SEARCH · Search PubMed

Results for “Models, Theoretical”

Search indexed PubMed citations on genomics, clinical trials, systematic reviews and public health. Explore titles, authors and supplied subject terms, then open the PubMed record.

SourceResearch connectionFrom yearThrough year

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

622 recordsLinked to original sources

PubMed2026

Toward real-time quantification of driving risks: a systematic review and research agenda of risk field theory.

In complex traffic systems, driving risk often evolves in a continuous and progressive manner prior to crash occurrence. How to effectively represent and analyze such latent risk states remains a central challenge in traffic safety research. In recent years, risk field-based approaches have introduced spatial and spatiotemporal continuous modeling paradigms, providing new perspectives for characterizing the distribution of traffic risk and its dynamic evolution. Motivated by the rapid growth of this research area and the lack of a systematic synthesis, this paper presents a comprehensive review of studies applying risk field theory to driving safety and traffic risk analysis. Following the PRISMA guidelines, relevant literature was collected through multi-database searches and analyzed using a combination of bibliometric analysis and qualitative review. The review systematically summarizes the theoretical foundations, modeling elements, data sources, analytical methods, and application domains of risk field-related research. Particular attention is given to studies that conceptualize traffic risk as a continuous field, complemented by a broader review of traffic risk factor literature to identify key elements and analytical dimensions involved in risk field modeling. On this basis, the paper synthesizes research progress in major application areas, including traffic safety state representation, driving behavior analysis, traffic conflict assessment, and autonomous driving and human-machine cooperative systems. Differences and commonalities among existing studies are compared in terms of modeling strategies, data support, and application scenarios. Through this systematic review, the paper clarifies the main research themes and methodological trends of risk field-based studies, providing a structured framework for understanding the evolution and application of this approach and offering methodological insights for risk perception modeling and safety-oriented decision support in intelligent transportation systems (ITS).

Humans↗

PubMed2025

An integrated multiscale air quality modelling framework for industrial park pollution: Linking local emissions to regional transport.

Capturing the spatiotemporal distribution of pollutants in industrial parks remains challenging for regional air quality models because of their coarse resolution (3 km), resulting in uncertainties in local emission quantification. To address this, we developed the Integrated Multiscale Air Quality Modelling System for Industry (IAQMS-Industry), coupling the regional Nested Air Quality Prediction Modelling System (NAQPMS) with a city-scale chemical transport model. This framework integrates point-source locations and Gaussian plume dispersion to simulate particulate matter with a diameter smaller than 2.5 micrometres (PM2.5) at 100 m resolution. Applied to the Beijing Yi Zhuang and Tangshan industrial parks and evaluated against observations. The coupled model achieved a normalized mean bias (NMB) ranging from 3.1 % to 6.2 %, improving upon NAQPMS (-16.9 % to -7.7 %). Spatial analysis revealed that coarse regional grids underestimated the PM2.5 concentrations at industrial sites by smoothing gradients, whereas IAQMS-Industry successfully resolved spatial patterns. Industrial point emissions accounted for 22.9 %-26.4 % of PM2.5 in the coupled model, which was significantly greater than the regional model estimates of 1.6 %-13.7 %. These findings indicate that regional models overestimate pollutant dispersion processes in industrial parks while underestimating local industrial impacts. By explicitly resolving point-source dynamics and linking them to regional transport, IAQMS-Industry provides a robust tool for designing targeted emission controls in industrial cities and balancing local air quality improvements with minimized regional pollution outflow. This study underscores the necessity of multiscale modelling for accurate source apportionment and informed environmental governance in industrial zones.

Air Pollution↗

PubMed2026

Identifying stakeholder behaviors for competency-based pharmacy education: A stage 1 behavior change wheel analysis.

