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Using data-driven machine learning (ML) models as surrogates in classical engineering is an emerging trend in the literature. However, effective surrogate modeling in path-dependent problems requires a deep understanding of the fundamental physical properties that naturally arise in data obtained from simulations or experiments. While generic ML architectures can capture nonlinear behavior, they may not inherently satisfy the specific temporal constraints dictated by physical processes. This study examines the characteristics of deformation paths generated through finite element simulations and identifies key modeling requirements for achieving physically meaningful predictions. One important requirement is that future inputs do not influence past outputs, a property typically satisfied by most surrogate ML models, yet rarely acknowledged or formalized. This requirement, often called the truncation condition, is essential for achieving physically meaningful predictions. Another closely related requirement is consistency across different time discretizations, which remains an active and important topic in deformation history modelling. To address these requirements, we propose a customized and adaptable Recurrent Neural Network (RNN) transition function that takes absolute strain inputs and is designed to enforce both truncation and consistency, ensuring robust predictions across varying temporal resolutions. This study contributes toward improving physically consistent damage initiation estimation and supports the development of more reliable surrogate models in computational mechanics.

The unique environments created within healthcare simulations may create distinct health and safety risks for patients, learners, simulation faculty and staff, and other participants involved in these activities. Guidance to aid simulationists to manage the physical risks arising from simulation activities is limited and there is no integrated synthesis of known risks or risk mitigation strategies. To identify and examine literature addressing the physical health and safety risks associated with healthcare simulation, in order to inform the development of effective safety management strategies. This review included published empirical research and non-empirical literature (e.g., commentaries, editorials) examining physical health and safety risks associated with any form of healthcare simulation programme, event, or facility. A multi-pronged search strategy was used including electronic databases, Google Scholar searches, and reference list searches of literature published between 2010 and 2025. Risks, contributory factors and mitigation strategies were identified and mitigation strategies were coded according to the Hierarchy of Controls framework. Sixteen articles were included. The literature most frequently identified physical health and safety risks to patients during in-situ simulation. Risks to participants and simulation staff or faculty were reported less often and primarily related to exposure to clinical equipment and musculoskeletal injury associated with the physical demands of simulation. Risks to simulated participants included clinical interventions being performed on them. Contributory factors included learner inexperience and failure to recognise risks, rule violations, pursuit of realism in simulation, poor simulation design, inadequate preparation, and lack of formal safety systems. Mitigation strategies were predominantly administrative, with rules, procedures, and checklists reported in 94% of sources, while elimination and engineering controls appeared in 37% and 19% respectively. There was call for clinical governance tools and processes to support a robust simulation health and safety approach. These findings highlight the need for co-designed, simulation-specific governance tools, including standardised risk assessments, adverse event reporting systems, and safety policies tailored to simulation environments. These tools should be embedded within, and aligned to the parent organisation's health and safety governance process, rather than constituting a separate or parallel governance process.

Hashimoto's thyroiditis (HT) is the most common autoimmune cause of hypothyroidism, often accompanied by persistent non-specific symptoms despite normalized thyroid hormone levels. Lifestyle factors, including mood, sleep, diet, and exercise, are increasingly considered in the supportive management of HT, but comprehensive characterization of lifestyle patterns in HT patients remains limited. To systematically assess the lifestyle patterns of HT patients, including psychological well-being, sleep quality, quality of life, dietary habits, and physical activity. A single-center, cross-sectional study was conducted with 226 participants (105 HT patients and 121 healthy controls). Emotional status was assessed using the GAD-7 and PHQ-9; sleep quality with the Pittsburgh Sleep Quality Index (PSQI); and health-related quality of life with the SF-36. Dietary habits and physical activity were evaluated using validated questionnaires. Serum TPOAb and TgAb levels were measured, and correlations with exercise parameters were analyzed using Spearman correlation and multivariable linear regression. All HT participants were biochemically euthyroid at enrollment; prior clinical history of hypothyroidism and LT4 treatment were extracted from medical records. HT patients exhibited higher anxiety and depression scores, poorer sleep quality, and lower quality of life compared with controls. They also reported distinct dietary patterns, including higher daily intake of meat, dairy, vegetables, and fruits and less frequent use of iodized salt. In terms of physical activity, HT patients showed more conservative exercise patterns, with greater time spent in low-intensity activity and less in moderate-intensity activity. Among HT patients, high-intensity exercise time was inversely associated with TPOAb levels after multivariable adjustment. HT patients displayed poorer psychological status, impaired sleep, and reduced quality of life, together with distinct dietary adjustments and conservative exercise patterns. In this biochemically euthyroid HT cohort, high-intensity exercise time was inversely associated with TPOAb levels. Not applicable.

