Diabetes resilience is key to adolescents' effective type 1 diabetes management. This descriptive, cross-sectional study examined the relationships between diabetes resilience scores and behavioral and health indicators and individual and family factors in 202 older adolescents (aged 14-18 years) with type 1 diabetes presenting to a pediatric endocrinology outpatient clinic and ward in Türkiye. Data were collected using a background information form and the Diabetes Strengths and Resilience Measure for Adolescents with Type 1 Diabetes (DSTAR-Teen). Higher DSTAR-Teen scores were associated with higher maternal education level, absence of sibling diabetes, shorter diabetes duration, at least four blood glucose self-checks daily, and three or fewer hyperglycemic episodes in the last month (p < 0.05). DSTAR-Teen scores negatively correlated with HbA1c (p = 0.001) and postprandial blood glucose levels (p < 0.05). Understanding the factors associated with diabetes resilience in adolescents with type 1 diabetes may enable the development of targeted interventions to support diabetes management.

At the third anniversary of the full-scale invasion of Ukraine by Russia, the prevalence of mental health problems (depression, anxiety, and posttraumatic stress disorder [PTSD]) in all regions of the country was explored, as well as individual and collective predictors of outcomes. Resources that assist civilians to cope with the chronic stress were also examined. An anonymous online survey was completed by Ukrainian civilians (N = 815) between February 10 and March 25, 2025, around the third anniversary of the full-scale invasion. Participants completed a questionnaire with background information; standardized scales of anxiety (Generalized Anxiety Disorder 7-item scale), depression (Patient Health Questionnaire-9), and PTSD (PTSD Checklist for Diagnostic and Statistical Manual of Mental Disorders, fifth edition); and assessments of individual and collective traumatic experiences. Levels of anxiety, depression, and PTSD did not differ significantly across the country. Overall, respondents reported a moderate level of anxiety (9.24), depressive symptoms (10.83), and PTSD symptoms (29.18). "Lack of future prospects," "media consumption," "personal loss of soldiers who died in the war," "age," "risk of occupation," and "risk of job loss" were significant predictors of the three outcomes investigated. "Lack of future prospects" was found to be the strongest predictor of anxiety and depression, while "risk of occupation" proved to be the strongest predictor of PTSD symptoms. Key resources associated with lower symptomatology were communicating with loved ones and engaging in physical activity. Ukrainian traumatization is best understood as multilayered, occurring at individual and collective levels, as well as chronic, dynamic, polytraumatic in nature, and significantly linked to exposure to the mass media. (PsycInfo Database Record (c) 2026 APA, all rights reserved).

The health system has been identified as a key lever for increasing access to birth registration (BR) directly after birth. BR is critical for upholding human rights and accessing essential services, yet many low- and middle-income countries struggle with low BR rates. In this realist synthesis, we reviewed literature on two separate interventions that are designed to increase access to BR services. This included (1) embedding or appointing an official to register births in health facilities and (2) incorporating education on BR in routine perinatal care programmes. This study aimed to support the knowledge base on how these interventions work, why, and for whom, within health facility contexts. We followed the RAMESES I publication guidelines for realist syntheses. Iterative searches were carried out in PubMed, Scopus and Web of Science. The search also included grey literature, policy documents and insights from a key informant in South Africa. We adopted an iterative cycle of searches guided by review team exchanges, stakeholders' insights and sequential snowballing searches for theory and background information. We developed two initial programme theories, presented as ICAMO configurations, that were then refined against empirical evidence from 17 included studies, with publication dates ranging from 1992 to 2021. Our review findings highlight that the interventions improved BR rates by lowering logistical barriers, raising awareness and supporting BR staff, (i.e., personnel responsible for conducting BR within health facilities). Literature from LMICs suggest that the effectiveness of facility-level BR interventions is often affected by access to care, travel distances, transportation costs, as well as by the availability of resources, BR infrastructure, and staff motivation within healthcare facilities. These factors may, in turn, affect BR demand, parents' self-efficacy, and the extent to which BR initiatives align with parents' real-life contexts.

