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The vertical load-bearing performance of slab–column joints is significantly affected by bottom reinforcement and concealed beams, but existing studies remain insufficient in analyzing their influence mechanisms. To address this, the effects of bottom reinforcement, concealed beam width, and punch-to-span ratio on the mechanical properties of joints are systematically investigated in this study through finite element analysis. Validating 2 experimental models and establishing 13 parametric models, the results shows that adding bottom reinforcement can enhance the late-stage bearing capacity and ductility of joints; increasing the ratio of top-to-bottom reinforcement improves bearing capacity but reduces ductility; a wider concealed beam leads to better bearing capacity and ductility performance of the joint; and under the same concealed beam width, a larger punching–span ratio reduces bearing capacity but improves ductility. This study reveals the critical role of bottom reinforcement and concealed beams in joint performance, providing a theoretical basis for optimizing design. Full article

(This article belongs to the Special Issue Seismic and Durability Performance of Steel Connections)

17 pages, 816 KiB

Open AccessArticle

Risk Stratification Using a Perioperative Nomogram for Predicting the Mortality of Bladder Cancer Patients Undergoing Radical Cystectomy

by Daniel-Vasile Dulf, Anamaria Larisa Burnar, Patricia-Lorena Dulf, Doina-Ramona Matei, Hendea Raluca Maria, Cătălina Bungărdean, Maximilian Buzoianu, Iulia Andraș, Tudor-Eliade Ciuleanu, Nicolae Crișan and Camelia Alexandra Coadă

J. Clin. Med. 2025, 14(16), 5810; https://doi.org/10.3390/jcm14165810 (registering DOI) - 16 Aug 2025

Abstract

Background: Perioperative factors significantly impact oncologic outcomes after radical cystectomy (RC) for bladder cancer. This study aimed to identify key perioperative predictors for overall (OS) and progression-free survival (PFS) and to develop a prognostic nomogram for the identification of high-risk patients adapted to the clinical routines and standard of care of our country. Methods: We retrospectively analyzed 121 patients undergoing RC (2014–2024). Data on patient demographics, comorbidities, tumor pathology, neoadjuvant treatments, extensive intraoperative factors, and postoperative events were assessed using COX models. A prognostic nomogram for 3-year OS was constructed. Results: Median follow-up was 44.33 months. Significant predictors for worse OS included lymphovascular invasion (LVI) (HR 2.22), higher T stage (HR 8.75), N+ status (HR 1.10), and intraoperative complications (HR 3.04). Similar predictors were noted for PFS. The developed nomogram incorporated T-, N-stages, sex, grade, intraoperative complications and early (12 months) recurrence, and was able to significantly identify patients with a higher mortality risk (p < 0.001) with a C-index of 0.74. Conclusions: Our nomogram for mortality prediction of BC patients offers a promising tool for individualized risk stratification. Further studies are required for its external validation. Full article

(This article belongs to the Special Issue Advances and Perspectives in Cancer Diagnostics and Treatment)

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28 pages, 3479 KiB

Open AccessArticle

Engineering in the Digital Age: A Career-Level Competency Framework Validated by the Productive Sector

by Nádya Zanin Muzulon, Luis Mauricio Resende, Gislaine Camila Lapasini Leal, Paulo Cesar Ossani and Joseane Pontes

Sustainability 2025, 17(16), 7425; https://doi.org/10.3390/su17167425 (registering DOI) - 16 Aug 2025

Abstract

This study investigates the essential competencies for engineers in the context of digital transformation, with the aim of proposing a refined framework to guide professional development across career levels. A mixed-methods, sequential approach was adopted: (1) a systematic literature review, conducted between 2014 and 2024, which identified 46 competencies organized into seven dimensions; (2) a quantitative survey with 392 engineers who self-assessed their level of mastery for each competency; (3) semi-structured interviews with 20 company representatives, who validated and contextualized the essential competencies according to hierarchical levels (junior, mid-level, and senior); (4) data triangulation, resulting in a final competency model by career level. The findings reveal a widespread deficit in digital competencies, regardless of hierarchical level. In total, 33 competencies assessed by career level showed statistically significant differences in employer perceptions and were identified as progressive throughout the career trajectory. Analysis of self-assessments and interviews indicates that for early-career engineers, there is a strong emphasis on personal and basic cognitive competencies. For mid-level engineers, the data show a significant valuation of social competencies. Senior engineers are perceived as having accumulated experience across all seven mapped dimensions. This study offers a practical model that can be used by educational institutions, companies, and professionals to align education, market demands, and career planning. Full article