INTRODUCTION/OBJECTIVES: Competency-Based Pharmacy Education (CBPE) is a strategic priority for preparing graduates to meet evolving healthcare needs. However, efforts to implement CBPE can stall due to behavioral challenges among faculty, administrators, preceptors, and learners. This study aimed to apply Stage 1 of the Behavior Change Wheel (BCW) to identify stakeholder-specific behaviors and associated determinants needed to implement the five core components of CBPE. METHODS: A multi-method approach grounded in the BCW, the Capability, Opportunity, Motivation - Behavior (COM-B) model, and the Theoretical Domains Framework (TDF) was used. Data were gathered through (1) targeted literature review; (2) structured focus groups with competency-based education experts and pharmacy education stakeholders; and (3) an iterative consensus process. Behaviors were mapped to the five CBPE components: (1) defined competencies, (2) developmental progression, (3) tailored instruction, (4) authentic experiential learning, and (5) programmatic assessment, and then mapped to COM-B and TDF constructs. RESULTS: Over fifty stakeholder-specific behaviors were identified and specified across the CBPE framework. This revealed shared barriers such as limited instructional design knowledge (psychological capability), insufficient assessment of infrastructure (physical opportunity), and misaligned professional identity (reflective motivation). Key TDF domains included knowledge, environmental context, beliefs about capabilities, and professional roles. The behavioral problem statements, specifications, and determinants were identified to support future intervention planning. CONCLUSION: This Stage 1 analysis provides a behaviorally grounded foundation for CBPE implementation by identifying stakeholder behaviors and conditions that enable change. These findings will inform the development of readiness-to-change assessments and targeted interventions (BCW Stages 2 and 3), supporting scalable and sustainable CBPE transformation in pharmacy education.

Education, Pharmacy↗

PubMed2026

Decoding the spatiotemporal patterns of food spoilage microbial communities: Integrating multi-omics and artificial intelligence to enable precision preservation.

In the global food supply chain, food wastage caused by spoilage has resulted in significant economic losses, food shortages, and environmental pressure. This process is fundamentally driven by the spatiotemporal dynamics of microbial communities. However, traditional research methods struggle to elucidate the complex mechanisms of spatial heterogeneity, interspecies interactions, and functional succession. This limits the development of effective preservation strategies. This review systematically reviews the cutting-edge progress of integrating multi-omics technologies and artificial intelligence (AI) to study food spoilage microbial communities, breaking through this bottleneck. We propose an intelligent theoretical framework that could potentially analyze microbial metabolic activities and predict dynamic shelf life if implemented. The conceptual framework integrates multidimensional data, including spatial metabolomics, temporal metatranscriptomics, single-cell transcriptomics, and longitudinal metagenomics. It can also be combined with AI models, such as graph neural networks. The article elaborates on the principles and applications of spatio-temporal monitoring technologies, such as nano secondary ion mass spectrometry, hyperspectral imaging, and the Internet of Things sensing. Through illustrative cases of typical perishable foods, it also explores how such a multi-omics - AI system might be applied to spoilage warning and precise intervention. Additionally, the article addresses the current challenges in data coverage, model generalization, and federated learning implementation. Then the research further explores emerging areas such as engineered probiotics, edge AI, and microfluidic sensing. These areas are targeted at transforming food preservation from an empirical control approach to a data-driven, precise regulatory framework. This transformation provides theoretical support and technical approaches for developing a smart, sustainable food preservation system.

Multiomics↗

PubMed2026

Granger connectivity and graph-theoretical analysis of scalp EEG across the preictal to ictal transition for presurgical evaluation.

OBJECTIVE: To assess the feasibility of estimating lateralization and localization of the epileptogenic zone (EZ) in temporal and extratemporal lobe epilepsy by combining Electric Source Imaging (ESI) with functional connectivity analysis of high-density EEG from the preictal to the ictal phase. METHODS: Adults with drug-resistant focal epilepsy and at least one recorded seizure during 40- or 64 channels EEG monitoring were retrospectively included. Granger causality and hubness centrality were computed over the 10-s preictal interval and the first 5 s of the ictal period, with ictal onset defined as the first EEG change identified by experienced epileptologists. The reference standard for EZ localization was based on resective surgical outcome or stereo-EEG findings. RESULTS: Thirteen patients (7 females; median age 35 years) were included. Connectivity analyses showed higher concordance with clinical findings during the preictal phase than during the ictal phase for both lateralization (91% vs 46%) and localization (73% vs 27%). Performance was highest in temporal (7/7 lateralization; 6/7 localization) and frontal lobe epilepsy (2/2 for both), and lower in parieto-occipital epilepsy (1/2 and 0/2, respectively). In two cases with poor surgical outcome or no surgical indication, connectivity findings were discordant with clinical estimates. CONCLUSIONS: Connectivity analysis across the preictal to ictal transition provides relevant lateralizing and localizing information, particularly in temporal and frontal lobe epilepsy, and may reveal clinically meaningful discordance. SIGNIFICANCE: Integrating high-density EEG, ESI, and functional connectivity during the phase preceding the first EEG change may support non-invasive presurgical evaluation.

