المؤلفون: Fujikawa, Joji, Morigaki, Ryoma, Yamamoto, Nobuaki, Nakanishi, Hiroshi, Oda, Teruo, Izumi, Yuishin, Takagi, Yasushi

مصطلحات موضوعية: Parkinson’s disease, essential tremor, diagnosis, stimulation, medical devices, machine learning

الوصف: Background: Parkinsonian tremors are sometimes confused with essential tremors or other conditions. Recently, researchers conducted several studies on tremor evaluation using wearable sensors and devices, which may support accurate diagnosis. Mechanical devices are also commonly used to treat tremors and have been actively researched and developed. Here, we aimed to review recent progress and the efficacy of the devices related to Parkinsonian tremors. Methods: The PubMed and Scopus databases were searched for articles. We searched for “Parkinson disease” and “tremor” and “device”. Results: Eighty-six articles were selected by our systematic approach. Many studies demonstrated that the diagnosis and evaluation of tremors in patients with PD can be done accurately by machine learning algorithms. Mechanical devices for tremor suppression include deep brain stimulation (DBS), electrical muscle stimulation, and orthosis. In recent years, adaptive DBS and optimization of stimulation parameters have been studied to further improve treatment efficacy. Conclusions: Due to developments using state-of-the-art techniques, effectiveness in diagnosing and evaluating tremor and suppressing it using these devices is satisfactorily high in many studies. However, other than DBS, no devices are in practical use. To acquire high-level evidence, large-scale studies and randomized controlled trials are needed for these devices.

العلاقة: http://repo.lib.tokushima-u.ac.jp/files/public/11/118913/20240117143045332211/life_13_1_78.pdfTest; http://repo.lib.tokushima-u.ac.jp/118913Test

الإتاحة: http://repo.lib.tokushima-u.ac.jp/118913Test

المؤلفون: Fujikawa, Joji, Morigaki, Ryoma, Miyake, Kazuhisa, Matsuda, Taku, Koyama, Hiroshi, Oda, Teruo, Yamamoto, Nobuaki, Izumi, Yuishin, Mure, Hideo, Goto, Satoshi, Takagi, Yasushi

الوصف: Abnormal skull shape has been reported in brain disorders. However, no studies have investigated cranial geometry in neurodegenerative disorders. This study aimed to evaluate the cranial geometry of patients with dystonia or Parkinson's disease (PD). Cranial computed tomography images of 36 patients each with idiopathic dystonia (IDYS), PD, and chronic subdural hematoma (CSDH) were analyzed. Those with IDYS had a significantly higher occipital index (OI) than those with CSDH (p = 0.014). When cephalic index (CI) was divided into the normal and abnormal groups, there was a significant difference between those with IDYS and CSDH (p = 0.000, α = 0.017) and between PD and CSDH (p = 0.031, α = 0.033). The age of onset was significantly correlated with the CI of IDYS (τ = − 0.282, p = 0.016). The Burke–Fahn–Marsden Dystonia Rating Scale motor score (BFMDRS-M) showed a significant correlation with OI in IDYS (τ = 0.372, p = 0.002). The cranial geometry of patients with IDYS was significantly different from that of patients with CSDH. There was a significant correlation between age of onset and CI, as well as between BFMDRS-M and OI, suggesting that short heads in the growth phase and skull balance might be related to the genesis of dystonia and its effect on motor symptoms.

العلاقة: http://repo.lib.tokushima-u.ac.jp/files/public/11/118394/20230720161926192353/srep_13_11003.pdfTest; http://repo.lib.tokushima-u.ac.jp/118394Test

الإتاحة: http://repo.lib.tokushima-u.ac.jp/118394Test

المؤلفون: Fujikawa, Joji, Morigaki, Ryoma, Yamamoto, Nobuaki, Oda, Teruo, Nakanishi, Hiroshi, Izumi, Yuishin, Takagi, Yasushi

المساهمون: Japan Society for the Promotion of Science, Terumo Foundation for Life Sciences and Arts

