This study analyzes the communication strategies of PR agencies that act as consultants in supporting the needs of clients in the technology industry. This research uses a descriptive qualitative approach through in-depth interviews with KlikPR managers and direct observation. The findings show that KlikPR's communication strategy follows three communication objectives, namely to secure understanding, to establish acceptance, and to motivate action. KlikPR prioritizes open communication, regular coordination, and participatory work practices to strengthen relationships with clients. This strategy is based on the theory of organizational communication flow through the aspect of downward communication, which serves to convey strategic directions and context, while upward communication facilitates the exchange of ideas, reporting, and evaluation. With an integrated and transparent communication approach, KlikPR is able to maintain client trust and build long-term partnerships.

In the present work, we developed a data-driven algorithm for the automated segmentation of fluorescence lifetime imaging microscopy (FLIM) images, enhancing the analysis of multifunctionalized nanoparticles (NPs) within living cancer cells. FLIM, a powerful microscopy technique, generates images that capture the fluorescence lifetime across a sample at the pixel level, revealing critical details about the molecular environment. Traditionally, FLIM image analysis has relied on manual segmentation with the phasor plot approach, a graphical representation of FLIM data in aGandScoordinate system, which is susceptible to user bias and inconsistency. Here, we present an automated and free of user-biased thresholding and segmentation algorithm that streamlines with clustering techniques to automatically identify phasor-clusters in the phasor plot space, reducing user dependency and providing a reproducible strategy under tested conditions image segmentation. We demonstrate its application in the context of FLIM images displaying a map of intensity heterogeneity where functionalized NPs affect the cellular metabolism of HeLa cells, reported by NADH, a bright and a dim fluorescent source, respectively, both of biological relevance. This algorithm provides a transparent and reproducible approach for FLIM image analysis, showing good agreement with expert-defined segmentation under sufficient contrast conditions, while presenting limitations in low-contrast or noisy regimes.

Integrated life cycle assessment for quantifying the carbon impact of rare earth elements mining.

Age equality was enshrined in UK law by the Equality Act of 2010, which defined age as a protected characteristic and age discrimination unlawful, unless objectively justified. To date relatively little attention has been paid to how this legal requirement is interpreted and applied in healthcare. Such a lack of clarity raises significant legal and ethical questions regarding how age affects access to and provision of care. This scoping review aims to address this gap. It examines how age and age groups are considered in healthcare policies and decision-making processes and how age equality duties are understood and implemented in practice. The scoping review draws on national and regional policy and decision-making documents, and relevant academic literature. It has two main objectives: first, to examine the explicit and implicit ways age influences healthcare provision. Second, to assess how different age-groups are represented or omitted in healthcare decision-making processes that determine access to care. Attention is given to age-based distinctions and omissions, their stated justifications or their absence in policy, and healthcare decision-making. Our findings indicate that despite legal protections, age equality is not yet consistently embedded in healthcare policy or practice with significant unresolved ethical tensions in approaches to age-equality. We identified gaps between stated policy commitments and implementation, limited attention to age-related inequalities and little clarity on when such inequalities may constitute inequities or raise concerns about unlawful discrimination. These include a lack of evidence for systematic monitoring of age-related inequalities and potential inequities, and lack of practical guidance to support healthcare decision-makers in meeting their equality duties. It remains unclear whether and how key ethical tensions in the pursuit of age equality are resolved by decision-makers. To support a more consistent and transparent approach to age equality in healthcare, efforts should focus on strengthened decision-making processes and clearer operational guidance. The routine use of age-sensitive equality impact assessments across services could enable a more systematic identification of age-related health inequalities and inequities that may be overlooked across the lifecourse.