Retrospective analysis of real-world clinical use of comprehensive genomic profiling in solid tumors in Finland 2017-2020

The use of Clinical Decision Support Systems (CDSS), such as clinical decision rules, algorithms, or machine learning-based applications, has gained attention in recent years. However, their adoption and effectiveness may vary across different health care systems and settings. For a CDSS to be adopted, it must effectively address the practical issues encountered by professionals; however, little research has been done to identify these needs and requirements. This study aims to describe and compare the current use of various decision-support and prediction tools in long-term care for older people across health professionals from 6 European countries and the United States. This study analyzed survey data from a CDSS pilot study in a purposive sample of health professionals working with older adults with complex chronic conditions from six European countries and the United States. The survey included participants' general background information, their current use of decision support tools, and their attitudes on the potential benefits of CDSS. About 20 participants were sampled per country. Closed responses were analyzed using correlation coefficients and regression models, while open-ended responses were clustered in a qualitative manner, categorizing each response. A total of 151 professionals (mean age 45.5, SD 11.6 years, 71.5%, 108/151 female) participated in the pilot study. Most participants were physicians (85/151, 56.3%) or nurses (57/151, 37.7%). About 51% (78/151) of the participants reported using CDSS, while 22.4% (34/151) used predictive CDSS, showing important variation across samples from the seven countries. The regression model for comfort with technology showed a positive association for openness to new technologies (β=0.622; P<.001), although an inverse significant association was found for age (β=-0.022; P<.001). No significant associations were found for the actual use of CDSS. Participants reported using CDSS mainly for diagnostic purposes or for guideline implementation, not aimed at prognostic information. In contrast, examples of prognostic tools were most frequently mentioned by respondents as being valuable improvements to clinical practice. While some countries' samples reported well-integrated digital health infrastructures and higher CDSS adoption rates, others still face challenges in implementing these. However, we found multiple examples of emerging tools, and at the same time, an important demand for predictive CDSS. Our findings highlight the need for improvement of current CDSS implementation and both development and implementation of particularly predictive CDSS.

See the small lesions: Frequency-guided spatial debiasing GAN for multimodal medical image fusion

ABSTRACT Guided tours are an established part of photography exhibitions, but little is known about how visitors use them, what they expect from them, and which formats they find helpful. This exploratory online survey examined 461 respondents, including 345 participants who reported attending guided tours in exhibitions of artistic photography at least once. The findings show that guided tours are not used regularly by all visitors, but they are evaluated positively by those who attend them. Visitors especially value background information on the works, insights into photographers’ biographies, distinctive stories, and information on artistic techniques. They are less interested in guided tours as primarily social events. The study identifies three visitor profiles: curious art explorers, reserved art skeptics, and reflective art experts. These profiles suggest that guided tours can serve different educational functions, including initial access, focused orientation, and deeper interpretive engagement. For museum educators and exhibition teams, the findings support compact, context-rich, and target-group-sensitive guided tours that combine selected works, clear contextual framing, and opportunities for dialogue.

Purpose Self-harm is a frequent feature of borderline personality disorder. In severe conditions comorbidity across mental disorders is common. Intellectual developmental disorder (IDD) may be an underlying condition with high frequent self-harming behavior. Based on extensively hospitalized severe self-harm patients, we aimed to 1) explore associations between possible IDD (identified by Hayes Ability Screening Index, HASI), and mental health disorders, and 2) investigate the ability to identify possible IDD with a low-effort HASI subscale. We hypothesized that possible IDD was significantly associated with greater mental health comorbidity. Method The study included 42 adults admitted to psychiatric hospitals for severe self-harm. Assessments included diagnostic interviews and IDD screening (HASI) by clinicians, and childhood trauma (patient self-report). Data were analyzed using Poisson regression. Validation of the HASI subscale background information was conducted using ROC analysis and exploratory data analysis. Results Possible IDD was significantly associated with higher incidence of mental disorders, even after adjusting for BPD, SUD, autism, and childhood trauma. Possible IDD and childhood trauma were independently associated with the number of mental disorders. The HASI background subscale demonstrated good predictive properties for identifying possible IDD. Conclusions Results highlight the importance of assessing IDD among severe self-harm inpatients. A diagnosis of IDD may open the door to a broader range of treatment adaptations specifically addressing the needs of individuals with complex developmental and psychological challenges. Given the small sample size, these findings should be viewed as preliminary and hypothesis-generating. Replication in larger samples is required for further confirmation.

