Characterizing nursing home care team communication via text messaging: A social network analysis.

To examine the role of health-related quality of life (HRQoL) assessments and patient-reported outcome measures (PROMs) in the economic evaluation of urological interventions, highlighting methodological challenges, and identifying areas for future development. This narrative review synthesises key frameworks for economic evaluation in urology, describes methods for utility derivation, and critically appraises the application of both generic and disease-specific PROMs. Evidence is drawn from major urological trials and model-based evaluations, with attention to their impact on health technology assessment decisions. Patient-reported outcome measures underpin cost-utility analyses across the full spectrum of urological conditions, including benign prostatic hyperplasia (BPH), urinary incontinence, overactive bladder, bladder cancer, renal cancer, and prostate cancer. Generic instruments such as the EuroQoL five Dimensions (EQ-5D) and 36-item Short-Form Health Survey (SF-36) facilitate cross-disease comparison but may lack sensitivity to urological symptoms. Disease-specific tools (e.g., International Prostate Symptom Score, Overactive Bladder Questionnaire, 26-item Expanded Prostate cancer Index Composite) provide granularity but often require mapping to utility values, introducing uncertainty. Trial-based evaluations (e.g., Trans European Research into the use of Management Policies for LUTS suggestive of BPH in Primary Healthcare [TRIUMPH], Single-Incision Mini-Slings [SIMS], Refractory Overactive Bladder: Sacral NEuromodulation vs BoTulinum Toxin Assessment [ROSETTA]) demonstrate that PROM-derived quality-adjusted life year differences can decisively influence cost-effectiveness. Surgery-related PROMs including the Consultation and Relational Empathy measure can capture important short-term recovery outcomes not measured by generic tools. Health-related quality of life and PROMs are foundational to value-based urological care. Accurate and context-sensitive measurement ensures that cost-utility analyses reflect patient experience as well as clinical outcomes. Future priorities include development of preference-based urology-specific PROMs, improved digital/electronic PROM capture, validation of mapping algorithms, and integration of surgery-related PROMs into economic evaluations.

Although specialised palliative care is recommended for patients with refractory or complex symptoms, only a minority receive such care. Consequently, most patients rely on general palliative care delivered in hospitals or primary care, yet this practice remains poorly described. This study aimed to explore how general palliative care is organised, practised and experienced across clinical departments in a university hospital. This qualitative study used semistructured interviews and qualitative content analysis in a Danish regional university hospital serving approximately 600 000 citizens. A total of 20 informants from all clinical departments, including nurse managers, senior physicians, floor nurses and nurses with advanced roles, participated in 10 individual and five dyad interviews. Three overarching themes emerged: 'a palliative care approach', 'general palliative care tools and practice' and 'intersections with specialised palliative care'. Patient identification relied primarily on implicit or explicit clinical triggers rather than systematic screening. Many departments provided palliative care without naming it as such, particularly needs assessments, life-sustaining treatment decisions and end-of-life planning. Collaboration with specialised palliative care was influenced by unclear referral criteria and capacity limitations, although informal consultations and teaching were highly valued. Participants expressed a strong wish for more knowledge transfer and structured guidance from specialised palliative care. General palliative care in hospitals is variably recognised and inconsistently organised. The pervasive presence of complexity in general palliative care challenges the utility of 'complexity' as an effective criterion for distinguishing general from specialised palliative care.

Higher-order statistical dependencies can encode cooperative structure in complex systems, but are often missed by pairwise analyses and supervised pipelines optimized for discrimination. We aim to develop an unsupervised feature ranking framework that prioritizes genes by higher-order synergy within transcriptomic communities. We combine PCA derived latent components with O-information to quantify gene level synergy within network communities. Using hepatocellular carcinoma (HCC) microarray data (GSE102079), we start from gene communities identified in our previous community detection workflow and analyze them without supervised pre filtering. For each community, we select latent components through permutation based criteria, compute O-information between each gene and the retained components, assess significance using surrogate testing, and rank genes by synergy. Top ranked genes are selected using knee based criteria on ranked synergy profiles. Synergy based ranking consistently highlights genes associated with functions related to HCC, such as metabolic remodeling, extracellular matrix dynamics, EMT, angiogenesis, immune modulation and oxidative stress responses. Several high-synergy genes supported by literature were previously discarded by Boruta, indicating that synergy prioritizes cooperative module roles rather than solely discriminative markers. The proposed method provides a reproducible, fully unsupervised tool for feature ranking based on higher-order interactions, complementing supervised feature selection approaches and offering a general domain agnostic tool applicable to any samples by features dataset.