Intellectual disability (ID) affects approximately 45% of children with cerebral palsy (CP), yet early identification is frequently hindered by severe motor and communication impairments. This study aimed to develop and validate an interpretable machine learning (ML) framework for predicting ID risk in children with CP. In this retrospective, registry-based study, data from 807 children with CP were analysed. To ensure temporal validity, all predictors were restricted to clinical and neuroimaging assessments confirmed by 2 years of age. Eight ML algorithms were trained and compared on an independent test set, and SHapley Additive exPlanations (SHAP) were applied to interpret model output at both the global and the individual levels. The optimized models achieved robust discriminative performance, with the highest area under the receiver operating characteristic curve (AUC) reaching 0.813 on the independent test set. SHAP analysis revealed a highly skewed distribution of predictive features: The inability to achieve independent sitting by age 2 was the most critical risk factor, followed by early-onset epilepsy, spastic quadriplegia and severe Gross Motor Function Classification System (GMFCS) levels. Baseline perinatal factors demonstrated lower direct predictive utility, and local SHAP analyses successfully mapped individualized risk trajectories. This transparent ML approach functions as a reliable decision-support tool, translating complex algorithmic output into clinically intuitive insights. It may empower clinicians to move from 'wait-and-see' approaches towards timely, personalized neurodevelopmental interventions for high-risk children.

Integrating palliative care into primary health care requires clear, feasible, and valid indicators. Consensus-based methods such as Delphi are commonly used for this purpose, but they may overlook differences in how heterogeneous expert panels prioritize indicators. This study aimed to strengthen the methodological approach used to select indicators for assessing the integration of palliative care into primary health care. This study aimed to strengthen the methodological approach used to select indicators for assessing the integration of palliative care into primary health care. A two-round modified Delphi was conducted with 23 experts from different regions of Colombia. Each indicator was rated for relevance, feasibility, and validity. A priori consensus was defined as ≥70% of ratings ≥7/10 and IQR ≤2. Global content validity indices (S-CVI) and Lawshe's content validity ratio (CVR) were calculated as supportive evidence. Expert profile was quantified using the Metric for Assessing Knowledge and Understanding (MAKU) and used to stratify analyses, To explore data patterns, separate 2×2 SOMs were trained for Strategic and Operational profiles using normalized medians and robust coefficients of variation. Neurons with higher median scores and lower dispersion were interpreted as favorable. Global S-CVI increased from 0.86 in Round 1 to 0.88 in Round 2. By dimension, relevance rose from 0.82 to 0.85, feasibility remained stable at 0.72, and validity increased from 0.62 to 0.71. Stratification by expert profile revealed meaningful differences in consensus patterns. SOM analysis showed both shared and differing prioritization patterns across profiles. After removing overlaps, a final set of eighteen indicators was retained from the dominant neurons of the profile-specific SOMs. Combining a modified Delphi with MAKU-based stratification and self-organizing maps provided a transparent and profile-sensitive approach to strengthen consensus-based indicator selection in a heterogeneous expert panel. Differences in expert profile influenced how indicators were prioritized. This approach supports more rigorous and context-aware selection of indicators for monitoring palliative care integration in primary health care.

Genotoxicity assessment is crucial for drug development and chemical safety evaluation. However, traditional experimental approaches are time-consuming, resource-intensive, and raise ethical concerns related to animal testing. Although computational models offer efficient alternatives, most existing approaches treat all data points equally, disregarding the heterogeneous quality of public database records and rarely addressing predictive uncertainty. We present a reliability-aware framework for genotoxicity prediction using a curated data set of 8,389 compounds annotated with experimental reliability tiers reflecting protocol quality, reproducibility, and review status. A two-step hierarchical learning strategy is employed. First, a message-passing neural network trained on high- and medium-reliability data is used to evaluate low-reliability samples and assign adaptive weights. These weights are then incorporated into conventional machine learning models, including random forest, support vector machine (SVM), and logistic regression, using molecular fingerprints. To address predictive uncertainty, we integrate conformal prediction, which provides distribution-free, finite-sample coverage guarantees for individual predictions. Random forest and RBF-kernel SVM achieved AUC values of 0.8613 and 0.8582, respectively, with Brier scores of 0.1523 and 0.1530. Conformal prediction attained 90.7% empirical coverage at α = 0.1 and identified 35.8% of test compounds as ambiguous. By incorporating data reliability and uncertainty quantification, the proposed framework provides a more transparent and uncertainty-aware approach.

