Data Integration Research Articles

Embedding Pharmacogenetics Into Clinical Practice to Improve Patient Outcomes.

Pharmacogenomics, the use of germline genomic data to guide prescription to improve effective and safer medication, holds promise as a clinical intervention. To date in most health systems, there has been limited uptake of pharmacogenomic testing confined to a few single drug-gene associations. Here, we describe the current reactive model of single gene testing and the potential for this to change to a pre-emptive panel or genome-based approach. For this change to occur, three major challenges need to be addressed-the pharmacogenomic testing approach, the digital and data integration, and service delivery models. We explore some of the potential solutions and how pharmacogenomics can be integrated into routine care at scale for patient benefit.

CrossAttOmics: Multi-Omics data integration with CrossAttention.

Advances in high throughput technologies enabled large access to various types of omics. Each omics provides a partial view of the underlying biological process. Integrating multiple omics layers would help have a more accurate diagnosis. However, the complexity of omics data requires approaches that can capture complex relationships. One way to accomplish this is by exploiting the known regulatory links between the different omics, which could help in constructing a better multimodal representation. In this article, we propose CrossAttOmics, a new deep-learning architecture based on the cross-attention mechanism for multi-omics integration. Each modality is projected in a lower dimensional space with its specific encoder. Interactions between modalities with known regulatory links are computed in the feature representation space with cross-attention. The results of different experiments carried out in this paper show that our model can accurately predict the types of cancer by exploiting the interactions between multiple modalities. CrossAttOmics outperforms other methods when there are few paired training examples. Our approach can be combined with attribution methods like LRP to identify which interactions are the most important. The code is available at https://github.com/Sanofi-Public/CrossAttOmics and https://doi.org/10.5281/zenodo.15065928. TCGA data can be downloaded from the Genomic Data Commons Data Portal. CCLE data can be downloaded from the depmap portal. Supplementary data are available at Bioinformatics online.

Computing multidimensional composite indicators for small areas in presence of missing variables: a data integration approach

Abstract We evaluate data integration methods to estimate small area composite indicators, when some of the single indicators cannot be computed due to completely missing variables needed for their computation. The parameter is a multidimensional poverty index, where some of the required variables are not available in the population Census, which is used as the main source to compute the indicator. We propose two approaches to generate these missing variables, considering an auxiliary sample survey. Specifically, the performance of an approach based on a generalized linear mixed model is compared with a two-step imputation technique. The measurement of multidimensional poverty, also including nonmonetary dimensions is crucial and aligned with the Sustainable Development Goals defined by the United Nations. We consider Colombia as a case study, which has a recent population Census providing most of the information necessary to compute the indicator at small area level. Our methodologies can be greatly of interest of other Latin American countries having similar indices, and other countries computing poverty indicators with missing variables. The approaches are evaluated via simulations. We show an application based on the National Population Census, 2018 and the Great Integrated Household Survey 2018 of Colombia, focusing on the Antioquia region.

Expanding the Potential of Gene Therapy for Duchenne Muscular Dystrophy.

Duchenne muscular dystrophy (DMD) is a severe X-linked neuromuscular disorder caused by mutations in the DMD gene, leading to progressive muscle degeneration, loss of ambulation, cardiomyopathy, and early mortality. While advances in multidisciplinary care and pharmacological interventions, including corticosteroids and exon-skipping therapies, have improved patient outcomes, current treatments primarily provide symptomatic relief without addressing the underlying genetic defect. Gene therapy has emerged as a promising approach to modify disease progression, particularly through the use of adeno-associated virus (AAV)-mediated delivery of micro-dystrophin constructs. These truncated genes retain essential functional domains, enabling the restoration of dystrophin expression within the packaging limits of AAV vectors. Early-phase clinical trials have demonstrated encouraging safety profiles and transgene expression; however, challenges such as immune responses, variability in functional improvement, and long-term durability remain. Recent innovations, including optimized AAV capsids, immunomodulatory strategies, and genome editing technologies like CRISPR-Cas9, are actively being explored to overcome these barriers. Additionally, scalable vector manufacturing and the integration of real-world data are essential for broader clinical translation. This review synthesizes current advancements, clinical milestones, and future directions in gene therapy for DMD, emphasizing the need for precise dosing, long-term efficacy, and equitable access to fully realize the therapeutic potential of these evolving strategies.