Humans↗

PubMed2026

The landscape of pruning for large language models: A systematic review and unified taxonomy.

Confronting the inherent tension between the exceptional capabilities and the immense computational costs of Large Language Models (LLMs), pruning has become a crucial technique for achieving efficient deployment. However, a systematic analytical framework dedicated specifically to LLM pruning remains absent. In this paper, we aim to bridge this gap. We first elucidate the theoretical foundations that underpin the effectiveness of pruning, namely overparameterization and redundancy, and then propose a multidimensional taxonomy that organizes existing approaches along the axes of granularity, timing, and criteria. Building upon this unified perspective, we further analyze performance recovery mechanisms and the broader evaluation ecosystem, while also exploring forward-looking challenges such as interpretability, automation, and hardware-algorithm co-design. Through this comprehensive synthesis, we seek to provide an integrated and coherent analytical lens for advancing both research and practice in LLM pruning.

Large Language Models↗

PubMed2025

Coupling of spectroscopy and nitrogen-oxygen isotopes unveils the mechanisms of dissolved organic matter and nitrate pollution in lakes within the agro-pastoral transition zone.

Lakes in arid and semi-arid regions are subjected to severe ecological stress, such as organic pollution, eutrophication, and salinization, due to climate change and human activities. This study investigates Chagannur Lake, a typical arid-region lake that is representative and ecologically sensitive in Northern China's agro-pastoral ecotone, to uncover its pollution characteristics and mechanisms. We employed fluorescence spectroscopy and stable isotope analysis to trace dissolved organic matter (DOM) and nitrate sources. The DOM composition was dominated by microbial metabolic byproducts and protein-like substances, suggesting that microbial processes are key to organic matter transformation. Source apportionment revealed that pollutants primarily originated from livestock and poultry manure (37.6 %), agricultural fertilizers (35.6 %), and soil erosion (24.7 %), with agricultural fertilizers contributing most significantly in the Gogstai River (63.3 %). A structural equation model (SEM) coupling spectral and mass spectrometric data revealed that microbial transformation significantly impairs the lake's self-purification capacity, thereby promoting pollutant accumulation (path coefficient = 0.91,*p < 0.05). Moreover, microbial processes link endogenous and exogenous pollution, a mechanism effectively traced by isotopic and fluorescence indices (path coefficient = 0.55, ⁎⁎p < 0.01). These findings enhance the understanding of pollution sources and transformation mechanisms in arid-region lakes and offer foundational theoretical support for policymakers engaged in pollution control strategies.

Lakes↗

PubMed2026

To longevity and beyond: A systems view of aging and stress resilience.

Aging is a dynamic and time-dependent process characterized by progressive functional decline across biological systems. Key hallmarks, including genomic instability, telomere attrition, loss of proteostasis, mitochondrial dysfunction, and immunosenescence, have been widely described, each reflecting distinct yet interconnected mechanistic frameworks. Rather than acting in isolation, these processes arise from complex interactions among cellular stressors, impaired repair mechanisms, and the cumulative burden of maladaptive responses. This system-level perspective explains the inter-individual variability in aging trajectories. Centenarians represent an extreme and informative model of successful aging, in which the balance between damage accumulation and repair is shifted toward the maintenance of physiological function. Their exceptional longevity is supported by coordinated genetic, epigenetic, metabolic, and immunological adaptations that enhance resilience to age-related stressors. Here, we summarize the biological drivers and theoretical frameworks of aging within an integrative context, focusing on mechanisms associated with extended healthspan in centenarians. We also examine the contribution of major animal models, highlighting their complementary roles in elucidating conserved and species-specific aging pathways. Overall, aging outcomes reflect a dynamic equilibrium between damage and repair processes. Understanding how this balance is modulated in long-lived individuals may inform strategies to promote healthy aging and delay the onset of age-related diseases.