ABSTRACT This article studies the relationship between creator-related cues and market outcomes – price, time to sale, and non-sale – of non-fungible tokens (NFTs) in a leading marketplace. We first extract textual and visual indicators and summarize them into cognitive and affective composites using principal component analysis. We then estimate hedonic regression and duration models with creator-level random effects and recover creator-related components using empirical Bayes shrinkage. These components provide a descriptive decomposition of market outcomes into variation linked to observable asset cues and residual variation systematically associated with creators. We find substantial heterogeneity in creator-related components for both price and liquidity, while simple social-media metrics account for only a small share of that heterogeneity. We also model non-sale probability and show that creators’ social media activity is modestly associated with sale failure. Methodologically, the paper offers a transparent approach to mapping creator-related heterogeneity when creator metadata and standard brand-equity measures are limited.

Robust nuclear power plant site selection using SWARA, MABAC, and SMART: A GIS-Based study on the Muria Peninsula

Organoids are three-dimensional (3D) in vitro models for studying tissue development, disease progression, and physiological responses. Holotomography (HT) enables long-term, label-free imaging of live organoids by reconstructing volumetric refractive-index (RI) maps, but quantitative analysis is limited by the missing-cone artifact, which introduces anisotropic resolution and axial distortion. Here, we present a quantitative analysis framework that addresses the missing-cone problem at the level of image representation rather than reconstruction. We introduce morphology-preserving holotomography (MP-HT), a torus-shaped spatial filtering strategy that emphasizes high-spatial-frequency RI texture while suppressing low-frequency components most susceptible to missing-cone-induced distortion. Based on MP-HT, we develop a 3D segmentation pipeline for robust separation of epithelial and luminal structures, together with a model-based RI quantification approach that incorporates the system point spread function to enable morphology-independent estimation of dry-mass density and total dry mass. We apply the framework to long-term imaging of live hepatic organoids undergoing expansion, collapse, and fusion. In representative organoids, the framework provides consistent segmentation across diverse geometries and enables quantitative characterization of epithelial-lumen remodeling, collapse-associated loss of morphometric stability, and transient biophysical fluctuations during fusion. Overall, this work establishes a physically transparent and reproducible approach for quantitative, label-free analysis of organoid dynamics in 3D.

Extractive activities drive land transformation in many mineralised river basins. However, linking these changes to observable and attributable water-quality outcomes remains methodologically challenging. This study applies an integrated monitoring framework to examine how multi-decadal Land-use and land-cover change (LULC) translates into spatially differentiated river water quality in the Ankobra River basin, Ghana. Using harmonised Landsat and Sentinel imagery, LULC dynamics was reconstructed for 1986, 2002, 2016, and 2025. Field-based measurements of key physico-chemical water-quality parameters were collected to support the analysis. Spatial interpolation using Ordinary Kriging and redundancy analysis was then applied to assess the extent to which land-use composition explains the observed variation in water quality. The results showed a shift from forest-dominated land cover towards agriculture, settlement, and mining-related disturbance during the study period. Bareland/Mining expanded from less than 1% of the basin in 1986 to approximately 3.7% by 2025 (>100 km2), while combined forest cover declined overall throughout the study period. Water-quality patterns exhibited strong spatial gradients, with turbidity ranging from approximately 114 to more than 1000 NTU and total suspended solids (TSS) from around 100 to nearly 3000 mg L-1. Redundancy analysis indicated that land-use composition explained approximately 47.5% of the variance in water quality, with the mining-related land cover exerting the strongest influence (F=13.66, p<0.001) and showing robust positive associations with turbidity and TSS. Closed forest cover displayed a significant buffering effect, while agricultural land use did not show significant association on the spatial scale examined. These findings demonstrate how integrated Earth observation and field data can move sustainability assessment beyond descriptive convergence towards diagnostic clarity. The analytical framework offers a transparent and scalable approach for prioritising regulatory attention and monitoring in extractive landscapes where environmental pressures are spatially uneven and governance capacity is constrained.