Transcriptome enhanced rice grain metabolic model identifies histidine level as a marker for grain chalkiness

Rising temperatures due to global warming can negatively impact rice grain quality and yield. This study investigates the effects of increased warmer night temperatures (WNT), a consequence of global warming, on the quality of rice kernel, particularly grain chalkiness. By integrating computational and experimental approaches, we used a rice grain metabolic network to discover the metabolic factors of chalkiness. For this, we reconstructed the rice grain genome-scale metabolic model (GSM), iOSA3474-G and incorporated transcriptomics data from three different times of the day (dawn, dawn 7 h, and dusk) for both control and WNT conditions with iOSA3474-G. Three distinct growth phases: anoxia, normoxia, and hyperoxia, were identified in rice kernels from the GSMs, highlighting the grain-filling pattern under varying oxygen levels. We predicted excess flux through histidine contributing to the biomass as a marker of normoxia, during which kernel chalkiness occurs. Moreover, similarly, we proposed tyrosine as a marker for the hyperoxic growth phase. We also proposed a potential link between monodehydroascorbate reductase, an enzyme with evolutionary significance dating back to the carboniferous era, in regulating the hyperoxic growth phase. Metabolic bottleneck analysis identified nucleoside diphosphate kinase as a central regulator of metabolic flux under different conditions. These findings provide targeted insights into the complex metabolic network governing rice grain chalkiness under WNT conditions. Integration of GSM and transcriptomics data, enhanced our understanding of the intricate relationship between environmental factors, metabolic processes, and grain quality and also offer markers that can be useful to develop rice with improved resilience.

Developmental and epileptic or epileptic encephalopathy with spike-wave activation in sleep: From the 'functional ablation' model to a neurodevelopmental network perspective.

The interplay between epilepsy and cognition is intricate and multifaceted, particularly in the context of childhood-onset epileptic disorders where epileptic activity can significantly interfere with and disrupt the delicate, highly plastic, and environment-related trajectories of neurodevelopment. Developmental and epileptic or epileptic encephalopathy with spike-wave activation during slow sleep (D/EE-SWAS), a spectrum of conditions including Landau-Kleffner syndrome,could serve as a valuable model to explore these complexities. Research to date has primarily examined its distinctive features, including genetic and structural etiological factors, electroencephalographic patterns, and cognitive phenotypes, often interpreted through simplified cause-effect paradigms. The adoption of a network perspective that aligns with neurodevelopmental trajectories is essential to grasp the full complexity of this evolving condition. Advancing research requires the integration of multimodal data, while leveraging tools such as artificial intelligence to develop sophisticated models in order to achieve a holistic understanding of D/EE-SWAS.

Leveraging real-world data for continuous evaluation of computational clinical practice guidelines.

There is a bidirectional interaction between clinical practice guidelines and clinical care, with each informing the other. Structural signalling of trends in guideline adherence in clinical practice is essential for advanced updates. Recent advances in computable care guidelines allow automated evaluation using real-world registry data. Here, we assess the feasibility by evaluating adherence to Dutch endometrial cancer (EC) guidelines. This retrospective cohort study uses real-world data of EC patients from the Netherlands Cancer Registry (NCR) between January 2010 and May 2022. The Dutch guideline for EC was parsed into clinical decision trees (CDTs). Primary outcome was guideline adherence for multiple (sub)populations, with secondary outcomes encompassing adherence trends, recommendation implementation pace, non-adherent treatment strategies and impact of additional non-guideline-based patient and tumour characteristics on adherence. The Dutch EC guideline was parsed into 10 CDTs, revealing 22 patient and disease characteristics and 46 interventions. NCR data were mapped to CDT data items. Four CDTs were successfully populated with NCR data, and 21 602 cases were assessed. Adherence levels were computed, which showed a mean adherence of 82.7% (range 44-100%). Three statistically significant trends in adherence were identified: two increasing trends in the 'non-adherent' compared with the 'adherent' group, and one decreasing trend. This study introduces a novel framework for continuously evaluating (non-)adherence to cancer guidelines. Future efforts should focus on the inclusion of health outcome measurements. Through the integration of real-world data with a computer-interpretable guideline, we effectively calculated various facets of adherence to guidelines for EC.