Background:Palliative care access remains limited in Lebanon, particularly outside the capital. Data on public palliative care knowledge and perceptions in general and among disadvantaged populations remain insufficient.Objective:This study aimed to assess palliative care awareness and knowledge among community users of a non-governmental organization serving socioeconomically disadvantaged populations in Tripoli, north of Lebanon.Methods:A cross-sectional study was conducted using a structured survey targeting 400 individuals using the Palliative Care Knowledge Scale (PaCKS) in the colloquial Arabic language. Data on palliative care awareness and background information were collected. Multivariable binary logistic regression using stepwise backward selection was conducted to identify predictors associated with palliative care knowledge levels and common misconceptions.Results:Low awareness of palliative care was observed, with 95.8% of participants reporting never having heard of it. PaCKS’s mean score was 9.24 ± 2.89; 40.8% (N = 163) had high scores (11–13), 42.8% (N = 171) had moderate scores (7–10), and 16.5% (N = 66) had low scores (0–6). Common misconceptions included perceiving palliative care to be limited to hospital care, to cancer, and to end of life. Higher educational attainment was a strong and stable predictor of palliative care knowledge and common misconceptions. Participants with middle or high school education had 1.754 higher odds (p = 0.013), and those with university education had 4.938 higher odds (p < 0.001) for higher knowledge compared to participants with no or primary education, potentially indicating a graded association. More than half of the participants expressed interest in having information about palliative care made generally available.Conclusion:Our results indicate low awareness and several misconceptions about palliative care along with the presence of health inequities. Low educational attainment, mainly linked to socioeconomic disadvantage, may limit health literacy, and as such, reduce access to information and the ability to understand complex concepts such as palliative care. These findings highlight the urgent need for a collaborative, multi-sectoral system-thinking, community-centered, and equity-driven approach that involves the community, researchers, healthcare providers, and policymakers.

This study proposes a hybrid deep learning framework for multi-class brain tumor classification using a combined MRI dataset constructed from Figshare, Br35H, and SARTAJ sources. The dataset includes 7023 MRI images belonging to four clinically important classes: glioma, meningioma, no-tumor, and pituitary tumor. In the proposed approach, a dedicated preprocessing pipeline is first applied to enhance tumor-related image regions and reduce irrelevant background information. Then, the Multi-scale GHOST Residual Attention Autoencoder (MS-GHOST-RAAE) is used for deep feature extraction. This architecture integrates multi-scale GHOST modules, residual connections, and attention mechanisms to obtain compact, stable, and discriminative feature representations. The extracted features are subsequently classified using conventional machine learning classifiers. Experimental results show that the proposed hybrid framework achieves 98.75% accuracy on the combined dataset, with a total processing time of 543 + 269.4571 s. These findings indicate that MS-GHOST-RAAE provides strong classification performance on heterogeneous MRI images and offers an effective computer-aided decision-support approach for brain tumor classification.