Accurate survival prediction in breast cancer is essential for patient risk stratification and personalized treatment planning. Although transcriptomic data offer valuable insights into tumor biology, existing predictive models often suffer from poor interpretability and limited integration of biological knowledge. Standard gene-level feature selection methods, such as variance filtering and regularized regression, are agnostic to functional relationships between genes and may overlook biologically meaningful patterns. In this study, we propose a pathway-guided Cox modeling framework that integrates curated biological knowledge to enhance the interpretability and performance of survival prediction. Using TCGA-BRCA transcriptomic and clinical data, we develop and compare three models: a baseline Cox model using high-variance genes, per-pathway Cox models built from curated KEGG and Reactome gene sets, and a composite model that aggregates top-performing pathways. Our results demonstrate that pathway-informed models consistently outperform the baseline in terms of cross-validated concordance index and survival stratification. Furthermore, the composite model highlights biologically plausible gene signatures and pathways associated with breast cancer prognosis, offering interpretable and clinically relevant insights. This work provides a modular, reproducible, and interpretable framework for survival modeling in high-dimensional genomics. By combining classical statistical models with biological structure, it offers a transparent approach for biomarker discovery and precision oncology.

Quantum Neural Networks (QNNs) represent a promising fusion of quantum computing and neural network architectures, offering speed-ups and efficient processing of high-dimensional, entangled data. A crucial component of QNNs is the encoder, which maps classical input data into quantum states. However, choosing suitable encoders remains a significant challenge, largely due to the lack of systematic guidance and the trial-and-error nature of current approaches. This process is further impeded by two key challenges: (1) the difficulty in evaluating encoded quantum states prior to training, and (2) the lack of intuitive methods for analyzing an encoder's ability to effectively distinguish data features. To address these issues, we introduce a novel visualization tool, XQAI-Eyes, which enables QNN developers to compare classical data features with their corresponding encoded quantum states and to examine the mixed quantum states across different classes. By bridging classical and quantum perspectives, XQAI-Eyes facilitates a deeper understanding of how encoders influence QNN performance. Evaluations across diverse datasets and encoder designs demonstrate XQAI-Eyes's potential to support the exploration of the relationship between encoder design and QNN effectiveness, offering a holistic and transparent approach to optimizing quantum encoders. Moreover, domain experts used XQAI-Eyes to derive two key practices for quantum encoder selection, grounded in the principles of pattern preservation and feature mapping.

Graduating nursing students frequently encounter a disconnect between academic preparation and the realities of clinical practice, often feeling underprepared, overwhelmed, and emotionally vulnerable. Contributing factors include inconsistent curricula, variable teaching quality, and limited access to high-fidelity simulation and mentorship. This fragmented preparation framework undermines resilience and readiness for today's complex healthcare environments. The aim of this scoping review is to identify and map the existing gaps in nurse education that impact the preparedness of student nurses entering professional practice. A scoping review conducted in December 2024 and December 2025 mapped the existing literature on nursing education to identify research gaps. The review comprised a comprehensive search of CINAHL, Informit, ProQuest, PsycINFO, PubMed, Scopus, and Web of Science, followed by analysis of the 81 relevant studies. The PRISMA-ScR guidelines ensured a systematic and transparent approach to the selection and inclusion of studies. The review identified overarching domains (curriculum, teaching, simulation, clinical education and student readiness) where specific barriers were evident, including the theory-practice gap, inconsistent supervision and feedback, limited evaluation transparency, and transition shock. Evidence suggests that integrating practice-proximal education (simulation and longitudinal placements) with structured supports (Dedicated Education Units, mentorship/residency programs) and robust feedback mechanisms provides a coherent pathway to strengthen graduate readiness and early-career outcomes. Nursing students face persistent challenges in achieving clinical readiness due to fragmented curricula, inconsistent pedagogy, and limited academic-clinical integration. Addressing these issues requires a future-focused, evidence-informed education model, such as the Professional Readiness and Education for Practice Framework, which embeds authentic clinical experiences, structured mentorship, cultural safety, and digital health competencies to prepare graduates for the realities of contemporary healthcare practice.

