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In addition to electronic cigarettes (e-cigarettes), tobacco companies have recently begun to sell heat-not-burn tobacco products, Ploom and iQOS in Japan. Previous research has reported an inverse association between combustible cigarette smoking and educational attainment, but little is known about the association for e-cigarettes, especially heat-not-burn tobacco products. Our objective was to analyze the relationship between educational attainment and e-cigarette and heat-not-burn tobacco use. An internet survey (randomly sampled research agency panelists) in Japan. A total of 7338 respondents aged 18-69 years in 2015 (3632 men and 3706women). Adjusted odds ratios (ORs) of educational attainment for current smoking (combustible cigarettes), e-cigarette ever-use, and heat-not-burn ever-use were calculated by multivariable logistic regression models using covariates including socio-demographic factors. Stratified analyses according to smoking status (combustible cigarettes) were additionally performed for e-cigarette ever-use and heat-not-burn tobacco product ever-use. Associations between educational attainment and e-cigarette ever-use or heat-not-burn tobacco ever-use are not straightforward, although these associations are not statistically significant except for one cell. For example, using \"graduate school\" education as a reference category, adjusted ORs for \"high school\" were 1.44 (95% confidence interval [CI]: 0.85-2.44) for e-cigarettes ever-use and 0.75 (95% CI:0.19-2.97) for heat-not-burn tobacco product ever-use. Among current smokers, compared with \"graduate school\" (reference), those with lower educational attainment showed 0.6 to 0.7 ORs for e-cigarette ever-use: e.g.,\"4-year university\"(OR = 0.54, 95% CI:0.24-1.24) and \"high school\" (OR = 0.69, 95% CI: 0.30-1.60). Among former smokers, lower education indicated higher ORs for both e-cigarettes and heat-not-burn tobacco ever-use. This study provides baseline information on educational gradients of e-cigarette and heat-not-burn tobacco products, ever-use. As heat-not-burn tobacco products are increasing their market share in Japan, continuous monitoring of these products will be necessary.

In daily life, huge costs can arise from just one incorrect performance on a visual search task (e.g., a fatal accident due to a driver overlooking a pedestrian). One potential way to prevent such drastic accidents would be for people to modify their decision criterion (e.g., placing a greater priority on accuracy rather than speed) during a visual search. The aim of the present study was to manipulate the criterion by creating an awareness of being watched by another person. During a visual search task, study participants were watched (or not watched) via video cameras and monitors. The results showed that, when they believed they were being watched by another person, they searched more slowly and accurately, as measured by reaction times and hit/miss rates. These findings also were obtained when participants were videotaped and they believed their recorded behavior would be watched by another person in the future. The study primarily demonstrated the role of being watched by another on the modulation of the decision criterion for responding during visual searches.

Wearing a sanitary mask tended, in the main, to reduce the wearer’s sense of perceived facial attractiveness before the COVID-19 epidemic. This phenomenon, termed the sanitary-mask effect, was explained using a two-factor model involving the occlusion of cues used for the judgment of attractiveness and unhealthiness priming (e.g., presumed illness). However, these data were collected during the pre-COVID-19 period. Thus, in this study, we examined whether the COVID-19 epidemic changed the perceived attractiveness and healthiness when viewing faces with and without sanitary masks. We also used questionnaires to evaluate beliefs regarding mask wearers. We found that the perception of mask-worn faces differed before versus after the onset of the COVID-19 epidemic. Specifically, mask-wearing improved wearers’ sense of the attractiveness of faces, which were rated as less attractive when a mask was not worn after the onset of the COVID-19 epidemic. Furthermore, mask-worn faces were rated as healthier after the onset of the COVID-19. The proportion of respondents with negative associations regarding mask-wearing (e.g., unhealthiness) decreased relative to before the epidemic. We suggest that the weakening of this association altered the sanitary-mask effect with a relative emphasis on the occlusion component, reflecting the temporal impact of a global social incident (the COVID-19 epidemic) on the perception of facial attractiveness.

“Clothes with horizontal (or vertical) stripes are perceived as wider and shorter (slimmer and taller).” This belief is common yet inconsistent with the Helmholtz illusion. It has often attracted attention from researchers of perception. Despite the controversy among empirical studies, it is persistently supported by the general public. This article explores the early appearance of this common belief in Japan in historical records. Consequently, we discovered the descriptions of the common belief in a Japanese beauty handbook titled “Miyako Fuzoku Kewai Den [Cosmetic manners and customs in Edo],” published in 1813. In Japan, this belief was not born in modern times. Instead, it was established over 200 years ago, when vertical striped patterns on clothes were popularized.

Associations between delirium and postoperative adverse events in cardiovascular surgery have been reported and the preoperative identification of high-risk patients of delirium is needed to implement focused interventions. We aimed to develop and validate machine learning models to predict post-cardiovascular surgery delirium. Patients aged ≥ 40 years who underwent cardiovascular surgery at a single hospital were prospectively enrolled. Preoperative and intraoperative factors were assessed. Each patient was evaluated for postoperative delirium 7 days after surgery. We developed machine learning models using the Bernoulli naive Bayes, Support vector machine, Random forest, Extra-trees, and XGBoost algorithms. Stratified fivefold cross-validation was performed for each developed model. Of the 87 patients, 24 (27.6%) developed postoperative delirium. Age, use of psychotropic drugs, cognitive function (Mini-Cog < 4), index of activities of daily living (Barthel Index < 100), history of stroke or cerebral hemorrhage, and eGFR (estimated glomerular filtration rate) < 60 were selected to develop delirium prediction models. The Extra-trees model had the best area under the receiver operating characteristic curve (0.76 [standard deviation 0.11]; sensitivity: 0.63; specificity: 0.78). XGBoost showed the highest sensitivity (AUROC, 0.75 [0.07]; sensitivity: 0.67; specificity: 0.79). Machine learning algorithms could predict post-cardiovascular delirium using preoperative data. Trial registration : UMIN-CTR (ID; UMIN000049390).

