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Nakato Y, Inoue T, Nakagawa S, Kitaichi Y, Kameyama R, Wakatsuki Y, Kitagawa K, Omiya Y, Kusumi I.Neuropsychiatr Dis Treat. 2016;12: 2173–2179. On pages 2175 and 2177, Figures 1 and 2 were transposed.The correct version of Figure 1 is shown. Read the original article

The effect of mechanical surface treatment and post-treatment polishing on tensile strength and fatigue life of Wonder Process Craft (WPC) and Micro-abrasive blasting (MB) treated tool steel SKH 51was studied. Surface polishing was performed on the ground, WPC, and MB treated specimens. MB treatment brought a higher surface roughness increase compared to WPC treatment, while polishing decreased the surface roughness. WPC and MB treatments increased the surface compressive residual stress. Fatigue life showed a negative relationship with surface roughness, and this relationship varies with residual stress condition. Fatigue life at similar residual stress conditions showed a variation based on the surface roughness condition. There were 1.68 times and 4.72 times average fatigue life increase due to the MB treatment and the WPC treatment compared to the ground specimen. Dimension controlled post-treatment polishing on the MB treated showed 6.73 times, and the WPC treated specimens showed more than 7.74 times improvement in average fatigue life compared to the ground specimens. [ABSTRACT FROM AUTHOR]

[Purpose] Although prone positioning is used to increase oxygenation in various respiratory conditions, this positioning can lead to facial and limb pressure ulcers. The aim in this study was to investigate body pressure variations in the prone position for different facial orientations and upper extremity positions. [Participants and Methods] Nineteen healthy young women participated in this study. Body pressure (maximum body pressure on the face, chest, elbows, and knees) was measured in six different prone positions with different face orientations and upper extremity positions, and the median value of each body pressure measurement was compared among postures. [Results] Face pressure tended to decrease when face orientation coincided with the raised side of the upper limb. In contrast, elbow pressure tended to be lower when the orientation of the face did not coincide with that of the raised side of the upper limb. [Conclusion] Pressure on the face and elbows can be reduced by placing the upper limbs in the prone position. This suggests that targeted and specific positioning may be useful for limiting the incidence and severity of pressure ulcers in these areas., Competing Interests: The authors have no conflicts of interest directly relevant to the content of this article., (2024©by the Society of Physical Therapy Science. Published by IPEC Inc.)

Background: This study aimed to systematically evaluate voice symptoms during heart failure (HF) treatments and to exploratorily extract HF-related vocal biomarkers., Methods and Results: This single-center, prospective study longitudinally acquired 839 audio files from 59 patients with acute decompensated HF. Patients' voices were analyzed along with conventional HF indicators (New York Heart Association [NYHA] class, presence of pulmonary congestion and pleural effusion on chest X-ray, and B-type natriuretic peptide [BNP]) and GOKAN scores based on the assessment of a cardiologist. Machine-learning (ML) models to estimate HF conditions were created using a Light Gradient Boosting Machine. Voice analysis identified 27 acoustic features that correlated with conventional HF indicators and GOKAN scores. When creating ML models based on the acoustic features, there was a significant correlation between actual and ML-derived BNP levels (r=0.49; P<0.001). ML models also identified good diagnostic accuracies in determining HF conditions characterized by NYHA class ≥2, BNP ≥300 pg/mL, presence of pulmonary congestion or pleural effusion on chest X-ray, and decompensated HF (defined as NYHA class ≥2 and BNP levels ≥300 pg/mL; accuracy: 75.1%, 69.1%, 68.7%, 66.4%, and 80.4%, respectively)., Conclusions: The present study successfully extracted HF-related acoustic features that correlated with conventional HF indicators. Although the data are preliminary, ML models based on acoustic features (vocal biomarkers) have the potential to infer various HF conditions, which warrant future studies., Competing Interests: This study was jointly researched with PST Inc. This work was partially supported by the LIP Yokohama Trial and research funding from The Murata Science Foundation. M.K. is a member of Circulation Reports’ Editorial Team., (Copyright © 2024, THE JAPANESE CIRCULATION SOCIETY.)

