Balance Measures Derived from Insole Sensor Differentiate Prodromal Dementia with Lewy Bodies

Dementia with Lewy bodies is the second most common type of neurodegenerative dementia, and identification at the prodromal stage$-$i.e., mild cognitive impairment due to Lewy bodies (MCI-LB)$-$is important for providing appropriate care. However, MCI-LB is often underrecognized because of its diversity in clinical manifestations and similarities with other conditions such as mild cognitive impairment due to Alzheimer's disease (MCI-AD). In this study, we propose a machine learning-based automatic pipeline that helps identify MCI-LB by exploiting balance measures acquired with an insole sensor during a 30-s standing task. An experiment with 98 participants (14 MCI-LB, 38 MCI-AD, 46 cognitively normal) showed that the resultant models could discriminate MCI-LB from the other groups with up to 78.0% accuracy (AUC: 0.681), which was 6.8% better than the accuracy of a reference model based on demographic and clinical neuropsychological measures. Our findings may open up a new approach for timely identification of MCI-LB, enabling better care for patients

Optimization of Ladle Tilting Speed for Preventing Temperature Drops in the Die Casting Process

In die casting, molten metal poured into a shot sleeve is pressed into a mold by a plunger at high speed. The temperature of the metal drops significantly while it is being poured from the ladle to the shot sleeve, resulting in casting defects such as misrun flow lines. Although it is important to control the temperature at all stages of the process, a method for minimizing temperature loss has not yet been clarified to date. In this study, the cause of the temperature drop in the shot sleeve was clarified, and a method of optimizing the ladle tilting speed was proposed to prevent temperature drop. First, experiments were conducted to measure the decrease in metal temperature in the sleeve during pouring. These experiments revealed that the metal cools significantly from the moment it touches the shot sleeve. Therefore, the time from the first contact between the shot sleeve and the metal to the start of pouring was set as the objective function. A genetic algorithm was then used to derive the optimal ladle tilting speed pattern to suppress the temperature drop. This analysis confirmed that the metal was poured without flowing out or running ahead and that the immediate liquid level vibration after pouring was suppressed, thus ensuring stable pouring

In-Stent Yellow Plaque at 1 Year After Implantation Is Associated With Future Event of Very Late Stent Failure The DESNOTE Study (Detect the Event of Very late Stent Failure From the Drug-Eluting Stent Not Well Covered by Neointima Determined by Angioscopy)

AbstractObjectivesThis study examined whether coronary angioscopy-verified in-stent yellow plaque at 1 year after drug-eluting stent (DES) implantation is associated with future event of very late stent failure (VLSF).BackgroundAtherosclerosis detected as yellow plaque by angioscopy has been associated with future events of acute coronary syndrome. Development of in-stent neoatherosclerosis is a probable mechanism of VLSF.MethodsThis study included 360 consecutive patients who received successful angioscopic examination at 1 year after implantation of a DES. They were clinically followed up for VLSF defined as cardiac death, acute myocardial infarction or unstable angina, or need for revascularization associated with the stent site.ResultsThe follow-up interval was 1,558 ± 890 days (4.3 ± 2.4 years). The incidence of VLSF was significantly higher in the patients with yellow plaque than in those without (8.1% vs. 1.6%; log rank p = 0.02). Multivariable analysis revealed the presence of yellow plaque (hazard ratio [HR]: 5.38; p = 0.02) and absence of statin therapy (HR: 3.25; p = 0.02) as risks of VLSF.ConclusionsIn-stent atherosclerosis evaluated by yellow plaque at 1 year after the implantation of DES and the absence of statin therapy were risks of VLSF. The underlying mechanism of VLSF appeared to be the progression of atherosclerosis as demonstrated by the yellow plaque

Establishment of a reborn MMV-microarray technology: realization of microbiome analysis and other hitherto inaccessible technologies

BACKGROUND: With the accelerating development of bioscience, the problem of research cost has become important. We previously devised and developed a novel concept microarray with manageable volumes (MMV) using a soft gel. It demonstrated the great potential of the MMV technology with the examples of 1024-parallel-cell culture and PCR experiments. However, its full potential failed to be expressed, owing to the nature of the material used for the MMV chip. RESULTS: In the present study, by developing plastic-based MMVs and associated technologies, we introduced novel technologies such as C2D2P (in which the cells in each well are converted from DNA to protein in 1024-parallel), NGS-non-dependent microbiome analysis, and other powerful applications. CONCLUSIONS: The reborn MMV-microarray technology has proven to be highly efficient and cost-effective (with approximately 100-fold cost reduction) and enables us to realize hitherto unattainable technologies

Screening of Mild Cognitive Impairment Through Conversations With Humanoid Robots: Exploratory Pilot Study

BackgroundThe rising number of patients with dementia has become a serious social problem worldwide. To help detect dementia at an early stage, many studies have been conducted to detect signs of cognitive decline by prosodic and acoustic features. However, many of these methods are not suitable for everyday use as they focus on cognitive function or conversational speech during the examinations. In contrast, conversational humanoid robots are expected to be used in the care of older people to help reduce the work of care and monitoring through interaction. ObjectiveThis study focuses on early detection of mild cognitive impairment (MCI) through conversations between patients and humanoid robots without a specific examination, such as neuropsychological examination. MethodsThis was an exploratory study involving patients with MCI and cognitively normal (CN) older people. We collected the conversation data during neuropsychological examination (Mini-Mental State Examination [MMSE]) and everyday conversation between a humanoid robot and 94 participants (n=47, 50%, patients with MCI and n=47, 50%, CN older people). We extracted 17 types of prosodic and acoustic features, such as the duration of response time and jitter, from these conversations. We conducted a statistical significance test for each feature to clarify the speech features that are useful when classifying people into CN people and patients with MCI. Furthermore, we conducted an automatic classification experiment using a support vector machine (SVM) to verify whether it is possible to automatically classify these 2 groups by the features identified in the statistical significance test. ResultsWe obtained significant differences in 5 (29%) of 17 types of features obtained from the MMSE conversational speech. The duration of response time, the duration of silent periods, and the proportion of silent periods showed a significant difference (P<.001) and met the reference value r=0.1 (small) of the effect size. Additionally, filler periods (P<.01) and the proportion of fillers (P=.02) showed a significant difference; however, these did not meet the reference value of the effect size. In contrast, we obtained significant differences in 16 (94%) of 17 types of features obtained from the everyday conversations with the humanoid robot. The duration of response time, the duration of speech periods, jitter (local, relative average perturbation [rap], 5-point period perturbation quotient [ppq5], difference of difference of periods [ddp]), shimmer (local, amplitude perturbation quotient [apq]3, apq5, apq11, average absolute differences between the amplitudes of consecutive periods [dda]), and F0cov (coefficient of variation of the fundamental frequency) showed a significant difference (P<.001). In addition, the duration of response time, the duration of silent periods, the filler period, and the proportion of fillers showed significant differences (P<.05). However, only jitter (local) met the reference value r=0.1 (small) of the effect size. In the automatic classification experiment for the classification of participants into CN and MCI groups, the results showed 66.0% accuracy in the MMSE conversational speech and 68.1% accuracy in everyday conversations with the humanoid robot. ConclusionsThis study shows the possibility of early and simple screening for patients with MCI using prosodic and acoustic features from everyday conversations with a humanoid robot with the same level of accuracy as the MMSE