Use of Artificial Intelligence in Current Fight Against Antimicrobial Resistance.

Antimicrobial resistance (AMR) poses a significant global health threat, with projections indicating it could surpass cancer in mortality rates by 2050 if left unaddressed. Optimizing antimicrobial dosing is critical to mitigate resistance and improve clinical outcomes. Traditional approaches, including population pharmacokinetics (PK) models and Bayesian estimation, are limited by mechanistic hypothesis requirements and complexity. Artificial intelligence (AI) and machine learning (ML) offer transformative solutions by leveraging large datasets to predict drug exposure accurately, refine sampling strategies, and enable real-time dose adjustments through therapeutic drug monitoring. This review highlights the role of ML models, in managing PK and pharmacodynamic variability across diverse patient populations. AI models often equal or outperform traditional methods in achieving therapeutic targets while minimizing toxicity, as demonstrated in some case studies involving ganciclovir, vancomycin, and daptomycin. Despite challenges such as data quality, interpretability, and integration with clinical workflows, AI's dynamic adaptability and precision underscore its potential. Future directions emphasize integrating multi-omics data, developing bedside decision-support tools, and expanding AI applications to broader drug categories and populations. Continued research and clinical validation are essential to harness AI's full potential in advancing precision medicine and combating AMR effectively.

Evaluating Usability and User Satisfaction in Hospital-Based EHR Systems: A Comparative Study of Two Usability Tools in the Context of the Valkyrie Project.

Usability and user satisfaction are critical for Electronic Health Records. This study evaluates DIPS Arena, a hospital-based EHR, and compares the System Usability Scale (SUS) and the National Usability-focused HIS-Scale (NuHISS) in predicting satisfaction. A cross-sectional survey of 127 users across three hospitals collected the SUS, the NuHISS, and satisfaction data. The SUS was a stronger predictor of satisfaction explaining 45% of variance. The SUS scores were higher and more consistent, while the NuHISS provided task-specific insights. The SUS effectively predicts satisfaction, but a combined usability approach is recommended. Interoperability challenges in DIPS Arena impact usability and Valkyrie's Virtual Health Record could enhance data integration. Future research should refine usability frameworks with subjective and objective measures.

Integration of Digital Phenotyping and Genomics for Dry Eye Disease: Protocol for a Prospective Cohort Study.