Background: In Japan, individuals with mild COVID-19 illness previously required to be monitored in designated areas and were hospitalized only if their condition worsened to moderate illness or worse. Daily monitoring using a pulse oximeter was a crucial indicator for hospitalization. However, a drastic increase in the number of patients resulted in a shortage of pulse oximeters for monitoring. Therefore, an alternative and cost-effective method for monitoring patients with mild illness was required. Previous studies have shown that voice biomarkers for Parkinson disease or Alzheimer disease are useful for classifying or monitoring symptoms; thus, we tried to adapt voice biomarkers for classifying the severity of COVID-19 using a dynamic time warping (DTW) algorithm where voice wavelets can be treated as 2D features; the differences between wavelet features are calculated as scores., Objective: This feasibility study aimed to test whether DTW-based indices can generate voice biomarkers for a binary classification model using COVID-19 patients' voices to distinguish moderate illness from mild illness at a significant level., Methods: We conducted a cross-sectional study using voice samples of COVID-19 patients. Three kinds of long vowels were processed into 10-cycle waveforms with standardized power and time axes. The DTW-based indices were generated by all pairs of waveforms and tested with the Mann-Whitney U test (α<.01) and verified with a linear discrimination analysis and confusion matrix to determine which indices were better for binary classification of disease severity. A binary classification model was generated based on a generalized linear model (GLM) using the most promising indices as predictors. The receiver operating characteristic curve/area under the curve (ROC/AUC) validated the model performance, and the confusion matrix calculated the model accuracy., Results: Participants in this study (n=295) were infected with COVID-19 between June 2021 and March 2022, were aged 20 years or older, and recuperated in Kanagawa prefecture. Voice samples (n=110) were selected from the participants' attribution matrix based on age group, sex, time of infection, and whether they had mild illness (n=61) or moderate illness (n=49). The DTW-based variance indices were found to be significant ( P <.001, except for 1 of 6 indices), with a balanced accuracy in the range between 79% and 88.6% for the /a/, /e/, and /u/ vowel sounds. The GLM achieved a high balance accuracy of 86.3% (for /a/), 80.2% (for /e/), and 88% (for /u/) and ROC/AUC of 94.8% (95% CI 90.6%-94.8%) for /a/, 86.5% (95% CI 79.8%-86.5%) for /e/, and 95.6% (95% CI 92.1%-95.6%) for /u/., Conclusions: The proposed model can be a voice biomarker for an alternative and cost-effective method of monitoring the progress of COVID-19 patients in care., Competing Interests: Conflicts of Interest: YO was employed by PST Inc. This study was conducted in collaboration between PST and Kanagawa University of Human Services, but no funding for this study was received from PST., (©Teruhisa Watase, Yasuhiro Omiya, Shinichi Tokuno. Originally published in JMIR Biomedical Engineering (http://biomsedeng.jmir.org), 06.11.2023.)

Background: Stereotactic body radiotherapy (SBRT) is a local treatment option for hepatocellular carcinoma (HCC). SBRT-induced focal reactions on the liver parenchyma have not been thoroughly evaluated using quantitative magnetic resonance imaging (MRI)., Purpose: To quantitatively evaluate liver parenchymal changes caused by SBRT for HCC using magnetic resonance elastography (MRE) and diffusion-weighted imaging (DWI)., Method: We retrospectively evaluated 22 adult patients who received SBRT for HCC and 27 who received locoregional therapy other than SBRT (controls). Liver stiffness by MRE and apparent diffusion coefficient (ADC) values by DWI of the liver parenchyma were measured before and after SBRT. Regions of interest (ROIs) were drawn on the two areas of radiation dose distribution levels, > 30 Gy and ≤ 30 Gy; a ROI was drawn in the control group. The two indices were compared before and after SBRT using a Wilcoxon matched-pairs signed-rank test., Results: Liver stiffness and ADC values were significantly increased after SBRT in the dose areas of > 30 Gy compared with those before SBRT (4.05 vs 4.85 kPa; p < 0.05 in liver stiffness, and 1.10 vs 1.40 ×10 -3 s/mm 2 ; p < 0.05 in ADC values). In the dose area of ≦ 30 Gy, liver stiffness showed a significant increase in one reader (p = 0.033) but not in another reader (p = 0.085); ADC value showed no significant difference before and after SBRT as per both readers (p > 0.05). The control group demonstrated no significant differences before and after treatment (p > 0.05)., Conclusion: MRE and DWI can be used to detect SBRT-induced liver parenchymal changes., (© 2023. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