Humans↗

PubMed2026

Measuring Coping Strategies in Daily Life: A Systematic Review of Experience Sampling Methodology and Daily Diary Studies.

Advances in daily diary methods and experience sampling method (ESM) have improved the study of coping strategies in daily life and their role in shaping health and well-being. In this review, we examine study designs, measurement approaches, and analytical practices used to investigate coping in natural contexts. We performed a systematic review of studies published before 5 December 2025 that used daily diary or ESM to measure coping strategies over multiple days or moments. Studies were examined with regard to sampling schemes, assessment frequency and duration, measurement of coping strategies, incorporation of stressor appraisals, and analytic techniques used to model coping processes. Fifty-five studies met the inclusion criteria. Results indicated that 80% employed end-of-day diary designs, generally lasting 1-3 weeks, whereas higher-frequency ESM protocols were less common and ranged 2-14 days. Coping strategies were often assessed using abbreviated or single-item measures, frequently adapted from established questionnaires. Many studies incorporated appraisals such as perceived stressor intensity or controllability, enabling tests of coping flexibility. Multilevel modelling was the dominant analytic approach, allowing researchers to distinguish within-person dynamics from between-person differences. However, analyses were predominantly concurrent, and temporally ordered models remained comparatively rare. Overall, the literature demonstrates substantial progress in capturing coping in everyday contexts, yet heterogeneity in measurement and limited use of temporal modelling constrain cumulative knowledge about the temporal links between coping and psychological and physiological health outcomes. Future research would benefit from greater alignment between theoretical assumptions, assessment strategies, and analytic methods.

Humans↗

PubMed2026

Gastrointestinal digestion governs insect protein hydrolysis and predicted bioactive peptide release: Species-dependent implications for functional food applications.

This study investigates the digestion of insect proteins and the release of predicted bioactive peptides during human gastrointestinal digestion. Using the Infogest in vitro model, mealworm, cricket, and black soldier fly larvae (BSFL) proteins were digested and analyzed through discovery proteomics and bioinformatics to identify predicted bioactive peptides. Sequential windowed acquisition of all theoretical fragment ion mass spectra (SWATH-MS) quantified insect proteins including predicted bioactive peptide precursor proteins, the precursors of predicted bioactive peptides. Results indicated that gastrointestinal digestion strongly influences peptide release, with the gastric phase exhibiting a richer predicted bioactive peptide profile than the small intestinal phase. Many predicted bioactive peptides were rapidly hydrolysed under small intestine conditions, which may lead to reduced stability or diminished activity in vivo, potentially explaining why certain peptides show strong bioactivity in vitro but limited effects in vivo. Additionally, predicted bioactive peptide release varied by insect species, influenced by genetic factors and peptide abundance. These findings highlight the importance of species selection and consideration of proteolytic digestion patterns in optimizing insect-derived bioactive peptides for functional foods and nutraceutical applications.

Animals↗

PubMed2026

Clinical applications of digital twin technology in In Vitro Fertilisation.