Dry eye disease (DED) is a common ocular condition with diverse and heterogeneous symptoms. Current treatment standards of DED include the post facto management of associated symptoms through topical eye drops. However, there is a need for predictive, preventive, personalized, and participatory medicine. The DryEyeRhythm mobile health app enables real-time data collection on environmental, lifestyle, host, and digital factors in a patient's daily environment. Combining these data with genetic information from biobanks could enhance our understanding of individual variations and facilitate the development of personalized treatment strategies for DED. This study aims to integrate digital data from the DryEyeRhythm smartphone app with the Tohoku Medical Megabank database to create a comprehensive database that elucidates the interplay between multifactorial factors and the onset and progression of DED. This prospective observational cohort study will include 1200 participants for the discovery stage and 1000 participants for the replication stage, all of whom have data available in the Tohoku Medical Megabank database. Participants will be recruited from the Community Support Center of Sendai, Miyagi Prefecture, Japan. Participant enrollment for the discovery stage was conducted from August 1, 2021, to June 30, 2022, and the replication stage will be conducted from August 31, 2024, to March 31, 2026. Participants will provide demographic data, medical history, lifestyle information, DED symptoms, and maximum blink interval measurements at baseline and after 30 days using the DryEyeRhythm smartphone app. Upon scanning a registration code, each participant's cohort ID from the Tohoku Medical Megabank database will be linked to their smartphone app, enabling data integration between the Tohoku Medical Megabank and DryEyeRhythm database. The primary outcome will assess the association between genetic polymorphisms and DED using a genome-wide association study. Secondary outcomes will explore associations between DED and various factors, including sociodemographic characteristics, lifestyle habits, medical history, biospecimen analyses (eg, blood and urine), and physiological measurements (eg, height, weight, and eye examination results). Associations will be evaluated using logistic regression analysis, adjusting for potential confounding factors. The discovery stage of participant enrollment was conducted from August 1, 2021, to June 30, 2022. The replication stage will take place from August 31, 2024, to March 31, 2026. Data analysis is expected to be completed by September 2026, with results reported by March 2027. This study highlights the potential of smartphone apps in advancing biobank research and deepening the understanding of multifactorial DED, paving the way for personalized treatment strategies in the future. DERR1-10.2196/67862.

First rangewide density estimate of the endemic and isolated Luangwa giraffe in Zambia

The Luangwa giraffe (Giraffa tippelskirchi thornicrofti), a subspecies of the Masai giraffe endemic to the Luangwa Valley of northeastern Zambia, inhabits an increasingly human-modified landscape. Accurate and current population estimates are critical to evaluating their status and identifying effective conservation strategies. However, sparse monitoring since the early 1900s has limited inferences about population size, structure, and range. To address this, we conducted the most spatially extensive and systematic survey to date of Luangwa giraffe across its distribution, extending survey effort 120 km south of their officially recognized extent. Using spatial capture recapture modeling, we estimated 651–890 giraffe and an overall density of 0.04–0.05 giraffe/km2. Density decreased to nought beyond 7.5 km from permanent rivers, consistent with preferred forage concentrated in riparian areas. Increasing giraffe density estimates up to a threshold of the Human Footprint Index suggested that limited human presence may have negligible consequences on movement and resource selection. This was likely due to suitable habitat and minimal conflict despite human presence. However, without mitigating land-use planning, rapid land conversion threatens human-giraffe coexistence. An even sex ratio and small proportion of subadults implied a stable population, but sex-biased and temporal dynamics in space use, impacts of predation, and stochastic risks necessitate continued monitoring. This study highlights the value of systematic large-scale monitoring and opportunities for data integration across long-term monitoring programs to evaluate factors driving Luangwa giraffe dynamics and to inform science-based conservation of this unique and isolated population.

Data-Driven Environmental Risk Management and Sustainability Analytics (Second Edition)

Environmental risk management (ERM) and sustainability analytics have undergone a paradigm shift from reactive, compliance-based frameworks to advanced, predictive, and data-driven methodologies. This second edition of "Data-Driven Environmental Risk Management and Sustainability Analytics" critically explores the integration of contemporary technologies such as machine learning (ML), artificial intelligence (AI), blockchain, Internet of Things (IoT), quantum computing, and cloud computing within ERM frameworks. The manuscript reviews the evolution of ERM strategies, emphasizing the transformative role of predictive analytics, real-time monitoring, and multi-stakeholder collaboration in addressing global environmental challenges including climate change, biodiversity loss, and resource depletion. Through empirical case studies on coastal flooding and urban water resource management, the research demonstrates the practical effectiveness of advanced analytics in mitigating environmental risks and enhancing resilience. Furthermore, the manuscript highlights key policy frameworks and governance models promoting transparency, data security, and sustainable development practices globally. The study concludes with actionable recommendations and identifies research gaps concerning data integration, quantum computing applications, and the ethical dimensions of emerging technologies in sustainability analytics. This edition aims to provide policymakers, researchers, practitioners, and industry professionals with actionable insights into designing and implementing robust, data-driven environmental risk management strategies aligned with sustainable development objectives.

