Publications by authors named "Yuji Iwamoto"

Nitride (N) or cyanamide (CN) based mixed-anion compounds stand as attractive materials due to their unique properties derived from the binary or multiple anions, although their synthesis remains challenging in incorporating the N or CN anions safely. This work highlights the first demonstration of in situ single phase formation of a LaCl(CN) mixed-anion compound from a stable single source precursor, melamine modified with LaCl preparable under aqueous conditions. The in situ formation of LaCl(CN) involves the chemical modification of melamine with LaCl to form a complex.

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Interest is growing in transition metal-free compounds for small molecule activation and catalysis. We discuss the opportunities arising from synthesizing sodium-doped amorphous silicon-boron-nitride (Na-doped a-SiBN). Na cations and 3-fold coordinated B moieties were incorporated into an amorphous silicon nitride network via chemical modification of a polysilazane followed by pyrolysis in ammonia (NH) at 1000 °C.

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The in situ confinement of nickel (Ni)-iron (Fe) nanoparticles (NPs) in a polymer-derived microporous silicon carboxynitride (Si-C-O-N)-based support is investigated to stimulate superior oxygen evolution reaction (OER) activity in an alkaline media. Firstly, we consider a commercial polysilazane (PSZ) and Ni and Fe chlorides to be mixed in N,N-dimethylformamide (DMF) and deliver after overnight solvent reflux a series of Ni-Fe : organosilicon coordination polymers. The latter are then heat-treated at 500 °C in flowing argon to form the title compounds.

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Herein, nanocomposites made of Ni nanoparticles distributed in an amorphous silicon nitride (Ni/a-SiN) matrix, on the one hand, and within an amorphous silicon dioxide (Ni/a-SiO) matrix, on the other hand, were synthesized from the same Ni-modified polysilazane precursor. In both compounds, the Ni/Si atomic ratio (0.06-0.

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Robot-assisted gait training is effective for walking independence in stroke rehabilitation, the hybrid assistive limb (HAL) is an example. However, gait training with HAL may not be effective for everyone, and it is not clear who is not expected to benefit. Therefore, we aimed to identify the characteristics of stroke patients who have difficulty gaining benefits from gait training with HAL.

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We synthesized nickel (Ni) nanoparticles (NPs) in a high specific surface area (SSA) p-block element-containing inorganic compound prepared the polymer-derived ceramics (PDC) route to dispatch the obtained nanocomposite towards oxygen evolution reaction (OER). The formation of Ni NPs in an amorphous silicon carboxynitride (Si-C-O-N(H)) matrix is allowed by the reactive blending of a polysilazane, NiCl and DMF followed by the subsequent thermolysis of the Ni : organosilicon polymer coordination complex at a temperature as low as 500 °C in flowing argon. The final nanocomposite displays a BET SSA as high as 311 m g while the structure of the NPs corresponds to face-centred cubic (fcc) Ni along with interstitial-atom free (IAF) hexagonal close-packed (hcp) Ni as revealed by XRD.

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Introduction: The effect of early initiation of gait training using hybrid assistive limb (HAL) remains unclear. This observational study aimed to investigate whether early initiation of gait training using HAL improves functional outcomes in patients with stroke.

Methods: We retrospectively analyzed patients with acute stroke admitted to our facility.

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Article Synopsis
  • The study explores how nickel (Ni) nanocrystallites are formed during the creation of amorphous silicon nitride at low temperatures (400 °C), using perhydropolysilazane (PHPS) and nickel chloride (NiCl) as key materials.
  • Nickel chloride acts as a catalyst to initiate reactions that combine silicon and nitrogen components in PHPS, ultimately creating a nanocomposite by forming silylamino groups and coordinating them with Ni(II) ions.
  • During pyrolysis, a nucleophilic reaction forms Ni-N bonds, leading to Ni nanocrystallite growth below 300 °C, which is enhanced by hydrogen-rich conditions resulting from the prior reactions, culminating in a strong SiN
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The Hybrid Assistive Limb (HAL) is used in training to improve walking ability for stroke patients; however, the quality of the evidence for its effects has not been fully critiqued to date. This study conducted a systematic review of randomized controlled trials to investigate the effectiveness of post-stroke gait training with the HAL. PubMed, the Cochrane Library, the Physiotherapy Evidence Database (PEDro), and the Cumulative Index to Nursing and Allied Health Literature (CINAHL) were searched for randomized controlled clinical trials evaluating the effect of HAL on gait training in stroke patients, published from the inception of each database until March 2021.

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Article Synopsis
  • The study investigates which stroke patients benefit most from robot-assisted upper limb rehabilitation, specifically using the hybrid assistive limb (HAL-SJ) technology.
  • Using a machine learning method called the CHAID model, researchers identified key factors like age, sex, and cognitive function that predict a patient's improvement in upper limb function after therapy.
  • Results showed that the initial Fugl-Meyer Upper Extremity Evaluation score was the best predictor of improvement, with the model achieving high accuracy, suggesting early intervention in patients with moderate dysfunction leads to better outcomes.
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Assistive exoskeleton robots are being widely applied in neurorehabilitation to improve upper-limb motor and somatosensory functions. During robot-assisted exercises, the central nervous system appears to highly attend to external information-processing (IP) to efficiently interact with robotic assistance. However, the neural mechanisms underlying this process remain unclear.

