Collaborative Care Model for Perinatal Wellness Support Services - Population-Level Upstream Systems Change (COMPASS+): A Hybrid Type 2 Cluster Randomized Trial.

Nurse-led multidisciplinary care improves recovery and quality of life after hepatectomy for liver cancer: A radiological imaging-based evaluation

Learning to share nicely: Leveraging the Nobel Prize-winning Shapley value to enhance collaboration and cooperation in healthcare systems.

Multidisciplinary collaborative acute knee clinic model leads to faster imaging, diagnoses and treatment decisions

Optimization of a refined power-to-gas integrated energy system considering reward-penalty stepped carbon trading mechanism

Diabetes mellitus is associated with a high burden of mental health issues, including both formal psychiatric disorders, such as depression and anxiety, and diabetes-specific psychological challenges like diabetes-related distress, psychological insulin resistance, and fear of hypoglycemia. These mental health difficulties significantly impair self-management, treatment adherence, and quality of life, increasing the risk of complications and mortality. The paper reviews the bidirectional relationship between diabetes and mental disorders, driven by biological and behavioural mechanisms. It highlights the high prevalence of depression, anxiety, cognitive impairment, and eating disorders among people with diabetes. The consequences of untreated mental health conditions include poor glycemic control, greater healthcare costs, increased hospitalization, and lower health-related quality of life. The review presents a range of evidence-based management strategies, including early screening, psychological and pharmacological interventions, lifestyle changes, and integrated collaborative care models. Particular emphasis is placed on the role of physicians and endocrinologists in recognizing and managing psychological difficulties. The paper also discusses challenges in low-resource settings and underscores the need for culturally adapted accessible interventions. It argues for a paradigm shift towards integrated, person-centred care models that address both physical and mental health needs in diabetes, offering practical recommendations for clinicians and policymakers.

Synthetic biology, as an emerging field that integrates life sciences and engineering technology, is driving profound transformations in global science, ethics, and legal systems. In international legal framework, the Biological Weapons Convention (BWC) and the Convention on Biological Diversity (CBD) have established initial hard law governance systems. However, these frameworks still face structural limitations in terms of technical adaptability, the scope of provisions, and institutional coordination. Soft law, with its flexibility, non-binding nature, and ability to build consensus, is increasingly becoming an essential supplement to the international response to the ethical risks of synthetic biology. International organizations, industry alliances, and non-governmental actors are constructing a multi-layered soft law governance network through ethical guidelines, policy recommendations, and codes of conduct, providing institutional support for risk identification, technology classification, and behavioral guidance. Soft law is well-suited to perform the roles of guiding and providing feedback in governance, while hard law should focus on the construction of systems of rights and responsibilities and the establishment of obligations. There is a collaborative governance model that integrates both soft and hard law. This model, characterized by “soft law guidance, hard law consolidation, and soft law feedback,” aims to create a flexible and enforceable governance framework. This approach ensures that soft law provides a timely and adaptive starting point, hard law offers a uniform and accountable foundation, and a feedback loop allows for continuous adjustment based on practical experience.

Efficient and safe disassembly of waste electric vehicle batteries (WEVB) is critical for sustainable recycling and resource recovery. However, current studies rarely consider the impact of resource constraints in disassembly scenarios on determining the optimal disassembly sequence. To rationalize the disassembly of used power batteries, this paper proposes a human-robot collaboration (HRC) model aiming to minimize time, cost, hazard index, and energy consumption. Firstly, the dismantling resource space limitation is considered to limit the assigned tasks to one human and one robot, and shop floor operators and robots coordinates to disassembly in the HRC model. In this article, a multi-objective evolutionary algorithm with an adaptive genetic differential evolution algorithm based on successful history (AGDE-ASH), combined with external archiving, is proposed to solve the model which is compared with three other commonly used algorithms for the disassembly of battery of Tesla Model 1 to prove its effectiveness and feasibility. Finally, an entropy-weight fuzzy hierarchical analysis (EWFHA) method, which is a combination of fuzzy analytic hierarchy process (FAHP) and entropy-weight method (EWM), is proposed to evaluate the effectiveness of the proposed approach. Eight Pareto solutions obtained through AGDE-ASH is employed to determine the optimal dismantling order.

