Concentration-Dependent Effects of Curcumin on Membrane Permeability and Structure.

Growing evidence suggests that many bioactive molecules can nonspecifically modulate the physicochemical properties of membranes and influence the action of embedded membrane proteins. This study investigates the interactions of curcumin with protein-free model membranes consisting of 1,2-dioleoyl-sn-glycero-3-phosphocholine (DOPC) and DOPC with cholesterol (4/1 mol ratio). The focus is on the capability of curcumin to modify membrane barrier properties such as water permeability assayed through the droplet interface bilayer (DIB) model membrane. For pure DOPC, our findings show a concentration-dependent biphasic effect: a reduction in water permeability is observed at low concentrations (up to 2 mol %), whereas at high concentrations of curcumin, water permeability increases. In the presence of cholesterol, we observed an overall reduction in water permeability. A combination of complementary experimental methods, including phase transition parameters studied by differential scanning calorimetry (DSC) and structural properties measured by attenuated total reflectance (ATR)-FTIR, provides a deeper understanding of concentration-dependent interactions of curcumin with DOPC bilayers in the absence and presence of cholesterol. Our experimental findings align with a molecular mechanism of curcumin's interaction with model membranes, wherein its effect is contingent on its concentration. At low concentrations, curcumin binds to the lipid-water interface through hydrogen bonding with the phosphate headgroup, thereby obstructing the transport of water molecules. Conversely, at high concentrations, curcumin permeates the acyl chain region, inducing packing disorders and demonstrating evidence of phase separation. Enhanced knowledge of the impact of curcumin on membranes, which, in turn, can affect protein function, is likely to be beneficial for the successful translation of curcumin into effective medicine.

Nontraditional Roles of Magnesium Ions in Modulating Sav2152: Insight from a Haloacid Dehalogenase-like Superfamily Phosphatase from Staphylococcus aureus.

Methicillin-resistant Staphylococcus aureus (MRSA) infection has rapidly spread through various routes. A genomic analysis of clinical MRSA samples revealed an unknown protein, Sav2152, predicted to be a haloacid dehalogenase (HAD)-like hydrolase, making it a potential candidate for a novel drug target. In this study, we determined the crystal structure of Sav2152, which consists of a C2-type cap domain and a core domain. The core domain contains four motifs involved in phosphatase activity that depend on the presence of Mg2+ ions. Specifically, residues D10, D12, and D233, which closely correspond to key residues in structurally homolog proteins, are responsible for binding to the metal ion and are known to play critical roles in phosphatase activity. Our findings indicate that the Mg2+ ion known to stabilize local regions surrounding it, however, paradoxically, destabilizes the local region. Through mutant screening, we identified D10 and D12 as crucial residues for metal binding and maintaining structural stability via various uncharacterized intra-protein interactions, respectively. Substituting D10 with Ala effectively prevents the interaction with Mg2+ ions. The mutation of D12 disrupts important structural associations mediated by D12, leading to a decrease in the stability of Sav2152 and an enhancement in binding affinity to Mg2+ ions. Additionally, our study revealed that D237 can replace D12 and retain phosphatase activity. In summary, our work uncovers the novel role of metal ions in HAD-like phosphatase activity.

Effects of noise exposure on stress hormone changes during task performance in young Korean men: quasi-experimental study.

Numerous studies have suggested that noise exposure might be associated with changes in stress hormone levels. However, quantitative evidence for these effects in humans is rare and remains controversial. This study aimed to investigate the acute effects of exposure to noise and its different levels on stress hormone changes in task performance. Quasi-experimental noise exposure environment was established for 90 male university student volunteers in their twenties, and each was exposed to different noise levels during task performance. The stress hormones tested included cortisol, adrenocorticotropic hormone (ACTH), adrenaline, and noradrenaline. A one-way ANOVA was performed to investigate differences in hormone levels measured in the three groups according to the noise exposure levels (35, 45, or 75 dB). Analysis of covariance (ANCOVA) was used to adjust for confounding factors that might affect hormone levels. After adjusting for confounders, significant exposure-dependent differences were found in hormone levels in salivary cortisol, serum cortisol, serum ACTH, and serum adrenaline. The amount of hormonal increase in 75 dB exposure group compared to 35 or 45 dB groups was detected. Similar results were also seen in the rate of change analysis. Our findings indicate that short-term noise exposure during task performance elevates stress hormone levels. Further, the extent of stress hormone alterations varies with noise exposure levels. Changes in hormone levels are an objective measure that may be used to identify health effects and stress responses in various noise environments.

