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Introduction: This study aimed to investigate the effectiveness of data augmentation to improve dementia risk prediction using machine learning models. Recent studies have shown that basic blood tests are cost-effective in predicting cognitive function. However, developing models that address various conditions poses challenges due to constraints associated with blood test results and cognitive assessments, including high costs, limited sample sizes, and missing data from tests not performed in certain facilities. Despite being often limited by small sample sizes, periodontal examination data have also emerged as a cost-effective screening tool., Methods: To address these challenges, this study explored the effectiveness of data augmentation using the Synthetic Minority Over-sampling Technique for Regression with Gaussian noise (SMOGN), a Generative Adversarial Network (GAN), and a Conditional Tabular GAN (CTGAN) on periodontal examination and blood test data. The datasets included parameters such as cognitive assessment results from the Mini-Mental State Examination (MMSE), demographic characteristics, periodontal examination data, and blood test results. Linear regression models, random forests, and deep neural networks were used to evaluate the effectiveness of the synthesized data., Results: This study used measured data from 108 participants and the synthesized data generated from the measured data. External validity was evaluated using a different dataset of 41 participants with missing items. The results suggested that normal GANs have the advantage of investigating models in data diversity, whereas CTGANs preserve the data structure and linear relationships in tabular data from the measured data, which drastically improves linear regression models., Discussion: Importantly, by interpolating sparse areas in the distribution, such as age, the synthesized models maintained prediction accuracy for test data with extreme inputs. These findings suggest that GAN-synthesized data can effectively address regression problems and improve dementia risk prediction., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Oyama, Isogai, Nakayama, Kobayashi, Kitano, Karako and Sakatani.)

Introduction: In this study, we investigated the correlation between serum albumin levels and cognitive function, and examined the impact of including serum albumin values in the input layer on the prediction accuracy when forecasting cognitive function using deep learning and other machine learning models., Methods: We analyzed the electronic health record data from Osaka Medical and Pharmaceutical University Hospital between 2014 and 2021. The study included patients who underwent cognitive function tests during this period; however, patients from whom blood test data was not obtained up to 30 days before the cognitive function tests and those with values due to measurement error in blood test results were excluded. The Mini-Mental State Examination (MMSE) was used as the cognitive function test, and albumin levels were examined as the explanatory variable. Furthermore, we estimated MMSE scores from blood test data using deep learning models (DLM), linear regression models, support vector machines (SVM), decision trees, random forests, extreme gradient boosting (XGBoost), and light gradient boosting machines (LightGBM)., Results: Out of 5,017 patients who underwent cognitive function tests, 3,663 patients from whom blood test data had not been obtained recently and two patients with values due to measurement error were excluded. The final study population included 1,352 patients, with 114 patients (8.4%) aged below 65 and 1,238 patients (91.6%) aged 65 and above. In patients aged 65 and above, the age and male sex showed significant associations with MMSE scores of less than 24, while albumin and potassium levels showed negative associations with MMSE scores of less than 24. Comparing MMSE estimation performance, in those aged below 65, the mean squared error (MSE) of DLM was improved with the inclusion of albumin. Similarly, the MSE improved when using SVM, random forest and XGBoost. In those aged 65 and above, the MSE improved in all models., Discussion: Our study results indicated a positive correlation between serum albumin levels and cognitive function, suggesting a positive correlation between nutritional status and cognitive function in the elderly. Serum albumin levels were shown to be an important explanatory variable in the estimation of cognitive function for individuals aged 65 and above., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Karako, Hata, Inoue, Oyama, Ueda and Sakatani.)