Familial hypercholesterolemia (FH) is a common autosomal dominant genetic disorder conferring a high risk of premature atherosclerotic cardiovascular disease (ASCVD), without early diagnosis and treatment. Underdiagnosis remains a major public health problem; cascade testing (systematically identifying blood relatives) can mitigate this burden. The Identification Methods, Patient Activation, and Cascade Testing for FH (IMPACT-FH, 5R01HL148246-04) study evaluated family communication strategies for FH cascade testing and significantly increased uptake in a population genomic screening setting. The current IMPACT-FH Renewal (2R01HL148246-05) will next adapt these strategies to individuals diagnosed with FH outside of population genomic screening, evaluate the cost and performance of detection strategies within health systems, and define elements which help sustain FH screening programs. A pragmatic trial guided by the Conceptual Model of Implementation Research will be conducted to evaluate generalized family communication strategies to facilitate FH cascade testing in primary care (Aim 1). We will apply economic evaluation modeling methods to compare implementation costs, efficiency, and performance of FH implementation strategies, and develop a generic modeling tool for other healthcare systems to support local decision-making (Aim 2). We will use a convergent mixed-methods approach guided by the Integrated Sustainability Framework to understand factors influential in sustaining FH screening programs at an international level (Aim 3). By addressing novel communication strategies, economic impact, and sustainability of FH cascade testing, this study seeks to develop generalizable approaches to improving identification of FH to reduce the high burden of FH-associated ASCVD.

Advances in spectral cytometry instrumentation and fluorescent reagents have led to the possibility of ultra-high-parameter panels exceeding 50 colors. However, panel size is limited in practice by unmixing-dependent spreading (UDS), a phenomenon which leads to a progressive deterioration of unmixed signal-to-noise ratios in panels that contain fluorochrome combinations with significant spectral overlap. So far, choosing spectrally compatible sets of fluorochromes that avoid UDS has been a complex and labor-intensive task involving substantial trial-and-error experimentation. Here, we provide a detailed explanation of UDS and practical strategies for handling UDS in large spectral panels. We describe the empirical hallmarks of UDS, demonstrate how to quantify its impact, and dissect its underlying mathematical cause in terms of spectral collinearity. We present practical tools derived from the regression literature that can be used to select optimal combinations of fluorochromes in a platform-agnostic fashion based on publicly available reference data, providing a general tool for spectral panel design.

Comparison of the performance of ChatGPT Plus, Gemini Pro, and experienced nurses in pediatric emergency triage using physician reference assessment: A prospective observational study.

The routine assessment of Quality of Life (QoL) is rarely performed in the clinical paediatric endocrine and needs further exploration. Between October 2022 and December 2023, 216 patients (M:F, 134:82) with a median age of 11.8 yrs (10th,90th: 5.6, 16) completed a total of 227 EQ5D questionnaires which included an assessment of five dimensions (mobility, self-care, participation in usual activities, pain or discomfort and anxiety/depression) as well as a visual analogue scale (VAS) assessment of overall health. Additional data were collected to assess presenting complaint, clinic outcome and number of encounters and specialties involved in the patient's care within the preceding 12 months. The median VAS was 90/100 (65,100) whilst the median Dimensions score was 6/15 (5,10) for <16yrs and 7/15 (5,9) for >16yrs. The VAS score was negatively associated with the Dimensions score with the strongest correlation in >16yrs group, (r=-0.64, p=0.002). The best VAS and Dimensions scores were reported in the Early Puberty group at 99/100 and 5/15 respectively while the worst scores were reported in the Excess Weight group at 77.5/100 and 8/15, respectively. In a multivariate analysis, the number of specialties involved over the previous year was associated with a worse VAS score (p=0.001) and Dimensions score (p<0.001). The EQ5D can be used in paediatric endocrinology with a clear association with burden of illness as reflected by the complexity of the condition. EQ5D may represent a useful generic tool for the routine assessment of QoL in paediatric endocrinology.