(This article belongs to the Section Psychology of Sustainability and Sustainable Development)

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25 pages, 2119 KiB

Open AccessReview

Targeting Lactylation: From Metabolic Reprogramming to Precision Therapeutics in Liver Diseases

by Qinghai Tan, Mei Liu and Xiang Tao

Biomolecules 2025, 15(8), 1178; https://doi.org/10.3390/biom15081178 (registering DOI) - 16 Aug 2025

Abstract

Lactylation, a recently identified post-translational modification (PTM) triggered by excessive lactate accumulation, has emerged as a crucial regulator linking metabolic reprogramming to pathological processes in liver diseases. In hepatic contexts, aberrant lactylation contributes to a range of pathological processes, including inflammation, dysregulation of lipid metabolism, angiogenesis, and fibrosis. Importantly, lactylation has been shown to impact tumor growth, metastasis, and therapy resistance by modulating oncogene expression, metabolic adaptation, stemness, angiogenesis, and altering the tumor microenvironment (TME). This review synthesizes current knowledge on the biochemical mechanisms of lactylation, encompassing both enzymatic and non-enzymatic pathways, and its roles in specific liver diseases. From a therapeutic perspective, targeting lactate availability and transport, as well as the enzymes regulating lactylation, has demonstrated promise in preclinical models. Additionally, combinatorial approaches and natural compounds have shown efficacy in disrupting lactylation-driven pathways, providing insights into future research directions for hepatic diseases. Although the emerging role of lactylation is gaining attention, its spatiotemporal dynamics and potential for clinical translation are not yet well comprehended. This review aims to synthesize the multifaceted roles of lactylation, thereby bridging mechanistic insights with actionable therapeutic strategies for liver diseases. Full article

(This article belongs to the Section Molecular Medicine)

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20 pages, 7030 KiB

Open AccessArticle

Integrating HBIM and GIS Through Object-Relational Databases for the Conservation of Rammed Earth Heritage: A Multiscale Approach

by F. Javier Chorro-Domínguez, Paula Redweik and José Juan Sanjosé-Blasco

Heritage 2025, 8(8), 336; https://doi.org/10.3390/heritage8080336 (registering DOI) - 16 Aug 2025

Abstract

Historic earthen architecture—particularly rammed earth—is underrepresented in digital heritage initiatives despite its widespread historical use and vulnerability to degradation. This paper presents a novel methodology for integrating semantic, geometric, and geospatial information from earthen heritage into a unified digital environment, bridging Heritage Building Information Modeling (HBIM) and Geographic Information Systems (GIS) through an object-relational database. The proposed workflow enables automated and bidirectional data exchange between Revit (via Dynamo scripts) and open-source GIS tools (QGIS and PostgreSQL/PostGIS), supporting semantic alignment and spatial coherence. The method was tested on seven fortified rammed-earth sites in the southwestern Iberian Peninsula, chosen for their typological and territorial diversity. Results demonstrate the feasibility of multiscale documentation and analysis, supported by a structured database populated with geometric, semantic, diagnostic, and environmental information, enabling enriched interpretations of construction techniques, material variability, and conservation status. The approach also facilitates the integration of HBIM datasets into broader territorial management frameworks. This work contributes to the development of scalable, open-source digital tools tailored to vernacular heritage, offering a replicable strategy for bridging the gap between building-scale and landscape-scale documentation in cultural heritage management. Full article

(This article belongs to the Section Architectural Heritage)

19 pages, 4994 KiB

Open AccessArticle

The Role of Nutritional Environment in Cryptococcus gattii Titan Cells’ Ultrastructure, Biophysical Properties, Molecular Features, and Virulence in Cryptococcosis

by Igor Avellar-Moura, Glauber R. de S. Araujo, Juliana Godoy, Vinicius Alves, Iara Bastos de Andrade, Juliana Soares, Bruno Pontes and Susana Frases

Infect. Dis. Rep. 2025, 17(4), 101; https://doi.org/10.3390/idr17040101 (registering DOI) - 16 Aug 2025