Distribution of volatile organic compounds by distance from industrial complexes and potential health impact on the residents.

Volatile organic compounds (VOCs) are the air pollutants emitted from the petrochemical industry known to pose adverse health effects on workers. The database based on the third phase of The Environmental Health Study in the Korean National Industrial Complexes (EHSNIC) in Ulsan conducted from 2018 to 2021 was used. Subjects were divided into the exposed and control group according to the estimated pollution level and distances from the industrial complexes. Ambient benzene, ethylbenzene, and xylene were significantly higher in the exposed group compared to the controls, as well as their metabolites. Risk of chronic disease and atopic dermatitis was higher in the exposed group which was supported by higher serum inflammatory markers and high hazard index of the exposed region. These results can draw attention to people engaged with environmental plans and used as primary data when making policies to reduce pollutant levels around industrial complexes.

The Role of Lipid Intrinsic Curvature in the Droplet Interface Bilayer.

Model bilayers are constructed from lipids having different intrinsic curvatures using the droplet interface bilayer (DIB) method, and their static physicochemical properties are determined. Geometrical and tensiometric measurements are used to derive the free energy of formation (ΔF) of a two-droplet DIB relative to a pair of isolated aqueous droplets, each decorated with a phospholipid monolayer. The lipid molecules employed have different headgroup sizes but identical hydrophobic tail structure, and each is characterized by an intrinsic curvature value (c0) that increases in absolute value with decreasing size of headgroup. Mixtures of lipids at different ratios were also investigated. The role of curvature stress on the values of ΔF of the respective lipid bilayers in these model membranes is discussed and is illuminated by the observation of a decrement in ΔF that scales as a near linear function of c02. Overall, the results reveal an association that should prove useful in studies of ion channels and other membrane proteins embedded in model droplet bilayer systems that will impact the understanding of protein function in cellular membranes composed of lipids of high and low curvature.

Drug-loaded adhesive microparticles for biofilm prevention on oral surfaces.

The oral cavity, a warm and moist environment, is prone to the proliferation of microorganisms like Candida albicans (C. albicans), which forms robust biofilms on biotic and abiotic surfaces, leading to challenging infections. These biofilms are resistant to conventional treatments due to their resilience against antimicrobials and immune responses. The dynamic nature of the oral cavity, including the salivary flow and varying surface properties, complicates the delivery of therapeutic agents. To address these challenges, we introduce dendritic microparticles engineered for enhanced adhesion to dental surfaces and effective delivery of antifungal agents and antibiofilm enzymes. These microparticles are fabricated using a water-in-oil-in-water emulsion process involving a blend of poly(lactic-co-glycolic acid) (PLGA) random copolymer (RCP) and PLGA-b-poly(ethylene glycol) (PLGA-b-PEG) block copolymer (BCP), resulting in particles with surface dendrites that exhibit strong adhesion to oral surfaces. Our study demonstrates the potential of these adhesive microparticles for oral applications. The adhesion tests on various oral surfaces, including dental resin, hydroxyapatite, tooth enamel, and mucosal tissues, reveal superior adhesion of these microparticles compared to conventional spherical ones. Furthermore, the release kinetics of nystatin from these microparticles show a sustained release pattern that can kill C. albicans. The biodegradation of these microparticles on tooth surfaces and their efficacy in preventing fungal biofilms have also been demonstrated. Our findings highlight the effectiveness of adhesive microparticles in delivering therapeutic agents within the oral cavity, offering a promising approach to combat biofilm-associated infections.

Radiation dose estimation with multiple artificial neural networks in dicentric chromosome assay.