Purpose: To evaluate the feasibility of folded image training strategy (FITS) and the quality of images reconstructed using the improved model-based deep learning (iMoDL) network trained with FITS (FITS-iMoDL) for abdominal MR imaging., Methods: This retrospective study included abdominal 3D T1-weighted images of 122 patients. In the experimental analyses, peak SNR (PSNR) and structure similarity index (SSIM) of images reconstructed with FITS-iMoDL were compared with those with the following reconstruction methods: conventional model-based deep learning (conv-MoDL), MoDL trained with FITS (FITS-MoDL), total variation regularized compressed sensing (CS), and parallel imaging (CG-SENSE). In the clinical analysis, SNR and image contrast were measured on the reference, FITS-iMoDL, and CS images. Three radiologists evaluated the image quality using a 5-point scale to determine the mean opinion score (MOS)., Results: The PSNR of FITS-iMoDL was significantly higher than that of FITS-MoDL, conv-MoDL, CS, and CG-SENSE (P < 0.001). The SSIM of FITS-iMoDL was significantly higher than those of the others (P < 0.001), except for FITS-MoDL (P = 0.056). In the clinical analysis, the SNR of FITS-iMoDL was significantly higher than that of the reference and CS (P < 0.0001). Image contrast was equivalent within an equivalence margin of 10% among these three image sets (P < 0.0001). MOS was significantly improved in FITS-iMoDL (P < 0.001) compared with CS images in terms of liver edge and vessels conspicuity, lesion depiction, artifacts, blurring, and overall image quality., Conclusion: The proposed method, FITS-iMoDL, allowed a deeper MoDL reconstruction network without increasing memory consumption and improved image quality on abdominal 3D T1-weighted imaging compared with CS images.

Appropriate intervention and care in detecting cognitive impairment early are essential to effectively prevent the progression of cognitive deterioration. Diagnostic voice analysis is a noninvasive and inexpensive screening method that could be useful for detecting cognitive deterioration at earlier stages such as mild cognitive impairment. We aimed to distinguish between patients with dementia or mild cognitive impairment and healthy controls by using purely acoustic features (i.e., nonlinguistic features) extracted from two simple phrases. Voice was analyzed on 195 recordings from 150 patients (age, 45-95 years). We applied a machine learning algorithm (LightGBM; Microsoft, Redmond, WA, USA) to test whether the healthy control, mild cognitive impairment, and dementia groups could be accurately classified, based on acoustic features. Our algorithm performed well: area under the curve was 0.81 and accuracy, 66.7% for the 3-class classification. Thus, our vocal biomarker is useful for automated assistance in diagnosing early cognitive deterioration., Competing Interests: D.M., T.Y., K.E., and Y.O. were employed by PST Inc. The remaining authors, K.T., M.O., and S.T. declare that the research was conducted in the absence of any commercial or financial relationships. This study was conducted in collaboration between PST Inc., Takeyama Hospital, and Honjo Kodama Hospital, but no funding for this study was received from PST., (© The Author(s) 2023.)