Established under the European Union (EU) Health Technology Assessment (HTA) Regulation, the EU Joint Clinical Assessment (JCA) aims to streamline evaluation of health technologies across member states. As a collaborative assessment, it must fulfill the data and analysis needs of all countries, leading to numerous statistical comparisons and raising the question of how to navigate this information effectively and transparently so results are credible and actionable. The EU HTA Coordination Group has released reporting requirements for multiplicity adjustment, describing technical statistical approaches to address the issue. However, we caution against viewing multiplicity as purely a technical discussion, as—unlike in the regulatory context—the HTA process involves addressing multiple distinct questions to support decision-making. In the multistakeholder JCA environment, it is crucial to enhance clarity and foster dialogue around member state priorities. This will support a more effective and transparent approach to multiplicity and build trust in the JCA process. Statistical analysis plans specifically designed for the JCA can be valuable tools: they help control the degrees of freedom available to the health technology developer (HTD) and health technology assessment bodies (HTABs), guide interpretation for stakeholders, and help mitigate challenges associated with multiplicity.

Abstract Gross primary productivity (GPP) and evapotranspiration (ET) represent two fundamental processes in coupled water and carbon cycles. The strong regulation of ecosystem carbon and water fluxes by stomata is well understood at the leaf level. However, the coupling is complex at regional or ecosystem scales. The objective of this study is to understand key environmental factors that control both water and carbon fluxes at regional scales and develop a robust resource‐constrained framework (RCF) for estimating climatology of ecosystem carbon and water fluxes consistently. Water balance data from 1927 catchments were obtained to parameterize the model and independent observations from 107 flux stations were used to validate the method. Results demonstrated robust model performance with Nash–Sutcliffe efficiency (NSE) of 0.65 for GPP and NSE of 0.55 for ET against independent flux observations. The RCF approach estimated global mean GPP and ET at 1,141 g C m −2 a −1 and 530 mm a −1 , respectively, corresponding to an annual terrestrial carbon uptake of 142.4 Pg C a −1 . Further analysis identified the ecosystem responsive regimes, with about 40% land areas energy‐responsive, 40% water‐responsive, and 20% co‐responsive for both GPP and ET across the globe. This study reveals consistent estimates of GPP and ET by disentangling the spatial interplay of energy and water constraints. The RCF approach provides a transparent and scalable approach to jointly estimate and attribute carbon and water fluxes, offering new insights into ecosystem functioning and a pathway to improve ecosystem modeling.

Setting regional disease-specific health research priorities is essential for guiding the development of the local health system and even the society through research. However, existing priority-setting approaches either rely heavily on opinions and lack transparent and reproducible procedures or demand intensive technical resources and are time-consuming. This study aimed to develop a structured, disease-specific, regionally adaptable methodological framework to address these gaps. A multidisciplinary working group with expertise in public health and clinical research methodology was established to develop the framework, with consultation from an expert panel in clinical medicine and health research administration. Through iterative discussions, the group defined five guiding principles: gap-oriented, evidence-based, disease-life-cycle-covered, aligned with clinical practice realities and reproducible and transparent. Based on these principles, the analytical steps of the framework were proposed, refined through expert review and pilot tested using gastric cancer and other diseases. A framework consisting of seven sequential steps and a generic health matrix template was developed and named the Regional Disease-Specific Health Research Priorities (RDHRP) Framework: (1) defining the research scope; (2) developing a disease-specific matrix for data search; (3) retrieving and processing indicator-based evidence; (4) synthesizing evidence and linking performance gaps to generate actionable research priorities; (5) drafting the preliminary report; (6) consulting experts and stakeholders; and (7) reporting, dissemination and monitoring. Applied to gastric cancer, the RDHRP Framework consistently identified system-level performance gaps between China and countries advanced in this area, explored underlying causes, reduced subjective topic selection and improved the alignment of proposed research questions with measurable health system needs. The case study produced a focused set of research priorities for China in gastric cancer research: community-based surveillance systems; scalable techniques for early identification, screening and diagnosis; Helicobacter pylori prevention and eradication strategies; and molecular classification, target discovery and innovative therapeutics development. The RDHRP Framework provides a transparent, reproducible and adaptable approach for generating regional disease-specific health research priorities anchored in objective evidence and causal reasoning. Future work is warranted to evaluate its utility in diverse global settings, recognizing that resulting priorities may vary depending on disease, regional context and evolving evidence.

