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Hang-up associated with FABP4 attenuates hyperoxia-induced bronchi injury and fibrosis by means of suppressing TGF-β signaling within neonatal test subjects.
Therefore, all three approaches together might achieve a complete assessment of the functional status of patients in clinical trials, as they each independently correlate with health-related quality of life and important clinical outcomes such as hospitalizations but are weakly associated among themselves.The world population is aging, and this phenomenon is expected to continue for the next decades. This study aimed to propose a simple and reliable method that can be used for daily in-home monitoring of frailty and cognitive dysfunction in the elderly based on their walking-in-place characteristics. Fifty-four community-dwelling elderly people aged 65 years or older participated in this study. The participants were categorized into the robust and the non-robust groups according to the FRAIL scale. The mini-mental state examination was used to classify the cognitive impairment and the non-cognitive impairment groups. The 3-axis acceleration and the 3-axis angular velocity signals were measured using the inertial measurement units attached to the foot, shank, thigh, and posterior pelvis while each participant was walking in place for 20 seconds. The walking-in-place spectrograms were acquired by applying time-frequency analysis to the lower body movement signals measured in one stride. Four-fold cross-validation was applied to 80% of the total samples and the remaining 20% were used as test data. The deep convolutional neural network-based classifiers trained with the walking-in-place spectrograms enabled to categorize the robust and the non-robust groups with 94.63% accuracy and classify the cognitive impairment and the non-cognitive impairment groups with 97.59% accuracy. This study suggests that the walking-in-place spectrograms, which can be obtained without spacious experimental space, cumbersome equipment, and laborious processes, are effective indicators of frailty and cognitive dysfunction in the elderly.As the world's older population grows dramatically, the needs of continuing care retirement communities increases. Studies show that privacy can be a major concern for adopting technologies, while the older population prefers smart homes [1]. In order to minimize the number of sensors to be installed in each house, we performed Principal Component Analysis (PCA) to filter out the relatively unimportant sensors. We applied a machine learning model to classify residents' activity types, using a different set of sensors chosen by PCA. Then, we validated the trade-off between the classification model accuracy and the number of sensors used in classification. Our experiment shows that feature engineering helps reduce accuracy degradation for activity type classification when using fewer sensors in smart homes.In this study, we propose a method for estimating lower extremity strength from daily gait movement. Gait movement is affected by sex and gait environment. Therefore, we examined correlation coefficient between lower extremity strength and gait movement based on sex and environment and created models for estimating lower extremity strength. As a result, when only male or female data were used for model constructing, the correlation coefficient between estimates and actual measurements of lower extremity strength were approximately 0.7 and the precision had a mean absolute error of approximately 0.1 N/kg. The accuracy of the estimates was higher than that when sex was considered.Frailty in old age is defined as the individual intrinsic susceptibility of having bad outcomes following a health problem. It relies on sarcopenia, mobility and activity. Recognizing and monitoring a range of physical activities is a necessary step which precedes the analysis of this syndrome. This paper investigates the optimal tools for this recognition in terms of type and placement of wearable sensors. Two machine learning procedures are proposed and compared on a public dataset. The first one is based on deep learning, where feature extraction is done manually, by constructing activity images from raw signals and applying convolutional neural networks to learn optimal features from these images. buy 4-Hydroxynonenal The second one is based on shallow learning, where hundreds of handcrafted features are extracted manually, followed by a novel feature selection approach to retain the most discriminant subset.Clinical relevance- This analysis is an indispensable prerequisite to develop efficacious way in order to identify people with frailty using sensors and moreover, to take on the challenge of frailty prevention, an actual world health organization priority.Since the 70s sensory substitution devices have been used for blind individuals to compensate for the lack of vision and enable them to perceive environment through intact sensory modalities. In this study, we present a rehabilitation device called Audio Visual Thumble (AVT), which is a small ring-like device with LED and buzzer, that can be worn on pharynx. We focus on a unique group of low-vision individuals with a black spot or scotoma in their visual field due to a disease called Macular Degeneration. The visual localization abilities of these individuals are highly impaired due to developing scotoma. We recently showed that also their audio localization skills are impaired [9]. Rehabilitation techniques developed so far for Macular Degeneration focus on visual modality only. Since audition can also be used to improve their spatial skills, we developed the AVT device. It permits to associate the multisensory information (audio and visual feedbacks) coming from the device with the own movement (proprioceptive feedback). We propose that the AVT has the potential to help people with visual dysfunctions to improve in the identification of audio and visual targets outside or at the edge of the residual visual field. AVT could be used for a wide range of applications combined with classical rehabilitation techniques in Macular Degeneration patients.Clinical relevance- This device can be an effective addition for low-vision rehabilitation experts and can be used combined with classical rehabilitation methods.Haptic feedback allows an individual to identify various object properties. In this preliminary study, we determined the performance of stiffness recognition using transcutaneous nerve stimulation when a prosthetic hand was moved passively or was controlled actively by the subjects. Using a 2x8 electrode grid placed along the subject's upper arm, electrical stimulation was delivered to evoke somatotopic sensation along their index finger. Stimulation intensity, i.e. sensation strength, was modulated using the fingertip forces from a sensorized prosthetic hand. Object stiffness was encoded based on the rate of change of the evoked sensation as the prosthesis grasped one of three objects of different stiffness levels. During active control, sensation was modulated in real time as recorded forces were converted to stimulation amplitudes. During passive control, prerecorded force traces were randomly selected from a pool. Our results showed that the accuracy of object stiffness recognition was similar in both active and passive conditions. A slightly lower accuracy was observed during active control in one subject, which indicated that the sensorimotor integration processes could affect haptic perception for some users.Phantom limb pain (PLP) is pain felt in the missing limb in amputees. Somatosensory input delivered as high-frequency surface electrical stimulation may provoke a significant temporary decrease in PLP. Also, transcutaneous electrical nerve stimulation (TENS) is a somatosensory input that may activate descending inhibitory systems and thereby relieve pain. Our aim was to investigate changes in cortical activity following long-time sensory TENS. Time-frequency features were extracted from EEG signals of Cz and C4 channels (contralateral to the stimulation site) with or without TENS (2 subjects). We found that the TENS caused inhibition of the spectral activity of the somatosensory cortex following TENS, whereas no change was found when no stimulation was applied.Clinical Relevance- Although our preliminary results show a depression of the cortical activity following TENS, a future study with a larger population is needed to provide strong evidence to evaluate the effectiveness of sensory TENS on cortical activity. Our results may be useful for the design of TENS protocols for relief of PLP.The major challenge in upper limbs neuroprosthetic improvement is the implementation of effective sensory feedback. Transcutaneous electrical nerve stimulation (TENS) of the median and ulnar nerves confirmed, with electroencephalographic (EEG) recordings, the presence of appropriate responses in relevant cortical areas with induced sensation successfully located in the innervation regions of each nerve. The characterization of these elicited responses could be used to recreate precise somatotopic feedback from hand protheses. Using TENS and EEG, the purpose of this study was to detect distinctions in time-frequency cortical dynamics and connectivity occurring after stimulation of hand nerves. Region of interest (ROI) were selected according to topographical distributions and Somatosensory Evoked Potentials (SEP) localization and were named Contralateral Parietal (Cont P), Central Frontal (Cent F) and Superior Parietal (Sup P). The analysis of cortical oscillations showed spectral inflections in theta [4-7 Hz] and alpha [7.5-12.5 Hz] band which occurred at 60 ms in Cont P and 300 ms in Sup P and prominent for the ulnar condition over the median one. The beta band decrease [16-30 Hz] which occurred in the same ROIs was especially significant after ulnar stimulation too. Effective connectivity measures did not differ significantly across conditions but exhibited some slight difference in the alpha-band causal flow coming from Cent F in direction to Cont P and Sup P. Although pending completion of multiple-subjects study, these results already suggest magnitude differences in somatosensory spectral fluctuations and sensorimotor interactions flows.Understanding the joint encoding of multiple tactile stimulus features (e.g., spatial position, amplitude, and frequency of vibration) is a major goal of somatosensory neuroscience, and the development of experimental set-ups to probe joint encoding is important. We describe in detail a microcontroller-based, piezoelectric bender device for tactile experiments. The device comprises an Arduino Due microcontroller board with a 32-bit ARM Cortex-M3 RISC processor, and two 12-bit digital-to-analog converters, enabling precise, independent stimulation of adjacent epithelial points. Using laser doppler vibrometry, we developed a model of the benders' structural mechanics, which we implemented on the device. We used the device to delivered precise, reliable somatosensory stimulation in an experimental setting, recording electrophysiological responses in the peripheral nervous system of the Gisborne cockroach (Drymaplaneta semivitta) to sinusoidal vibration of tibial spines. We plotted tuning curves and derived bandwidths of multi-unit populations. We also stimulated rat facial vibrissae ex vivo. This microcontroller-based, low-cost, open-source system leverages a large developer community associated with Arduino, and may help speed advances in systems neuroscience.
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