Abstract

Background/Objectives: Cryptococcus gattii presents a significant threat to healthy individuals. Titan cell formation, a key virulence factor, is influenced by the nutritional environment and plays a critical role in immune evasion and stress resistance. This study investigates the molecular and biophysical changes in titanized C. gattii cells grown in nutrient-rich Neurobasal™ medium, a potent inducer of titan cells. Methods: An integrative approach was used, combining scanning electron microscopy, optical tweezers, fluorescence microscopy, and physicochemical methods to analyze C. gattii cells grown in Neurobasal™ medium and minimal media. Results: Cells grown in Neurobasal™ medium exhibited significant differences compared to those grown in minimal media. These included a thicker and more defined polysaccharide capsule, enhanced capsule elasticity, and the secretion of more elastic polysaccharides. Furthermore, cells grown in the enriched medium showed reduced susceptibility to antifungals and delayed mortality in infection models. Conclusions: C. gattii adapts to nutritional cues by forming titan cells, thereby enhancing its pathogenicity. Targeting nutritional sensing pathways may offer novel therapeutic strategies against cryptococcal infections. Full article

21 pages, 556 KiB

Open AccessArticle

A Quadratic Programming Model for Fair Resource Allocation

by Yanmeng Tao, Bo Jiang, Qixiu Cheng and Shuaian Wang

Mathematics 2025, 13(16), 2635; https://doi.org/10.3390/math13162635 (registering DOI) - 16 Aug 2025

Abstract

In collaborative projects, traditional resource allocation methods often rely on company-assigned contribution rates, which can be subjective and lead to unfair outcomes. To address this, we propose a quadratic programming model that integrates participants’ self-reported rankings of their contributions across projects with company evaluations. The model aims to minimize deviations from company-assigned rates while ensuring consistency with participants’ perceived contribution rankings. Extensive simulations demonstrate that the proposed method reduces allocation errors by an average of 50.8% compared to the traditional approach and 21.4% against the method considering only individual estimation tendencies. Additionally, the average loss reduction in individual resource allocation ranges from 40% to 70% compared to the traditional method and 10% to 50% against the estimation-based method, with our approach outperforming both. Sensitivity analyses further reveal the model’s robustness and its particular value in flawed systems; the error is reduced by approximately 75% in scenarios where company evaluations are highly inaccurate. While its effectiveness is affected by factors such as team size variability and self-assessment errors, the approach consistently provides more equitable allocation of resources that better reflects actual individual contributions, offering valuable insights for improving fairness in team projects. Full article

(This article belongs to the Special Issue Data-Driven Decision Making: Models, Methods and Applications, 2nd Edition)

16 pages, 8293 KiB

Open AccessArticle

Thermodynamic Modeling of Microstructural Design of Lightweight Ferritic Steels

by Tamiru Hailu Kori, Adam Skowronek, Jarosław Opara, Ana Paula Domingos Cardoso and Adam Grajcar

Metals 2025, 15(8), 912; https://doi.org/10.3390/met15080912 (registering DOI) - 16 Aug 2025

Abstract

Ferritic lightweight steels are an emerging class of low-density steels (LDSs) with promising mechanical properties. The study aimed to develop two ferritic lightweight steels with different Mn concentrations. Al was incorporated to achieve the lightweighting effect due to its relatively low atomic mass of substitutional solutions. The C concentration was kept at a minimum level to avoid the precipitation of carbides and the Mn addition was intended to increase solid solution strengthening. Thermodynamic calculations (Thermo-Calc) were employed to design the composition, analyze the phase constituents, and predict the phase transformation behavior. Microstructural investigation and hardness tests were conducted to experimentally verify the calculations. Both produced alloys exhibited a fully ferritic microstructure. Compared to industrially produced DP980 steel, a density reduction of about 7.2% and 8.3% was attained for the Fe-0.04C-5.5Al-1.6Mn-0.075Nb and Fe-0.04C-5.6Al-5.5Mn-0.08Nb steels, respectively. The steel with the higher Mn content showed increased hardness attributed to its solution strengthening effect. An increase in the hardness values was also measured with the progress in hot-rolling thickness reductions for both alloys. The alloying elements influenced the microstructural characteristics, phase transformation behavior, density, and hardness of the newly designed lightweight steels. Full article

(This article belongs to the Special Issue Thermodynamic Modeling of Phase Equilibrium in Metallic Materials)

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22 pages, 2826 KiB

Open AccessArticle

Toward a Consensus Model of Cognitive–Reading Achievement Relations Using Meta-Structural Equation Modeling

by Daniel B. Hajovsky, Christopher R. Niileksela, Dawn P. Flanagan, Vincent C. Alfonso, William Joel Schneider and Jacob Robbins