Recent advances in technology and the impact of COVID-19 have expanded the adoption of digital health. Synchronous collaborative telehealth between optometry and ophthalmology has been used to expedite specialist eye care in Western Australia since 2011. Optometrists perform an in-person assessment and participate in videoconferencing with ophthalmology to facilitate shared decision-making. This study utilises existing implementation theory to assess factors influencing implementation success, sustainability and scalability of this telehealth model. Regional, rural, and remote Western Australia. In-depth interviews were undertaken using an interview guide based on the Consolidated Framework for Implementation Research (CFIR). Deductive analysis was used to code themes to the framework, and inductive analysis was used for themes relating to sustainability and scalability. Sixteen clinical and non-clinical staff with experience in telehealth for eye care in rural Western Australia. Key themes identified using the CFIR included the relative advantage of telehealth (innovation), optimising workflow and role clarification (implementation process), funding (external setting), technological infrastructure (inner setting), a champion, and ownership and adaptability (individuals). Stakeholder buy-in, resource allocation, integration into public health infrastructure, asynchronous telehealth, and artificial intelligence were themes pertaining to both sustainability and scalability. This study proposes key considerations in the implementation and maintenance of a collaborative telehealth model for eye care in rural areas which may be used as a basis for guideline development or to replicate the model in other contexts.

Proficiency in cytopathologic diagnosis depends heavily on extensive hands-on practice and immediate error correction. Traditional teaching models, however, are constrained by limited practice opportunities and delayed feedback, which fails to meet the core skill-development needs of residents. In total, 45 pathology residents were enrolled and assigned to two groups. The experimental group (n=20) adopted a tripartite teacher-artificial intelligence-resident collaborative teaching model, whereas the control group (n=25) received conventional instruction. Both groups underwent an identical 8-week teaching cycle. The questionnaire results from the experimental group indicated that 19 of 20 residents (9%) deemed the new model highly necessary, and 15 of 20 (75%) believed it significantly improved their diagnostic competence. Semistructured interviews further revealed that the model enhanced diagnostic ability, facilitated personalized learning, and alleviated learning anxiety. For objective metrics, the experimental group demonstrated a significantly higher postintervention concordance rate for gray-zone cell identification (78.65%) compared with both their preintervention baseline (64.38%) and the contemporaneous control group (66.84%; t=8.962; p<.001). In addition, the experimental group exhibited a markedly faster diagnostic speed (mean±standard deviation, 3.05±0.52 minutes per case) compared with their preintervention performance (5.92±0.85 minutes per case) and the control group (5.63±0.79 minutes per case; t=14.821; p<.001). No statistically significant changes were observed in the control group (p> .05). This study demonstrates that artificial intelligence technology integrated with real-time visual interaction effectively improves the cytopathologic diagnostic skills of residents and merits wider promotion in pathology education.

The integration of artificial intelligence in healthcare has transformed clinical practice and research methodologies. However, concerns regarding algorithmic accountability, interpretability, and safety have necessitated human oversight in AI systems. Human in the loop artificial intelligence represents a collaborative paradigm where human expertise and machine intelligence converge to enhance decision making while maintaining ethical standards and clinical safety. This review synthesizes current evidence on human in the loop AI in healthcare delivery and research, examining implementation frameworks, clinical outcomes, comparative advantages over fully automated and clinician-only approaches, and challenges. A comprehensive narrative review was conducted using PubMed, Scopus, Web of Science, and IEEE Xplore databases covering studies from 2018 to 2025. Data were thematically synthesized to identify patterns, frameworks, and outcomes. This narrative approach enables comprehensive conceptual synthesis across diverse HITL-AI applications and contexts. Human in the loop AI demonstrates significant applications across diagnostic imaging, clinical decision support, patient monitoring, drug discovery, and research data analysis. Evidence indicates improved diagnostic accuracy, reduced medical errors, enhanced patient safety, and increased clinician trust compared to both automated AI and traditional approaches. Implementation requires EHR interoperability, clear liability frameworks, adaptive training protocols, and quantum-safe cryptographic security. Challenges include workflow integration, regulatory gaps for adaptive systems, and sustainability concerns. This review advances the field by synthesizing cross-domain implementation patterns, mapping collaboration models to risk-stratified contexts, identifying regulatory gaps for adaptive systems, and proposing future directions including post-quantum cryptographic integration, AI-driven adaptive architectures, and multi-center scalability frameworks for optimizing human-machine collaboration in healthcare.