HighlightsWhat are the main findings?A hierarchical GWACF decomposition and layer-specific fusion strategy significantly enhance concealed object contrast in multi-polarization PMMW images while preserving edge integrity.Experimental results confirm superior detection accuracy and robustness over mainstream methods, demonstrating reliable concealed object imaging on the human body.What are the implications of the main findings?The significant boost in concealed-object contrast and edge fidelity reduces both false alarms and missed detections, establishing multi-polarization PMMW imaging as a robust, privacy-safe solution for operational security screening.The proposed hierarchical decomposition and fusion framework provides a robust, generalizable strategy for fully exploiting multi-polarization information, establishing a new reference for processing polarimetric passive imagery in security and remote sensing applications.Passive millimeter-wave (PMMW) imaging technology has become a highly promising technology that can protect privacy in human body security inspections. However, most existing methods rely on single-pixel and single-polarization processing mechanisms, which often lead to discrete false-alarm pixels or missed detections in practical applications. Although multi-polarization information can provide richer distinguishing features, the current methods typically depend on limited Stokes parameters or artificially designed polarization features, lacking a systematic framework to fully exploit the intrinsic potential of multi-polarization information. In this paper, we propose a novel multi-scale edge-preserving decomposition model, termed Gaussian and weighted average curvature filtering (GWACF), to hierarchically decompose a multi-polarization PMMW image into three structural layers: base structural (BS) layer, coarse structural (CS) layer, and fine structural (FS) layer. Furthermore, we also propose a fusion strategy in which a gradient-domain pulse-coupled neural network (PCNN) is employed to fuse the texture-rich CS and FS layers, while the energy attribute fusion method is applied to the BS layer where primary structure and background information play a dominant role. This method effectively leverages complementary polarimetric information without introducing artifacts or compromising edge sharpness. Experimental results demonstrate that the proposed method effectively enhances the brightness temperature (BT) contrast of concealed objects. Compared with existing mainstream methods, it exhibits notable advantages in both detection accuracy and robustness.

ABSTRACT Background Information about integrated care for patients with atrial fibrillation (AF) in low- and middle-income countries is scarce. Methods We analyzed multicentre data from 699 patients with AF treated in São Paulo, Brazil. ABC compliance was defined as (1) adequate anticoagulation (‘A’ component); (2) controlled AF symptoms (‘B’ component); and (3) comorbidities treatment (‘C’ component). We built logistic regression models to identify characteristics associated with ABC compliance. Results Mean age was 69.4 ± 14.4 years (45.8% women; 57.4% from private healthcare). Compliance with ABC pathway, and with the ‘A,’ ‘B’ and ‘C’ components occurred in 20.9%, 42.3%, 81.7% and 50.1% of the participants, respectively. Lack of ABC compliance was associated with female sex (adjusted odds ratio [aOR]: 1.66; p = 0.015), intermediate (aOR: 3.37; p < 0.001) and high (aOR: 9.17; p < 0.001) bleeding risk. Patients with AF treated in public units had worse performance for the ‘A’ component (aOR: 2.50; p < 0.001) and better performance for the ‘C’ component (aOR: 0.49; p < 0.001) compared to those using private healthcare. Conclusions Compliance with the ABC pathway in São Paulo, Brazil was low. Lack of ABC compliance was more common in women and individuals with high bleeding risk. We found a mixed pattern of ABC compliance in public and private units.

This represents a substantial development of the guideline on the topic first published in 2016. The following notable points have been updated: the sections on background information; the clinical benefit of the method; the resulting differential diagnostic considerations; the outlook for possible future extensions of the indication spectrum; the quantitative analysis of the PET images; the embedding of the method in diagnostic pathways; and the relation to alternative biomarker methods such as amyloid measurement in CSF/blood.

The recently updated AORN "Guideline for the safe use of surgical energy devices" provides perioperative nurses with background information on surgical energy devices and ways to prevent patient and staff member injury during use. This article provides an overview of the guideline and discusses recommendations for assessment, planning, and injury prevention; surgical energy device generators; accessories; return electrodes; implanted electronic devices; and education. It also includes a clinical scenario that describes how perioperative nurses apply these recommendations in practice to prevent thermal injury and ensure the safe management of implanted electronic devices. Perioperative nurses should review the guideline in its entirety and apply the recommendations when using surgical energy devices.

