Determinants of Weight Status and Body, Health and Life Satisfaction in Young Adults.

Health behaviors include behavioral patterns and habits that relate to health maintenance, restoration and improvement. They do not only affect the physical condition; they are also associated with life satisfaction. In our study, we focused on young adulthood, a specific lifespan period for establishing long-term health behavior patterns. The aim of the present study was to investigate depressive symptoms, lifestyle and eating behaviors and delineate their associations with overweight/obesity and body, health and life satisfaction in young adults in Poland. We enrolled 800 students (81.4% females and 18.6% males). Diet, physical activity, depressive symptoms, eating behaviors and body, health and life satisfaction were assessed. Multivariate logistic regression models were employed. Almost half of the participants in our study had at least mild symptoms of depression. Symptoms of depression significantly reduced the odds of satisfaction with body, health and life, whereas physical activity increased them. Overweight/obesity significantly reduced the odds of body and health satisfaction. In women, a history of depression and emotional eating increased the odds of being overweight/obese. The results of our study may contribute to the development of educational programs and intervention strategies for young adults.

Determinants of Attitude to a Humanoid Social Robot in Care for Older Adults: A Post-Interaction Study.

BACKGROUND While there is a growing body of research examining opinions on social robots in elderly care, there is a lack of comprehensive studies investigating the underlying factors influencing these opinions. The Godspeed Questionnaire Series (GQS) measures perceptions related to human-robot interactions (HRIs). The Comprehensive Geriatric Assessment (CGA) is widely used to evaluate physical, cognitive, and social functions of older patients. The EASYCare 2010 Standard (EC) is a tool for assessing unmet needs in older individuals. TIAGo, a social humanoid robot, integrates perception, navigation, and HRI capabilities. This study aimed to identify the determinants of perception following interactions between older individuals and TIAGo, utilizing the GQS, selected CGA items, and EC. MATERIAL AND METHODS We analyzed a database of opinions from older individuals who interacted with TIAGo, based on the Users' Needs, Requirements, and Abilities Questionnaire. We examined the relationships between the robot's roles (companion/assistant/useful device), its assistive/social functions, and various characteristics of the older participants. RESULTS The study included 161 participants (mean age: 75.2±9.8 years), comprising 89 women and 113 institutionalized individuals. Positive correlations were observed between the robot's role, its functions, and the participants'; perceptions across most evaluated parameters (Anthropomorphism, Animacy, Likeability, Perceived intelligence, Perceived safety). Only a few individual correlations were found for other parameters. CONCLUSIONS The primary determinant of older individuals' opinions was their perception of the robot. Therefore, involving older adults in the co-design process of such robots is crucial. Additionally, a paradigm shift is needed in the study of humanoid social robots, focusing on successful aging rather than deficits associated with aging.

GMM-Based Expanded Feature Space as a Way to Extract Useful Information for Rare Cell Subtypes Identification in Single-Cell Mass Cytometry.

Cell subtype identification from mass cytometry data presents a persisting challenge, particularly when dealing with millions of cells. Current solutions are consistently under development, however, their accuracy and sensitivity remain limited, particularly in rare cell-type detection due to frequent downsampling. Additionally, they often lack the capability to analyze large data sets. To overcome these limitations, a new method was suggested to define an extended feature space. When combined with the robust clustering algorithm for big data, it results in more efficient cell clustering. Each marker's intensity distribution is presented as a mixture of normal distributions (Gaussian Mixture Model, GMM), and the expanded space is created by spanning over all obtained GMM components. The projection of the initial flow cytometry marker domain into the expanded space employs GMM-based membership functions. An evaluation conducted on three established cellular identification algorithms (FlowSOM, ClusterX, and PARC) utilizing the most substantial publicly available annotated dataset by Samusik et al. demonstrated the superior performance of the suggested approach in comparison to the standard. Although our approach identified 20 cell clusters instead of the expected 24, their intra-cluster homogeneity and inter-cluster differences were superior to the 24-cluster FlowSOM-based solution.

The Effects of Stakeholder Perceptions on the Use of Humanoid Robots in Care for Older Adults: Postinteraction Cross-Sectional Study.