PURPOSE: The dicentric chromosome assay (DCA), often referred to as the 'gold standard' in radiation dose estimation, exhibits significant challenges as a consequence of its labor-intensive nature and dependency on expert knowledge. Existing automated technologies face limitations in accurately identifying dicentric chromosomes (DCs), resulting in decreased precision for radiation dose estimation. Furthermore, in the process of identifying DCs through automatic or semi-automatic methods, the resulting distribution could demonstrate under-dispersion or over-dispersion, which results in significant deviations from the Poisson distribution. In response to these issues, we developed an algorithm that employs deep learning to automatically identify chromosomes and perform fully automatic and accurate estimation of diverse radiation doses, adhering to a Poisson distribution. MATERIALS AND METHODS: The dataset utilized for the dose estimation algorithm was generated from 30 healthy donors, with samples created across seven doses, ranging from 0 to 4 Gy. The procedure encompasses several steps: extracting images for dose estimation, counting chromosomes, and detecting DC and fragments. To accomplish these tasks, we utilize a diverse array of artificial neural networks (ANNs). The identification of DCs was accomplished using a detection mechanism that integrates both deep learning-based object detection and classification methods. Based on these detection results, dose-response curves were constructed. A dose estimation was carried out by combining a regression-based ANN with the Monte-Carlo method. RESULTS: In the process of extracting images for dose analysis and identifying DCs, an under-dispersion tendency was observed. To rectify the discrepancy, classification ANN was employed to identify the results of DC detection. This approach led to satisfaction of Poisson distribution criteria by 32 out of the initial pool of 35 data points. In the subsequent stage, dose-response curves were constructed using data from 25 donors. Data provided by the remaining five donors served in performing dose estimations, which were subsequently calibrated by incorporating a regression-based ANN. Of the 23 points, 22 fell within their respective confidence intervals at p < .05 (95%), except for those associated with doses at levels below 0.5 Gy, where accurate calculation was obstructed by numerical issues. The accuracy of dose estimation has been improved for all radiation levels, with the exception of 1 Gy. CONCLUSIONS: This study successfully demonstrates a high-precision dose estimation method across a general range up to 4 Gy through fully automated detection of DCs, adhering strictly to Poisson distribution. Incorporating multiple ANNs confirms the ability to perform fully automated radiation dose estimation. This approach is particularly advantageous in scenarios such as large-scale radiological incidents, improving operational efficiency and speeding up procedures while maintaining consistency in assessments. Moreover, it reduces potential human error and enhances the reliability of results.

Differential Effects of Soy Isoflavones on the Biophysical Properties of Model Membranes.

The effects that the main soy isoflavones, genistein and daidzein, have upon the biophysical properties of a model lipid bilayer composed of 1,2-dioleoyl-sn-glycero-3-phosphocholine (DOPC) or DOPC with cholesterol (4 to 1 mol ratio) have been investigated by transbilayer water permeability, differential scanning calorimetry, and confocal Raman microspectroscopy. Genistein is found to increase water permeability, decrease phase transition temperature, reduce enthalpy of transition, and induce packing disorder in the DOPC membrane with an increasing concentration. On the contrary, daidzein decreases water permeability and shows negligible impact on thermodynamic parameters and packing disorder at comparable concentrations. For a cholesterol-containing DOPC bilayer, both genistein and daidzein exhibit an overall less pronounced effect on transbilayer water permeability. Their respective differential abilities to modify the physical and structural properties of biomembranes with varying lipid compositions signify a complex and sensitive nature to isoflavone interactions, which depends on the initial state of bilayer packing and the differences in the molecular structures of these soy isoflavones, and provide insights in understanding the interactions of these molecules with cellular membranes.

Evaluation of adipogenesis over time using a novel bioabsorbable implant without the addition of exogenous cells or growth factors.

Background: Breast reconstruction is crucial for patients who have undergone mastectomy for breast cancer. Our bioabsorbable implants comprising an outer poly-l-lactic acid mesh and an inner component filled with collagen sponge promote and retain adipogenesis in vivo without the addition of exogenous cells or growth factors. In this study, we evaluated adipogenesis over time histologically and at the gene expression level using this implant in a rodent model. Methods: The implants were inserted in the inguinal and dorsal regions of the animals. At 1, 3, 6, and 12 months post-operation, the weight, volume, and histological assessment of all newly formed tissue were performed. We analyzed the formation of new adipose tissue using multiphoton microscopy and RNA sequencing. Results: Both in the inguinal and dorsal regions, adipose tissue began to form 1 month post-operation in the peripheral area. Angiogenesis into implants was observed until 3 months. At 6 months, microvessels matured and the amount of newly generated adipose tissue peaked and was uniformly distributed inside implants. The amount of newly generated adipose tissue decreased from 6 to 12 months but at 12 months, adipose tissue was equivalent to the native tissue histologically and in terms of gene expression. Conclusions: Our bioabsorbable implants could induce normal adipogenesis into the implants after subcutaneous implantation. Our implants can serve as a novel and safe material for breast reconstruction without requiring exogenous cells or growth factors.