J. Intell. 2025, 13(8), 104; https://doi.org/10.3390/jintelligence13080104 (registering DOI) - 16 Aug 2025

Abstract

Cognitive tests measure psychological constructs that predict the development of academic skills. Research on cognitive–reading achievement relations has primarily been completed with single-test batteries and samples, resulting in inconsistencies across studies. The current study developed a consensus model of cognitive–reading achievement relations using meta-structural equation modeling (meta-SEM) through a cross-sectional analysis of subtest correlations from English-language norm-referenced tests. The full dataset used for this study included 49,959 correlations across 599 distinct correlation matrices. These included correlations among 1112 subtests extracted from 137 different cognitive and achievement test batteries. The meta-SEM approach allowed for increased sampling of cognitive and academic reading skills measured by various test batteries to better inform the validity of construct relations. The findings were generally consistent with previous research, suggesting that cognitive abilities are important predictors of reading skills and generalize across different test batteries and samples. The findings are also consistent with integrated cognitive–reading models and have implications for assessment and intervention frameworks. Full article

(This article belongs to the Special Issue Intelligence Testing and Assessment)

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21 pages, 1845 KiB

Open AccessArticle

SRoFF-Yolover: A Small-Target Detection Model for Suspicious Regions of Forest Fire

by Lairong Chen, Ling Li, Pengle Cheng and Ying Huang

Forests 2025, 16(8), 1335; https://doi.org/10.3390/f16081335 (registering DOI) - 16 Aug 2025

Abstract

The rapid detection and confirmation of Suspicious Regions of Forest Fire (SRoFF) are critical for timely alerts and firefighting operations. In the early stages of forest fires, small flames and heavy occlusion lead to low accuracy, false detections, omissions, and slow inference in existing target-detection algorithms. We constructed the Suspicious Regions of Forest Fire Dataset (SRFFD), comprising publicly available datasets, relevant images collected from online searches, and images generated through various image enhancement techniques. The SRFFD contains a total of 64,584 images. In terms of effectiveness, the individual augmentation techniques rank as follows (in descending order): HSV (Hue Saturation and Value) random enhancement, copy-paste augmentation, and affine transformation. A detection model named SRoFF-Yolover is proposed for identifying suspicious regions of forest fire, based on the YOLOv8. An embedding layer that effectively integrates seasonal and temporal information into the image enhances the prediction accuracy of the SRoFF-Yolover. The SRoFF-Yolover enhances YOLOv8 by (1) adopting dilated convolutions in the Backbone to enlarge feature map receptive fields; (2) incorporating the Convolutional Block Attention Module (CBAM) prior to the Neck’s C2fLayer for small-target attention; and (3) reconfiguring the Backbone-Neck linkage via P2, P4, and SPPF. Compared with the baseline model (YOLOv8s), the SRoFF-Yolover achieves an 18.1% improvement in mAP@0.5, a 4.6% increase in Frames Per Second (FPS), a 2.6% reduction in Giga Floating-Point Operations (GFLOPs), and a 3.2% decrease in the total number of model parameters (#Params). The SRoFF-Yolover can effectively detect suspicious regions of forest fire, particularly during winter nights. Experiments demonstrated that the detection accuracy of the SRoFF-Yolover for suspicious regions of forest fire is higher at night than during daytime in the same season. Full article

(This article belongs to the Section Natural Hazards and Risk Management)

28 pages, 2148 KiB

Open AccessArticle

Analyzing the Causal Relationships Among Socioeconomic Factors Influencing Sustainable Energy Enterprises in India

by T. A. Alka, Raghu Raman and M. Suresh

Energies 2025, 18(16), 4373; https://doi.org/10.3390/en18164373 (registering DOI) - 16 Aug 2025

Abstract

Sustainable energy entrepreneurs promote sustainable development by focusing more on energy efficiency. This study examines the interdependence and driving–dependent relationships among the socioeconomic factors (SEFs) influencing sustainable energy enterprises (SEEs). A mixed-methods approach is used, beginning with a literature review and expert consensus, followed by total interpretive structural modeling (TISM) and cross-impact matrix multiplication applied to classification (MICMAC) analysis. Seven key SEFs are finalized through interviews with 12 experts. Data are then collected from 11 SEEs. The study reveals that the regulatory and institutional framework emerges as the primary driving factor influencing other SEFs, including financial accessibility, market demand, technological innovation, and infrastructure readiness. Social and cultural acceptance is identified as the most dependent factor. The study proposes future research directions by identifying the United Nations sustainable development goals (SDGs) related to the antecedents, decisions, and outcomes with theoretical linkages through the Antecedents–Decisions–Outcomes (ADO) framework. The major SDGs identified are SDG 4 (education), SDG 7 (energy), SDG 9 (industry), SDG 11 (communities), and SDG 13 (climate). The study highlights that regulatory support, funding access, skill development, and technology transfer are required areas for strategic focus. Understanding the hierarchy of SEs supports business model innovation, investment planning, and risk management. Full article