Metal clusters are of broad interest across fields in that they possess unique properties making them appealing for a variety of applications; additionally, they provide fundamental understanding of the structure, bonding, and reactivity of the extended metal systems at an atomic level. Pioneering studies on metal clusters were directed toward the rationalization of their (electronic) structure from symmetry considerations and computations. The need to relate these theoretical models to specific spectroscopic signatures of metal clusters in relation to their (electronic) structure has not yet been addressed. In this work, we studied a family of structurally related Ptn(0) species with different nuclearities (up to 15); we were able to show how 195Pt solid-state NMR can be used as a general tool to distinguish among metal sites in metal clusters and how their computed MO diagrams can be validated experimentally through the analysis of their NMR fingerprints. We applied this approach to known Pt phosphine/carbonyl 'Chini' clusters as well as to an unprecedented Pt5 square-pyramidal cluster. We propose that this approach can be extended to several other classes of metal clusters containing NMR-active centers, as well as (un)supported metal-based nanostructures.

BackgroundWith the increasing burden of chronic diseases, self-management education (SME) is crucial. Traditional SME based on face-to-face delivery by clinicians is resource-intensive, and general digital tools such as web-based platforms often provide limited interactivity for patient learning. Although chatbots based on large language models (LLMs) show promise in interactivity, their real-world effectiveness lacks empirical evidence.ObjectiveThis study aimed to explore the feasibility and preliminary effectiveness of an LLM-based chatbot specifically designed for osteoporosis SME.MethodsA formative randomized controlled trial was conducted in a tertiary hospital from February 2024 to March 2025. Adults aged ≥18 years with osteoporosis were recruited and randomly assigned (1:1) to either the intervention (OPBot) group or a control group receiving traditional health education. The chatbot provided interactive educational content and question-and-answer support, while the control group received face-to-face education and written materials. Osteoporosis knowledge was assessed using the Osteoporosis Knowledge Assessment Tool at baseline and discharge. Nurses’ time spent on health education was self-recorded during each intervention session and aggregated across sessions. Adherence to disease management was assessed at 1, 3, and 6 months after discharge via telephone using Likert-scale questionnaires. The reliability of OPBot responses was evaluated by 2 clinician assessors using a 5-point Likert scale, with interrater agreement calculated using Cohen κ. Group comparisons were conducted using 2-tailed independent t tests, Mann-Whitney U tests, and chi-square tests, and adherence outcomes were analyzed using mixed-effects models.ResultsA total of 100 participants were randomized; 12% (12/100) were excluded due to loss to follow-up, refusal of the second knowledge assessment, or death, leaving 88% (88/100) participants for analysis (n=45, 51.1% in the OPBot group and n=43, 48.9% in the control group). The OPBot group showed significantly higher postintervention knowledge scores than the control group (median 80.0, IQR 70.0-89.0 vs median 75.0, IQR 65.5-80.0; P=.01). Nurses in the OPBot group spent lesser time on SME than those in the control group (median 5.0, IQR 2.0-17.0 vs median 23.0, IQR 20.0-25.0 minutes; P<.001). For adherence outcomes, a significant group×time interaction was observed for calcium supplement intake (odds ratio 1.49, 95% CI 1.08-2.06; P=.02), indicating differing adherence trajectories over time. The OPBot group also showed higher odds of consuming calcium-rich foods across time points (odds ratio 2.87, 95% CI 1.04-7.89; nominal P=.04), although this association did not remain significant after Holm correction. No significant effects were observed for sun exposure (P=.56), exercise (P=.79), or total adherence scores (P=.33). In the question-and-answer module, most OPBot responses were rated as highly reliable 89.4% (76/85), with high interrater agreement (Cohen κ=0.83).ConclusionsLLM-based chatbots specifically designed for osteoporosis SME may improve patient knowledge, supporting adherence behaviors, and reducing healthcare workload. However, further large-scale studies are needed to confirm these findings.