Purpose: To assess inter-observer agreement and accuracy of LI-RADS v2018 for differentiating tumor in vein (TIV) from bland thrombus on gadoxetic acid-enhanced magnetic resonance imaging (Gx-MRI). Secondarily, to determine whether a multi-feature model improves accuracy compared to LI-RADS., Methods: We retrospectively identified consecutive patients at risk for hepatocellular carcinoma with venous occlusion(s) reported on Gx-MRI. Five radiologists independently classified each occlusion as TIV or bland thrombus using the LI-RADS TIV criterion (enhancing soft tissue in vein). They also evaluated imaging features suggestive of TIV or bland thrombus. Intra-class correlation coefficient (ICC) was calculated for individual features. A multi-feature model was developed based on consensus scores of features with > 5% consensus prevalence and > 0.40 ICC. Sensitivity and specificity of the LI-RADS criterion and of the cross-validated multi-feature model were compared., Results: Ninety-eight patients with 103 venous occlusions (58 TIV, 45 bland thrombus) were included. The LI-RADS criterion provided 0.63 ICC and, depending on the reader, 0.62-0.93 sensitivity and 0.87-1.00 specificity. Five other features had > 5% consensus prevalence and > 0.40 ICC, including three LI-RADS suggestive features and two non-LI-RADS features. The optimal multi-feature model incorporated the LI-RADS criterion and one LI-RADS suggestive feature (occluded or obscured vein contiguous with malignant parenchymal mass). After cross-validation, the multi-feature model did not improve sensitivity or specificity compared to the LI-RADS criterion (P = 0.23 and 0.25, respectively)., Conclusion: Using Gx-MRI, the LI-RADS criterion for TIV provides substantial inter-observer agreement, variable sensitivity, and high specificity for differentiating TIV from bland thrombus. A cross-validated multi-feature model did not improve diagnostic performance., (© 2023. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

In this study, the technique associated with the capturing involuntary changes in voice elements caused by diseases is applied to diagnose them and a voice index is proposed to discriminate mild cognitive impairments. The participants in this study included 399 elderly people aged 65 years or older living in Matsumoto City, Nagano Prefecture, Japan. The participants were categorized into healthy and mild cognitive impairment groups based on clinical evaluation. It was hypothesized that as dementia progressed, task performance would become more challenging, and the effects on vocal cords and prosody would change significantly. In the study, voice samples of the participants were recorded while they were engaged in mental calculational tasks and during the reading of the results of the calculations written on paper. The change in prosody during the calculation from that during reading was expressed based on the difference in the acoustics. Principal component analysis was used to aggregate groups of voice features with similar characteristics of feature differences into several principal components. These principal components were combined with logistic regression analysis to propose a voice index to discriminate different mild cognitive impairment types. Discrimination accuracies of 90% and 65% were obtained for discriminations using the proposed index on the training and verification data (obtained from a population different from the training data), respectively. Therefore, it is suggested that the proposed index may be utilized as a means for discriminating mild cognitive impairments., Competing Interests: This study received funding from MS&AD Insurance Group Holdings Co., Ltd. The funder had the following involvement with the study: data collection., (Copyright © 2023 Higuchi, Nakamura, Omiya and Tokuno.)

Severe intraoral pain induces difficulty in eating and speaking, leading to a decline in the quality of life. However, the molecular mechanisms underlying intraoral pain remain unclear. Here, we investigated gene modulation in the trigeminal ganglion and intraoral pain-related behavior in a rat model of acetic acid-induced oral ulcerative mucositis. Oral ulceration was observed on day 2 after acetic acid treatment to the oral mucosa of male Wistar rats, causing spontaneous pain and mechanical allodynia. Deoxyribonucleic acid microarray analysis of trigeminal ganglion tissue indicated that Hamp (a hepcidin gene that regulates cellular iron transport) was the most upregulated gene. In the oral ulcerative mucositis model, the upregulation of Hamp was also induced in the ulcer region but not in the liver, with no increase in hepcidin levels in the plasma and saliva, indicating that hepcidin was produced locally in the ulcer region in the model. Systemic antibiotic pretreatment did not increase the mRNA levels of Hamp in the trigeminal ganglion and ulcer regions. Hepcidin injection into the oral mucosa enhanced neuronal excitability in response to noxious mechanical stimulation of the oral mucosa in trigeminal spinal subnucleus interpolaris/caudalis neurons. These results imply that oral ulcerative mucositis induces oral mucosal pain because of infectious inflammation of the ulcerative area and potentiates Hamp, which represents anti-bacterial and anti-peptidase gene expression in the ulcer region and trigeminal ganglion. The regulation of cellular iron transport by hepcidin is likely involved in oral ulcerative mucositis-induced pain., Competing Interests: The authors declare that this study was conducted without any commercial or financial relationships construed as potential competing interests (Financial support from Tsumura & Co. was outside the last 5-year time frame). This does not alter our adherence to PLOS ONE policies on sharing data and materials., (Copyright: © 2023 Hitomi et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Voice-based depression detection methods have been studied worldwide as an objective and easy method to detect depression. Conventional studies estimate the presence or severity of depression. However, an estimation of symptoms is a necessary technique not only to treat depression, but also to relieve patients' distress. Hence, we studied a method for clustering symptoms from HAM-D scores of depressed patients and by estimating patients in different symptom groups based on acoustic features of their speech. We could separate different symptom groups with an accuracy of 79%. The results suggest that voice from speech can estimate the symptoms associated with depression.