BACKGROUND: Efficient use of humanoid social robots in the care for older adults requires precise knowledge of expectations in this area. There is little research in this field that includes the interaction of stakeholders with the robot. Even fewer studies have compared the perceptions of older people (as care recipients) and professional caregivers (representing those taking care of older adults in teams with robots). OBJECTIVE: The aim of this study was to analyze whether specific aspects of the perceptions about humanoid robots influence attitudes after interacting with the robot and to compare the opinions of different stakeholders (older people and their professional caregivers) on this topic. We analyzed the potential impact of the differences in perception of the robot between stakeholder groups with respect to how the robot should be designed and tailored to fit the specific needs of future users. We also attempted to define areas where targeted educational activities could bring the attitudes of the two groups of stakeholders closer to each other. METHODS: The studied group was a conveniently available sample of individuals who took part in the presentation of and interaction with a humanoid social robot. Among them, there were 48 community-dwelling older adults (aged ≥60 years), who were participants of day care units (which may signal the presence of self-care needs), and 53 professional caregivers. The participants were asked to express their views after an interaction with a humanoid social robot (TIAGo) using the Users' Needs, Requirements and Abilities Questionnaire (UNRAQ) and the Godspeed Questionnaire Series (GQS). RESULTS: Compared to the caregivers, older adults not only assessed the robot more positively with respect to its roles as a companion and assistant (P=.009 and P=.003, respectively) but also had higher scores on their need to increase their knowledge about the robot (P=.049). Regarding the robot's functions, the greatest differences between groups were observed for the social aspects on the UNRAQ, including decreasing the sense of loneliness (P=.003) and accompanying the user in everyday activities (P=.005). As for the GQS, the mean scores of the Animacy, Likeability, and Perceived Intelligence scales were significantly higher for older participants than for caregivers (P=.04, P<.001, and P<.001, respectively). The only parameter for which the caregivers' scores were higher than those of the older adults was the Artificial-Lifelike item from the Anthropomorphism scale of the GQS (P=.03). CONCLUSIONS: The acceptance of the social functions of a humanoid robot is related to its perception in all analyzed aspects, whereas the expected usefulness of a care robot is not linked to aspects of anthropomorphism. Successful implementation of robots in the care for older people thus depends on considering not only the fears, needs, and requirements of various stakeholders but also on the perceptions of the robot. Given the differences between the stakeholders, targeted and properly structured educational and training activities for caregivers and prospective users may enable a seamless integration of robotic technologies in care provision.

The Godspeed Questionnaire Series in the Assessment of the Social Robot TIAGo by Older Individuals.

(1) Background: A robot in care for older adults requires solid research confirming its acceptance. The aim of this study was to present the Polish version of the Godspeed Questionnaire Series (GQS) and assess the perception of the social robot TIAGo; (2) Methods: The study involved older individuals living in the community and care homes and measured perception after interaction with TIAGo using five series of GQS (S1: Anthropomorphism, S2: Animacy, S3: Likeability, S4: Perceived intelligence, and S5: Perceived safety); (3) Results: We studied 178 individuals (age: 75.2 ± 9.6 years, 103 women). Good internal consistency was found. Cronbach's Alpha was 0.90 for the entire tool (from 0.75 to 0.94 for the individual series). Mean scores for S1 and S2 were comparable but lower than all others (p < 0.001). Average scores for S3 and S4 did not differ but were higher than those of S5. Age, gender and education did not impact the answers, as did the ease of use of technology and self-assessment of independence. Solely, the place of residence influenced the results of S3 and S5; people living in institutions scored higher (p < 0.05 and p < 0.001, respectively); (4) Conclusions: Acceptance does not go hand in hand with the perception of anthropomorphism and animacy.

Diet and physical activity and metabolic disorders in patients with schizophrenia and bipolar affective disorder in the Polish population.