Understanding how women navigate induced abortion care pathways is critical to ensuring person-centred, quality reproductive health services. Evidence indicates that persistent abortion stigma, the lack of choices of abortion methods and respectful care during abortion remain a global challenge to reproductive healthcare. Yet there is minimal evidence regarding abortion care pathways. This study explored induced abortion care pathways in Addis Ababa healthcare facilities. We used a descriptive qualitative approach, adopting purposive sampling techniques to recruit women who sought induced abortion care from seven facilities. The data were collected from May to July 2024. In-depth semi-structured interviews with sixteen women were digitally recorded and transcribed into the local language before being translated into English. Data were coded, organised, and analysed using inductive thematic analysis. Five main themes and their corresponding subthemes were developed through data analysis. Themes were: (i) social and emotional support, (ii) moral and social meanings shaping abortion care, (iii) accessibility and service delivery, (iv) perceived competency of abortion providers, and (v) physical and emotional effects of abortion. Many women attended the clinic alone, without their families, and received no support. Women often sought care at clinics away from their community due to concerns related to fear of stigma and social pressure. This study found long waiting times to receive abortion care, a lack of medicine and ultrasound at some facilities and limited availability of second-trimester abortions. Women reported that many providers were welcoming and competent, while others reported poor communication, the use of medical jargon, and stigmatising behaviours. Participant reported pressure to accept methods they did not want during contraceptive counselling and fear of breaches in privacy and confidentiality. Participants also described physical symptoms such as bleeding and pain, and felt ashamed and upset after the abortion, which could be associated with negative experiences. Inadequate social support, abortion stigma, and barriers to accessing abortion services, such as long waiting times and insufficient resources, were identified as significant gaps. These findings emphasised the need to strengthen person-centred abortion care and address systemic and socio-cultural barriers that undermine the quality of care. Abortion care should be easy to access, fair for everyone, and respectful of women’s needs. Kind communication and emotional support during abortion enhance the quality of care. This study explored abortion care experiences in healthcare facilities in Addis Ababa.We spoke with women who came for abortion care. We conducted face-to-face interviews employing open-ended questions. We analysed the data by thoroughly reading and checking the information to identify common patterns in women’s experiences.Women had varied experiences of support. Some received strong support from family or friends, which made them feel less worried and more confident. Some went through the abortion procedure alone as they feared pressure or shame. Many women felt abortion was a “sin” or morally wrong, while others felt confident that they had made the best decision for their lives. Women reported waiting times and service availability as challenges to accessing quality care. In addition, negative experiences such as feeling judged and ignored, as well as poor communication from providers, are reported. Women explained physical symptoms such as bleeding, fatigue and emotional outcomes including anxiety, guilt and self-blame after abortion. At the same time, some felt relieved after the abortion and satisfied with the care received.This study found that women faced challenges such as stigma and judgment, long waiting times, and limited availability of abortion services in some places. Improving the quality of abortion care can help women feel supported, reduce emotional distress, and protect their health and dignity.

Binge Eating Disorder (BED) is prevalent among adults with overweight/obesity, yet its detection remains limited by the scarcity of validated tools for Spanish-speaking populations. This study aimed to culturally adapt and evaluate the psychometric properties of the Binge Eating Disorder Screener-7 (BEDS-7) in Chilean adults with overweight/obesity and recurrent binge-eating episodes. The BEDS-7 was translated, back-translated, reviewed by expert judges, and pilot tested to ensure linguistic and cultural adequacy. Content validity evaluation also led to the exploratory addition of two DSM-5-based items. In total, 1,497 adults were recruited; after applying eligibility criteria, 435 were retained. Participants completed the Spanish BEDS-7 and online measures. Content validity was evaluated through expert judgment, structural validity using confirmatory factor analysis and exploratory structural equation modeling, internal consistency using omega coefficients, measurement invariance using multigroup analyses across gender, BMI, and physical activity, and concurrent validity through associations with negative affect. Content validity analyses indicated adequate clarity, coherence, and relevance, although lower sufficiency supported the exploratory inclusion of two additional items. For the BEDS-7, a two-factor structure showed excellent fit (CFI = 0.995; TLI = 0.998; RMSEA = 0.014) and acceptable internal consistency (ω = 0.706), with factors labeled "loss of control" and "emotional distress." Measurement invariance was supported across gender, BMI, and physical activity groups, and negative affect was positively associated with both factors. The Spanish BEDS-7 demonstrated adequate psychometric properties for assessing binge-eating symptomatology in Chilean adults with overweight/obesity and recurrent binge-eating. This study provides novel evidence from Latin America and contributes to cross-cultural comparability. Future research should assess diagnostic accuracy and temporal stability. Binge-eating involves eating large amounts of food while feeling unable to stop. It is common among adults living with overweight or obesity, but many people are not identified early because very few short screening tools exist in Spanish. To help address this gap, we adapted the Binge Eating Disorder Screener-7 (BEDS-7) for use in Chile. Nearly 1,500 adults took part, and 435 met the criteria needed for the analysis. We first checked whether the questions were clear and meaningful for Chilean adults, which led to the addition of two items. We then examined how the questions grouped together and found two main areas: loss of control around eating and emotional distress related to eating. The questionnaire also worked in a similar way for people of different genders, different body-weight categories, and different levels of physical activity. These findings suggest that the Spanish BEDS-7 can be a useful tool for assessing binge-eating symptoms in Chilean adults with overweight or obesity and recurrent binge-eating.