BACKGROUND: Digital twin technology, originating from aerospace and manufacturing industries, has emerged as a transformative tool in healthcare. In vitro fertilisation (IVF) faces persistent challenges including suboptimal embryo selection, unpredictable treatment outcomes, and limited personalisation of protocols. Despite advances in assisted reproductive technology, existing literature exhibits fragmentation: artificial intelligence applications in embryo selection, ovarian stimulation, and endometrial assessment have been developed independently without systematic integration into comprehensive treatment frameworks. Digital twin technology offers unprecedented opportunities to create virtual replicas of biological systems, enabling real-time monitoring, predictive modelling, and personalised treatment strategies. AIM: This narrative review aims to critically examine the current applications of digital twin technology in IVF, evaluate its potential benefits and limitations, synthesize existing evidence into an integrative conceptual model, and identify future directions for implementation in reproductive medicine. METHOD: A comprehensive narrative review was conducted using PubMed, Scopus, Web of Science, and IEEE Xplore databases. A narrative review approach was selected over systematic review to accommodate the heterogeneity of evidence types in this emerging field, including theoretical frameworks, simulation studies, and proof-of-concept implementations that would be excluded from systematic reviews. Search terms included "digital twin," "IVF," "in vitro fertilisation," "assisted reproductive technology," "embryo selection," and "predictive modelling." Studies published between 2015 and 2025 were included, focusing on original research articles, systematic reviews, and proof-of-concept studies describing digital twin applications in reproductive medicine. RESULTS: Digital twin technology in IVF demonstrates significant potential across multiple domains including embryo development simulation, ovarian response prediction, endometrial receptivity modelling, and personalised stimulation protocols. Current applications integrate artificial intelligence, machine learning algorithms, time-lapse imaging, and omics data to create comprehensive virtual models. Early evidence suggests improvements in embryo selection accuracy, ovarian response prediction, and treatment protocol optimization, though large-scale randomized controlled trials remain limited. Implementation challenges include data integration complexity, computational requirements, regulatory considerations, and validation requirements. CONCLUSION: Digital twin technology represents a paradigm shift in IVF practice, offering personalised, predictive, and precision medicine approaches. This review synthesizes existing evidence to propose an integrative conceptual model for digital twin implementation across the IVF treatment spectrum, identifies critical knowledge gaps, and establishes research priorities to advance clinical translation. Despite current limitations, continued advancement promises improved success rates and patient outcomes.

Humans↗

PubMed2026

A systematic review of human avoidance learning: Cognition, computation, and methods.

Avoidance behaviour is fundamental for survival but can become maladaptive in clinical conditions. A large body of literature has accumulated on the dynamics of human avoidance learning. However, current theories and overviews do not provide an exhaustive account of this evidence. In this systematic review, we identify N = 116 studies on human avoidance learning. We analyse these studies with the goal of distilling robust empirical phenomena as a basis for theory-building, and examine their diagnostic value in differentiating between competing theories. We find that the evidence is difficult to reconcile with foundational two-factor and classical safety-signal accounts, and most strongly supports expectancy- and inference-based views, in which avoidance responses are selected with respect to represented consequences. At the same time, no current framework provides a complete account of the evidence: several findings point to an additional role for operant valuation, Pavlovian influences, and contextual or latent-state control over the expression of avoidance. Methodologically, we observe that the problem setting in the most common experimental paradigms is radically simpler than real-world avoidance and therefore unlikely to expose the limits of inferential or reflective mechanisms. Consequently, we argue that paradigms with greater computational demands and more realistic action affordances are required to identify the mechanisms underlying avoidance learning. Collectively, these insights provide a foundation for theoretical refinement, computational modelling, and methodological innovation, with implications for advancing interventions targeting maladaptive avoidance.

Humans↗

PubMed2026

Integrative machine learning and transcriptomic analysis reveals molecular mechanisms underlying low survival rate in larval Chinese Bahaba (Bahaba taipingensis).