Introduction: There are numerous reports of a higher prevalence of metabolic disorders in patients with schizophrenia and bipolar disorder (BD), yet its connections to diet and physical activity remain not fully explained. This article aimed to evaluate diet, physical activity and selected biochemical and anthropometric parameters associated with metabolism in patients with schizophrenia and BD and to analyse the relationships between these variables in the subjects. Materials and Methods: A total of 126 adults participated in the study: 47 patients with schizophrenia, 54 patients with BD and 25 patients in mental illness remission (reference group). Data were collected on the underlying illness and concomitant illnesses, and the severity of symptoms of the current episode was assessed using the following scales: PANSS, MADRS and YMRS. An assessment of the subjects' diet (KomPAN questionnaire) and their physical activity (International Physical Activity Questionnaire) was carried out. Anthropometric and blood pressure measurements were taken and BMI and WHR were calculated. Serum concentrations of fasting glucose, TSH, total cholesterol, LDL and HDL fractions, triglycerides and leptin, ghrelin and resistin were determined. For statistical analysis, the significance level was set at 0.05. For multiple comparisons one way ANOVA or Kruskal Wallis were used with post hoc Tukey and Dunn tests, respectively. To determine correlation of variables, Pearson's linear correlation coefficient or Spearman's rank correlation coefficient were used. Results: A total of 50.8% of the subjects had at least one metabolic disorder-most commonly excessive body weight (66.7%) and abdominal obesity (64.3%). Patients did not differ significantly in terms of physical activity, but they did differ in mean time spent sitting-with this being significantly longer for all groups than in the general population. The subjects differed in diet: patients with BD consumed less unhealthy foods than patients with schizophrenia. The highest correlations between physical activity, diet and variables defining metabolic disorders were found in patients with BD. Only in patients with schizophrenia were there significant correlations between the course of the disease and physical activity. Discussion: The results suggest the existence of associations between diet, physical activity, and metabolic disorders in both BD and schizophrenia patients. They also suggest a tendency among those patients to spend long periods of time sitting.

Pathological changes or technical artefacts? The problem of the heterogenous databases in COVID-19 CXR image analysis.

BACKGROUND: When the COVID-19 pandemic commenced in 2020, scientists assisted medical specialists with diagnostic algorithm development. One scientific research area related to COVID-19 diagnosis was medical imaging and its potential to support molecular tests. Unfortunately, several systems reported high accuracy in development but did not fare well in clinical application. The reason was poor generalization, a long-standing issue in AI development. Researchers found many causes of this issue and decided to refer to them as confounders, meaning a set of artefacts and methodological errors associated with the method. We aim to contribute to this steed by highlighting an undiscussed confounder related to image resolution. METHODS: 20 216 chest X-ray images (CXR) from worldwide centres were analyzed. The CXRs were bijectively projected into the 2D domain by performing Uniform Manifold Approximation and Projection (UMAP) embedding on the radiomic features (rUMAP) or CNN-based neural features (nUMAP) from the pre-last layer of the pre-trained classification neural network. Additional 44 339 thorax CXRs were used for validation. The comprehensive analysis of the multimodality of the density distribution in rUMAP/nUMAP domains and its relation to the original image properties was used to identify the main confounders. RESULTS: nUMAP revealed a hidden bias of neural networks towards the image resolution, which the regular up-sampling procedure cannot compensate for. The issue appears regardless of the network architecture and is not observed in a high-resolution dataset. The impact of the resolution heterogeneity can be partially diminished by applying advanced deep-learning-based super-resolution networks. CONCLUSIONS: rUMAP and nUMAP are great tools for image homogeneity analysis and bias discovery, as demonstrated by applying them to COVID-19 image data. Nonetheless, nUMAP could be applied to any type of data for which a deep neural network could be constructed. Advanced image super-resolution solutions are needed to reduce the impact of the resolution diversity on the classification network decision.

POLCOVID: a multicenter multiclass chest X-ray database (Poland, 2020-2021).

The outbreak of the SARS-CoV-2 pandemic has put healthcare systems worldwide to their limits, resulting in increased waiting time for diagnosis and required medical assistance. With chest radiographs (CXR) being one of the most common COVID-19 diagnosis methods, many artificial intelligence tools for image-based COVID-19 detection have been developed, often trained on a small number of images from COVID-19-positive patients. Thus, the need for high-quality and well-annotated CXR image databases increased. This paper introduces POLCOVID dataset, containing chest X-ray (CXR) images of patients with COVID-19 or other-type pneumonia, and healthy individuals gathered from 15 Polish hospitals. The original radiographs are accompanied by the preprocessed images limited to the lung area and the corresponding lung masks obtained with the segmentation model. Moreover, the manually created lung masks are provided for a part of POLCOVID dataset and the other four publicly available CXR image collections. POLCOVID dataset can help in pneumonia or COVID-19 diagnosis, while the set of matched images and lung masks may serve for the development of lung segmentation solutions.