Health-related quality of life (HRQoL) is a vital indicator of evaluating care outcomes and prognosis, yet little is understood about its developmental trajectories in older patients with chronic pain. This study aimed to identify latent HRQoL trajectories and their predictors, and to develop explainable machine learning models for predicting HRQoL deterioration. This prospective cohort study assessed 608 older patients with chronic pain at admission and at 1, 3, and 6 months post-admission, collecting data on HRQoL, general characteristics, pain level, activities of daily living (ADL), depression, and perceived social support. Growth mixture modeling was applied to identify trajectories of physical and mental HRQoL. Predictors were selected using LASSO regression and SVM-RFE. Nine explainable machine learning models were developed for both components, and SHAP interpreted the outputs. An HRQoL decision-support dashboard was developed to facilitate potential clinical application. Three physical HRQoL trajectories were identified: Stable High, Decline and Low Stability, alongside two mental HRQoL trajectories: Improvement and Decline. Key predictors included education level, pain duration, pain level, ADL, depression, and perceived social support, with ADL and pain level being the most influential for physical and mental HRQoL, respectively. This dual-trajectory study identified five distinct HRQoL patterns in older patients with chronic pain, elucidating key predictors via explainable machine learning. The proposed HRQoL decision-support dashboard may provide an interpretable tool to support understanding of predictive relationships and assist healthcare professionals in HRQoL assessment. Not applicable.

Neuroinflammation has historically been viewed through a biochemical lens, governed by cytokines and danger signals. While this paradigm has provided foundational insights, integrating the physical dimension of tissue stiffening, hemodynamic shear stress, and compressive forces offers a more complete understanding of the pathological microenvironment of the central nervous system (CNS). In this Review, we examine Piezo1 as an important mechanosensitive channel that can translate such physical cues into neuroinflammatory responses. We synthesize emerging evidence showing that Piezo1-mediated calcium signaling can regulate activation, migration, and metabolic reprogramming in resident CNS cells and infiltrating immune populations. Importantly, the strength of evidence is not uniform across all cell types: genetic studies provide the strongest support in microglia, oligodendrocyte-lineage cells, and endothelial cells, whereas roles in astrocytes, dendritic cells, and T cells remain more context-dependent and emerging.Furthermore, we outline the distinct mechanical signatures across major CNS pathologies and discuss how Piezo1 may shape context-dependent outcomes-from plaque-associated microglial responses in Alzheimer's disease to mechanically restricted remyelination in multiple sclerosis, acute vascular or parenchymal injury in stroke and trauma, and emerging links to seizure-associated swelling and hyperexcitability in epilepsy. Finally, we propose an integrated "Mechanical Alterations-Piezo1-Immune Regulation" framework and discuss how mechano-therapeutic strategies might be used to modulate, rather than uniformly enhance or suppress, neuroinflammatory responses.