Chinese Bahaba (Bahaba taipingensis) is a Class I protected marine fish endemic to China. Low larvae survival during artificial breeding severely hinder population recovery. To investigate the molecular mechanism of high mortality in larval fish, this study performed RNA-seq on liver from naturally deceased (ND) and mass-dead (MD) individuals, combined with least absolute shrinkage and selection operator (LASSO) regression and random forest (RF) algorithms to screen for core signature genes. A total of 873 differentially expressed genes (DEGs) were identified, including 112 upregulated and 761 downregulated genes. GO and KEGG enrichment analyses revealed significant enrichment in amino acid metabolism disorders, one‑carbon folate pool impairment, PPAR signaling abnormalities, ECM-receptor interaction, focal adhesion pathway, indicating widespread metabolic suppression accompanied by extracellular matrix remodeling and signaling disturbances in the livers of MD fish. MAD pre-filtering combined with dual machine learning algorithms yielded 18 robust core signature genes, among which SLC38A4, MMP1, FADD, FKBP5, and APOB were consistently identified as high-frequency core genes by both algorithms. SLC38A4 exhibited the highest importance score in the RF model and was significantly downregulated, making it the primary molecule distinguishing ND from MD phenotypes. ROC curve analysis showed that both models achieved an AUC of 1.000 (95% CI lower bound: 0.610), confirming the precise discriminatory ability of the core genes. GSEA further demonstrated significant enrichment of this core gene set in ND samples. This study provides the first systematic elucidation of the molecular mechanisms underlying liver dysfunction in low survival rate B. taipingensis, characterized by amino acid transport impairment, metabolic reprogramming, and structural remodeling, offering theoretical foundations for health assessment, early mortality risk warning, and artificial breeding conservation of this species.

Animals↗

PubMed2026

From fear to empowerment: the impact of employees AI awareness on workplace well-being - a new insight from the JD-R model.

PURPOSE: The primary purpose of the study was to explore the impact of health workers' awareness of artificial intelligence (AI) on their workplace well-being, addressing a critical gap in the literature. By examining this relationship through the lens of the Job demands-resources (JD-R) model, the study aimed to provide insights into how health workers' perceptions of AI integration in their jobs and careers could influence their informal learning behaviour and, consequently, their overall well-being in the workplace. The study's findings could inform strategies for supporting healthcare workers during technological transformations. DESIGN/METHODOLOGY/APPROACH: The study employed a quantitative research design using a survey methodology to collect data from 420 health workers across 10 hospitals in Ghana that have adopted AI technologies. The study was analysed using OLS and structural equation modelling. FINDINGS: The study findings revealed that health workers' AI awareness positively impacts their informal learning behaviour at the workplace. Again, informal learning behaviour positively impacts health workers' workplace well-being. Moreover, informal learning behaviour mediates the relationship between health workers' AI awareness and workplace wellbeing. Furthermore, employee learning orientation was found to strengthen the effect of AI awareness on informal learning behaviour. RESEARCH LIMITATIONS/IMPLICATIONS: While the study provides valuable insights, it is important to acknowledge its limitations. The study was conducted in a specific context (Ghanaian hospitals adopting AI), which may limit the generalizability of the findings to other healthcare settings or industries. Self-reported data from the questionnaires may be subject to response biases, and the study did not account for potential confounding factors that could influence the relationships between the variables. PRACTICAL IMPLICATIONS: The study offers practical implications for healthcare organizations navigating the digital transformation era. By understanding the positive impact of health workers' AI awareness on their informal learning behaviour and well-being, organizations can prioritize initiatives that foster a learning-oriented culture and provide opportunities for informal learning. This could include implementing mentorship programs, encouraging knowledge-sharing among employees and offering training and development resources to help workers adapt to AI-driven changes. Additionally, the findings highlight the importance of promoting employee learning orientation, which can enhance the effectiveness of such initiatives. ORIGINALITY/VALUE: The study contributes to the existing literature by addressing a relatively unexplored area - the impact of AI awareness on healthcare workers' well-being. While previous research has focused on the potential job displacement effects of AI, this study takes a unique perspective by examining how health workers' perceptions of AI integration can shape their informal learning behaviour and, subsequently, their workplace well-being. By drawing on the JD-R model and incorporating employee learning orientation as a moderator, the study offers a novel theoretical framework for understanding the implications of AI adoption in healthcare organizations.

Humans↗

PubMed2026

Characterization and functional insights of histone deacetylases in bivalves: implications for temperature and immune response in Chlamys nobilis.

