Search Results (1,944)

Background/Objectives: Automated insulin delivery (AID) systems have improved the management of type 1 diabetes (T1D), but exercise and nutrition remain challenging because they rapidly alter glucose flux, substrate oxidation, hepatic glucose output, insulin requirements, and fuel availability. This narrative review aimed to synthesize current evidence on the interaction between AID systems, physical activity, and nutritional strategies from a metabolism-oriented perspective. Methods: A narrative bibliographic approach was used to integrate evidence from clinical trials, observational studies, technical studies, consensus statements, and reviews involving people with T1D across different life stages, including pediatric, adolescent, adult, and pregnancy-related contexts, when available. The review focused on AID systems, exercise physiology, nutritional strategies, meal announcement, bolus adjustment, dual-hormone systems, metabolic biomarkers, and emerging metabolomic approaches. Results: AID systems generally improve time in range and reduce hypoglycemia across several user groups, although most exercise- and nutrition-specific evidence comes from adult and pediatric/adolescent cohorts rather than pregnancy-specific exercise studies. Exercise-related glucose responses remain highly dependent on user input, exercise modality, insulin on board, meal timing, and metabolic state. Planned exercise announcement, prandial bolus reduction before postprandial activity, and individualized carbohydrate intake remain key strategies. Biomarkers such as lactate, ketone bodies, non-esterified fatty acids, and counter-regulatory hormones may help explain interindividual variability and support future personalization. Conclusions: Nutrition and exercise management in AID users should be interpreted as a dynamic metabolic interface among exogenous insulin, endogenous counter-regulation, substrate availability, and algorithmic control. Emerging approaches, including activity sensors, adaptive algorithms, dual-hormone systems, digital twins, and metabolomics-informed personalization, may improve safety and reduce user burden, but several remain exploratory and require further validation in diverse free-living conditions. Full article

Multi-criteria group decision making (MCGDM) under linguistic uncertainty remains a fundamental challenge in applied mathematics, where decision makers seldom assign crisp numerical evaluations and frequently exhibit heterogeneous risk attitudes shaped by behavioural factors. An integrated mathematical framework, hereafter PLR-3WBC (Probabilistic Linguistic Regret-driven Three-Way Bayesian Consensus), is developed to systematically integrate four methodological components that have each been individually validated in the MCGDM literature: representation of decision information with explicit probability mass on linguistic terms; quantification of decision-maker regret and rejoice psychology under linguistic uncertainty; classification of alternatives into three actionable decision regions rather than a single-valued ranking; and group consensus reaching with credal weight aggregation. Each component has demonstrated its effectiveness in its respective domain; the present framework capitalises on their complementary strengths by embedding them within a single pipeline equipped with formal guarantees, an integration that has not been previously reported. The framework integrates five methodological components: probabilistic linguistic term sets (PLTS) for information representation; the Bayesian best–worst method (BBWM) for credal criterion weighting; a regret–rejoice value function adapted to the linguistic domain for behavioural evaluation; three-way decision (3WD) thresholds derived from a loss-function model for actionable classification; and a distance-based consensus reaching process with feedback mechanism for group convergence. A case study on age-friendliness evaluation of twelve aging urban residential communities under an indicator system of five dimensions and eighteen criteria, with four expert decision makers, demonstrates that PLR-3WBC delivers an actionable three-way classification, recovers a transparent group consensus, and produces rankings broadly consistent with classical TOPSIS, VIKOR, PROMETHEE-II, and BWM-TOPSIS (Spearman rank correlation exceeding 0.97), thereby confirming that the integrated framework preserves the ordinal reliability of these established methods, while additionally delivering three outputs that arise from the methodological integration: an actionable three-way classification enabling discrete budget-aligned decisions, credal weight intervals quantifying the depth of expert agreement on criterion importance, and a behavioural reordering of borderline non-dominated alternatives that reflects the loss-averse psychology of the decision panel and would remain hidden under single-method deployment. Sensitivity analyses with respect to the regret aversion coefficient, the loss function parameters, and the consensus threshold confirm that the qualitative classification is stable across a wide parameter envelope, supporting the practical deployment of PLR-3WBC in age-friendly community renewal programmes. Full article