Visual Guidance for User Placement in Avatar-Mediated Telepresence between Dissimilar Spaces.

Rapid advances in technology gradually realize immersive mixed-reality (MR) telepresence between distant spaces. This paper presents a novel visual guidance system for avatar-mediated telepresence, directing users to optimal placements that facilitate the clear transfer of gaze and pointing contexts through remote avatars in dissimilar spaces, where the spatial relationship between the remote avatar and the interaction targets may differ from that of the local user. Representing the spatial relationship between the user/avatar and interaction targets with angle-based interaction features, we assign recommendation scores of sampled local placements as their maximum feature similarity with remote placements. These scores are visualized as color-coded 2D sectors to inform the users of better placements for interaction with selected targets. In addition, virtual objects of the remote space are overlapped with the local space for the user to better understand the recommendations. We examine whether the proposed score measure agrees with the actual user perception of the partner's interaction context and find a score threshold for recommendation through user experiments in virtual reality (VR). A subsequent user study in VR investigates the effectiveness and perceptual overload of different combinations of visualizations. Finally, we conduct a user study in an MR telepresence scenario to evaluate the effectiveness of our method in real-world applications.

Population Weighting in Statistical Analysis.

This Guide to Statistics and Methods provides an overview of weighted analyses of population-based surveys, which can help achieve statistically valid, representative population-based findings.

Implementation of a multidisciplinary nutritional support team and clinical outcomes in critically ill patients with COVID-19.

BACKGROUND & AIMS: The South Korean government established the multidisciplinary nutritional support teams (NST) system for enhancing the evaluation and adequate supply of nutritional support to patients at high risk of malnutrition. However, the impact of the NST on clinical outcomes in critically ill patients with coronavirus disease 2019 (COVID-19) remains unclear. We aimed to investigate whether NST implementation affects survival outcomes in patients with COVID-19 requiring intensive care unit (ICU) admission. METHODS: Using data from the National Health Insurance Service and Korea Disease Control and Prevention Agency in South Korea, adult patients with COVID-19 admitted to the ICU between October 8, 2020, and December 31, 2021, were included. The NST comprised four professional personnel (physicians, full-time nurses, full-time pharmacists, and full-time clinical dietitians). Patients admitted to ICUs with and without the NST system were assigned to the NST and non-NST groups, respectively. RESULTS: A total of 13,103 critically ill adult patients were included in the final analysis; among them, 10,103 (77.1 %) and 3,000 (22.9 %) patients were included in the NST and non-NST groups, respectively. In the NST group, 2,803 (27.7 %) critically ill patients with COVID-19 were prescribed enteral or parenteral nutrition by the NST. In a covariate-adjusted multivariable model, the NST group showed a 40 % lower in-hospital mortality rate than the non-NST group (odds ratio: 0.60, 95 % confidence interval: 0.51, 0.71; P < 0.001). In subgroup analyses, compared with the non-NST group, the NST group showed significantly lower in-hospital mortality rates at 2, 3, 4, and 5 points on the World Health Organization clinical progression scale among patients with acute respiratory distress and mechanical ventilatory support. CONCLUSIONS: NST implementation was associated with improved survival outcomes in critically ill patients with COVID-19; accordingly, it may be recommended for improving adequate nutritional support and evaluation in critically ill patients.

Aspirin Interacts with Cholesterol-Containing Membranes in a pH-Dependent Manner.