Background: Patients with chronic pain suffer from psychological effects such as anxiety due to the pain itself. Pain can not only impair activities of daily living (ADL) and quality of life (QOL), but also impair cognitive function. Therefore, in this study, we aimed to estimate the cognitive function of chronic pain patients using a deep neural network (DNN) model that has already been implemented in society. We investigated the characteristics of patients presumed to have mild cognitive impairment (MCI) and, at the same time, verified the relationship with the questionnaire commonly used in chronic pain research, which is administered by 43 university affiliated hospitals and medical institutions participating in the chronic pain research group of the Ministry of Health, Labor and Welfare in Japan (assessment batteries)., Method: The study included 114 outpatients from a multidisciplinary pain clinic, and we estimated their Mini-Mental State Examination (MMSE) scores based on age and basic blood test data (23 items). Furthermore, we classified the estimated MMSE scores of chronic pain patients into two groups based on a cutoff score of 27, which indicates MCI, and compared the blood data and assessment batteries. Additionally, we used a control group of 252 healthy adults aged 45 years or older who visited a dementia prevention outpatient clinic for comparison with the MMSE scores of chronic pain patients., Result: The MMSE scores in chronic pain patients were below the cutoff for MCI. When classified into two groups based on the estimated MMSE score of 27 points, WBC, RBC, Hb, Hct, PLT, UA, BUN, creatinine, Triglyceride, and γ-GT were significantly higher in the blood data. In the MCI group, PDAS values were significantly lower. Furthermore, only in the non-MCI group, a significant correlation was found between the estimated MMSE value and BPI, PDAS, and Locomo. The estimated MMSE scores were significantly lower in chronic pain patients than in healthy adults ( p  = 0.04)., Conclusion: Patients with chronic pain may exhibit cognitive impairment due to systemic metabolic disturbances. This suggests that chronic pain affects activities of daily living, resulting in systemic metabolic disorders., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Moriya, Hu, Sakatani and Kitahara.)

We conducted a randomized controlled trial to investigate the potential of Bifidobacterium breve M-16 V to improve mood in humans. In this evaluation, we incorporated the use of near-infrared spectroscopy (NIRS), which has been used to evaluate mood states in studies with small sample sizes. Participants were given B. breve M-16 V (20 billion cells/day) for 6 weeks, and their mood state was assessed before and after ingestion. NIRS data were collected at rest and during a mental arithmetic task (under stress). Intake of B. breve M-16 V decreased the heart rate under stress and increased levels of the GABA-like substance pipecolic acid in stool samples. In addition, B. breve M-16 V improved mood and sleep scores in participants with high anxiety levels. These results suggest that B. breve M-16 V affects the metabolites of the gut microbiota and has the potential to modulate the autonomic nervous system and to improve mood and sleep., Competing Interests: Declaration of Competing Interest N.M., C.X., K.K., S.A., N.I. and M.T. are employed by Morinaga Milk Industry, Co., Ltd. Y. J. had a consulting contract with Morinaga Milk Industry, Co., Ltd. M.M., R.I., K.S. and S.W. had no conflicts of interest., (Copyright © 2023 The Authors. Published by Elsevier B.V. All rights reserved.)

Recent research has linked systemic metabolic disorders to cognitive decline and dementia risk, including Alzheimer's. This is suspected to be due to lifestyle-related vascular impairments from atherosclerosis and other factors, such as malnutrition and anaemia. Applying deep learning using 2897 cases from a rehabilitation hospital and health screenings, we trained a model to predict cognitive function [mini-mental state examination (MMSE) scores] and brain atrophy [Brain Healthcare Quotient (BHQ) scores] from basic blood tests and age. The deep learning model accurately estimated MMSE and BHQ from these inputs, with age, nutritional information, and organ function indicators being top predictors. These findings highlight the relationship of dementia with systemic metabolic disorders and suggest the potential of using routine blood tests for dementia risk assessment. Furthermore, personalised dietary interventions could be tailored based on blood test anomalies. This holistic view mirrors traditional Chinese medicine, which considers brain disorders systemic, that is related to vital organs but not the brain itself., (© 2024. Oxygen Transport to Tissue International.)

The purpose of this study was to examine the effects on prefrontal cortex (PFC) activity of listening to pleasant sounds (PS) while walking, gum chewing (GCh), or performing the dual task of walking and gum chewing at the same time (walking + GCh). A total of 11 healthy adult male volunteers participated in the study (mean age: 29.54 ± 3.37). The block design of the trial consisted of a 30-sec rest, a 60-sec task (target task or control task), and a 30-sec rest. There were three target task conditions: walking, GCh, and the dual task. All of these were performed while listening to PS. The control condition was rest (no exercise) while listening to PS. The outcomes measured and measurements used were PFC activity using two-channel near-infrared spectroscopy and participant self-evaluation of the pleasantness of the experience using the visual analogue scale (VAS). Compared to the control condition, there was significantly greater PFC activation during the GCh and the walking + GCh tasks. Compared to the control condition, GCh and walking + GCh showed significantly greater activation on the VAS measure. In conclusion, listening to PS while GCh or walking + GCh increases PFC activity in the lower central region and induces positive emotional change., (© 2024. Oxygen Transport to Tissue International.)