Background: Immune mechanisms are increasingly implicated in the heterogeneity of schizophrenia spectrum disorders. Cytomegalovirus (CMV), a latent immunomodulatory herpesvirus, is linked to cognitive and immunological alterations, but its electrophysiological correlates remain largely unexplored. This study investigates the relationships among CMV serostatus, EEG features, inflammatory markers, and clinical–cognitive variables. Methods: In this prospective cross-sectional study, 123 patients with schizophrenia spectrum disorders underwent integrated clinical, cognitive, laboratory, and qualitative visual EEG assessments. CMV exposure was determined via IgG serology. Results: Global electroencephalographic EEG organization did not differ by CMV serostatus. However, a descriptive increase in resting-state sharp-wave discharges was observed in CMV-seronegative patients, independent of baseline cortical rhythms. Immunologically, CMV-seropositive individuals exhibited significantly higher total leukocyte counts, consistent with latent viral immune remodeling rather than overt systemic inflammation. Clinically, CMV-seropositive patients demonstrated descriptively higher scores on the disorganization dimension derived from the PANSS (Positive and Negative Syndrome Scale) five-factor consensus model. While these variations did not retain statistical significance after multiple testing correction, separate dimensional analyses revealed that patients exhibiting sharp waves demonstrated better overall cognitive functioning and superior performance within a memory-related item grouping. Notably, the presence of sharp-wave activity was independent of both peripheral inflammatory profiles and treatment-resistant status, underscoring a distinct electrophysiological phenotype. Conclusions: CMV exposure represents a modulating biological background associated with corrected leukocyte elevations and subtle electrophysiological variability, rather than a direct determinant of global clinical severity. The nominal EEG variations and their independent link to better-preserved memory performance highlight non-linear neuroimmune interactions. Given the cross-sectional design, these exploratory patterns warrant a non-causal interpretation but outline a foundation for future longitudinal investigations. Full article

Background/Objectives: The trainer–doctor model (TDM) is a participatory paradigm in which the physician acts as a mentor and educator. Effective health communication and patient engagement are key determinants of treatment adherence and health outcomes. Based on this conceptual framework, the present study aimed to assess preferences for the Trainer–Doctor Model among dental practitioners and patients, examine the influence of demographic variables, and provide a preliminary psychometric evaluation of the TDM questionnaire in accordance with the COSMIN (COnsensus-based Standards for the selection of health Measurement Instruments) criteria. Methods: A cross-sectional survey was conducted in Romania between May 2023 and April 2024. The study included dental practitioners recruited during scientific dental conferences and patients recruited from a private dental practice in Alba Iulia, Romania. Eligible participants were adults aged 18 years or older who provided written informed consent and completed the data protection requirements. Individuals younger than 18 years of age or those who did not provide complete informed consent were excluded. Participants completed a 12-item Likert-type questionnaire assessing preferences toward the Trainer–Doctor Model. Results: Both groups showed high TDM preference (practitioners: 43.93 ± 5.56; patients: 44.77 ± 4.84; p = 0.195); 71–76% of responses were high-preference (≥4). Cronbach’s α with reverse-scored items was 0.752/0.651. EFA (KMO = 0.740; Bartlett’s p < 0.001) identified a 3-factor structure, explaining 51.3% of the variance. Patients scored significantly higher on items A (p = 0.002), B (p = 0.022), and F (p = 0.005). Conclusions: Both groups demonstrate a strong, consistent preference for TDM across demographics. The preliminary psychometric evaluation indicates acceptable internal consistency and structural validity; however, further validation, including Delphi-based content validation and confirmatory factor analysis, is required. Full article