Postoperative delirium (POD) is associated with complications following cardiovascular surgery, and its psychomotor subtypes differ in clinical course and treatment responsiveness, underscoring the importance of personalized preventive strategies. We aimed to investigate preoperative neurophysiological characteristics associated with POD and its psychomotor subtypes in patients undergoing cardiovascular surgery. This prospective, single-center study included 209 patients undergoing cardiovascular surgery. Preoperative frontal electroencephalography (EEG) was recorded using a patch-type device with three electrodes (left, center, and right). Standardized mean power spectral density of the following frequency bands was calculated: theta (4–8 Hz), alpha (8–13 Hz), beta (13–30 Hz), gamma1 (30–59 Hz), and gamma2 (61–75 Hz). Delirium and its psychomotor subtypes were assessed until postoperative day 7. EEG features were compared between patients with and without POD and among psychomotor subtypes, with adjustment for multiple comparisons. POD occurred in 70 patients (33.5%). No significant differences in preoperative EEG power were observed between patients with and without POD. However, patients with the hyperactive-or-mixed subtype demonstrated significantly higher theta and lower alpha activity than those with the hypoactive or no motor subtypes. These findings may identify neurophysiological markers associated with underlying psychomotor symptoms in delirium and may inform preoperative subtype risk stratification. Trial registration : UMIN-CTR (ID: UMIN000049390).

Mild cognitive impairment (MCI) and dementia pose significant health challenges in aging societies, emphasizing the need for accessible, cost-effective, and noninvasive diagnostic tools. Electroencephalography (EEG) is a promising biomarker, but traditional systems are limited by size, cost, and the need for skilled technicians. This study proposes a deep-learning-based approach using data from a portable EEG device to distinguish healthy volunteers (HVs) from patients with dementia-related conditions. We analyzed EEG data from 233 participants, including 119 HVs and 114 patients, and transformed the signals into frequency-domain features using a short-time Fourier transform. A customized transformer-based model was trained and evaluated using 10-fold cross-validation and a holdout dataset. In the cross-validation, the model achieved an area under the curve (AUC) of 0.872 and a balanced accuracy (bACC) of 80.8% in distinguishing HVs from patients. Subgroup analyses were conducted for HVs versus patients stratified by dementia severity and by clinical diagnosis, yielding AUCs ranging from 0.812 to 0.898 and bACCs from 74.9 to 86.4%. Comparable results were obtained in the holdout dataset. These findings suggest that portable EEG data combined with deep learning may serve as a practical tool for the early detection and classification of dementia-related conditions.

Wearing face masks in public has become the norm in many countries post-2020. Although mask-wearing is effective in controlling infection, it has the negative side effect of occluding the mask wearer’s facial expressions. The purpose of this study was to investigate the effects of wearing transparent masks on the perception of facial expressions. Participants were required to categorize the perceived facial emotion of female (Experiment 1) and male (Experiment 2) faces with different facial expressions and to rate the perceived emotion intensity of the faces. Based on the group, the participants were assigned to, the faces were presented with a surgical mask, a transparent mask, or without a mask. The results showed that wearing a surgical mask impaired the performance of reading facial expressions, both with respect to recognition and perceived intensity of facial emotions. Specifically, the impairments were robustly observed in fear and happy faces for emotion recognition, and in happy faces for perceived intensity of emotion in Experiments 1 and 2. However, the impairments were moderated by wearing a transparent mask instead of a surgical mask. During the coronavirus disease 2019 (COVID-19) pandemic, the transparent mask can be used in a range of situations where face-to-face communication is important.

Diagnosing Alzheimer’s disease (AD) through pathological markers is typically costly and invasive. This study aims to find a noninvasive, cost-effective method using portable electroencephalography (EEG) to detect changes in AD-related biomarkers in cerebrospinal fluid (CSF). A total of 102 patients, both with and without AD-related biomarker changes (amyloid beta and phosphorylated tau), were recorded using a 2-minute resting-state portable EEG. A machine-learning algorithm then analyzed the EEG data to identify these biomarker changes. The results showed that the machine learning model could distinguish patients with AD-related biomarker changes, achieving 68.1% accuracy (AUROC 0.75) for amyloid beta and 71.2% accuracy (AUROC 0.77) for phosphorylated tau, with gamma activities being key features. When excluding cases with idiopathic normal pressure hydrocephalus, accuracy improved to 74.1% (AUROC 0.80) for amyloid beta and 73.1% (AUROC 0.80) for phosphorylated tau. This study suggests that portable EEG combined with machine learning is a promising noninvasive and cost-effective tool for early AD-related pathological marker screening, which could enhance neurophysiological understanding and diagnostic accessibility.