Incarcerated populations face greater health challenges, including higher rates of communicable and mental diseases. However, traditional health measures like disease prevalence and life expectancy do not capture their physical, mental, emotional, and social well-being. This scoping review will summarize the health-related quality of life (HRQoL) outcomes in incarcerated populations using preference-based HRQoL instruments (and measures that can be used to derive utility scores), providing insights for health policies and economic evaluations. A scoping review was conducted following PRISMA-ScR guidelines. Six electronic databases and three health technology assessment agencies were searched for peer-reviewed studies reporting preference-based HRQoL or HRQoL scores that can be used to generate health state utility values in incarcerated populations. Eligibility and data extraction were performed by two independent researchers.Findings were synthesized to identify knowledge gaps. Twenty-two articles met the inclusion criteria, primarily focusing on male and white populations. Ten studies targeted disease-specific populations, with mental health disorders (n = 7) being the most prevalent. Across studies, inmates generally reported lower HRQoL scores than the general population, especially those with mental health issues. Female and Indigenous inmates had lower HRQoL scores than male and non-Indigenous inmates. The variety in HRQoL instruments used, with each assessing different domains, hinders direct comparisons between studies. Validating instruments specific to incarcerated populations may be needed for future research. Overall, incarcerated populations, especially women and Indigenous inmates, demonstrate poorer HRQoL than the general population. There is a need for more diverse, inclusive studies to address these gaps. Incarcerated populations face greater health issues that are not fully captured by traditional health measures. Health-related quality of life (HRQoL) provides a more comprehensive view of their physical, mental, emotional and social well-being. This study summarizes HRQoL research in incarcerated populations using standardized tools. The purpose of this study is to provide a scoping review of the HRQoL outcomes of incarcerated populations, summarizing existing research and identifying gaps in the literature. Our findings reveal that inmates generally have lower HRQoL scores compared to the general population, and those with mental health issues reporting the lowest scores. Additionally, female and Indigenous inmates tend to have poorer HRQoL than male and non-Indigenous inmates. The findings highlight the need for HRQoL tools specifically tailored to incarcerated populations and call for more diverse studies, particularly for underrepresented groups.

The evolving global disease landscape, in conjunction with the significant impact of an aging population, has led to mental&#x2012;physical multimorbidity, imposing unprecedented pressures on healthcare systems and economies. This study aimed to investigate the interrelationships among multimorbidity, depression, and catastrophic health expenditure (CHE) and to test whether the intensity of CHE mediates these links. The analysis employed data from the China Health and Retirement Longitudinal Study (CHARLS), which conducted a longitudinal survey from 2011 to 2018, tracking 5,274 participants aged 45 years and older over a seven-year timeframe. Multimorbidity was ascertained through self-reported data from participants, whereas depression was evaluated via the 10-item Center for Epidemiologic Studies Depression Scale (CES-D-10). The intensity of CHE was calculated as the ratio of out-of-pocket (OOP) payments to the capacity to pay (CTP), adjusted for a catastrophic threshold of 40%. The relationships among the three variables were analysed via an extension of the random intercept cross-lagged panel model (RI-CLPM), which includes covariates to predict the observed variables. Mediation via the intensity of CHE was tested using 5,000 bootstrap resamples. At the between-person level, multimorbidity and depression were positively correlated (Model 1 r&#x2009;=&#x2009;0.349; Model 2 r&#x2009;=&#x2009;0.246; both p&#x2009;<&#x2009;0.001), whereas the intensity of CHE showed negligible between-person associations with either variable. At the within-person level, all variables showed significant autoregressive stability, with multimorbidity demonstrating the strongest persistence (&#x3b2;&#x2009;=&#x2009;0.808 in Model 1 and 0.936 in Model 2). Cross-lagged associations were clearly asymmetric, with prior multimorbidity exerting the largest prospective effects on the intensity of CHE (&#x3b2;&#x2009;=&#x2009;3.028) and subsequent depression (&#x3b2;&#x2009;=&#x2009;0.646 in Model 1 and &#x3b2;&#x2009;=&#x2009;0.789 in Model 2), whereas prior depression and prior intensity of CHE had much smaller effects on later multimorbidity. Mediation analyses indicated that the intensity of CHE (T) partially mediated the association from multimorbidity (T&#x2011;1) to depression (T&#x2009;+&#x2009;1) (indirect effect&#x2009;=&#x2009;0.063, 95% CI [0.042, 0.084]), but showed negligible mediation for the reverse pathway from depression (T&#x2011;1) to multimorbidity (T&#x2009;+&#x2009;1) (indirect effect&#x2009;=&#x2009;0.001, 95% CI [0.000, 0.001]). The study identified asymmetric bidirectional relationships among multimorbidity, depression, and the intensity of CHE in Chinese middle-aged and older adults, with effects predominantly running from multimorbidity to increased intensity of CHE and later depression; the intensity of CHE explained only a small portion of the multimorbidity&#x2192;depression effect and virtually none of the depression&#x2192;multimorbidity pathway. Policies that integrate multimorbidity management with routine depression screening could help reduce the combined physical, psychological, and financial burdens among middle-aged and older adults.