This study investigates the effectiveness of data augmentation to improve dementia risk prediction using deep neural networks (DNNs). Previous research has shown that basic blood test data were cost-effective and crucial in predicting cognitive function, as indicated by mini-mental state examination (MMSE) scores. However, creating models that can accommodate various conditions is a significant challenge due to constraints related to blood test and MMSE results, such as high costs, limited sample size, and missing data from specific tests not conducted in certain facilities. Periodontal examinations have also emerged as a cost-effective tool for mass screening. To address these issues, this study explores the use of generative adversarial networks (GANs) for generating synthesised data from blood test and periodontal examination results. We used DNNs with four hidden layers to compare prediction accuracy between real and GAN-synthesised data from 108 participants at Nihon University Itabashi Hospital. The GAN-synthesised DNNs achieved a mean absolute error (MAE) of 1.91 ± 0.30 compared to 2.04 ± 0.37 for real data, indicating improved accuracy with synthesised data. Importantly, synthesised data showcased enhanced robustness against missing important variables including age information, and better managed data imbalances. Considering the difficulties in amassing extensive medical data, the augmentation approach is promising in refining dementia risk prediction., (© 2024. Oxygen Transport to Tissue International.)

Background: Recently, a method using deep learning has been developed to estimate the risk of developing dementia. This method uses general blood test data from routine health examinations that reveal lifestyle-related diseases, which can lead to vascular cognitive impairment via arteriosclerosis, as well as systemic metabolic disorders that are unrelated to lifestyle, such as nutritional disorders. In this study, we investigated the differences in the accuracy of estimating the risk of dementia based on the presence or the absence of blood test parameters reflecting nutritional disorders while focusing on the association between malnutrition and the risk of dementia in frail, elderly individuals., Objectives: The objective of this study was to evaluate the impact of including or excluding serum albumin, which reflects nutritional status, on the accuracy of predicting cognitive function in older adults using blood test data., Methods: We estimated cognitive function, as measured by the Mini-Mental State Examination (MMSE), using the deep learning model (DLM). The estimation was performed based on general blood test data, including complete blood tests and basic metabolic panels, obtained from a selection of 1287 patients admitted to Osaka Medical and Pharmaceutical University Hospital. The data were divided into two groups: individuals aged 65 and above and those aged below 65. The impact of including or excluding serum albumin on the predictive performance of MMSE was examined within each group., Results: In those aged below 65, the mean squared error (MSE) of the DLM was 5.33 without albumin and 4.62 with albumin, showing a -0.71 improvement with albumin. In those aged 65 and above, the MSE of the DLM was 6.38 without albumin and 6.28 with albumin, showing a -0.1 improvement with albumin., Discussion: The present study demonstrated that including serum albumin in the input data resulted in lower estimation errors for MMSE across all applied algorithms in the group aged 65 and above. This is consistent with previously reported studies that have shown the adverse effects of malnutrition on cognitive function in older adults., Conclusions: This study highlighted the significance of serum albumin, which reflects nutritional status, as an important assessment variable for estimating MMSE from blood test data, particularly in individuals aged 65 and above., (© 2024. Oxygen Transport to Tissue International.)