The duration of genetic counseling (GC) sessions can vary significantly. However, which factors contribute to sessions being abbreviated or extended and how that affects quality of care has not been well studied. This study explored variability in long versus short GC patient care time, defined as time spent in the session with the patient, across various specialties and settings. Sessions from the Genetic Counseling Processes Result in Outcomes (GC-PRO) study were purposively sampled based on session time to include nine short (<25 min) and nine long (>45 min) sessions. Audio recordings of these 18 sessions were transcribed and then analyzed using a reflexive thematic coding approach according to Braun and Clarke, involving the study team familiarizing themselves with the data, coding and recoding, developing themes, revising and refining in relation to the coded data and then the full dataset. Four themes were generated: (1) genetic counselor behaviors influence session content and patient involvement, (2) patient behaviors vary based on levels of patient engagement and impact session direction, (3) patient characteristics are an opportunity for customized communication strategies, and (4) contextual factors impact patient care time and can be moderated via alternative information gathering approaches. This research suggests that GC sessions often involve substantial time spent gathering medical information and providing extensive information to patients (e.g., background information on genetics, genetic testing technologies) and genetic counselors engage patients variably with regard to questions and psychosocial prompts. Findings uncovered potentially modifiable factors-including the quantity of information provided, the extent of history collected, and unintentionally inhibiting patient questions-that may improve efficiency and, consequently, patient-centered care quality by providing more space for patients to engage, ask questions, and express emotions.

Diabetic Retinopathy (DR) is the leading cause of blindness in middle-aged and elderly individuals in Asia. Screening for retinopathy is a cost-effective method to prevent this condition. This study aims to identify the factors that are associated with the uptake of Diabetic Retinopathy Screening (DRS) among patients with type 2 diabetes in northern Iraq, using the attitude-social influence-self-efficacy (ASE) model. This cross-sectional study was conducted in 2023 in the city of Sulaymaniyah, northern Iraq, and involved 602 patients with type 2 diabetes. The patients were selected using a simple random sampling method from those who referred to the diabetes clinic in Sulaimaniyah city. Data was collected through written questionnaires including background and demographic information, DRS uptake, and ASE determinants based on patient interviews. The collected data was analyzed using SPSS version 16, as well as, STAT-17 and statistical tests including Independent Samples t Test, Pearson correlation coefficient, and univariate and multivariate logistic regression were used. The mean age of respondents was 53.17 ± 9.38 years, ranged from 30 to 77 years. The mean of diabetes duration was 7.92 ± 4.90 years, ranged from 1 to 24 years. 58.1% (350/602) of the participants had DRS uptake. Social influence (OR: 1.089 and P: 0.039), self-efficacy (OR: 1.502 and P < 0.001), barriers (OR: 0.891 and P: 0.002), and intention (OR: 1.215 and P: 0.012) were found to be significantly associated with the DRS uptake. Goodness of fit model indices included sensitivity, specificity, positive predictive value, negative predictive value and correctly classified were equal to 83.67%, 74.80%, 82.23%, 76.67% and 79.97% respectively. Our findings suggest that in the development of interventions to promote DRS uptake, it should focus on strategies to promote social influences and self-efficacy, along with reducing barriers to DRS uptake. The online version contains supplementary material available at 10.1007/s40200-026-01971-y.

Implementation and Validation of the Multi-scale Gradient Smoothing Strategy for Breast Full Waveform Inversion.