المصدر: Frontiers in Aging Neuroscience ; volume 14 ; ISSN 1663-4365

مصطلحات موضوعية: Cognitive Neuroscience, Aging

الوصف: Background Pharmacotherapy is the first-line treatment option for Parkinson’s disease, and levodopa is considered the most effective drug for managing motor symptoms. However, side effects such as motor fluctuation and dyskinesia have been associated with levodopa treatment. For these conditions, alternative therapies, including invasive and non-invasive medical devices, may be helpful. This review sheds light on current progress in the development of devices to alleviate motor symptoms in Parkinson’s disease. Methods We first conducted a narrative literature review to obtain an overview of current invasive and non-invasive medical devices and thereafter performed a systematic review of recent randomized controlled trials (RCTs) of these devices. Results Our review revealed different characteristics of each device and their effectiveness for motor symptoms. Although invasive medical devices are usually highly effective, surgical procedures can be burdensome for patients and have serious side effects. In contrast, non-pharmacological/non-surgical devices have fewer complications. RCTs of non-invasive devices, especially non-invasive brain stimulation and mechanical peripheral stimulation devices, have proven effectiveness on motor symptoms. Nearly no non-invasive devices have yet received Food and Drug Administration certification or a CE mark. Conclusion Invasive and non-invasive medical devices have unique characteristics, and several RCTs have been conducted for each device. Invasive devices are more effective, while non-invasive devices are less effective and have lower hurdles and risks. It is important to understand the characteristics of each device and capitalize on these.

الإتاحة: https://doi.org/10.3389/fnagi.2022.807909Test

المؤلفون: Saga, Norihiko, Doi, Atsushi, Oda, Teruo, Kudoh, Suguru N.

المساهمون: JKA Foundation

المصدر: Frontiers in Neurorobotics ; volume 14 ; ISSN 1662-5218

مصطلحات موضوعية: Artificial Intelligence, Biomedical Engineering

الوصف: Non-invasive brain–computer interfaces (BCIs) based on common electroencephalography (EEG) are limited to specific instrumentation sites and frequency bands. These BCI induce certain targeted electroencephalographic features of cognitive tasks, identify them, and determine BCI's performance, and use machine-learning to extract these electroencephalographic features, which makes them enormously time-consuming. In addition, there is a problem in which the neurorehabilitation using BCI cannot receive ambulatory and immediate rehabilitation training. Therefore, we proposed an exploratory BCI that did not limit the targeted electroencephalographic features. This system did not determine the electroencephalographic features in advance, determined the frequency bands and measurement sites appropriate for detecting electroencephalographic features based on their target movements, measured the electroencephalogram, created each rule (template) with only large “High” or small “Low” electroencephalograms for arbitrarily determined thresholds (classification of cognitive tasks in the imaginary state of moving the feet by the size of the area constituted by the power spectrum of the EEG in each frequency band), and successfully detected the movement intention by detecting the electroencephalogram consistent with the rules during motor tasks using a fuzzy inference-based template matching method (FTM). However, the electroencephalographic features acquired by this BCI are not known, and their usefulness for patients with actual cerebral infarction is not known. Therefore, this study clarifies the electroencephalographic features captured by the heuristic BCI, as well as clarifies the effectiveness and challenges of this system by its application to patients with cerebral infarction.

الإتاحة: https://doi.org/10.3389/fnbot.2020.607706Test

المؤلفون: Fujikawa, Joji, Morigaki, Ryoma, Yamamoto, Nobuaki, Nakanishi, Hiroshi, Oda, Teruo, Izumi, Yuishin, Takagi, Yasushi

المصدر: Life (2075-1729); Jan2023, Vol. 13 Issue 1, p78, 25p

مصطلحات موضوعية: PARKINSON'S disease, ELECTRIC stimulation, DEEP brain stimulation, MACHINE learning, DIAGNOSIS