Feature selection represents a critical step in developing accurate and interpretable models for early breast cancer detection. Despite extensive research in the field of mammographic image analysis, no consensus has yet been reached on the optimal feature subsets that distinguish normal from malignant tissues. To address this gap, the present study aims to identify the most discriminative and significant features through a comprehensive multi-criterion selection framework. The aim is to integrate, as new frameworks, different combinations of t-test, ANOVA, Mutual Information (MI), and Equal Grouping Methods (EGM) to rank 19 linear and nonlinear features extracted from mammographic images. The objective is to maximize feature relevance while minimizing redundancy and enhancing diagnostic and healthcare systems. Linear features were assessed alongside nonlinear descriptors. A framework combining t-test, ANOVA, and EGM, guided by MI relevance, was employed to balance feature contributions across categories. The experimental results demonstrated that hybrid feature selection significantly enhanced diagnostic accuracy using optimal linear and nonlinear attributes. The optimization results suggested using a hybrid of six linear and eight nonlinear features. Linear features were highly accurate for detecting cancer. Haralick entropy obtained the highest average accuracy and performance, 94.14% and 93.45%; followed by kurtosis, 93.49% and 92.59%; perimeter irregularity, 93.43% and 92.65%; skewness, 93.01% and 92.25%; and volume/area, 92.82% and 91.92%. Despite the reliable discriminative power of linear descriptors, their overall effectiveness in representing intricate tissue characteristics was limited. The comparison of statistical characteristics shows a distinct performance benefit of nonlinear descriptors over linear ones for detecting breast cancer. Nonlinear descriptors, however, showcased higher accuracy and performance, with an average accuracy of 97.81% in contrast to 94.43% for linear approaches. Local phase congruency achieved the top average accuracy and performance, 97.81% and 96.61%, respectively; succeeded by wavelet entropy, 97.62% and 96.42%; Laplacian spectrum features, 97.52% and 96.32%; nonlinear diffusion, 97.10% and 95.90%; and clustering coefficient, 96.70% and 95.50%; then Shannon, Tsallis, and Rényi entropies. The results indicate that statistically validated nonlinear characteristics significantly outperform linear ones across accuracy and performance measures. Their combination significantly improves the strength and discriminative power of computer-assisted breast cancer diagnostic systems, affirming their suitability for integration into sophisticated machine learning and deep learning models. The results also show that the new multi-criterion framework’s early detection performance surpassed that of the statistical and deep learning models explored, with an average of 98.6% accuracy, 98% sensitivity, 98.9% precision, and 98.4% F1 score of early detection of breast cancer. The incorporation of statistically validated nonlinear descriptors, particularly local phase congruency and wavelet entropy, improves the discriminative ability, robustness, and clinical understanding of breast cancer computer-assisted diagnostic systems. Overall, the proposed framework confirms that integrating hybrid features substantially enhances robustness and plays a pivotal role in computer-assisted breast cancer detection. These selected features may be fed to more advanced algorithms in the future, potentially yielding improved performance. Full article

Human activities and economic development require large amounts of water and energy. The analysis of the nexus between water and energy flows can improve the understanding of the quantitative relationship between the two resources and guide actions and policies to obtain better results with lower risks. This article aimed to analyse and evaluate the use of rainwater in urban environments and its relationship with the water–energy nexus through a literature review. The PRISMA guidelines were used to structure the research, and the RStudio programme was used for the bibliometric analysis. A total of 118 articles published between 2013 and 2023 were identified in the Scopus and Web of Science databases, of which 30 met the eligibility criteria and were included in the review. The risk of bias in the studies included was assessed by two independent reviewers, and disagreements were resolved by consensus. The results were synthesized in a narrative and descriptive way, and organized in a table containing the authors, year, country, and main findings. The studies were grouped according to the theme addressed and the results related to the use of rainwater and the water–energy nexus were compared. The results indicate that the main use of rainwater is for non-drinkable purposes, to reduce the demand for potable water, lessen the pressure on water resources and contribute to environmental sustainability. Climate change can affect rainfall regimes and, consequently, the feasibility of systems. By decentralizing water supply services, the use of rainwater can save drinking water. When assessing energy savings, the use of rainwater is not always the best option, as system configurations and pump specifications are determining factors. Regarding the environmental impacts, all stages of the urban water cycle consume energy for their operation, and the environmental impact is directly related to the energy source used. Policies and regulations focused on rational use, water conservation, demand reduction, and tax incentives for the installation of rainwater harvesting systems, together with awareness campaigns, are necessary for the widespread adoption of rainwater harvesting systems. Finally, there is consensus regarding saving drinking water, but there is still a lack of studies and specifications regarding energy savings. The findings highlight the need for future longitudinal and simulation-based studies to strengthen knowledge of water–energy nexus dynamics in buildings. Full article