• Proposed Gated CNN with Spatial Attention enhances lane feature focus. • Hybrid Dice-Focal-Tversky Loss improves thin lane boundary segmentation. • Outperforms six methods on BDD100K, CULane, and TUSimple benchmarks. Lane detection plays an important role in modern self-driving systems, directly bearing on vehicle control, navigation stability, and road safety. All autonomous steering, adaptive cruise control, and lane-keeping assistance were reliant on lane detection. CNN-based models, particularly with encoder-decoder architectures like UNet and SegNet, have inherent limitations in their performance. These limitations become apparent in challenging conditions such as varying lighting, occlusions, faded markings, tight corners, and urban clutter, which can negatively impact their accuracy and robustness. Thus, the present study, introduce a novel deep learning method that utilizes a spatial attention mechanism in combination with Gated Convolutional Neural Networks (Gated CNNs) to mitigate these challenges. Gated convolutional layers enable adaptive feature selection by suppressing irrelevant background information, while the spatial attention module enhances localization of lane-relevant regions. Moreover, a hybrid dice-focal-Tversky loss function is employed to attenuate class imbalance and improve thin lane boundary segmentation. The proposed model was evaluated on three extensive benchmark datasets: BDD100K, CULane, and TUSimple, which collectively represent a wide range of real-world circumstances, including nighttime driving, lane occlusions, and crowded driving conditions. As a result, the model consistently outperformed multiple baseline approaches, in terms of accuracy, F1-score, and intersection over union (IoU) across segmentation experiments. The current framework demonstrates that Gated CNN with Spatial Attention provides a more robust, accurate, and generalizable lane detection approach, applicable in real-time autonomous driving scenarios.

It can be difficult for some students to comprehend maximal aerobic capacity (V̇o2max) and endurance performance. We present a project-based learning example used in our advanced human physiology course. This project is based largely on a seminal paper but utilizes chapters from the course textbook and additional peer-reviewed studies. After we cover background information on topics such as muscle physiology, metabolic pathways, expired air analysis, V̇o2max, and lactate threshold, students join one of three groups to prepare and deliver presentations. Those topics and groups are based on 1) history of V̇o2max, 2) rate limiters of V̇o2max, and 3) additional determinants of endurance performance. The instructor covered topics from the textbook on sports physiology including oxygen uptake at the onset, during, and after exercise. The course instructor also provided a brief review of sport-specific V̇o2max and lactate threshold testing in hockey players. Students in the class agreed to hold one class period in the laboratory for direct use of expired air analysis. The lab emphasized the relationship between intensity and carbohydrate versus fat utilization. During the project presentations in the final week of class, group 1 summarized key points such as the use of a Douglas bag and the difference between V̇o2max and V̇o2peak, while group 2 summarized central (e.g., cardiac output) versus peripheral (e.g., muscle mitochondria) limitations, and group 3 summarized other key factors for endurance performance such as lactate threshold and running economy. This project-based assignment was an excellent way for students to better comprehend V̇o2max and endurance performance.NEW & NOTEWORTHY This project-based assignment provided students a unique way to comprehend human metabolism, cardiorespiratory and muscle physiology, and applied exercise science concepts such as maximal aerobic capacity (V̇o2max), lactate threshold, and running economy. Topics covered during this assignment are important for health, performance, and longevity. Students were excellent at working in small groups, basing presentations on peer-reviewed literature, and applying their communication skills. Students strongly agreed with "teaching methods" and scored well on final exam questions related to the project.

Visible–thermal tiny pedestrian detection in UAV aerial images is crucial for online decision-making in urban security and disaster response. However, the extremely small scale and sparse distribution of pedestrians cause discriminative cues to be submerged by dominant low-frequency background and contextual redundancy during feature learning. Meanwhile, cross-modal spatial misalignment and spatially varying modality reliability hinder stable fine-grained correspondence, thereby degrading fusion quality. To address these issues, QA2FDet is proposed as a quality-aware adaptive alignment fusion network comprising three modules: spectrum-spatial decoupled enhancement module (SDE), cross-modal correspondence mining module (CCM), and prior-informed gated fusion (PGF). SDE leverages the discrete cosine transform to disentangle redundant low-frequency background information, while deep semantic gating propagates high signal-to-noise ratio details into shallow representations to enhance subtle cues of tiny pedestrians and suppress high-frequency noise. To establish fine-grained neighborhood correspondences under slight spatial offsets, thermal-guided local asymmetric cross-attention is designed in CCM. Finally, region-level quality and modality discrepancy are jointly modeled for adaptive cross-modal fusion in PGF. Extensive experiments on multiple UAV-based RGBT detection benchmarks demonstrate that QA2FDet achieves state-of-the-art performance and exhibits strong robustness in challenging aerial scenes.