مستخلص: Background: Parkinsonian tremors are sometimes confused with essential tremors or other conditions. Recently, researchers conducted several studies on tremor evaluation using wearable sensors and devices, which may support accurate diagnosis. Mechanical devices are also commonly used to treat tremors and have been actively researched and developed. Here, we aimed to review recent progress and the efficacy of the devices related to Parkinsonian tremors. Methods: The PubMed and Scopus databases were searched for articles. We searched for "Parkinson disease" and "tremor" and "device". Results: Eighty-six articles were selected by our systematic approach. Many studies demonstrated that the diagnosis and evaluation of tremors in patients with PD can be done accurately by machine learning algorithms. Mechanical devices for tremor suppression include deep brain stimulation (DBS), electrical muscle stimulation, and orthosis. In recent years, adaptive DBS and optimization of stimulation parameters have been studied to further improve treatment efficacy. Conclusions: Due to developments using state-of-the-art techniques, effectiveness in diagnosing and evaluating tremor and suppressing it using these devices is satisfactorily high in many studies. However, other than DBS, no devices are in practical use. To acquire high-level evidence, large-scale studies and randomized controlled trials are needed for these devices. [ABSTRACT FROM AUTHOR]

: Copyright of Life (2075-1729) is the property of MDPI and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

مستخلص: A concentration sensor device includes a sensor unit, a substrate, and a sedimentation limit unit. The sensor unit detects a concentration of a specific component contained in liquid. The substrate has a face to which the sensor unit is arranged. The sedimentation limit unit is integrally arranged with the sensor unit or arranged at an upstream side of the sensor unit in a flowing direction of the liquid. The sedimentation limit unit is configured to prevent sedimentation of a foreign object on the sensor unit. The sedimentation limit unit includes a piezoelectric element to vibrate when electricity is supplied so as to promote the foreign object to be separated from the sensor unit. The substrate has a recess recessed in a thickness direction of the substrate.

مستخلص: An ultrasonic sensor includes a transmitting device, receiving devices arranged in an array, and a circuit device. One receiving device is configured as a reference receiving device. The circuit device includes a reference signal generator and first and second synchronous detectors. The reference signal generator generates a reference signal by using a received signal of the reference receiving device. The first synchronous detector performs synchronous detection of a received signal of one of the receiving devices based on the reference signal to detect a distance to an object. The second synchronous detector performs synchronous detection of received signals of the receiving devices except the reference receiving device based on the reference signal to detect a direction of the object.

مستخلص: A concentration sensor device includes a sensor unit, a substrate, and a sedimentation limit unit. The sensor unit detects a concentration of a specific component contained in liquid. The substrate has a face to which the sensor unit is arranged. The sedimentation limit unit is integrally arranged with the sensor unit or arranged at an upstream side of the sensor unit in a flowing direction of the liquid. The sedimentation limit unit is configured to prevent sedimentation of a foreign object on the sensor unit. The sedimentation limit unit includes a piezoelectric element to vibrate when electricity is supplied so as to promote the foreign object to be separated from the sensor unit. The substrate has a recess recessed in a thickness direction of the substrate.

مستخلص: A semiconductor device includes: a semiconductor substrate; a flow sensor having a first heater for detecting a flow rate of fluid; and a humidity sensor for detecting a humidity of the fluid. The flow sensor and the humidity sensor are disposed on the semiconductor substrate. The flow sensor is disposed around the humidity sensor. The humidity sensor is disposed on an upstream side of the first heater. Since the device includes the humidity sensor, moisture in the fluid is compensated so that detection accuracy of the flow rate is improved.

مستخلص: An ultrasonic sensor includes a transmitting device, receiving devices arranged in an array, and a circuit device. One receiving device is configured as a reference receiving device. The circuit device includes a reference signal generator and first and second synchronous detectors. The reference signal generator generates a reference signal by using a received signal of the reference receiving device. The first synchronous detector performs synchronous detection of a received signal of one of the receiving devices based on the reference signal to detect a distance to an object. The second synchronous detector performs synchronous detection of received signals of the receiving devices except the reference receiving device based on the reference signal to detect a direction of the object.