Existing cross-chain bridges over-rely on static collateralization and post-event penalties, leaving them vulnerable to concealed on–off attacks and rational group collusion. To address these limitations, this paper proposes a Dynamic Trust Evaluation and Risk Control (DTERC) mechanism for heterogeneous cross-chain relay nodes. First, DTERC develops a multidimensional trust quantification model that combines temporal decay, robust multi-observer latency aggregation, verification accuracy, online stability, and an asymmetric one-strike penalty triggered only by cryptographic evidence. Second, DTERC constructs a threshold-aware N-player evolutionary game model to characterize the k-of-N signature structure of cross-chain relay consensus and introduces a dynamic staking function to reduce the economic incentive for collusion under bounded attack-value and parameter conditions. Third, DTERC designs a threshold-preserving FastPath mechanism to reduce redundant verification for low-risk transactions while retaining committee-level confirmation and challenge-based fallback. The empirical evaluation combines multi-agent simulation, smart-contract prototype testing, whitelist-compromise stress tests, malicious-oracle robustness analysis, network-jitter experiments, repeated trials, and parameter-sensitivity analysis. The results show that, under the tested settings, DTERC reduces the malicious transaction success rate to 0.15% under a 50% initial collusion scenario, lowers core contract Gas overhead by 35.7%, and reduces average end-to-end latency by approximately 10% in benign FastPath conditions. These findings indicate that DTERC improves the security–efficiency trade-off of heterogeneous cross-chain relay networks while making its assumptions and limitations explicit. Full article

The pervasive integration of artificial intelligence across e-commerce ecosystems has fundamentally transformed the competitive landscape, rendering systematic and reproducible platform evaluation frameworks an operational necessity rather than an academic exercise. Conventional multi-criteria decision analysis approaches for e-commerce evaluation remain structurally constrained by their dependency on human expert panels, which introduce recruitment costs, cognitive biases, limited reproducibility, and the practical infeasibility of assembling genuinely multidisciplinary panels spanning e-commerce strategy, machine learning engineering, and financial technology simultaneously. This study proposes a novel decision support framework that integrates Large Language Model (LLM) multi-agent simulation with the Best–Worst Method (BWM) to derive reproducible priority weights for AI-driven e-commerce platform evaluation within a rigorous business intelligence architecture. Twelve domain-differentiated LLM agents—organized into three expertise groups representing e-commerce management, AI and machine learning technology, and digital payment systems—were instantiated with structured system prompts encoding professional domain knowledge and deployed across three independent simulation rounds to perform BWM pairwise comparisons across a comprehensive six-dimensional, 30-sub-criterion evaluation hierarchy. Inter-agent consensus was synthesized through geometric mean aggregation, with consistency verification conducted via BWM’s xi* indicator and inter-round stability assessed through coefficient of variation analysis. Results reveal that Transaction Security and Trust achieves the highest dimension-level weight (w = 0.248), followed by AI Recommendation Effectiveness (w = 0.213), with Personal Data Protection (G = 0.0750), Recommendation Accuracy (G = 0.0607), and Transaction Transparency (G = 0.0549) emerging as the three highest globally ranked sub-criteria. The aggregated consistency indicator xi* = 0.062 confirms logical coherence of the multi-agent judgment consensus, and all dimension weights exhibit CV values below 2.8%, demonstrating exceptional inter-round stability. Spearman rank correlations among the three domain-expertise groups exceed 0.92, confirming strong inter-group convergence. Sensitivity analysis under perturbations of ±10% and ±20% demonstrates that the top-five priority indicators are structurally stable. This study establishes LLM multi-agent BWM simulation as a methodologically rigorous, institutionally accessible, and computationally reproducible alternative to traditional expert elicitation for complex platform evaluation tasks. Full article

Multiple North American and European societies now endorse a combined serological and imaging-based clinical care pathway for non-invasive risk stratification of patients with metabolic dysfunction-associated steatotic liver disease (MASLD). A multidisciplinary group of Canadian radiologists, hepatologists, family physicians, and other health professionals have recently published consensus guidelines for identification and risk stratification of patients with suspected MASLD. Screening should be performed with the FIB-4 score, and those with an indeterminate FIB-4 (between 1.32.67) should undergo imaging-based liver stiffness evaluation either with transient elastography (FibroScan), ultrasound shear wave elastography, or magnetic resonance elastography as a second step. While the implementation of these techniques for measuring liver stiffness differ, there is no clinically significant difference in their diagnostic performance. This narrative review, intended for Family Physicians, summarizes recommendations for serological investigations and imaging modalities of liver steatosis and stiffness. Practical guidance includes an algorithm with thresholds. We discuss current challenges and future directions of risk-stratifying patients with MASLD in the community. Full article