An Overview of the State of the Art of Mobile XR and Lessons Learnt So Far

(Trendsetter Presentation) Mobile extended reality, encompassing augmented reality, virtual reality, and mixed reality, has advanced significantly over the past decade. Transitioning from high-cost, tethered systems into increasingly accessible, mobile-first platforms, these technologies have been integrated into education, training, and cultural sectors with varied results. As smartphones, head-mounted displays, and web-based frameworks evolved, so too did the opportunities and constraints of delivering extended reality experiences at scale. This presentation provides a structured overview of the current state of mobile extended reality. It is organised around five key developmental phases, beginning with early mobile visualisation and simple three degrees of freedom experiences, followed by the emergence of marker-based augmented reality and physical-digital hybrid approaches using 3D printing. Subsequent phases focus on mobile-first immersive learning, collaborative virtual environments, affective biometric feedback, and the recent shift toward spatial computing with artificial intelligence integration. The presentation draws on over twenty published case studies in education, health, construction, design, and cultural heritage. These include simulations for remote paramedic skill development, mobile augmented reality heritage tours, immersive cinema projects using heart rate and brainwave data, and international comparisons of mobile web-based augmented reality access and usability. Each case study is used to identify specific design challenges and pedagogical implications, including issues of onboarding, user interface complexity, physical discomfort, and cultural and ethical sensitivity. Lessons from these implementations are critically examined. Key successes include measurable skill transfer, high learner engagement, and the development of comparative visualisation pedagogy. Challenges include limited scalability beyond pilot deployments, insufficient inclusion of diverse learner needs, and difficulty sustaining cross-institutional collaboration. Design-based research and multimodal evaluation approaches have played an essential role in identifying these factors. The presentation also reflects on how mobile extended reality intersects with policy and standards. It includes examples from international safety guidelines, ethical design frameworks, and institutional deployment models. These considerations are used to inform a broader discussion of where mobile extended reality currently stands and where future development is needed. Participants will leave with a comprehensive understanding of the trajectory of mobile extended reality technologies and their application in real-world contexts. The session will be of particular relevance to educators, researchers, designers, and institutional leaders seeking evidence-informed guidance for designing, evaluating, or scaling mobile immersive learning environments. It will also identify future directions for research and development, including the need for longitudinal studies, integration of cognitive and emotional data, and improved accessibility for all learners.

Predictive athlete performance modeling with machine learning and biometric data integration

The Purpose of this study is to propose a new integrative framework for athletic performance prediction based on state-of-the-art machine learning analysis and biometric data biometric scanning. By merging physiological signals i.e., Heart rate variability, oxygen consumption, muscle activation patterns, with psychological signals i.e., mental toughness, athlete engagement, group cohesion along with contextual training data, we create a hybrid model that performs superiorly as compared to traditional unidimensional models. Our exercisers were trained using a gradient boosting and neural network to learn the very complex non-linear relationships that exist between the physical and the psychological performance drivers. With a rich sample set of 480 athletes from different sports, the proposed model achieved 90% accuracy (R2 = 0.90) in predicting performance outcomes and outdid the conventional methods with statistical approaches R2 = 0.77) and machine learning based methods (R2 = 0.77) And machine whiz methods. The robust results achieved from the model (over 90%) as compared to conventional nor statistical methods. From the analysis of the features’ importance, the strongest predictor of performance are the Provided Dedicated Athletes’ scores of the Functional Movement Screening (13.7%), athlete dedication (11.5%), maximum acceleration capabilities (10.2%), which verify the relationship along biomechanical, preconceived explosive power and psychological commitment. This reflects the finding whereby deep categorized athletic talent prediction requires a multi-dimensional approach by sophisticated fusion techniques. The framework is useful to coaches and sports scientists because it allows for the individualized design of injury risk mitigation and physiologically and psychologically-focused interpersonal help. This approach integrates multiple factors and constitutes an important progress in sports analytics by providing a comprehensive perspective on the complex realities which influence elite athletic performance.