BackgroundFunctional near-infrared spectroscopy (fNIRS) studies demonstrated that regulation of stress response of the autonomic nervous system is mediated by the left-right asymmetry of prefrontal cortex (PFC) activity. However, it is not yet clear whether PFC regulation of stress response is functioning only when the subject was under stress or even at rest without stress. In addition, the temporal responsivity of PFC regulation of stress response is not known.AimThis study aims to investigate the relationship between the left-right asymmetry of PFC activity and heart rate during both resting state and stressful state while performing a working memory task.ApproachTwenty-nine subjects were recruited to rest and conduct 2-back task, during which fNIRS and ECG were measured simultaneously.ResultsWe found weak correlation (r = 0.28, p = 0.137) between laterality index (LI) and heart rate in the task session, but no correlation in rest sessions at a group level. Moreover, weak but significant correlation was found only in the task session for all analysis intervals ranged from 2 s to 1 min.ConclusionIt is suggested that regulation of stress responses was mediated by the left-right asymmetry of PFC activity only when the subject was under stress stimuli and embody stress response did not affect PFC in reverse. This regulation can be observed at an analysis interval of no less than 2 s., (© 2023. The Author(s), under exclusive license to Springer Nature Switzerland AG.)

Systemic metabolic disorders, including lifestyle-related diseases, are known risk factors for dementia. Furthermore, oral diseases such as periodontal disease and tooth decay are also associated with systemic metabolic disorders such as lifestyle-related diseases, and have also been reported to be indicators of risk factors for developing dementia. In this study, we investigated the relationship between cognitive function, oral conditions and systemic metabolic function in the elderly. We investigated the number of healthy teeth, the number of prosthetic teeth fitted, the number of missing prosthetic teeth, etc., in 41 elderly patients (69.7 ± 5.6 years old). Cognitive function was evaluated by the Mini Mental State Examination (MMSE). We also estimated MMSE scores for each subject using deep learning-based assessment of MMSE scores. This deep learning method enables the estimation of the MMSE score based on basic blood test data from medical examinations and reflects the systemic metabolic state including lifestyle-related diseases. The estimated MMSE score correlated negatively with age (r = -0.381), correlated positively with the number of healthy teeth (r = 0.37), and correlated negatively with the number of missing prosthetic teeth (r = -0.39). This relationship was not found in the measured MMSE scores. A negative correlation (r = -0.36) was found between age and the current number of teeth and a positive correlation (r = 0.37) was found between age and the number of missing prosthetic teeth. A positive correlation was found between the number of teeth requiring prosthesis and lifestyle-related diseases. The deep learning-based estimation method of cognitive function clearly demonstrated the close relationship between oral health condition, systemic metabolic function and the risk of cognitive impairment. It was determined that the smaller the number of existing teeth and the larger the number of missing prosthetic teeth, the higher is the risk of cognitive impairment. Systemic metabolic function is presumed to affect oral health and cognitive function. Interestingly, no such relationship was found in the measured MMSE scores. There are two possible reasons for this. The first is that MMSE is a subjective test and is less accurate in assessing cognitive function. The second is that because the MMSE estimated based on blood data using deep learning is calculated based on the metabolic function, it has a stronger correlation with the oral health condition affected by the metabolic function. In conclusion, oral health condition may predict cognitive impairment in the elderly., (© 2023. The Author(s), under exclusive license to Springer Nature Switzerland AG.)

Background: Based on the assumption that systemic metabolic disorders affect cognitive function, we have developed a deep neural network (DNN) model that can estimate cognitive function based on basic blood test data that do not contain dementia-specific biomarkers. In this study, we used the same DNN model to assess whether basic blood data can be used to estimate cerebral atrophy., Methods: We used data from 1,310 subjects (58.32 ± 12.91years old) enrolled in the Brain Doc Bank. The average Mini Mental State Examination score was 28.6 ± 1.9. The degree of cerebral atrophy was determined using the MRI-based index (GM-BHQ). First, we evaluated the correlations between the subjects' age, blood data, and GM-BHQ. Next, we developed DNN models to assess the GM-BHQ: one used subjects' age and blood data, while the other used only blood data for input items., Results: There was a negative correlation between age and GM-BHQ scores (r = -0.71). The subjects' age was positively correlated with blood urea nitrogen (BUN) (r = 0.40), alkaline phosphatase (ALP) (r = 0.22), glucose (GLU) (r = 0.22), and negative correlations with red blood cell counts (RBC) (r = -0.29) and platelet counts (PLT) (r = -0.26). GM-BHQ correlated with BUN (r = -0.30), GLU (r = -0.26), PLT (r = 0.26), and ALP (r = 0.22). The GM-BHQ estimated by the DNN model with subject age exhibited a positive correlation with the ground truth GM-BHQ (r = 0.70). Furthermore, even if the DNN model without subject age was used, the estimated GM-BHQ showed a significant positive correlation with ground truth GM-BHQ (r = 0.58). Age was the most important variable for estimating GM-BHQ., Discussion: Aging had the greatest effect on cerebral atrophy. Aging also affects various organs, such as the kidney, and causes changes in systemic metabolic status, which may contribute to cerebral atrophy and cognitive impairment. The DNN model may serve as a new screening test for dementia using basic blood tests for health examinations. Finally, the blood data reflect systemic metabolic disorders in each subject-this method may thus contribute to personalized care., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Sakatani, Oyama, Hu and Warisawa.)

