four-temporary-waterslide-designs-adapted-to-different-slope

Four Temporary Waterslide Designs Adapted to Different Slope Conditions to Encourage Child Socialization in Playgrounds

Increased urbanization has decreased children's access to various natural outdoor environments. To counteract this deficiency in early life experiences, ...

a-common-marmoset-model-mother-infant-intervention-for-breastfeeding

A Common Marmoset Model of Mother-Infant Intervention for Breastfeeding Disorders in the Presence of Paternal Inhibition and Maternal Neglect

Parents&#39; psychological stress during the perinatal and neonatal periods continues to increase in an environment of declining birthrates, aging ...

Tohoku University

Determining the resectable region and volume of the liver prior to anatomical resection is important. The synapse Vincent (SV) system is the current method for surgical liver resection that relies on the surgeon's individual experience and skill. Additionally, in cases involving abnormal liver function, the resectable region is limited due to deteriorating liver function, thus making the determination of the hepatectomy region challenging. The current study outlines a novel 3D Hariyama-Shimoda Soft (HSS) simulation software that can be used to automatically simulate the optimal hepatectomy region under a limited resectable liver volume. The current study recruited patients with hepatic malignant tumors that were scheduled for anatomical resection. The influence of the tumor on each portal vein point was quantified in accordance with the tumor domination ratio (TDR). The resectable region was subsequently determined so that the sum of the TDR was the maximum estimated resectable liver volume (ERLV). The maximum ERLV settings utilized were within Makuuchi's criteria. ERLV was compared with the actual resected liver volume (ARLV) using SV and HSS. A total of 15 patients were included in the present study. The median ERLV was not significantly different between the two groups (P=0.15). However, the correlation between ERLV and ARLV, for SV and HSS, was statistically significant [SV ERLV (ml) = 1.139 × HSS ERLV (ml) + 30.779 (P=0.001)]. In conclusion, HSS may be an effective 3D simulation system. TDR and ERLV were indicated to be novel factors that may be incorporated into simulation software for use in anatomical resection surgery.

Development of new software enabling automatic identification of the optimal anatomical liver resectable region, incorporating preoperative liver function.

Prevalence of tooth loss has increased due to population aging. Tooth loss negatively affects the overall physical and social well-being of older adults. Understanding the role of socio-demographic and other predictors associated with tooth loss that are measured in non-clinical settings can be useful in community-level prevention. We used high-dimensional epidemiological data to investigate important factors in predicting tooth loss among older adults over a 6-year period of follow-up. Data was from participants of 2010 and 2016 waves of the Japan Gerontological Evaluation Study (JAGES). A total of 19,407 community-dwelling functionally independent older adults aged 65 and older were included in the analysis. Tooth loss was measured as moving from a higher number of teeth category at the baseline to a lower number of teeth category at the follow-up. Out of 119 potential predictors, age, sex, number of teeth, denture use, chewing difficulty, household income, employment, education, smoking, fruit and vegetable consumption, community participation, time since last health check-up, having a hobby, and feeling worthless were selected using Boruta algorithm. Within the 6-year follow-up, 3013 individuals (15.5%) reported incidence of tooth loss. People who experienced tooth loss were older (72.9 ± 5.2 vs 71.8 ± 4.7), and predominantly men (18.3% vs 13.1%). Extreme gradient boosting (XGBoost) machine learning prediction model had a mean accuracy of 90.5% (±0.9%). A visual analysis of machine learning predictions revealed that the prediction of tooth loss was mainly driven by demographic (older age), baseline oral health (having 10-19 teeth, wearing dentures), and socioeconomic (lower household income, manual occupations) variables. Predictors related to wide a range of determinants contribute towards tooth loss among older adults. In addition to oral health related and demographic factors, socioeconomic factors were important in predicting future tooth loss. Understanding the behaviour of these predictors can thus be useful in developing prevention strategies for tooth loss among older adults.

Importance of socioeconomic factors in predicting tooth loss among older adults in Japan: Evidence from a machine learning analysis.

This study was carried out to identify biomarkers that distinguish Hunner-type interstitial cystitis from non-Hunner-type interstitial cystitis patients.

Supervised machine learning algorithm identified KRT20, BATF and TP63 as biologically relevant biomarkers for bladder biopsy specimens from interstitial cystitis/bladder pain syndrome patients.

Adverse health effects resulting from falls are a major public health concern. Although studies have identified risk factors for falls, none have examined long-term prediction of fall risk. Furthermore, recent evidence suggests that there are additional risk factors, such as psychosocial factors.

An Interpretable Machine Learning Approach to Predict Fall Risk Among Community-Dwelling Older Adults: a Three-Year Longitudinal Study.

Single-channel electrophysiological recordings provide insights into transmembrane ion permeation and channel gating mechanisms. The first step in the analysis of the recorded currents involves an "idealization" process, in which noisy raw data are classified into two discrete levels corresponding to the open and closed states of channels. This provides valuable information on the gating kinetics of ion channels. However, the idealization step is often challenging in cases of currents with poor signal-to-noise ratios and baseline drifts, especially when the gating model of the target channel is not identified. We report herein on a highly robust model-free idealization method for achieving this goal. The algorithm, called adaptive integrated approach for idealization of ion-channel currents (AI2), is composed of Kalman filter and Gaussian mixture model clustering and functions without user input. AI2 automatically determines the noise reduction setting based on the degree of separation between the open and closed levels. We validated the method on pseudo-channel-current datasets that contain either computed or experimentally recorded noise. We also investigated the relationship between the noise reduction parameter of the Kalman filter and the cutoff frequency of the low-pass filter. The AI2 algorithm was then tested on actual experimental data for biological channels including gramicidin A, a voltage-gated sodium channel, and other unidentified channels. We compared the idealization results with those obtained by the conventional methods, including the 50%-threshold-crossing method.

Article Total : 2	Year
Publication title	2022
Publication title	2023

Article	Year
Development of new software enabling automatic identification of the optimal anatomical liver resectable region, incorporating preoperative liver function. Oncology letters\| PubMed ID: 31788120	2019
Importance of socioeconomic factors in predicting tooth loss among older adults in Japan: Evidence from a machine learning analysis. Social science & medicine (1982)\| PubMed ID: 34700121	2021
Supervised machine learning algorithm identified KRT20, BATF and TP63 as biologically relevant biomarkers for bladder biopsy specimens from interstitial cystitis/bladder pain syndrome patien... International journal of urology : official journal of the Japanese Urological Association\| PubMed ID: 35102612	2022
An Interpretable Machine Learning Approach to Predict Fall Risk Among Community-Dwelling Older Adults: a Three-Year Longitudinal Study. Journal of general internal medicine\| PubMed ID: 35112279	2022
Model-free idealization: Adaptive integrated approach for idealization of ion-channel currents. Biophysical journal\| PubMed ID: 37634080	2023

Masanori Hariyama

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