Histone deacetylases serve as pivotal epigenetic regulators that modulate chromatin remodeling and gene transcription, playing critical roles in immune defense and environmental stress responses in aquatic organisms. However, the evolutionary characteristics and functional roles of the HDAC family in bivalves remain poorly understood. In this study, genome-wide identification of the HDAC family across 30 bivalve species yielded 558 HDAC genes. Phylogenetic reconstruction categorized these genes into four conserved groups and revealed a unique, bivalve-specific SIRT8 clade. Using the noble scallop Chlamys nobilis as a representative model, expression profiling revealed distinct expression patterns among CnHDAC members. Class I and most Class III members were predominantly expressed in the gonads, while Class II members were enriched in immune-related tissues, implying their potential involvement in bivalve immunity. Upon temperature stress, CnHDAC1/2, CnHDAC11-1, CnHDAC11-2, CnSIRT2-1, CnSIRT4, CnSIRT6, and CnSIRT8-3 were significantly induced, highlighting their critical roles in temperature adaptation. Upon Vibrio exposure, CnHDAC1/2, CnHDAC8, CnSIRT4, and CnSIRT6 were upregulated, while CnHDAC4/5/7/9, CnHDAC6/10, CnSIRT2-2, CnSIRT5, CnSIRT7, and CnSIRT8-3 were downregulated, suggesting a coordinated epigenetic regulatory mechanism underlying host immune defense. In conclusion, this study systematically elucidates the evolutionary landscape of the HDAC family and underscores its potential involvement in environmental resilience and host immunity, providing a theoretical basis for the breeding of disease-resistant and stress-tolerant aquaculture bivalves.

Animals↗

PubMed2026

Mechanisms of impact of mental health peer support in high-, middle- and low-income settings: mediation analysis of the UPSIDES randomised controlled trial.

AIMS: While there is growing evidence for the effectiveness of peer support (PS) in improving psychosocial outcomes among individuals with severe mental health conditions, the mechanisms through which these effects occur remain insufficiently understood. This study examines whether social inclusion, hope and empowerment mediate the relationship between PS, personal recovery and health and social functioning. METHODS: Data were collected from 565 adults with severe mental health conditions who participated in the multicentre UPSIDES randomised controlled trial across six sites in Germany, Uganda, Tanzania, India and Israel. Participants in the intervention group received structured PS from trained peer workers over a 6- to 8-month period. Standardised, self-report measures of social inclusion, hope, empowerment and personal recovery, as well as clinician-rated health and social functioning, were administered at baseline, 4 months, end of intervention (8 months) and 12-month follow-up. Cross-lagged panel modelling was used to explore longitudinal associations and mediating pathways. RESULTS: The cross-lagged models showed strong autoregressive effects across all variables, indicating high temporal stability. There were no significant direct effects of PS on recovery or health and social functioning. However, mediation analysis identified significant indirect effects of PS on personal recovery via social inclusion (β = 0.114, 95% confidence interval [CI] [0.049, 0.194], P < 0.05) and hope (β = 0.037, 95% CI [0.001, 0.086], P < 0.05). Similar indirect effects were observed for health and social functioning (via social inclusion: β = -0.035, 95% CI [-0.064, -0.013]; via hope: β = -0.026, 95% CI [-0.052, -0.006]; both P < 0.05). CONCLUSIONS: Findings suggest that PS affects recovery-related outcomes primarily through intermediate mechanisms of enhanced hope and social inclusion. These results support theoretical models positing indirect pathways of change in PS interventions and highlight the value of targeting social and psychological domains when designing and implementing PS in mental health services. Individuals with lower baseline levels of hope and social inclusion may particularly benefit from PS.

Humans↗

PubMed2026

Experiences of stigma, bias, and communication challenges among pregnant healthcare workers: A systematic review of qualitative evidence.