Robots for Elderly Care: Review, Multi-Criteria Optimization Model and Qualitative Case Study.

This paper focuses on three areas: the first is a review of current knowledge about social and service robots for elderly care. The second is an optimization conceptual model aimed at maximizing the efficiency of assigning robots to serve the elderly. The proposed multi-criteria optimization model is the first one proposed in the area of optimization for robot assignment for the elderly with robot utilization level and caregiver stress level. The third is the findings of studies on the needs, requirements, and adoption of technology in elderly care. We consider the use of robots as a part of the ENRICHME project for long-term interaction and monitoring of older persons with mild cognitive impairment, to optimize their independence. Additionally, we performed focus group discussions (FGD) to collect opinions about robot-related requirements of the elderly and their caregivers. Four FDGs of six persons were organized: two comprising older adults, and two of the other formal and informal caregivers, based on a detailed script. The statements of older participants and their caregivers were consistent in several areas. The analysis revealed user characteristics, robot-related issues, functionality, and barriers to overcome before the deployment of the robot. An introduction of the robot must be thoroughly planned, include comprehensive pre-training, and take the ethical and practical issues into account. The involvement of future users in the customization of the robot is essential.

CMB-HUNT: Automatic detection of cerebral microbleeds using a deep neural network.

Cerebral microbleeds (CMBs) are gaining increasing interest due to their importance in diagnosing cerebral small vessel diseases. However, manual inspection of CMBs is time-consuming and prone to human error. Existing automated or semi-automated solutions still have insufficient detection sensitivity and specificity. Furthermore, they frequently use more than one magnetic resonance imaging modality, but these are not always available. The majority of AI-based solutions use either numeric or image data, which may not provide sufficient information about the true nature of CMBs. This paper proposes a deep neural network with multi-type input data for automated CMB detection (CMB-HUNT) using only susceptibility-weighted imaging data (SWI). Combination of SWIs and radiomic-type numerical features allowed us to identify CMBs with high accuracy without the need for additional imaging modalities or complex predictive models. Two independent datasets were used: one with 304 patients (39 with CMBs) for training and internal system validation and one with 61 patients (21 with CMBs) for external validation. For the hold-out testing dataset, CMB-HUNT reached a sensitivity of 90.0%. As results of testing showed, CMB-HUNT outperforms existing methods in terms of the number of FPs per case, which is the lowest reported thus far (0.54 FPs/patient). The proposed system was successfully applied to the independent validation set, reaching a sensitivity of 91.5% with 1.9 false positives per patient and proving its generalization potential. The results were comparable to previous studies. Our research confirms the usefulness of deep learning solutions for CMB detection based only on one MRI modality.

Quantifying Spatial Heterogeneity of Tumor-Infiltrating Lymphocytes to Predict Survival of Individual Cancer Patients.

Tumor-infiltrating lymphocytes (TILs), identified on HE-stained histopathological images in the cancer area, are indicators of the adaptive immune response against cancers and play a major role in personalized cancer immunotherapy. Recent works indicate that the spatial organization of TILs may be prognostic of disease-specific survival and recurrence. However, there are a limited number of methods that were proposed and tested in analyses of the spatial structure of TILs. In this work, we evaluated 14 different spatial measures, including the one developed for other omics data, on 10,532 TIL maps from 23 cancer types in terms of reproducibility, uniqueness, and impact on patient survival. For each spatial measure, 16 different scenarios for the definition of prognostic factor were tested. We found no difference in survival prediction when TIL maps were stored as binary images or continuous TIL probability scores. When spatial measures were discretized into a low and high category, a higher correlation with survival was observed. Three measures with the highest cancer prognosis capability were spatial autocorrelation, GLCM M1, and closeness centrality. Most of the tested measures could be further tuned to increase prediction performance.