When the whole exceeds the sum of its parts: Squeezing greater cumulative benefit from cross-technology partnerships in bacterial infection.

Current intrusion detection in industrial control system (ICS) typically relies on network flows or traffic packets, often neglecting differences in payloads of functional fields and their heterogeneous responses under attacks. Moreover, most methods depend on manually crafted features, limiting the utilization of raw traffic byte streams and constraining detection performance. This paper proposes a multi-view correlation intrusion detection model that incorporates spatiotemporal features to enhance detection in ICS. By integrating byte streams with parsed field data, the model leverages traffic information through multi-view collaborative modeling. A fine-grained hierarchical feature framework is developed to extract behavior patterns from each field attribute, and cross-attention mechanisms capture inter-view relationships to construct a comprehensive representation of traffic content. A spatial feature extractor based on convolutional neural network (CNN) and a temporal extractor using Transformer architecture are employed to learn deep spatiotemporal features. A focal loss function is adopted to compute anomaly scores, which support the final intrusion detection decisions. Experiments on the water distribution testbed dataset show that the proposed model achieves superior performance compared to state-of-the-art methods, enabling accurate and efficient intrusion detection in ICS environments.

The Global Parkinson's Genetics Program (GP2) is an international initiative funded by Aligning Science Across Parkinson's (ASAP), in partnership with the Michael J Fox Foundation for Parkinson's Research (MJFF), to accelerate genetic discovery and improve ancestral representation in Parkinson's disease (PD) and related diseases through collaboration, open data sharing, and research capacity building. Since its launch in 2020, GP2 has assembled the largest and most ancestrally diverse PD dataset to date, integrating genotyping, sequencing, and harmonized clinical data from over 240 cohorts worldwide. Through its structured monogenic and complex disease networks, the program spans rare and common variant discovery to advance understanding of PD genetics. Recent GP2-supported studies have identified more than 50 novel genetic risk factors for PD, including a remarkably common GBA1 risk variant among people of African ancestry, and have confirmed new candidate causal genes such as RAB32. Ongoing efforts include whole-genome burden testing, multiple ancestrally-diverse genome-wide association studies (GWAS), polygenic risk modeling, and expansion into atypical Parkinsonism and prodromal cohorts. Beyond discovery, GP2 has invested extensively in research infrastructure and training, supporting more than 270 early-career investigators through workshops, hackathons, and a trainee-to-trainer mentorship framework. These initiatives build local capacity and empower researchers, particularly in underrepresented regions, to lead future genetic studies. GP2 provides an equitable, collaborative model for accelerating the field's understanding of the genetics of PD and related disorders. Continued expansion will enhance population diversity, refine mechanistic insights, better delineate disease onset and progression, and advance progress toward precision medicine across the Parkinsonian spectrum.

People living with advanced heart failure in rural areas have poorer quality of care as their disease progresses, which may be due to lack of access to specialty palliative care. A collaborative care model, connecting specialty palliative care clinicians to embedded complex care teams in primary care practices, may increase access to palliative care in this population. Research objectives To explore perspectives on a proposed collaborative palliative care intervention and whether this intervention would be appropriate for people living with heart failure in rural areas. We conducted a qualitative study (n=26), including patients with heart failure (n=7), caregivers (n=2), complex care team members working in primary care practices (n=5), primary care providers (n=5), interdisciplinary specialty palliative care clinicians (n=4), and cardiologists (n=3) all living and/or practicing in a rural community in Maine. Interviews were audio recorded and professionally transcribed. We used Max-QDA, line-by-line coding, and grounded theory analysis. We found people living and working in rural areas wanted palliative care integrated into primary care. Participants voiced suspicion about care "from outsiders" and that introduction of a specialty palliative care team into their medical care might not be well received. As one primary care provider noted "rural [people] are less influenced by … seeing the latest specialist," so describing palliative care as a specialty may not be appealing. However, participants felt that patients would be open to receiving palliative care delivered by their primary care teams. Palliative care specialists and primary care clinical staff were enthusiastic about a collaborative care model to navigate patients' desire to avoid a new team while increasing access to specialty palliative care expertise. A collaborative palliative care model may be welcomed by patients, caregivers, and clinicians in rural areas.