Aspirin has been used for broad therapeutic treatment, including secondary prevention of cardiovascular disease associated with increased cholesterol levels. Aspirin and other nonsteroidal anti-inflammatory drugs have been shown to interact with lipid membranes and change their biophysical properties. In this study, mixed lipid model bilayers made from 1-palmitoyl-2-oleoyl-sn-glycero-3-phosphatidylcholine (POPC) or 1,2-dioleoyl-sn-glycero-3-phosphatidylcholine (DOPC) comprising varying concentrations of cholesterol (10:1, 4:1, and 1:1 mole ratio of lipid:chol), prepared by the droplet interface bilayer method, were used to examine the effects of aspirin at various pH on transbilayer water permeability. The presence of aspirin increases the water permeability of POPC bilayers in a concentration-dependent manner, with a greater magnitude of increase at pH 3 compared to pH 7. In the presence of cholesterol, aspirin is similarly shown to increase water permeability; however, the extent of the increase depends on both the concentration of cholesterol and the pH, with the least pronounced enhancement in water permeability at high cholesterol levels at pH 7. A fusion of data from differential scanning calorimetry, confocal Raman microspectrophotometry, and interfacial tensiometric measurements demonstrates that aspirin can promote significant thermal, structural, and interfacial property perturbations in the mixed-lipid POPC or DOPC membranes containing cholesterol, indicating a disordering effect on the lipid membranes. Our findings suggest that aspirin fluidizes phosphocholine membranes in both cholesterol-free and cholesterol-enriched states and that the overall effect is greater when aspirin is in a neutral state. These results confer a deeper comprehension of the divergent effects of aspirin on biological membranes having heterogeneous compositions, under varying physiological pH and different cholesterol compositions, with implications for a better understanding of the gastrointestinal toxicity induced by the long term use of this important nonsteroidal anti-inflammatory molecule.

Development of new bioabsorbable implants with de novo adipogenesis.

Poly-L-lactic acid (PLLA) mesh implants containing collagen sponge (CS) were replaced with autologous adipose tissue regeneration in vivo. Herein, we investigated the optimal external frames and internal fillings using poly (lactic-co-ε-caprolactone) (P (LA/CL)), PLLA, and low-molecular-weight PLLA (LMW-PLLA) as the external frame and polyglycolic acid (PGA) nanosheets and CS as the internal filling. We prepared six implants: P (LA/CL) with PGA nano, PLLA with PGA nano, PLLA with CS, PLLA with 1/2 CS, PLLA with 1/4 CS, and LMW-PLLA with CS, and evaluated adipogenesis at 6 and 12 months using a rat inguinal model. The internal spaces in the P (LA/CL) and LMW-PLLA implants collapsed at 6 months, whereas those in the other four implants collapsed at 12 months. Adipose tissue regeneration was not significantly different between the PLLA-implanted groups at 6 and 12 months and was greater than that in the P (LA/CL) with PGA nano and LMW-PLLA with CS groups. The PGA nanosheet inside PLLA was comparable to the CS inside PLLA in the regeneration of adipose tissue and macrophage infiltration. In summary, PLLA is a promising external frame material in which the internal space can be replaced with adipose tissue. Thus, PGA nanosheets are an alternative internal filling material for adipose tissue regeneration.

Long term observation of de novo adipogenesis using novel bioabsorbable implants with larger size in a porcine model.

Introduction: The regeneration of adipose tissue in patients after breast cancer surgery would be desirable without the use of growth factors or cells to avoid potential recurrence and metastasis. We reported that prolate spheroidal-shaped poly-L-lactic acid (PLLA) mesh implants of approximately 18-mm polar diameter and 7.5-mm greatest equatorial diameter containing collagen sponge (CS) would be replaced by regenerated adipose tissue after implantation, thereby suggesting an innovative method for breast reconstruction. Our study aimed to evaluate the adipose tissue regeneration ability of implant aggregates in a porcine model. Methods: We prepared implant aggregates consisting of thirty PLLA mesh implants containing CS packed in a woven poly (glycolic acid) bag. The implant aggregates were inserted under the mammary glands in the porcine abdomen for a year. Single and double groups were classified by inserting either one or two implant aggregates on each side of the abdomen, respectively. Results: In both groups, the volume of the implant aggregates decreased over time, and the formation of adipose tissue peaked between 6 and 9 months. Histologically, the formation of adipose tissue was confirmed in the area that was in contact with native adipose tissue. Conclusions: Our implant aggregates could induce the autologous adipose tissue after long term implantation in vivo, without the use of any growth factor or cell treatment, presenting a potential novel method of breast reconstruction.

Ultra-Processed Food Intakes Are Associated with Depression in the General Population: The Korea National Health and Nutrition Examination Survey.