Mini Review: Highlight of Recent Advances and Applications of MALDI Mass Spectrometry Imaging in 2024.

Matrix-assisted laser desorption/ionisation mass spectrometry imaging (MALDI-MSI) is an emerging imaging tool that allows visualisation of hundreds of analytes unbiasedly in a single experiment. This paper highlights the adaptations of MALDI-MSI in different context in 2024, such as clinical diagnostic, pharmacology, forensics applications, plant metabolism and biology. Challenges and advancements were also discussed regarding sample preparation, instrumentations, data analysis, and integration of machine learning in the trend of single cell resolution and multi-omics. There are still rooms for improvements in sensitivity, spatial resolution, acquisition algorithm and data integration across multi-omics data to enable MALDI-MSI at subcellular level.

The predictive power of profiling the DNA methylome in human health and disease.

Early and accurate diagnosis significantly improves the chances of disease survival. DNA methylation (5mC), the major DNA modification in the human genome, is now recognized as a biomarker of immense clinical potential. This is due to its ability to delineate precisely cell-type, quantitate both internal and external exposures, as well as tracking chronological and biological components of the aging process. Here, we survey the current state of DNA methylation as a biomarker and predictor of traits and disease. This includes Epigenome-wide association study (EWAS) findings that inform Methylation Risk Scores (MRS), EpiScore long-term estimators of plasma protein levels, and machine learning (ML) derived DNA methylation clocks. These all highlight the significant benefits of accessible peripheral blood DNA methylation as a surrogate measure. However, detailed DNA methylation biopsy analysis in real-time is also empowering pathological diagnosis. Furthermore, moving forward, in this multi-omic and biobank scale era, novel insights will be enabled by the amplified power of increasing sample sizes and data integration.

Advanced mapping of mango orchards in Chittoor district through object-based image analysis approach using Sentinel-2 imagery

Mango is crucial in India's agricultural economy, particularly in the Chittoor District of Andhra Pradesh. As mango cultivation transitions from subsistence farming to large-scale commercial operations, accurate mapping and monitoring of mango plantations are essential for sustainable agricultural management. This study explored the application of Object-Based Image Analysis (OBIA) using high-resolution Sentinel-2 satellite imagery for mapping mango plantations in Chittoor District. OBIA, a more advanced approach than traditional pixel- based methods, integrates spectral, spatial and contextual data, enabling the identification of mango orchards with higher accuracy. Sentinel-2 multispectral bands were utilized to distinguish mango plantations based on canopy density, inter-row spacing and orchard layout. Ground-truth data collected from 531 points across the district validated the classification process. The results show that OBIA achieved an overall accuracy of 87.0 % with a Kappa index of 0.74, signifying strong agreement with the ground truth data and the total mango area mapped in Chittoor District is 97,006 hectares. This study highlights the effectiveness of OBIA for accurate mango area estimation and suggests potential improvements, including the integration of hyperspectral data and advanced algorithms. This study offers valuable insights for agricultural management, resource optimization and policy planning, with implications for broader crop mapping and precision agriculture applications.

Hybrid Mode Sensor Fusion for Accurate Robot Positioning

Robotic systems are becoming increasingly crucial in applications requiring high precision. While a robot can operate using basic sensor feedback under controlled conditions, achieving micro-level accuracy requires more comprehensive data integration, especially in dynamic environments. The fusion of data from a variety of sensors is necessary for improving the positioning accuracy of a robot because the accuracy of one type of sensor is insufficient. The field of micro-positioning presents new challenges and tasks that have been gradually explored in the recent literature published from 2015 to 2025. Micro-positioning is a complex operation that involves factors such as mechanical drift, environmental effects, and sensor signal errors. Hybrid fusion is a sensor fusion technique that combines elements of fusion at different levels. For the effective deployment of robots in such contexts, it is essential to integrate multiple sensors and ensure reliable data fusion between them. This involves the use of different sensors, advanced fusion algorithms, and accurate calibration methods through sensor fusion and sophisticated data processing techniques. This literature review presents an analysis of the sensor data fusion methods for precise robot micro-positioning. The focus is on the investigated sensors, the applied synthesis methods, and the developed algorithms and their practical application to identify the existing gaps for future system improvements. Finally, discussions and conclusions based on the collected ideas are presented.