Low back pain (LBP) is a complex, multifactorial condition with numerous contributors across biopsychosocial domains. To advance understanding of this complexity, we synthesized diverse expert knowledge on treatment effectiveness and underlying mechanisms using a systems-based, collaborative modeling approach. Twenty-nine experts from diverse disciplines created individual fuzzy cognitive maps (FCMs) to represent their understanding of factors affecting pain, disability, and quality of life (QoL), along with treatment mechanisms. These maps were aggregated into a meta-model comprising 142 Components and 1,161 weighted Connections. Centrality was used to quantify the relative contribution of each domain within the meta-model. Simulations with the meta-model based on expert knowledge (1) estimated the relative effectiveness of treatments on pain, disability, and QoL and (2) identified key Mediators and mediating Domains based on their relative contribution to mediating treatment effects. Psychological, biomechanical, and social/contextual Domains were central to expert conceptualizations of LBP. Simulation indicated cognitive behavioral therapy was considered the most effective among all interventions. Most interventions were mediated by Components across multiple Domains, with psychological factors frequently serving as mediators. The structure of the conceptual meta-model reflected both the multifactorial complexity of LBP and the diversity of expert perspectives regarding factors that influence treatment effectiveness. The developed meta-model provides a novel, systems-based representation of expert knowledge about LBP, enabling quantitative exploration of treatment effects and underlying mechanisms. This conceptual framework also offers a foundation for advancing research on multi-modal, personalized care.

Purpose The study analyses the dynamics of collaboration between research infrastructures (RIs) and industry through the lens of Open Innovation in Science. It aims to identify collaboration typologies, barriers and enablers, as well as the knowledge and technology transfer mechanisms that contribute to innovation outcomes. Design/methodology/approach The study adopts a two-stage qualitative research design. First, a scoping literature review was conducted to map the existing body of knowledge on RI–industry collaboration. Second, a qualitative case study was carried out to explore the dynamics of collaboration within ACTRIS, a pan-European RI in the environmental domain. The analysis drew on extensive secondary data, including EU project deliverables, policy documents and strategic reports. Findings Multiple collaboration models emerge across the RI lifecycle, supporting both scientific advancement and innovation. The findings highlight the centrality of open and FAIR data, standardized methodologies, access to state-of-the-art instrumentation as key enablers of collaboration. Intellectual property management and limited SME access underscore the need for more flexible and adaptive governance frameworks. Research limitations/implications The focus on a single case study and the reliance on secondary data limit the generalizability of the findings. Longitudinal and multi-case approaches based on primary data would provide a more comprehensive understanding of RI–industry collaboration across different stages of the research and innovation process. Practical implications Flexible governance frameworks are crucial for addressing the diverse needs of industrial partners. Strengthening user support systems, enhancing visibility through innovation portfolios, and supporting the intermediary role of research performing organizations can reduce access barriers – particularly for SMEs – and foster regional innovation ecosystems. Social implications This study highlights how RIs in the environmental domani play a pivotal role in tackling pressing societal challenges, including climate change and air quality. By fostering collaboration with industry and providing open access to high-quality data and advanced facilities, RIs support technological innovation and contribute to broader societal well-being. Originality/value This study provides a new analytical approach for examining cross-sector innovation. It sheds light on collaboration models, governance challenges, and innovation outcomes, advancing current understanding of how RIs function as open, mission-oriented innovation platforms.

The Primary Eye Care (PEC) model aimed to right-site patients with non-complex eye conditions by upskilling optometrists. However, there is a paucity of studies examining healthcare professionals' and patients' perceptions and context that influenced implementation of community-based eye care model. This study aimed to explore contextual factors (e.g., interprofessional relationships, patients' perceptions) that influenced implementation of PEC and potential strategies to the identified barriers. Qualitative research design was adopted to elicit the experiences of patients and healthcare professionals. Eight focus group discussions (FGDs) and four in-depth interviews (IDIs) were conducted with 32 healthcare professionals, and 12 IDIs were conducted with 14 patients/caregivers between June 2023 to March 2024. Interviews were transcribed and analysed thematically using an inductive approach. Three themes were identified that influenced the implementation of PEC. First, a bidirectional ophthalmologist-optometrists partnership supported implementation and maintenance of high-quality care. PEC's high-quality care experienced by the ophthalmologists influenced their motivation to refer patients to PEC paving the way for responsibility transfer and role expansion. Second, limited interprofessional interactions between primary care physicians and optometrists impacted the awareness of PEC's services and quality of care, dampening referral motivation. Third, patients' awareness of PEC integration with hospital services and optometrists' capability influenced acceptance of PEC referrals. To enhance primary care physicians' awareness and address patients' concern on PEC integration with hospital services, PEC team will need to prioritise active engagement with primary care physicians and dissemination of interactive patient education.