We have demonstrated that machine learning allows us to predict cognitive function in aged people using near-infrared spectroscopy (NIRS) data or basic blood test data. However, the following points are not yet clear: first, whether there are differences in prediction accuracy between NIRS and blood test data; second, whether there are differences in prediction accuracy for cognitive function in linear models and non-linear models; and third, whether there are changes in prediction accuracy when both NIRS and blood test data are added to the input layer. We used a linear regression model (LR) for the linear model and random forest (RF) and deep neural network (DNN) for the non-linear model. We studied 250 participants (mean age = 73.3 ± 12.6 years) and assessed cognitive function using the Mini Mental State Examination (MMSE) (mean MMSE scores = 22.9 ± 6.1). We used time-resolved NIRS (TNIRS) to measure absolute concentrations of hemoglobin and optical pathlength at rest in the bilateral prefrontal cortices. A basic blood test was performed on the same day. We compared predicted MMSE scores and grand truth MMSE scores; prediction accuracies were evaluated using mean absolute error (MAE) and mean absolute percentage error (MAPE). We found that (1) the DNN-based prediction using TNIRS data exhibited lower MAE and MAPE compared with those using blood test data, (2) the difference in MAPE between TNIRS and blood test data was only 0.3%, (3) adding TNIRS data to the blood test data of the input layer only improved MAPE by 1.0% compared to the use of blood test data alone, whereas the use of the blood test data alone exhibited the prediction accuracy with 81.8% sensitivity and 91.3% specificity ( N = 202, repeated five-fold cross validation). Given these findings and the benefits of using blood test data (low cost and large-scale screening possible), we concluded that the DNN model using blood test data is still the most suitable for mass screening., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Oyama and Sakatani.)

The Internet of Medical Things (IoMT) system plays a role in various areas of social activity, including healthcare. Telemetry of cardiovascular function, such as blood pressure and pulse, in daily life is useful in the treatment of cardiovascular disease and stress management. However, until now, brain function monitoring technology has not been installed in the IoMT system.In this study, we used near-infrared spectroscopy (NIRS) installed in the IoMT system to evaluate whether consumers who are not medical experts can measure their own brain function correctly. In addition, the IoMT system was used to assess the long-term effects of physical exercise on physical and mental health.We studied a total of 119 healthy adults recruited from a fitness gym in Koriyama, Japan. After receiving instruction in the usage of the IoMT monitoring system including NIRS, the subjects monitored their physical and mental conditions by themselves when they visited the gym. We evaluated the relations between blood pressure (BP), pulse rate (PR), body weight (BW) and age. In addition, we evaluated the left/right asymmetry of the prefrontal cortex (PFC) at rest and BP. We calculated the laterality index at rest (LIR) for assessment of left/right asymmetry of PFC activity; a positive LIR (>0) indicates right-dominant PFC activity associated with higher stress responses, while a negative LIR (<0) indicates left-dominant PFC activity associated with lower stress responses. We studied 47 out of 119 cases who monitored their physiological conditions before and after physical exercise for 6 months for this study.The results showed that the systolic blood pressure and mean blood pressure (p < 0.05) were significantly reduced after the physical exercise for 6 months; body weight did not change significantly (p > 0.05). In addition, NIRS demonstrated that LIR changed to plus values from minus values after exercise (p < 0.01).These results show that (1) consumers who are not-medical experts can measure their own brain function correctly using NIRS; (2) after long-term physical exercise, systemic blood pressure decreased, associated with modulation of PFC activity (i.e., from right-dominant PFC activity to left-dominant activity), indicating that long-term physical exercises caused relaxation in the brain and the autonomic nervous system., (© 2022. Springer Nature Switzerland AG.)