Validation of an anorectal malformation trainer - Can a high-fidelity model simulate real life?

Patient-reported outcome measures in persons with epilepsy - Part I: detection of depression and anxiety.

In germinal centers, activated B cells modify their antigen receptors through somatic hypermutation (SHM), followed by antigenic selection that favors expansion of high affinity B cells. The affinity maturation process is critical for development of broadly neutralizing antibodies (bnAbs) against the human immunodeficiency virus-1 (HIV-1). BnAbs have been isolated from some people living with HIV-1. Because these antibodies target conserved epitopes of the HIV-1 Envelope (Env) protein, they inhibit a broad spectrum of viruses. Eliciting bnAbs by vaccination is a top priority for HIV-1 prevention, but reproducing the lengthy maturation of bnAbs is a major challenge. The problem is typified by VRC01 class antibodies, which recognize the CD4 binding site of HIV-1 Env protein. To reach the CD4 binding site, antibodies need to navigate through adjacent glycans. Accommodating the glycans requires multiple SHMs in germinal center (GC) B cells, including infrequent events. For this reason, VRC01 vaccine development often stalls at this point. We have generated a mouse model aimed at providing a potential solution for navigating this vaccine design impediment. To this end, we made a mouse model that expresses a stalled VRC01 intermediate conditionally in GC B cells. This system has three advantages: 1) direct expression of the intermediate obviates prior immunization steps, thereby shortening the immunization scheme; 2) the conditional expression system bypasses tolerance control checkpoints that sometimes delete B cells expressing bnAbs; 3) the intermediate responds to immunization in GCs, the physiological site of affinity maturation. With this model, we established an immunization method to mature the VRC01 intermediate into heterologous neutralizing antibodies against viruses with a native glycan shield. Since high mutation load is common among bnAbs, the germinal center conditional expression system could provide a general tool for boost immunogen design to overcome roadblocks in the maturation pathway.

Disturbances can occur as short‐lived pulses (e.g., storms) or sustained presses (e.g., chronic drought). Much work in ecology has developed methods to help predict how natural populations respond to disturbances, but analyses of pulse and press disturbances have been largely disconnected. We present a matrix framework that links press and pulse perturbations within the same analytical approach, showing how transient nonlinearities and demography shape fitness. We find that transient responses to pulse perturbations accumulate to determine the long‐term response to press disturbances. For structured population models, this cumulative change is given by a new transient response matrix (TRM). Strikingly, the TRM also yields the second derivatives of the population growth rate with respect to matrix elements. Thus, there is an intimate but unexpected relationship between nonlinear selection pressures on demographic rates, and the transient dynamics of populations. This relationship yields a strong correlation between TRM and generation time across 439 unique plant and animal species (2690 population models). We also show that the TRM is directly related to Cohen's cumulative distance measure for populations converging to stability. Our framework provides ecologists with a general tool to predict population responses to diverse environmental changes.