Introduction: Remote education is increasingly used in ostomy care, but its components, timing, governance, and evaluation remain inconsistently defined. This study aimed to develop practice-oriented recommendations for implementing remote patient education for people living with an ostomy. Methods: An Italian expert consensus using a modified Delphi method and reported according to the ACCORD guidelines was conducted. An expert panel (n = 11), recruited nationally, included stomatherapists (n = 6) and people living with an ostomy (n = 5). Round 1 comprised a remotely conducted focus group to generate and refine statements informed by a targeted literature search. Rounds 2 and 3 were anonymous online surveys in which panelists rated statements on a four-point Likert scale and could provide comments or propose additional items. Consensus was predefined as ≥75% agreement. Results: Response rates were 100% across the three rounds (October–November 2025). The panel achieved consensus on 8 definitions and 14 statements, organized into six domains: (1) model of care and eligibility; (2) privacy and data protection; (3) program structure, outcomes, and evaluation; (4) educational content and teaching strategies; (5) timing, intensity, follow-up, and caregiver involvement; and (6) dignity, relational quality, and professional and organizational requirements. Recommendations supported a hybrid-by-default model with eligibility criteria, privacy-by-design using secure platforms and traceable documentation, structured programs with tailored multimodal content, staged pathways lasting 2–6 months after an initial in-person foundation, dignity-preserving options during remote encounters, professional training in communication and digital empathy, and integration into clinical planning and records. Conclusions: This consensus provides the first ostomy-specific, implementation-focused recommendations for standardizing remote patient education in Italy, with an emphasis on equity, privacy, dignity, evaluation, and workforce competencies. Full article

Podophyllum hexandrum Royle (1834) (Himalayan Mayapple), a key Himalayan medicinal plant and source of podophyllotoxin for anticancer drugs, is declining due to overharvesting, habitat loss, and climate change. This study, conducted from May to September 2024 across nine populations in Swat, Pakistan, assessed its ethnobotanical importance and conservation status. A total of 331 participants (270 individual surveys + 61 group discussions) were included. Using ethnobotanical surveys, IUCN-CMP threat frameworks, and spatial analysis, results showed high cultural value (Use Value = 0.63–0.92) and strong consensus for rheumatism (ICF = 0.91) and fever (ICF = 0.89). Fidelity levels were 94% for rheumatism and 88% for fever. Only 35% of respondents demonstrated conservation awareness. Overharvesting was the main threat, followed by habitat degradation and climate change. The species showed restricted distribution (EOO = 4250 km²; AOO = 295 km²), high fragmentation (0.68), and a 35% population decline over 10 years. It is assessed as Endangered (EN B1ab (iii, v)). This study provides the first integrated ethnobotanical–GIS assessment of P. hexandrum in the Hindu Kush–Himalaya region of Pakistan, offering measurable conservation baselines and community perception data previously unavailable. Findings align with global medicinal plant decline trends and support integration with CBD, SDGs (3 and 15), and potential CITES listing. Urgent conservation actions are required, including community-based management, habitat restoration, sustainable harvesting, ex situ conservation, and policy enforcement. Full article

Background: Despite global interest in the fast-emerging field of culinary nutrition (CN) and culinary medicine (CM), frameworks and competencies are underdeveloped. Due to the interprofessional nature and diverse research and practice applications, there is a need for a broad consideration of relevant competencies with input from experts across multiple disciplines. Aims: The aims of this study were to explore existing standards from related fields and create a draft framework of competency domains and elements required for CN and CM interprofessional practice and to consult with a panel of international experts in the field as a first step towards achieving consensus. Methods: Nine competency standards relevant to interprofessional CN and CM practice informed the mapping and development of a draft framework. A modified online Nominal Group Technique (NGT) with ten international experts was performed with facilitated discussion around the domains, elements, and guiding statements of the framework. Results: The resulting Interprofessional Framework for Culinary Nutrition and Culinary Medicine Competencies outlines a preliminary framework intended to support future refinement and validation, and acts as a prompt to consider extensive capabilities across an interprofessional team. Conclusions: To the authors’ knowledge, this is the first study to investigate an interprofessional framework with competency domains and elements for professionals delivering CN and CM. The framework provides an initial expert-informed foundation to support the refinement, external validation, and development of multiple specific competency standards and the evolution of future consensus regarding the delivery of CN and CM, which will be distinct to disciplines, settings, organizations, population groups, and practice areas. Full article