Depression is the most common mental illnesses worldwide. The consumption of ultra-processed food (UPF) has increased globally due to its affordability and convenience; however, only a few studies have investigated the link between UPF intake and depression in the general population. We investigated the associations between UPF and depression using the Korea National Health and Nutrition Examination Survey. A total of 9463 individuals (4200 males and 5263 females) aged above 19 years old participated in this study. The prevalence of depression was identified using the Patient Health Questionnaire-9. Dietary intake was assessed through a 24-h recall interview. The percentage of energy from UPFs was ascertained based on the NOVA classification. The associations between the quartile ranges of UPF intake and depression were estimated using logistic regression models. Individuals in the highest quartile had a 1.40 times higher likelihood of having depression, with marginal significance (95% confidence intervals (CIs) = 1.00-1.96). In a sex-specific stratification, only females demonstrated a significant association (odds ratio (OR) = 1.51, 95% CI 1.04-2.21), even after adjusting for confounders (p-value for trend = 0.023). Our findings revealed a significant association between higher UPF intake and depression among females but not among males in the Korean general population.

Association between CLOCK Gene Polymorphisms and Insomnia Risk According to Food Groups: A KoGES Longitudinal Study.

Food intake could mitigate or exacerbate the risk for insomnia associated with the CLOCK gene. This study investigated the associations between the clock circadian regulator (CLOCK) polymorphisms rs12649507 and rs4580704 and the risk of insomnia, as well as its interactions with food groups. Among 1430 adults, new insomnia cases were identified between 2005 and 2012. Single nucleotide polymorphisms were genotyped, and dietary intake was assessed. Next, Cox proportional hazard models were established. The fruit and meat groups significantly mitigated the risk of insomnia associated with rs12649507 among males (pinteraction = 0.006 in a recessive model; p = 0.010 in a dominant model). In contrast, among females the beverage group significantly increased the risk of insomnia (p = 0.041 in a dominant model). As for rs4580704, among males the fruit and meat groups modified the risk of insomnia (p = 0.006 in a recessive model; p = 0.001 in a dominant model). However, among females, the beverage group exacerbated the risk of insomnia associated with rs4580704 (p = 0.004 in a dominant model). In this longitudinal study, we observed a significantly modified insomnia risk associated with the CLOCK gene depending on food groups. Notably, in a general population the risks were modified according to both the fruit and meat intake among 775 males but exacerbated with beverage intake among 655 females.

Machine learning-based predictive models for the occurrence of behavioral and psychological symptoms of dementia: model development and validation.

The behavioral and psychological symptoms of dementia (BPSD) are challenging aspects of dementia care. This study used machine learning models to predict the occurrence of BPSD among community-dwelling older adults with dementia. We included 187 older adults with dementia for model training and 35 older adults with dementia for external validation. Demographic and health data and premorbid personality traits were examined at the baseline, and actigraphy was utilized to monitor sleep and activity levels. A symptom diary tracked caregiver-perceived symptom triggers and the daily occurrence of 12 BPSD classified into seven subsyndromes. Several prediction models were also employed, including logistic regression, random forest, gradient boosting machine, and support vector machine. The random forest models revealed the highest area under the receiver operating characteristic curve (AUC) values for hyperactivity, euphoria/elation, and appetite and eating disorders; the gradient boosting machine models for psychotic and affective symptoms; and the support vector machine model showed the highest AUC. The gradient boosting machine model achieved the best performance in terms of average AUC scores across the seven subsyndromes. Caregiver-perceived triggers demonstrated higher feature importance values across the seven subsyndromes than other features. Our findings demonstrate the possibility of predicting BPSD using a machine learning approach.

Developing a Multimodal Monitoring System for Geriatric Depression: A Feasibility Study.

The Internet of Medical Things is promising for monitoring depression symptoms. Therefore, it is necessary to develop multimodal monitoring systems tailored for elderly individuals with high feasibility and usability for further research and practice. This study comprised two phases: (1) methodological development of the system; and (2) system validation to evaluate its feasibility. We developed a system that includes a smartphone for facial and verbal expressions, a smartwatch for activity and heart rate monitoring, and an ecological momentary assessment application. A sample of 21 older Koreans aged 65 years and more was recruited from a community center. The 4-week data were collected for each participant (n = 19) using self-report questionnaires, wearable devices, and interviews and were analyzed using mixed methods. The depressive group (n = 6) indicated lower user acceptance relative to the nondepressive group (n = 13). Both groups experienced positive emotions, had regular life patterns, increased their self-interest, and stated that a system could disturb their daily activities. However, they were interested in learning new technologies and actively monitored their mental health status. Our multimodal monitoring system shows potential as a feasible and useful measure for acquiring mental health information about geriatric depression.