Computer vision has been extensively applied to sheep behavior detection in recent years. However, the dense distribution of Hu sheep poses detection challenges, while imbalanced behavioral categories in datasets affect classification accuracy for detection tasks in intensive farming scenarios, resulting in high misclassification rates. Current models often rely on over-parameterization to achieve satisfactory detection performance, which increases computational burden and limits practical deployment. To address these challenges, this study introduces the Hu Sheep Behavior Dataset (HSBD), specifically designed for intensive farming environments. The dataset comprises 280 images capturing four behaviors across 6,766 Hu sheep: standing, lying, eating, and drinking. Building upon this foundation, we developed the KT-YOLO model, which utilizes a novel Kernel-Team Fusion (KTF) method to enhance the YOLOv8n detection framework. By employing four different convolution kernel sizes, this method effectively captures multi-scale features and addresses Hu sheep occlusion challenges. To mitigate accuracy degradation caused by dataset imbalance, KT-YOLO incorporates a SlideLoss function during classification, effectively addressing this challenge. Comparative experiments demonstrate that KT-YOLO achieved a mean Average Precision (mAP50) of 86.4%, representing a 6.3 percentage point improvement over YOLOv8n, with SlideLoss contributing an additional 1 percentage point improvement. Further comparison with YOLOv13n demonstrates KT-YOLO’s superior performance in dense Hu sheep behavior detection. By introducing HSBD and developing the innovative KT-YOLO, this study significantly enhances both accuracy and efficiency of dense Hu sheep behavior detection, demonstrating the potential and practical value of deep learning technologies in intensive farming environments.

This study aimed to characterize comorbidity distribution patterns and assess their clinical impact on therapeutic effectiveness in elderly pulmonary tuberculosis (PTB) patients, establishing an evidence base for risk-stratified clinical decision-making. A retrospective cohort study was conducted among 1,340 hospitalized PTB patients (aged ≥ 60 years) from January 2020 to January 2024. Demographic characteristics, comorbidity data, and treatment outcomes were extracted from electronic medical records. Patients were categorized into treatment success (n = 1,105) and adverse outcome (n = 235) groups according to WHO criteria. Propensity score matching (PSM) was employed to balance baseline confounders, followed by multivariate logistic regression to evaluate associations between comorbidities and adverse outcomes. Comorbidities were prevalent in 81.64% (1,094/1,340) of patients, with stratification as follows: single comorbidity (30.67%, 411), dyad (26.64%, 357), triad (18.13%, 243), and complex multimorbidity (≥4 conditions: 6.19%, 83). The most frequent comorbidities were chronic heart diseases (31.12%), chronic lung diseases (27.84%), and hypertension (26.49%), followed by diabetes mellitus(20.30%) and psychiatric disorders (15.07%). The adverse outcome rate was 17.54% (235/1,340), comprising 98 treatment failures (7.31%), 89 deaths (6.64%), and 48 treatment terminations (3.58%).Multivariate analysis identified the following independent risk factors: diabetes mellitus(adjusted odds ratio [aOR]=2.73, 95% confidence interval [CI]:1.91-3.90), chronic kidney diseases (aOR = 6.31, 95% CI:4.00-9.96), active malignancy (aOR = 3.27, 95% CI:2.07-5.14), and multimorbidity (≥3 comorbidities; aOR = 1.71, 95% CI:1.20-2.46) (all p < 0.05). Elderly PTB patients exhibit a high comorbidity burden. Diabetes mellitus, chronic kidney diseases, active malignancy, and multimorbidity significantly increase the risk of adverse treatment outcomes. These findings underscore the necessity for multidisciplinary collaborative management models and early comorbidity screening to optimize clinical interventions. Not applicable.