The vascular occlusion test (VOT) with peripheral near-infrared spectroscopy (NIRS) is a non-invasive method to evaluate peripheral microcirculation. Statin therapy is widely used for patients with dyslipidaemia and contributes to reducing low-density lipoprotein cholesterol (LDL-C) levels and adverse cardiovascular events. However, it is not yet clear whether statin treatment improves peripheral microcirculation assessed by VOT with NIRS. In the present study, using VOT with NIRS, we evaluated the effect of statin therapy on peripheral microcirculation in patients with dyslipidaemia before and after statin therapy., Methods: A total of six consecutive patients with dyslipidaemia who had not received statin therapy (6 males, mean age 71.8 ± 7.4 years) were enrolled. All patients were administered atorvastatin and their peripheral microcirculation assessed using VOT with NIRS (NIRO-200NX, Hamamatsu Photonics K.K., Japan) before and after statin therapy. The NIRS probe was attached to the right thenar eminence and brachial artery blood flow was blocked for 3 min at 50 mmHg above the resting systolic blood pressure. Maximum and minimum values of NIRS parameters after the VOT were used to determine concentration changes for total haemoglobin (ΔcHb), oxyhaemoglobin (ΔO 2 Hb), deoxyhaemoglobin (ΔHHb), and tissue oxygenation index (ΔTOI)., Results: During the follow-up period (mean 30.3 ± 6.5 days), LDL-C level decreased from 129.7 ± 26.3 to 67.5 ± 20.2 mg/dL (p-value = 0.031), ΔTOI increased from 24.0 ± 5.3 to 33.7 ± 6.3% (p-value = 0.023), and ΔO 2 Hb increased from 16.4 ± 5.3 to 20.0 ± 6.6 μmol/L (p-value = 0.007). ΔcHb and ΔHHb did not change significantly., Conclusion: ΔO 2 Hb and ΔTOI were significantly increased during the follow-up period. These findings suggest that ΔO 2 Hb and ΔTOI could assess the improvement of peripheral microcirculation by statin therapy. Compared to ΔTOI, ΔO 2 Hb seems to be a more useful parameter to evaluate peripheral microcirculation., (© 2022. Springer Nature Switzerland AG.)

Autonomic disorders such as orthostatic hypotension often become a problem during the early mobilization of poststroke patients. We reported that the prefrontal cortex (PFC) oxyhemoglobin changes at rest are often on the right, and a positive correlation was observed between the left and right activity balance and the change in oxy-Hb. In this study, we focused on the asymmetrical changes associated with the standing load from rest. We assessed the left-right asymmetry of the PFC oxyhemoglobin changes at rest and standing load by calculating the Laterality Index at Rest (LIR) and laterality index during activity (LIA); positive values indicate the right-dominant activity, while negative values indicate left-dominant activity. As for left-right asymmetry LIA, the active dominant PFC was reversed in five patients. It should be noted that in almost all of the 13 cases, the active PFC and the lesion side matched. The detailed mechanism of overactivity up to the prefrontal cortex on the lesion side is unknown, but it may be a recovery mechanism that elicits plasticity in the brain network.