Speech Understanding in Manic and Depressive Episodes of Mood Disorders.

OBJECTIVE: The aim of this study was to assess the perception of speech in adverse acoustic conditions during manic and depressive episodes of mood disorders. METHODS: Forty-three patients with bipolar disorder (mania, N=20; depression, N=23) and 32 patients with unipolar depression were included for analyses. Thirty-five participants served as the control group. The study of speech understanding was carried out using the Polish Sentence Matrix Test, allowing for the determination of the speech reception threshold (SRT). The test was performed in the clinical groups both during an acute episode and remission; during remission, patients underwent audiometric evaluation. RESULTS: Compared with control subjects, patients with mood disorders had worse speech understanding (higher SRT), regardless of the episode or remission. A manic episode in the course of bipolar disorder was not associated with worse speech understanding compared with remission of mania. However, an episode of depression in the course of both bipolar disorder and unipolar depression was associated with worse speech understanding compared with remission of depression. In bipolar depression, this correlated with age, duration of the disorder, number of episodes, and number of hospitalizations, as well as in remission with age and duration of illness. In unipolar depression, poor speech understanding was more severe in individuals with hearing impairment. CONCLUSIONS: These findings revealed that patients with mood disorders had impaired speech understanding, even while in remission, and manic episodes in the course of bipolar disorder were not associated with impaired speech understanding compared with mania remission.

Robots in Eldercare: How Does a Real-World Interaction with the Machine Influence the Perceptions of Older People?

(1) Background: Using autonomous social robots in selected areas of care for community-dwelling older adults is one of the promising approaches to address the problem of the widening care gap. We posed the question of whether a possibility to interact with the technology to be used had an impact on the scores given by the respondents in various domains of needs and requirements for social robots to be deployed in care for older individuals. (2) Methods: During the study, the opinions of older people (65+; n = 113; with no severe cognitive impairment) living in six social care institutions about a robot in care for older people were collected twice using the Users' Needs, Requirements and Abilities Questionnaire (UNRAQ): after seeing a photo of the robot only and after a 90−150 min interaction with the TIAGo robot. (3) Results: Mean total scores for both assistive and social functions were higher after the interaction (p < 0.05). A positive correlation was found between opinion changes in social and assistive functions (r = 0.4842; p = 0.0000). (4) Conclusions: Preimplementation studies and assessments should include the possibility to interact with the robot to provide its future users with a clear idea of the technology and facilitate necessary customisations of the machine.

Studies on candidate genes for lithium prophylaxis performed at the Poznan University of Medical Sciences. / Badania genów kandydujacych profilaktyki litem przeprowadzone na Uniwersytecie Medycznym w Poznaniu.

Lithium is a drug of choice as a mood-stabilizer for the maintenance treatment of bipolar disorder. The prophylactic efficacy of lithium can be determined by genetic factors, partially related to a predisposition to bipolar disorder. In the field of psychiatric genetics, the first decade of the 21st century was dominated by the "candidate gene" research. In this paper, the studies on candidate genes connected with lithium prophylaxis performed at the Poznan University of Medical Sciences in 2005-2018 are presented. During this time, the polymorphisms of multiple genes have been investigated, many of which are also connected with a predisposition to bipolar illness. The associations with lithium prophylactic efficacy were found for the polymorphisms in 5HTT, ACP1, ARNTL, BDNF, COMT, DRD1, FKBP5, FYN, GLCC, NR3C1, and TIM, genes, but not those in 5HT2A, 5HT2C, DRD2, DRD3, DRD4, GRIN2B, GSK-3ß, MMP-9, and NTRK2 genes. The polymorphism of the GSK-3ß gene was found to be associated with the kidney side-effects occurring during lithium therapy. Possible roles for these genes in both the mechanism of lithium prophylactic activity and pathogenesis of bipolar mood disorder were discussed.

Long-Term Survival of Older Patients Hospitalized for COVID-19. Do Clinical Characteristics upon Admission Matter?