The authors are currently conducting research on methods to estimate psychiatric and neurological disorders from a voice by focusing on the features of speech. It is empirically known that numerous psychosomatic symptoms appear in voice biomarkers; in this study, we examined the effectiveness of distinguishing changes in the symptoms associated with novel coronavirus infection using speech features. Multiple speech features were extracted from the voice recordings, and, as a countermeasure against overfitting, we selected features using statistical analysis and feature selection methods utilizing pseudo data and built and verified machine learning algorithm models using LightGBM. Applying 5-fold cross-validation, and using three types of sustained vowel sounds of /Ah/, /Eh/, and /Uh/, we achieved a high performance (accuracy and AUC) of over 88% in distinguishing "asymptomatic or mild illness (symptoms)" and "moderate illness 1 (symptoms)". Accordingly, the results suggest that the proposed index using voice (speech features) can likely be used in distinguishing the symptoms associated with novel coronavirus infection.

Purpose : This study aims to identify the hallmarks of gadoxetate disodium-enhanced magnetic resonance imaging distinguishing hepatocellular carcinoma (HCC) from hypervascular liver metastases (HLMs). Methods : Between January 2008 and October 2020, among patients who underwent gadoxetate disodium-enhanced MRI, those who met the following criteria were retrospectively included: without chronic hepatitis or liver stiffness ≤ 2.5 kPa on magnetic resonance elastography or F0/F1 on pathological assessment. Two blinded radiologists reviewed the imaging findings to judge the presence or absence of the enhancing capsule, nonperipheral washout, corona enhancement, hypointensity in the transitional/hepatobiliary phase (HBP), hyperintensity on T2-weighted/diffusion-weighted imaging (DWI), mosaic architecture, and blood products/fat in mass. The lesion-to-liver signal intensity ratios in HBP and DWI were also calculated. Univariate and multivariate analyses were performed to identify the imaging hallmarks distinguishing HCC from HLM. Interobserver agreement was calculated using kappa values and intraclass correlation coefficients (ICCs). Results : The final study cohort comprised 72 lesions in 44 patients (mean age, 65.0±11.9 years). Univariate analysis revealed higher frequencies of the following features in HCC than in HLM (P < .10): nonperipheral washout, corona enhancement, transitional phase hypointensity, mosaic architecture, and fat in mass (P = .002-.073). Multivariate analysis revealed that nonperipheral washout and mosaic architecture favored the diagnosis of HCC over that of HLM with odds ratios of 7.66 and 14.6, respectively (P = .038 and .029, respectively). The interobserver agreement for each item was moderate or substantial (kappa or ICC = .447-.792). Conclusion : Peripheral washout and mosaic architecture may be reliable imaging hallmarks for distinguishing HCC from HLM.