Mental disorders caused by chronic stress are difficult to identify, and colleagues in the work environment may suddenly report symptoms. Social barriers exist including the financial cost of medical services and the lack of a perceived need for treatment even if potential patients have a desire to receive mental healthcare. Self-report inventories such as the Beck Depression Inventory (BDI-II) and State-Trait Anxiety Inventory (STAI) can assess the emotional valence for mental health assessment, but medical expertise may be required for interpretation of the results. Contingency plans for clinical supervision and referral sources are necessary for sufficient mental healthcare using self-report inventories. On the other hand, the laterality index at rest (LIR) has been proposed for evaluation of the mental stress level from near-infrared spectroscopy (NIRS) data in the prefrontal cortex in the resting state. However, the potential for long-term monitoring has not been investigated with sufficient evaluation results. In this study, feature values were extracted from both NIRS and EEG signals each week for 10 weeks in four young participants with an average BDI-II score of 17.7, i.e., indicative of mild depression. Temporal changes in LIR and heart rate (HR) were compared with STAI-Y1 and BDI-II scores. We found cross-correlations between the time series of LIR and STAI-Y1 within one-week delay. In addition, the time series of LIR was also correlated with BDI-II with one-week delay. Importantly, by annotating the larger changes in LIR and HR on daily life events, the changes in LIR and HR were different depending on the type of life event that affected these moods.

Background: In order to develop a new screening test of cognitive impairment, we studied whether cognitive function can be estimated from basic blood test data by applying deep learning models. This model was constructed based on the effects of systemic metabolic disorders on cognitive function. Methods: We employed a deep neural network (DNN) to predict cognitive function based on subject's age and blood test items (23 items). We included 202 patients (73.48 ± 13.1 years) with various systemic metabolic disorders for training of the DNN model, and the following groups for validation of the model: (1) Patient group, 65 patients (73.6 ± 11.0 years) who were hospitalized for rehabilitation after stroke; (2) Healthy group, 37 subjects (62.0 ± 8.6 years); (3) Health examination group, 165 subjects (54.0 ± 8.6 years) admitted for a health examination. The subjects underwent the Mini-Mental State Examination (MMSE). Results: There were significant positive correlations between the predicted MMSE scores and ground truth scores in the Patient and Healthy groups ( r = 0.66, p < 0.001). There were no significant differences between the predicted MMSE scores and ground truth scores in the Patient group ( p > 0.05); however, in the Healthy group, the predicted MMSE scores were slightly, but significantly, lower than the ground truth scores ( p < 0.05). In the Health examination group, the DNN model classified 94 subjects as normal (MMSE = 27-30), 67 subjects as having mild cognitive impairment (24-26), and four subjects as having dementia (≤ 23). In 37 subjects in the Health examination group, the predicted MMSE scores were slightly lower than the ground truth MMSE ( p < 0.05). In contrast, in the subjects with neurological disorders, such as subarachnoid hemorrhage, the ground truth MMSE scores were lower than the predicted scores. Conclusions: The DNN model could predict cognitive function accurately. The predicted MMSE scores were significantly lower than the ground truth scores in the Healthy and Health examination groups, while there was no significant difference in the Patient group. We suggest that the difference between the predicted and ground truth MMSE scores was caused by changes in atherosclerosis with aging, and that applying the DNN model to younger subjects may predict future cognitive impairment after the onset of atherosclerosis., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2020 Sakatani, Oyama and Hu.)

The purpose of this study was to investigate the changes in patient subjective happiness and satisfaction with cataract surgery and evaluate the association between satisfaction and types of cataract. This study surveyed 247 participants (mean age, 67.9 years) and they completed questionnaires on their satisfaction with the surgery, the subjective happiness scale (SHS) and the Pittsburgh Sleep Quality Index (PSQI) before and after surgery. The SHS increased postoperatively from 4.6 ± 0.7 to 4.8 ± 0.7 (P = 0.007) and 83.4% of patients were satisfied with the surgical results and the average satisfaction score was 4.2 out of a possible 5.0. Multiple regression analysis showed that patient satisfaction was significantly associated with the postoperative SHS (β = 0.380; P < 0.001), the postoperative PSQI (β = -0.041; P = 0.035) and the presence of a posterior subcapsular cataract (PSC) (β = 0.277; P = 0.026). This study clarified that cataract surgery may improve both visual function and patient happiness and that patient satisfaction was affected by postoperative sleep quality and the disappearance of a PSC.