Health-related quality of life (HRQoL) is an important measure in healthcare that captures the multidimensional effects of health on individuals' lives. While HRQoL measures are widely used globally, there is limited consolidated evidence of their application in Africa. This review aimed to explore and map the available evidence on the utilization of HRQoL measures in Africa, with a focus on understanding their application across diverse populations, health conditions, and geographical regions while identifying gaps, methodological inconsistencies, and opportunities for future research. The scoping review was conducted following the Arksey and O'Malley framework. Electronic databases, including Medline, Embase, Emcare, CINAHL, and Cochrane Library, and other sources, were searched from the inception up until August 2025. Results were synthesized narratively with descriptive statistics, presented using tables and graphs. Out of 20,438 records identified, 731 studies met the inclusion criteria. The geographical distribution revealed significant disparities, with over half of the studies originating from Nigeria (21.8%), Ethiopia (14.8%), and South Africa (14.5%). Most studies (74.2%) employed descriptive designs and were conducted in hospital settings (70.3%). HRQoL instruments were used across a wide range of health conditions, with HIV/AIDS (20.5%), cancer (10.1%), and diabetes (7.1%) being the most frequently studied. Generic HRQoL tools such as WHOQOL-BREF (19.7%), SF-36 (16.4%), and EQ-5D (13.1%) were the most commonly utilized, accounting for nearly half of all studies. The analysis of information reporting practices for EQ-5D instruments revealed inconsistencies, with only 30.2% of studies reporting all three components (descriptive profile, index scores, and EQ-VAS). This review highlighted the increasing use of HRQoL measures in African health research over the past two decades while identifying significant gaps. Most studies were descriptive, concentrated in a few countries, and primarily conducted in healthcare settings, limiting broader applicability. The frequent use of generic HRQoL instruments underscores their importance and cultural adaptation. Additionally, inconsistencies in reporting practices and methodological limitations hinder the comparability and utility of findings. To advance HRQoL research and its application, it is essential to address these gaps through standardized guidelines, broader geographical representation, and improved methodological rigor.

Preeclampsia is a prevalent issue causing significant suffering and deaths in Uganda. The Ministry of Health has introduced a policy mandating the use of low-dose aspirin for its prevention. Being a new intervention, the acceptability of this drug among the women for whom it is prescribed is still unexplored in Southwestern Uganda. This study aimed to explore the lived experiences of women on low-dose aspirin during their most recent pregnancy, identify any challenges encountered and recommend appropriate solutions. We used a descriptive phenomenological design to explore the lived experiences of women. Data was collected using purposive sampling from women in the antenatal clinic and postnatal units of a tertiary hospital in Southwestern Uganda. A self-developed interview guide and an audio recorder were used to collect data. Data collection continued until saturation was reached at 16 participants. Participants provided narratives about their knowledge of preeclampsia, their perceived susceptibility, severity, benefits, barriers and their experiences with aspirin prescription (use, or non-use). Analysis was done using the Colaizzi's' method of analyzing qualitative data. Data was collected in two months between the 4th of October and the 20th of December 2025. The study included 16 participants (mean age, 31 years; SD, 6.3; range, 22-43). Most participants were married, residents of Mbarara city and had attained secondary education. The parity ranged from 1 to 8 (mean = 4, SD = 2). Half of the participants had experienced an abortion, and 5 (31.3%) had lost 75% or more of their previous pregnancies. Aspirin 75mg and 150 mg were initiated at a mean gestation age of 20 weeks (SD 1.8), and 81% (95% CI: 0.53-0.94) of those who used aspirin progressed to preeclampsia. This study generated five themes: perceived clinical benefit and self-efficacy, suboptimal provider guidance, delayed LDA initiation, resilient therapeutic optimism and side effects-based non-adherence. This study highlights perceived barriers and implementation challenges that may contribute to suboptimal uptake of aspirin prophylaxis and the high prevalence of preeclampsia in Southwestern Uganda. Mitigating these challenges requires consistent capacity building for maternal healthcare providers, robust community sensitization, and incorporating a screening tool within the antenatal card. In addition, improving medication adherence through active follow-up and strengthening health systems are essential to promote early antenatal care utilization. Future research can establish the population-specific risk factors for southwestern Uganda to adjust the general screening tool provided by the Ministry of Health so as to address preeclampsia at the regional level.