Physical frailty is an aging-related clinical syndrome involving decreases in body weight, mobility, activity, and walking speed that occurs in individuals with sarcopenia and is accelerated by increased oxidative stress. Ninjin'yoeito, a traditional Japanese Kampo medicine, is used for treating conditions, including anemia and physical weakness. Here, we investigated whether ninjin'yoeito could improve physical frailty by controlling oxidative stress in the senescence-accelerated mouse prone 8 (SAMP8) model. First, SAMP8 mice were divided into two groups, ninjin'yoeito treated and untreated, with the former consuming a diet containing 3% ninjin'yoeito from 3 months of age. At 7 months of age, body weight, motor function, locomotor activity, and mean walking speed were measured. Subsequently, mice were euthanized and measured for muscle weight, 8-hydroxy-2'-deoxyguanosine levels in muscle and brain, and cleaved caspase-3 expression in brain. The results showed reductions in weight, locomotor function, locomotion, and average walking speed in the untreated group, which were significantly improved by ninjin'yoeito. Furthermore, 8-hydroxy-2'-deoxyguanosine levels were reduced in muscle and brain from ninjin'yoeito-treated mice, compared with the levels in untreated mice; cleaved caspase-3 expression was similarly reduced in brain from the treated mice, indicating reduced apoptosis. Our findings suggest that ninjin'yoeito inhibits sarcopenia-based physical frailty through its antioxidant effects.

In general, it is common knowledge that people's feelings are reflected in their voice and facial expressions. This research work focuses on developing techniques for diagnosing depression based on acoustic properties of the voice. In this study, we developed a composite index of vocal acoustic properties that can be used for depression detection. Voice recordings were collected from patients undergoing outpatient treatment for major depressive disorder at a hospital or clinic following a physician's diagnosis. Numerous features were extracted from the collected audio data using openSMILE software. Furthermore, qualitatively similar features were combined using principal component analysis. The resulting components were incorporated as parameters in a logistic regression based classifier, which achieved a diagnostic accuracy of ~90% on the training set and ~80% on the test set. Lastly, the proposed metric could serve as a new measure for evaluation of major depressive disorder.

As both physiological and psychological factors influence age-associated declines in older people, the development of drug therapy with multifaceted effects is required. To investigate the utility of ninjin'yoeito (NYT) against geriatric syndromes, we evaluated the effects of NYT on age-related declines in old C57BL/6 mice (88-week-old) as a preclinical model of frailty progression. Here, we showed that NYT reversed the decline of rectal temperature in old mice and also improved forelimb grip strength compared with that in the old control group without affecting skeletal muscle loss. Moreover, NYT significantly increased the duration of grooming after a sucrose solution was sprayed, which reflected self-care motivation. Finally, we revealed the antioxidant effects of NYT using a cell-free assay. These results suggest that NYT can improve both physiological and psychological declines associated with aging, and the mechanism may include antioxidant effects. NYT may have potential utility for maintaining the health of older people., (© The Author(s) 2022. Published by Oxford University Press on behalf of Japan Society for Bioscience, Biotechnology, and Agrochemistry.)

The ventral tegmental area (VTA), substantia nigra pars compacta (SNpc) and nucleus accumbens (NAc) are involved in the regulation of appetite and motivational behaviors. A traditional Japanese (Kampo) medicine, ninjin'yoeito (NYT), has been reported to improve decreased motivation and anorexia in patients with Alzheimer's disease and apathy-like model mice. Thus, NYT may affect the activities of neurons in the VTA, SNpc and NAc. However, little is known about the underlying mechanisms of NYT. Here, we investigated the effects of NYT on the electrophysiological properties of dopaminergic neurons in the VTA and SNpc, as well as on those of medium spiny neurons (MSNs) in the NAc (core and shell subregions), by applying the patch-clamp technique in the brain slices. NYT reduced the resting membrane potential of VTA and SNpc dopaminergic neurons. In contrast, NYT increased the firing frequency of NAc MSNs accompanied by shortened first spike latency and interspike interval. Furthermore, NYT attenuated the inward rectification and sustained outward currents. In conclusion, NYT may directly influence the excitability of dopaminergic neurons in the VTA and SNpc, as well as MSNs in the NAc (core and shell). NYT may modulate dopamine signals in appetite and motivational behaviors.