global strains induced by uniaxial loading of murine tibiae is presented. Micro strain gauges and digital image correlation (DIC) were tested to measure local strains, while a moving coil motor-based length transducer was employed to measure relative global shortening. Local strain is the crucial parameter to be measured when dealing with bone cell mechanotransduction, so we characterized these techniques in the experimental conditions known to activate cell mechanosensing in vivo. The experimental tests were performed using tibia samples excised from twenty-two C57BL/6 mice. To evaluate measurement repeatability we computed the standard deviation of ten repetitive compressions to the mean value. This value was lower than 3% for micro strain gauges, and in the range of 7%-10% for DIC and the length transducer. The coefficient of variation, i.e., the standard deviation to the mean value, was about 35% for strain gauges and the length transducer, and about 40% for DIC. These results provided a comprehensive characterization of three methodologies for local and global bone strain measurement, suggesting a possible field of application on the basis of their advantages and limitations.

Anisotropy of the Passive and Active Rat Vagina Under Biaxial Loading.

Pelvic organ prolapse, the descent of the pelvic organs from their normal anatomical position, is a common condition among women that is associated with mechanical alterations of the vaginal wall. In order to characterize the complex mechanical behavior of the vagina, we performed planar biaxial tests of vaginal specimens in both the passive (relaxed) and active (contracted) states. Specimens were isolated from virgin, female Long-Evans rats (n = 16) and simultaneously stretched along the longitudinal direction (LD) and circumferential direction (CD) of the vagina. Tissue contraction was induced by electric field stimulation (EFS) at incrementally increasing values of stretch and, subsequently, by KCl. On average, the vagina was stiffer in the CD than in the LD (p < 0.001). The mean maximum EFS-induced active stress was significantly higher in the CD than in the LD (p < 0.01). On the contrary, the mean KCl-induced active stress was lower in the CD than in the LD (p < 0.01). When comparing the mean maximum EFS-induced active stress to the mean KCl-induced active stress, no differences were found in the CD (p = 0.366) but, in the LD, the mean active stress was much higher in response to the KCl stimulation (p < 0.001). Collectively, these results suggest that the anisotropic behavior of the vaginal tissue is determined not only by collagen and smooth muscle fiber organization but also by the innervation.

Measuring Gait Quality in Parkinson's Disease through Real-Time Gait Phase Recognition.

Monitoring gait quality in daily activities through wearable sensors has the potential to improve medical assessment in Parkinson's Disease (PD). In this study, four gait partitioning methods, two based on thresholds and two based on a machine learning approach, considering the four-phase model, were compared. The methods were tested on 26 PD patients, both in OFF and ON levodopa conditions, and 11 healthy subjects, during walking tasks. All subjects were equipped with inertial sensors placed on feet. Force resistive sensors were used to assess reference time sequence of gait phases. Goodness Index (G) was evaluated to assess accuracy in gait phases estimation. A novel synthetic index called Gait Phase Quality Index (GPQI) was proposed for gait quality assessment. Results revealed optimum performance (G < 0.25) for three tested methods and good performance (0.25 < G < 0.70) for one threshold method. The GPQI resulted significantly higher in PD patients than in healthy subjects, showing a moderate correlation with clinical scales score. Furthermore, in patients with severe gait impairment, GPQI was found higher in OFF than in ON state. Our results unveil the possibility of monitoring gait quality in PD through real-time gait partitioning based on wearable sensors.

On the Reliability and Repeatability of Surface Electromyography Factorization by Muscle Synergies in Daily Life Activities.

Muscle synergy theory is a new appealing approach for different research fields. This study is aimed at evaluating the robustness of EMG reconstruction via muscle synergies and the repeatability of muscle synergy parameters as potential neurophysiological indices. Eight healthy subjects performed walking, stepping, running, and ascending and descending stairs' trials for five repetitions in three sessions. Twelve muscles of the dominant leg were analyzed. The "nonnegative matrix factorization" and "variability account for" were used to extract muscle synergies and to assess EMG goodness reconstruction, respectively. Intraclass correlation was used to quantify methodology reliability. Cosine similarity and coefficient of determination assessed the repeatability of the muscle synergy vectors and the temporal activity patterns, respectively. A 4-synergy model was selected for EMG signal factorization. Intraclass correlation was excellent for the overall reconstruction, while it ranged from fair to excellent for single muscles. The EMG reconstruction was found repeatable across sessions and subjects. Considering the selection of neurophysiological indices, the number of synergies was not repeatable neither within nor between subjects. Conversely, the cosine similarity and coefficient of determination values allow considering the muscle synergy vectors and the temporal activity patterns as potential neurophysiological indices due to their similarity both within and between subjects. More specifically, some synergies in the 4-synergy model reveal themselves as more repeatable than others, suggesting focusing on them when seeking at the neurophysiological index identification.

Measuring Neuromuscular Junction Functionality.

Neuromuscular junction (NMJ) functionality plays a pivotal role when studying diseases in which the communication between motor neuron and muscle is impaired, such as aging and amyotrophic lateral sclerosis (ALS). Here we describe an experimental protocol that can be used to measure NMJ functionality by combining two types of electrical stimulation: direct muscle membrane stimulation and the stimulation through the nerve. The comparison of the muscle response to these two different stimulations can help to define, at the functional level, potential alterations in the NMJ that lead to functional decline in muscle. Ex vivo preparations are suited to well-controlled studies. Here we describe an intensive protocol to measure several parameters of muscle and NMJ functionality for the soleus-sciatic nerve preparation and for the diaphragm-phrenic nerve preparation. The protocol lasts approximately 60 min and is conducted uninterruptedly by means of a custom-made software that measures the twitch kinetics properties, the force-frequency relationship for both muscle and nerve stimulations, and two parameters specific to NMJ functionality, i.e. neurotransmission failure and intratetanic fatigue. This methodology was used to detect damages in soleus and diaphragm muscle-nerve preparations by using SOD1G93A transgenic mouse, an experimental model of ALS that ubiquitously overexpresses the mutant antioxidant enzyme superoxide dismutase 1 (SOD1).