To evaluate whether a custom agentic artificial intelligence (AI) pipeline can overcome the limitations of general-purpose large language model tools, when compared with a generic commercial tool (Google NotebookLM [NBLM]), for generating podcast-style summaries of radiology research articles. Twenty-two PDF-format original research articles published in the April 2025 issue of Radiology were processed using our Programmable, Phoneme-Aware PDF-to-Podcast Pipeline (P5) and NBLM to generate 44 audio episodes. P5 utilizes a multi-agent workflow for script generation, quality assurance, pronunciation enhancement, and audio synthesis. Four radiologists from a pool of 25 (7 generalists and 18 specialists) were randomly assigned to evaluate each blinded audio episode, yielding 176 total evaluations. The primary outcomes were the number of hallucinations (factual errors) per episode and the percentage of hallucination-free episodes. Secondary outcomes included the number of inappropriate statements, mispronunciations, and flow disruptions; the composite quality score (Quality Assessment of Educational Podcasts [QAEP]); the key results coverage score; and overall listener preference. Data were analyzed using generalized linear mixed models. The P5 method produced significantly fewer hallucinations per episode compared with NBLM (mean, 0.32 vs. 0.93; P < 0.001) and a higher proportion of hallucination-free episodes (71.6% [63/88] vs. 56.8% [50/88]; P = 0.013), consistently across generalists and specialists. P5 demonstrated significantly fewer mispronunciations (mean, 0.11 vs. 1.62; P < 0.001) and flow disruptions (mean, 0.19 vs. 1.06; P < 0.001) per episode, as well as higher mean QAEP composite scores (4.62 vs. 4.28; P < 0.001), compared with NBLM, consistently across generalists and specialists. Raters preferred P5 over NBLM in 72.7% (64/88) of comparisons (P = 0.003). Our custom agentic AI pipeline generated podcast-style summaries of radiology research articles with significantly higher quality and greater listener preference than the generic commercial tool.

68Ga]Ga-NOTA-ATH001 positron emission tomography as a general tool for non-invasive detection of platelet derived growth factor receptor beta in mesenchymal cells.

Background: Maternal mental health impacts caregiving, attachment and feeding practices. There is limited literature available on the influence of maternal mental health on feeding choices, specifically in peri-urban townships such as Khayelitsha. Aim: This study aimed to investigate the relationship between the mental health status of mothers and their infants' feeding choices attending a primary healthcare centre in Khayelitsha, South Africa. Methods: This longitudinal prospective study consisted of two phases and included mothers with infants aged 0-8 weeks (Phase 1) and then followed up at 14-24 weeks (Phase 2). Maternal mental health was screened using the Edinburgh Postnatal Depression Scale and the General Anxiety Disorder-7 screening tools, which are validated for use in South Africa. Infant anthropometric measurements (weight and height) were recorded and interpreted using z-scores. Results: A sample of 121 mother-infant dyads was recruited for this study. Exclusive breastfeeding rates, while initially high during Phase 1 (n=91, 75.2%), declined by Phase 2 (n=63, 58.3%). Of the participants recruited, 18.2% (n=22) opted to exclusively formula feed their infants at Phase 1; this number increased to 31.5% (n=34) by Phase 2. Among those who opted for mixed feeding at Phase 2 (n=11), 81.8% (n=9) provided both breast milk and infant formula. Probable maternal anxiety rates were 28.9% (Phase 1: n=35) and 14.9% (Phase 2: n=16). Maternal probable anxiety did not influence feeding practices (p=0.953). Probable depression rates were very low in this cohort (Phase 1: 0%; Phase 2: 1.9%). Infant growth outcomes highlighted stunting (Phase 1: n=36, 39.2%; Phase 2: n=21, 19.4%) and being overweight or obese accounted for 25.6% (n=31) at Phase 1 and 13.89% (n=15) at Phase 2. Conclusion: In this cohort of mothers, symptoms of anxiety were common, while probable depression was infrequent. No significant association was identified between maternal mental health status and infant feeding choices across the study period. This study reinforces the importance of integrating maternal mental health support in routine primary healthcare services.

Developing a reliable EEG-based Brain Computer Interface (BCI) system typically requires large and diverse training datasets, but collecting sufficient data remains challenging due to subject fatigue and interindividual variability. To address these limitations, this study proposes a reinforcement learning-enhanced EEG diffusion (RLED) framework for adaptive data augmentation in endogenous EEG tasks, with a focus on motor imagery and emotion recognition. The framework integrates a reinforcement learning mechanism to dynamically regulate the diffusion training process and achieve a flexible balance among temporal, spectral, and class-related features. Experiments on four datasets demonstrate that the proposed method generates high-quality synthetic EEG signals and consistently improves classification performance. These findings show that the proposed RLED framework may serve as a promising tool for EEG data augmentation and generalization in practical BCI applications.