BackgroundCrown preparation is a core but challenging skill for dental students, often assessed through subjective methods with limited reliability. Analytical rubrics provide a structured and transparent approach to teaching and evaluation. This study assessed the effectiveness of a rubric-based assessment system in preclinical crown preparation training.MethodsA randomized controlled trial was conducted with 90 fourth-year undergraduate dental students who met predefined eligibility criteria (mean age: 22 ± 1.2 years). Participants were randomly allocated in a 1:1 ratio using a computer-generated sequence to a control group (n = 45) evaluated conventionally and a test group (n = 45) evaluated using a structured rubric. Both groups prepared an all-ceramic crown on tooth #21 following standard instruction. The primary outcome was the total crown preparation score. Assessments were performed independently by calibrated examiners at baseline and post-training; student blinding was not feasible. No adverse events were reported. Independent and paired t-tests with Bonferroni correction were applied, effect sizes calculated, and post hoc power analysis performed using a 95% confidence interval. Student perceptions were collected using a six-item Likert-scale questionnaire.ResultsBaseline comparisons had no significant differences (p = 1.0). At the end of training, the primary outcome show, test group demonstrated significantly higher scores across all parameters (all p < 0.001). Mean total score was 30.53 ± 4.99 in the test group versus 10.56 ± 4.34 in the control group (p < 0.001; Cohen’s d = 4.21). Both groups improved significantly, with greater gains in the test group (22.91 ± 6.42 vs 2.93 ± 2.72). Post hoc power exceeded 0.99 for all outcomes. Student feedback was highly positive, with 95% affirming improved objectivity, self-reflection, and clarity of evaluation criteria.ConclusionsThe use of an analytical rubric was associated with higher crown preparation assessment scores and positive student perceptions regarding clarity and objectivity of evaluation. These findings suggest that rubric-based assessment may support structured performance evaluation and learner understanding in preclinical prosthodontic training.Trial registrationNot registered. This study evaluated an educational assessment intervention in a preclinical academic setting and did not involve patients or health-related clinical interventions as defined under ICMJE criteria.

Development of decision support framework to prioritize GHG emission Scope in sub-national inventories

ABSTRACTThe ProblemTransportability considerations are increasingly important to answer research questions in comparative effectiveness research (CER) to support the transfer of evidence on medicinal products across countries or settings. As drug development costs rise and healthcare systems and professionals face economic and resource pressures, leveraging existing data and evidence generated across borders or settings can improve efficiency and inform decisions in product development and decision‐making (regulatory, health technology assessment [HTA] and clinical care). Differences in population characteristics, healthcare systems, and data availability, including coding discrepancies, may present significant challenges to the transportability of CER evidence. Without rigorous methodological approaches, the utility of transportability analyses is limited.What we DidThis article provides a structured framework for considering transportability exercises in CER analyses. We outlined key methodological principles, when and why to use transportability exercises in CER, feasibility assessments including effect modifier identification and causal inference techniques such as weighting, outcome regression, and combined methods to guide transportability analytical approaches in CER. By synthesizing existing literature and expert insights, we identified opportunities and trade‐offs in applying transportability methods to support decisions across the product lifecycle.Strategies to Disseminate and Facilitate UseTo enhance the adoption of transportability analyses, we proposed best practices for researchers, regulators, HTA bodies, and industry stakeholders. These include early engagement with regulatory agencies and HTA bodies, transparent documentation of data assumptions, quality, fitness, and comparability assessments while ensuring robust analytical approaches. We also emphasized the need for standardized reporting guidelines and cross‐country collaborations to validate transportability methods in real‐world settings and communicate uncertainty in transported evidence.ConclusionsTransportability analyses offer a powerful tool for extending the applicability of CER findings across healthcare systems, improving evidence generation efficiency, and supporting global drug development and evaluation. By implementing best practices that promote a rigorous and transparent approach to the design and conduct of such analyses, stakeholders can maximize the value of transported treatment effects while ensuring scientific rigor and decision‐making relevance. Future research should focus on empirical testing and validation of transportability methods targeting different questions across the product lifecycle and the development of harmonized regulatory and HTA methodological standards. “This manuscript is endorsed by the International Society for Pharmacoepidemiology (ISPE).” Official Endorsement was received on 5/13/26.