Oral health has significant implications for pulmonary outcomes, particularly among individuals with dysphagia who are at risk for aspiration. Moreover, oral health and condition affect nutrition accessibility and status. Inadequate oral hygiene promotes bacterial colonization, plaque accumulation, and aspiration-related respiratory complications. This narrative review aimed to explore current evidence and expert perspectives across palliative medicine, pulmonary and critical care, and dentistry on the role of oral hygiene in supporting pulmonary health and maintaining opportunities for oral nutrition. A comprehensive literature search was conducted through the Texas Tech University Health Sciences Center digital library using Cochrane Library (Wiley), EBSCO Discovery, Embase, Ovid databases, PubMed, SCOPUS, ScienceDirect, Web of Science, and Google Scholar between 14 January 2026 and 1 April 2026. From 1287 identified records, 70 studies were selected to be highlighted in the manuscript after duplicate removal and eligibility screening. Relevant literature was reviewed to examine associations among dysphagia, oral health and condition, oral hygiene and care protocols, feeding route, salivary composition and function, and respiratory outcomes. Emphasis was placed on studies addressing pneumonia, oral versus tube feeding, and evidence-based oral care practices. Findings indicate that pneumonia, depression, and mortality rates are higher in patients receiving tube feeding compared to oral feeding. Evidence-based oral care practices inclusive of mechanical plaque disruption, oral cleansing products (Chlorhexidine, hydrogen peroxide, and sodium bicarbonate), and structured oral hygiene protocols can reduce pulmonary consequences of aspiration and support safer/least risk oral intake. Saliva plays a pivotal role in plaque breakdown, microbial defense, and host immunity; oral feeding helps to preserve salivary function. Results of this review highlight the importance of oral hygiene in both restorative and palliative care contexts. This review establishes a framework for embedding oral cleansing agents and protocols into a nutrition-focused health care infrastructure. Based on the literature analysis and inter- and multidisciplinary clinical expertise of the author group, the manuscript proposes consensus statements intended as expert guidance rather than formal clinical practice guidelines. Adherence to best practices in oral care can mitigate pulmonary consequences of aspiration amid dysphagia, make oral nutrition more accessible and comfortable, sustain opportunities for least risk oral feeding across diagnoses and health care settings, and improve quality of life for patients with dysphagia amid life-limiting illness. Full article

Despite overwhelming scientific consensus that human activity drives climate change, public opinion in the United States remains sharply polarized along political lines. This project tests whether a theory-driven narrative intervention can reduce divergence between individuals skeptical of climate change and those who accept the scientific consensus. Guided by narrative transportation theory, we hypothesize that an inclusive, character-driven video grounded in the authentic language of skeptical audiences will reduce polarization and increase civic engagement. The study proceeds in three phases. Phase 1 uses focus group discussions to identify words, phrases, and perspectives used by skeptical and accepting participants. Phase 2 integrates these findings into the production of a 2–3 min narrative short film, refined through iterative audience testing. Phase 3 employs a stratified online experiment assessing climate attitudes, policy support, and activism behaviors before exposure, immediately after, and one week later. Mediators include narrative transportation, perceived similarity, and character identification. We test whether pre-exposure divergence narrows over time and whether engagement mechanisms explain observed changes. Findings will inform climate communication policy, intervention design, and broader research on depolarization in polarized public issues. Full article

Pathology is foundational to biomarker-driven and translational oncology research, yet systemic barriers limit full pathology engagement in Canadian cancer clinical trials, compromising the tissue-based questions such trials are designed to answer. This commentary and white paper synthesizes the perspectives of a national pathology working group convened at the 2025 Canadian Cancer Trials Group (CCTG) Annual General Meeting with a descriptive internal audit of the CCTG Tumour Tissue Data Repository (TTDR), in which biospecimen attrition was tabulated at the patient level by disease site; no inferential testing was performed. The TTDR data revealed substantial attrition across disease sites, with no tissue submitted for 32–44% of patients and slides submitted in place of formalin-fixed paraffin-embedded blocks for up to 51% of cases, reflecting persistent misalignment between protocol expectations and laboratory capacity. From these observations, five interrelated gaps were identified—in trial design, funding and resourcing, digital pathology infrastructure, academic recognition, and knowledge translation around consent and ethics governance. Five corresponding strategies are proposed to align research demands with pathology capacity, reduce attrition, and strengthen biomarker-driven trials. As a consensus- and experience-driven analysis rather than a systematic review, these recommendations are intended to frame a national conversation and a starting point for prospective evaluation. Full article