Older adults are particularly susceptible to COVID-19 in terms of both disease severity and risk of death. To compare clinical differences between older COVID-19 hospitalized survivors and non-survivors, we investigated variables influencing mortality in all older adults with COVID-19 hospitalized in Poznan, Poland, through the end of June 2020 (n = 322). In-hospital, post-discharge, and overall 180-day mortality were analyzed. Functional capacity prior to COVID-19 diagnosis was also documented. The mean age of subjects was 77.5 ± 10.0 years; among them, 191 were females. Ninety-five (29.5%) died during their hospitalization and an additional 30 (9.3%) during the post-discharge period (up to 180 days from the hospital admission). In our study, male sex, severe cognitive impairment, underlying heart disease, anemia, and elevated plasma levels of IL-6 were independently associated with greater mortality during hospitalization. During the overall 180-day observation period (from the hospital admission), similar characteristics, excluding male sex and additionally functional impairment, were associated with increased mortality. During the post-discharge period, severe functional impairment remained the only determinant. Therefore, functional capacity prior to diagnosis should be considered when formulating comprehensive prognoses as well as care plans for older patients infected with SARS-CoV-2.

UNRAQ-A Questionnaire for the Use of a Social Robot in Care for Older Persons. A Multi-Stakeholder Study and Psychometric Properties.

(1) Background: while there exist validated measures to assess the needs of older people, there are comparatively few validated tools to assess needs and requirements for the use of robots. Henceforth, the aim of the study is to present and validate such a tool. (2) Methods: The study group included 720 subjects (mean age 52.0 ± 37.0, 541 females) who agreed to fill the Users' Needs, Requirements, and Abilities Questionnaire (UNRAQ). The validation part of the study included 125 persons. (3) Results: the acceptance of the robot was good in the whole group. The social functions were rated worse than assistive ones. A correlation was found between the scores of social and assistive functions. The respondents claimed that older adults were not prepared to interact with the robot and not very good at handling it, and were sceptical about their willingness to learn to operate the robot. The Cronbach alpha value for the whole questionnaire was 0.95 suggesting excellent internal consistency, and the ICC value of 0.88 represents excellent agreement; (4) Conclusions: We observed a good overall acceptance of the robot across the studied group. There is considerable demand for the use of a social robot in care for older people.

Perinatal Mental Health during COVID-19 Pandemic: An Integrative Review and Implications for Clinical Practice.

The COVID-19 pandemic and measures implemented to decelerate its spread have consequences for mental health of societies. The aim of our review was to analyze depressive and anxiety symptoms in perinatal women. The search used PubMed and Web of Science databases. Most studies showed an increase in the prevalence of depression and/or anxiety symptoms. Risk factors identified in our study were mainly related to the possibility of COVID-19 infection, changes in the organization of perinatal care, social isolation and financial problems. Protective factors included social support, the woman's own activity and knowledge about COVID-19. The results of our study point to the importance of the mental health screening including suicide risk assessment in perinatal women. Much of the mental health needs of perinatal women can be met in primary or perinatal care services; however, women with mental health issues should be offered psychiatric consultations and psychological support, and sometimes urgent psychiatric hospitalization is necessary. Healthcare professionals should provide information addressing uncertainty about COVID-19, organization of midwifery and medical care as well as mental health problems and how to get help. Mental health interventions in pregnant women may involve planning physical activity and encouraging to engage in online social activities.

Needs of older persons living in long-term care institutions: on the usefulness of cluster approach.