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Article Synopsis
  • The study investigates the impact of different frequencies of using the single-joint hybrid assistive limb (HAL-SJ) on recovery of upper limb motor function and daily living activities in stroke patients.
  • Patients were divided into high and low frequency use groups, and improvements were assessed after 30 days, showing that high-frequency users had better results in specific motor functions and daily living assessments.
  • However, frequent use of the HAL-SJ didn’t significantly increase the number of patients achieving clinically meaningful improvements, suggesting that while frequency may aid recovery, it doesn't guarantee better outcomes for everyone.
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Article Synopsis
  • The study aimed to assess the effectiveness of five supervised machine learning algorithms in predicting the possibility of home discharge for stroke patients based on a dataset of 481 individuals.
  • The algorithms evaluated included decision tree, linear discriminant analysis, k-nearest neighbors, support vector machine, and random forest, measured by classification accuracy, area under the curve (AUC), and F1 score.
  • Results indicated that the k-NN model had the highest classification accuracy (84.0%), while the SVM model excelled in AUC and sensitivity, with both models showing promise for predicting home discharge outcomes.
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Background: Functional issues (impairments, functional limitations, and disabilities) gradually occur with age. Nonetheless, maintaining physical capability may help prevent locomotion disabilities at an older age. The present study aimed to determine whether reductions in muscle strength and range of motion (ROM) cause locomotion disability via locomotion-related functional limitations among healthy older adults.

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The usefulness of magnetic resonance imaging (MRI) in predicting gait ability in stroke patients remains unclear. Therefore, MRI evaluations have not yet been standardized in stroke rehabilitation. We performed a systematic review to consolidate evidence regarding the use of MRIs in predicting gait ability of stroke patients.

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Article Synopsis
  • The study investigates how physical environmental factors impact the home discharge of stroke patients who use wheelchairs, suggesting that these factors are often overlooked.
  • The researchers utilized machine learning, specifically the CHAID algorithm, to analyze data from stroke patients in rehabilitation, aiming to identify what influences successful home discharge.
  • Key findings showed that house renovations and the presence of sloping roads are significant factors that impact whether wheelchair users can be discharged to home, highlighting the need for clinicians to consider these environmental elements in their assessments.
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To develop a conceptual model that confirms whether mobility-related activities of daily living (ADLs) abilities are strongly associated with reduced health-related quality of life (HRQOL) in older adults. A total of 153 participants (63 men, 90 women) were analyzed. The mobility-related ADL survey from the Ministry of Education, Culture, Sports, Science, and Technology of Japan was used.

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Background And Purpose: The importance of environmental factors for stroke patients to achieve home discharge was not scientifically proven. There are limited studies on the application of the decision tree algorithm with various functional and environmental variables to identify stroke patients with a high possibility of home discharge. The present study aimed to identify the factors, including functional and environmental factors, affecting home discharge after stroke inpatient rehabilitation using the machine learning method.

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This work highlights the first demonstration of a low-temperature in situ formation of Co nanocrystallites embedded within an amorphous silicon nitride matrix through careful control of the chemistry behind material design using perhydropolysilazane (PHPS) as a Si3N4 precursor further coordinated with CoCl2 and ammonia as a pyrolysis atmosphere. The Co nucleation was allowed to proceed at temperatures as low as 400 °C via thermal decomposition of Co2N pre-formed in situ by the reaction of CoCl2 with the Si centers of PHPS at the early stage of pyrolysis (220-350 °C).

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Introduction: Neovascular glaucoma is characterized by neovascularization of the iris and anterior angle chamber. Intravitreal anti-vascular endothelial growth factor agents may decrease intraocular pressure (IOP) and improve neovascularization. The VENERA study assessed the efficacy and safety of intravitreal aflibercept (IVT-AFL) in patients with neovascular glaucoma.

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Introduction: Neovascular glaucoma is characterized by neovascularization of the iris and the anterior angle chamber. Intravitreal anti-vascular endothelial growth factor agents may improve intraocular pressure (IOP) and neovascularization.

Methods: The VEGA trial assessed the efficacy and safety of intravitreal aflibercept (IVT-AFL) in patients with neovascular glaucoma in a 13-week, randomized, double-masked, sham-controlled, phase 3 study performed at multiple sites in Japan that enrolled patients with anterior segment neovascularization and IOP > 25 mmHg.

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This paper reports on a unique reversible reducing and oxidizing (redox) property of Co(III) in Co-doped amorphous SiO/γ-AlO composites. The Fenton reaction during the HO-catalyzed sol-gel synthesis utilized in this study lead to the partial formation of Co(III) in addition to Co(II) within the composites. High-resolution transmission electron microscopy (HRTEM) and high-angle annular dark-field scanning transmission electron microscopy (HAADF-STEM) analyses for the composite powder sample with a composition of Al:Si:Co = 85:10:5 showed the amorphous state of the Co-doped SiO that modified γ-AlO nanocrystalline surfaces.

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Solar hydrogen production via the photoelectrochemical water-splitting reaction is attractive as one of the environmental-friendly approaches for producing H. Since the reaction simultaneously generates H and O, this method requires immediate H recovery from the syngas including O under high-humidity conditions around 50 °C. In this study, a supported mesoporous γ-AlO membrane was modified with allyl-hydrido-polycarbosilane as a preceramic polymer and subsequently heat-treated in Ar to deliver a ternary SiCH organic-inorganic hybrid/γ-AlO composite membrane.

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Background And Purpose: Accurate prediction using simple and changeable variables is clinically meaningful because some known-predictors, such as stroke severity and patients age cannot be modified with rehabilitative treatment. There are limited clinical prediction rules (CPRs) that have been established using only changeable variables to predict the activities of daily living (ADL) dependence of stroke patients. This study aimed to develop and assess the CPRs using machine learning-based methods to identify ADL dependence in stroke patients.

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