This study aimed to identify subgroups of health-related quality of life (HRQoL) and examine predictors of latent class membership among low- and high-income cancer survivors using latent class analysis (LCA). We analyzed data from the Korea National Health and Nutrition Examination Survey (2013-2020), including 1075 cancer survivors. HRQoL patterns were identified using LCA based on the EuroQol five-dimension questionnaire. Analyses were conducted separately for low- and high-income groups to examine income-stratified HRQoL patterns. Sociodemographic characteristics and chronic disease status were included as covariates in the latent class models to examine predictors of class membership within each income group. Income-stratified LCAs suggested differential HRQoL patterns. Three latent HRQoL classes were identified in the low-income group: Good HRQoL, Pain and Mobility Impairment, and Poor HRQoL. In the high-income group, two classes were identified: Good HRQoL and Pain and Mobility Impairment. While education was a common predictor in both groups, other predictors varied by income level. In the low-income group, older age, unemployment, and multimorbidity were significantly associated with impaired HRQoL classes, whereas female sex predicted membership in the Pain and Mobility Impairment class in the high-income group. These findings highlight differences in HRQoL patterns and associated factors across income-stratified groups of cancer survivors, underscoring the limitations of relying solely on average HRQoL scores in survivorship care. Survivorship strategies should be tailored to the differential HRQoL patterns and associated predictors identified within each income group, thereby promoting more targeted and equitable care for cancer survivors. Health-related quality of life (HRQoL) is a key measure for understanding cancer survivors&#x2019; well-being after cancer. However, most studies use a single total score of HRQoL, which can mask important differences across physical, psychological, and social aspects. Considering its multidimensional nature, examining patterns across HRQoL domains can provide a more comprehensive understanding of survivors&#x2019; well-being. Moreover, since socioeconomic status&#x2014;especially income&#x2014;strongly influences HRQoL, it is important to explore how these patterns differ by income level. Therefore, this study examined HRQoL patterns among low- and high-income Korean cancer survivors and identified factors related to each pattern. Three patterns were found among low-income survivors&#x2014;Good HRQoL, Pain and Mobility Impairment, and Poor HRQoL&#x2014;and two among high-income survivors&#x2014;Good HRQoL and Pain and Mobility Impairment. Education was associated with better HRQoL patterns in both groups, but other predictors differed. Among low-income survivors, older age, unemployment, and multiple chronic diseases were linked to poorer HRQoL patterns. Among high-income survivors, women were more likely to belong to the Pain and Mobility pattern. These findings highlight the need for survivorship strategies that address heterogeneity across income-stratified groups, promoting equitable and comprehensive care for cancer survivors, even when overall HRQoL scores appear satisfactory.

The aim of this study was to examine the community participation levels (frequency and involvement) of children with Down syndrome (DS) and the restrictive and supportive factors affecting their participation levels and compare them with typically developing (TD) children. This cross-sectional study included parents of 70 children (35 with DS; 35 TD children) aged 8-17&#x2009;years. Parents of all children completed the community module of the Participation and Environment Measure for Children and Youth, which is used to assess participation and environmental factors in the community. Children with DS participated less frequently (organizations, groups, clubs, or leadership activities and out-of-school classes and lessons) and less involved (getting together with children, organizations, groups, clubs, or leadership activities and out-of-school classes and lessons) in community activities compared to TD children, and parents of children with DS were less satisfied with their children's participation in community activities. Children with DS faced more barriers (the physical layout, the sensory quality and weather conditions) and had insufficient resources (information and supplies) compared to their TD peers in the community. After adjusting for the child's age, parental education level and average income, the group (DS vs. TD) had an independent effect on frequency, involvement and desire for change. Children with DS are negatively affected in terms of community participation (both) compared to TD children. Beyond sociodemographic factors, being a child with DS may reduce community participation (both). The community environmental factors were influential for the community participation of children with DS. Community participation of children with DS can be increased through environmental regulations.

With rising demand for remote work and education, smartphones and other portable photographic devices are increasingly used to capture physical documents, which are then shared as electronic files. However, shadows in such images hinder reading. Currently available shadow removal datasets exhibit certain limitations. This paper creates a semi-synthetic dataset (SSD-DIS) with 12,224 image sets. Using Blender for shadow masks, multi-source shadow-free images, and adjusted shadow intensity/color, it simulates real-world shadow scenarios. Experiments show SSD-DIS enhances neural networks' learning of document shadow features; models trained on it outperform those using traditional datasets, supporting document shadow removal algorithm research.