BACKGROUND: Healthcare work environments are fraught with occupational hazards that can impact pregnant healthcare workers' health as well as patient care. Despite the feminization of healthcare globally, systematic discrimination against pregnant workers persists across diverse healthcare settings and cultural contexts. The intersection of stigma, bias, and communication challenges creates substantial barriers to career advancement and wellbeing. However, no systematic review has synthesized qualitative evidence on how these three constructs interact across healthcare professions and cultural contexts using an integrated theoretical framework. OBJECTIVE: To systematically review and synthesize qualitative evidence on experiences of stigma, bias, and communication challenges among pregnant healthcare workers across different healthcare settings and cultural contexts using an integrated theoretical framework. DESIGN: Systematic review of qualitative studies following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines with thematic synthesis. DATA SOURCES: Seven databases were searched from inception to January 2026. REVIEW METHODS: Included qualitative studies were appraised using the Critical Appraisal Skills Programme (CASP) checklist and synthesized through theory-guided thematic synthesis. Confidence was assessed using the Grading of Recommendations Assessment, Development and Evaluation-Confidence in the Evidence from Reviews of Qualitative research (GRADE-CERQual) approach. RESULTS: Fourteen studies encompassing 1223 participants across 17 countries revealed four major themes: (1) professional identity stigma and workplace discrimination through systematic labeling and stereotyping; (2) gender-based institutional bias rooted in masculine organizational logic; (3) multilevel communication failures creating fear-based climates; and (4) individual and collective resistance strategies developed despite constraints. Occupational hazards specific to pregnancy included exposure to infectious diseases, imaging, physical tasks, cleaning products, patient violence, and medication administration. Support from coworkers and supervisors was identified as the most critical facilitator for avoiding hazards and making necessary modifications, while the desire to be 'supernurses' and fear of consequences emerged as significant barriers. These patterns were consistent across healthcare professions, settings, and cultural contexts, with specialty culture and healthcare system type moderating discrimination intensity. Confidence in core findings was rated high using GRADE-CERQual. CONCLUSIONS: Pregnant healthcare workers globally experience interconnected stigma, bias, and communication challenges that are systematically embedded within healthcare organizational structures. These challenges operate synergistically, requiring comprehensive multilevel interventions beyond policy compliance. Healthcare organizations must implement evidence-based strategies addressing stigma reduction, bias interruption, and communication transformation simultaneously to retain skilled workers and ensure quality patient care.

Female↗

PubMed2026

Dual-Reporter Gene-Based Multimodal Imaging for Tracking Mesenchymal Stem Cells in Diabetic Skin Wound Repair.

BACKGROUND: Diabetic foot ulcer (DFU) is a clinically challenging complication characterized by poor healing outcomes, and conventional therapies provide limited benefit. Mesenchymal stem cell (MSC) transplantation offers a promising strategy for DFU repair. However, the low survival of transplanted MSCs in the hostile wound microenvironment, coupled with the lack of real-time, non-invasive methods to track these cells in vivo, severely hampers their therapeutic efficacy and clinical translation. METHODS: We engineered MSCs to co-express a dual reporter system comprising near-infrared fluorescent protein (iRFP) and ferritin heavy chain (FTH1). These modified cells were then integrated with a fibrin glue (FG) scaffold to create a unified platform that supports both multimodal imaging and therapeutic function within skin wounds. First, FTH1 overexpression enhances the antioxidant capacity of MSCs, while the FG scaffold provides structural support; this combination enhances cell survival and retention. Second, the iRFP/FTH1 dual reporter enables near-infrared fluorescence imaging and MRI-based localization, establishing a multimodal platform for real-time cell tracking. RESULTS: In a full-thickness skin defect model in diabetic mice, multimodal imaging revealed that transplanted cells persisted in the wound area for approximately seven days. Treatment with iRFP/FTH1-MSCs/FG significantly accelerated wound closure and promoted hair follicle regeneration and angiogenesis. Additionally, local iron deposition resulting from FTH1 expression enhanced fibroblast migration and collagen synthesis, further facilitating extracellular matrix remodeling. Mechanistic studies demonstrated that this therapy drives macrophage polarization toward the anti-inflammatory M2 phenotype and activates the PI3K-AKT-VEGF signaling pathway. These complementary effects synergistically enhance tissue regeneration and systematically improve diabetic wound healing. CONCLUSIONS: Collectively, this multimodal stem cell-scaffold system effectively integrates dynamic cell tracking with stem cell therapy during skin wound repair. It addresses a critical technical gap in visualizing stem cells within the wound microenvironment and provides valuable methodological and theoretical foundations for optimizing regenerative strategies for diabetic skin wounds.

Animals↗