Single-cell and spatial transcriptomics reveal correlation between RNA methylation-related miRNA risk model and immune infiltration in hepatocellular carcinoma

IntroductionIncreasing evidence highlights the pivotal role of RNA methylation and miRNAs in hepatocellular carcinoma (HCC). However, the risk associated with RNA methylation-related miRNAs (RMRMs) in the HCC immune microenvironment remains largely unknown. Here, we predicted the correlation between RMRM risk and immune cell infiltration in HCC using machine learning. MethodsMiRNA sequencing data was used to identify RMRMs. A risk score model of HCC was developed utilizing four RMRMs, including miR-551a, miR-4739, miR-326, and miR-210-3p.ResultsPatients with high-risk scores exhibited poorer prognoses. Single-cell RNA sequencing (scRNA-seq) analysis revealed the high-risk group exhibited increased infiltration levels of several immune cell subtypes, including myeloid-derived suppressor cell (MDSC), macrophage, and T cells. The data integration of scRNA-seq and bulk RNA-seq showed the decreased TIDE score in the high-risk patients and the elevated levels of Macro-secreted phosphoprotein 1 (SPP1), MDSC-meiotic nuclear divisions 1 (MND1), γδ T cells, and Macro-complement C1q C chain (C1QC) predicted adverse prognosis. ScRNA-seq and spatial transcriptomics data integration unveiled the spatial distribution of RMRMs risk scores and their correlation with immune cell subtype localization. Risk model-based clustering of HCC samples revealed that cluster 2, characterized by a higher risk score, correlated with a poorer prognosis and reduced immune and stromal scores. In vitro, the overexpression of miR-4739 in Huh-7 cells significantly induced SPP1+ macrophages, and the culture medium derived from SPP1+ macrophages further promoted the proliferation and migration of Huh-7 cells. Furthermore, miR-4739 reduced m1A methylation by inhibiting tRNA methyltransferase 61A (TRMT61A) expression. DiscussionOur study reveals that the RMRM risk model could effectively predict the prognosis of HCC, and SPP1+ macrophages regulated by miR-4739-RNA methylation promote the proliferation and migration of HCC cells. These results highlight the potential of RMRMs in predicting the prognosis of HCC.

GIS-based integration of marine data for assessment and management of a highly anthropized coastal area

Monitoring coastal marine environments by evaluating and comparing their chemical, physical, biological, and anthropogenic components is essential for ecological assessment and socio-economic development. In this study, we conducted an integrated multivariate analysis to assess the descriptors of the Marine Strategy Framework Directive at a regional scale in the Tyrrhenian Sea (Italy), with a specific focus on the densely populated coastal zone of the Campania region. Physical, chemical, and biological data were collected and analyzed in 22 sampling sites during three oceanographic surveys in the Gulf of Gaeta (GoG), Naples (GoN), and Salerno (GoS) in autumn 2020. Our results indicated that these three gulfs were distinct overall, with GoN being more divergent and heterogeneous than GoG and GoS. The marine area studied in the GoN had more favorable hydrographic and trophic conditions and food web characteristics, except for the mesozooplankton biomass, and was closer to socio-economic factors compared to the GoS and GoG. Our analysis helped us find the key ecological features that define different sub-regions and connect them to social and economic factors, including human activities. We highlighted the relevance of primary and secondary variables in terms of the comprehensive ecological assessment of a marine area and its impact on specific socio-economic activities. These findings support the need to describe and integrate multiple descriptors at the spatial scale.