BACKGROUND: Long-term care units' residents do not constitute a homogeneous population. Providing effective care, tailored to individual needs, is crucial in this context. It can be facilitated by suitable tools and methods, which include needs assessment along with the physical, psychological and social aspects of care. We thus applied a cluster approach to identify their putative groupings to enable the provision of tailored care. METHODS: The needs of 242 residents of care homes in four Polish cities (Poznan, Wroclaw, Bialystok and Lublin), aged 75-102 years (184 females), with the Mini-Mental State Examination (MMSE) score ≥ 15 points, were assessed with the CANE (Camberwell Assessment of Need for the Elderly) questionnaire. Their independence in activities of daily living was evaluated by the Barthel Index (BI), and symptoms of depression by the Geriatric Depression Scale (GDS). The results of MMSE, BI and GDS were selected as variables for K-means cluster analysis. RESULTS: Cluster 1 (C1), n = 83, included subjects without dementia according to MMSE (23.7 ± 4.4), with no dependency (BI = 85.8 ± 14.4) and no symptoms of depression (GDS = 3.3 ± 2.0). All subjects of cluster 2 (C2), n = 87, had symptoms of depression (GDS = 8.9 ± 2.1), and their MMSE (21.0 ± 4.0) and BI (79.8 ± 15.1) were lower than those in C1 (p = 0.006 and p = 0.046, respectively). Subjects of cluster 3 (C3), n = 72, had the lowest MMSE (18.3 ± 3.1) and BI (30.6 ± 18,8, p < 0.001 vs. C1 & C2). Their GDS (7.6 ± 2.3) were higher than C1 (p < 0.001) but lower than C2 (p < 0.001). The number of met needs was higher in C2 than in C1 (10.0 ± 3.2 vs 8.2 ± 2.7, p < 0.001), and in C3 (12.1 ± 3.1) than in both C1 and C2 (p < 0.001). The number of unmet needs was higher in C3 than in C1 (1.2 ± 1.5 vs 0.7 ± 1.0, p = 0.015). There were also differences in the patterns of needs between the clusters. CONCLUSIONS: Clustering seems to be a promising approach for use in long-term care, allowing for more appropriate and optimized care delivery. External validation studies are necessary for generalized recommendations regarding care optimization in various regional perspectives.

Psychosocial and cultural determinants of dietary intake in community-dwelling older adults: A Determinants of Diet and Physical Activity systematic literature review.

OBJECTIVES: Knowledge of factors determining dietary intake is important to develop targeted strategies to prevent malnutrition and age-related diseases. The aim of the present systematic review was to analyze the state of the art regarding the role of social status, cultural aspects, and psychological distress on dietary intake in community-dwelling older adults. METHODS: A systematic search was performed per the Preferred Reporting Items for Systematic Reviews and Meta-Analyses procedure. Titles, abstracts, and full texts were screened for predefined inclusion and exclusion criteria. RESULTS: Thirty-nine studies were included. Seven different groups of psychosocial and cultural determinants were associated with dietary intake. Family structure and living situation (e.g., loneliness, marital status), educational level, and income were the most important determinants associated with dietary choices and eating behavior. Less frequently, social assets, demographic parameters, psychosocial status, and awareness of current dietary recommendations were associated with the quality of the eating pattern. CONCLUSIONS: The results of our review indicate heterogeneity of the studies in the field of social and psychological determinants of dietary patterns in older adults, but some important conclusions can be drawn. Further research harmonizing and integrating approaches and methodologies are required to better understand the determinants of dietary intake and the complexity of their interactions.

Exemplar scoring identifies genetically separable phenotypes of lithium responsive bipolar disorder.

Predicting lithium response (LiR) in bipolar disorder (BD) may inform treatment planning, but phenotypic heterogeneity complicates discovery of genomic markers. We hypothesized that patients with "exemplary phenotypes"-those whose clinical features are reliably associated with LiR and non-response (LiNR)-are more genetically separable than those with less exemplary phenotypes. Using clinical data collected from people with BD (n = 1266 across 7 centers; 34.7% responders), we computed a "clinical exemplar score," which measures the degree to which a subject's clinical phenotype is reliably predictive of LiR/LiNR. For patients whose genotypes were available (n = 321), we evaluated whether a subgroup of responders/non-responders with the top 25% of clinical exemplar scores (the "best clinical exemplars") were more accurately classified based on genetic data, compared to a subgroup with the lowest 25% of clinical exemplar scores (the "poor clinical exemplars"). On average, the best clinical exemplars of LiR had a later illness onset, completely episodic clinical course, absence of rapid cycling and psychosis, and few psychiatric comorbidities. The best clinical exemplars of LiR and LiNR were genetically separable with an area under the receiver operating characteristic curve of 0.88 (IQR [0.83, 0.98]), compared to 0.66 [0.61, 0.80] (p = 0.0032) among poor clinical exemplars. Variants in the Alzheimer's amyloid-secretase pathway, along with G-protein-coupled receptor, muscarinic acetylcholine, and histamine H1R signaling pathways were informative predictors. This study must be replicated on larger samples and extended to predict response to other mood stabilizers.