Vascular cognitive impairment (VCI) plays an important role in dementia in elderly people, and refers to the contribution of vascular pathology to the entire spectrum of cognitive disorders, ranging from mild cognitive impairment to severe dementia, as well as the pathological spectrum, from 'pure' Alzheimer disease through degrees of vascular comorbidity to 'pure' vascular dementia. In the present study, we investigated the relationship between cognitive dysfunction and systemic metabolic disorders, by employing deep learning (DL). We studied 202 patients (73.4 ± 13.0 years), 94.6% of whom were undergoing treatment for lifestyle diseases, and 68.8% of whom had a history of cerebrovascular disorder. We evaluated cognitive dysfunction by performing a Mini Mental State Examination (MMSE). We performed general blood examination, including Complete Blood Count and Basic Metabolic Panel, and measured cerebral blood oxygenation in the prefrontal cortex (PFC) using time-resolved near infrared spectroscopy (TNIRS). We then used deep neural networks to assess the MMSE scores of the subjects based on the TNIRS parameters and the blood examination data, independently. Next, we compared predicted MMSE scores based on the TNIRS and the blood examination. There was a significant positive correlation between the TNIRS parameters and the blood examination data (r = 0.6, p < 0.01). These observations suggest that cognitive dysfunction in patients with VCI may be caused by combinations of systemic metabolic disorders such as energy and oxygen metabolisms and cerebral circulatory disturbance due to arteriosclerosis resulting from lifestyle-related diseases.

Recent guidelines on cardiopulmonary resuscitation (CPR) have stressed the necessity to improve the quality of CPR. Our previous studies demonstrated the usefulness of monitoring cerebral blood oxygenation (CBO) during CPR by near-infrared spectroscopy (NIRS). The present study evaluates whether the NIRO-CCR1, a new NIRS device, is as useful in the clinical setting as the NIRO-200NX. We monitored CBO in 20 patients with cardiac arrest by NIRS. On the arrival of patients at the emergency department, the attending physician immediately assessed whether the patient was eligible for this study after conventional advanced life support and, if eligible, measured CBO in the frontal lobe by NIRS. We found that in all patients, the cerebral blood flow waveform was in synchrony with the chest compressions. Moreover, the tissue oxygenation index increased following cardiopulmonary bypass (CPB) in patients undergoing CPB, including one patient in whom CBO was monitored using the NIRO-CCR1. In addition, although the NIRO-CCR1 could display the pulse rate (Tempo) in real time, Tempo was not always detected, despite detection of the cerebral blood flow waveform. This suggested that chest compressions may not have been effective, indicating that the NIRO-CCR1 also seems useful to assess the quality of CPR. This study suggests that the NIRO-CCR1 can measure CBO during CPR in patients with cardiac arrest as effectively as the NIRO-200NX; in addition, the new NIRO-CCR1 may be even more useful, especially in prehospital fields (e.g. in an ambulance), since it is easy to carry.

Changes in NIRS signals are related to changes in local cerebral blood flow or oxy-Hb concentration. On the other hand, recent studies have revealed the effect of chewing gum on cognitive performance, stress control etc. which accompanied brain activity in the prefrontal cortex (PFC). However, these relationships are still controversial. To evaluate the chewing effect on PFC, NIRS seems to be a suitable method of imaging such results. When measuring NIRS on PFC, blood volume in superficial tissues (scalp, skin, muscle) might have some affect. The aim of the present study was to clarify the effect of the anterior temporal muscle on NIRS signals during gum chewing. Eight healthy volunteers participated. Two-channel NIRS (HOT-1000, NeU, Japan), which can distinguish total-Hb concentrations in deep tissue and superficial tissue layers, was used. In addition to a conventional optode separation distance of 3.0 cm, Hot 1000 has a short distance of 1.0 cm (NEAR channel) to measure NIRS signals that originate exclusively from surface tissues. NIRS probes were placed at Fp1 and Fp2 in the normal probe setting. The headset was displaced to the left in order to allow the left probe to be placed over the left anterior temporal muscle. In the normal setting, the superficial signal curve shows no notable change; however, the neural (calculated and defined in HOT-1000) and deep curves show an increase during the gum chewing task. At the deviated setting, all three signals show marked changes during the task. Total-Hb concentration in the deviated probe setting is significantly large (p < 0.05) than that of in the normal probe setting. When using gum chewing as a task, it would be better to consider a probe position carefully so that the influence of muscle activity on NIRS signal can be distinguished.