(This article belongs to the Special Issue Energy Policies and Sustainable Development)

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16 pages, 871 KiB

Open AccessArticle

The Synergistic Impact of 5G on Cloud-to-Edge Computing and the Evolution of Digital Applications

by Saleh M. Altowaijri and Mohamed Ayari

Mathematics 2025, 13(16), 2634; https://doi.org/10.3390/math13162634 (registering DOI) - 16 Aug 2025

Abstract

The integration of 5G technology with cloud and edge computing is redefining the digital landscape by enabling ultra-fast connectivity, low-latency communication, and scalable solutions across diverse application domains. This paper investigates the synergistic impact of 5G on cloud-to-edge architectures, emphasizing its transformative role in revolutionizing sectors such as healthcare, smart cities, industrial automation, and autonomous systems. Key advancements in 5G—including Enhanced Mobile Broadband (eMBB), Ultra-Reliable Low-Latency Communication (URLLC), and Massive Machine-Type Communications (mMTC)—are examined for their role in enabling real-time data processing, edge intelligence, and IoT scalability. In addition to conceptual analysis, the paper presents simulation-based evaluations comparing 5G cloud-to-edge systems with traditional 4G cloud models. Quantitative results demonstrate significant improvements in latency, energy efficiency, reliability, and AI prediction accuracy. The study also explores challenges in infrastructure deployment, cybersecurity, and latency management while highlighting the growing opportunities for innovation in AI-driven automation and immersive consumer technologies. Future research directions are outlined, focusing on energy-efficient designs, advanced security mechanisms, and equitable access to 5G infrastructure. Overall, this study offers comprehensive insights and performance benchmarks that will serve as a valuable resource for researchers and practitioners working to advance next-generation digital ecosystems. Full article

(This article belongs to the Special Issue Innovations in Cloud Computing and Machine Learning Applications)

31 pages, 2084 KiB

Open AccessArticle

Spatial-Temporal Forecasting of Air Pollution in Saudi Arabian Cities Based on a Deep Learning Framework Enabled by AI

by Rafat Zrieq, Souad Kamel, Faris Al-Hamazani, Sahbi Boubaker, Rozan Attili and Marcos J. Araúzo-Bravo

Toxics 2025, 13(8), 682; https://doi.org/10.3390/toxics13080682 (registering DOI) - 16 Aug 2025

Abstract

Air pollution is steadily increasing due to industrialization, economic activities, and transportation. High levels pose a significant threat to human health and well-being worldwide. Saudi Arabia is a growing country with air quality indices ranging from moderate to unhealthy. Although there are many monitoring stations distributed throughout the country, mathematical modeling of air pollution is still crucial for health and environmental decision-making. From this perspective, in this study, a data-driven approach based on pollutant records and a Deep Learning (DL) Long Short-Term Memory (LSTM) algorithm is carried out to perform temporal modeling of selected pollutants (PM₁₀, PM_2.5, CO and O₃) based on time series combined with a spatial modeling focused on selected cities (Riyadh, Jeddah, Mecca, Rabigh, Abha, Dammam and Taif), covering ~48% of the total population of the country. The best forecasts were provided by LSTM in cases where the datasets used were of relatively large size. Numerically, the obtained performance metrics such as the coefficient of determination (R²) ranged from 0.2425 to 0.8073. The best LSTM results were compared to those provided by two ensemble methods, Random Forest (RF) and eXtreme Gradient Boosting (XGBoost), where the merits of LSTM were confirmed mainly in terms of its ability to capture hidden relationships. We also found that overall, meteorological factors showed a weak association with pollutant concentrations, with ambient temperature exerting a moderate influence. However, incorporating ambient temperature into LSTM models did not lead to a significant improvement in predictive accuracy. The developed approach can be used to support decision-making in environmental and health domains, as well as to monitor pollutant concentrations based on historical time series records. Full article

(This article belongs to the Special Issue Unraveling the Health Impacts of Air Pollution: Assessments and Mechanisms)

26 pages, 2865 KiB

Open AccessArticle

Extra Tree Regression Algorithm for Simulation of Iceberg Draft and Subgouge Soil Characteristics

by Hamed Azimi and Hodjat Shiri

Water 2025, 17(16), 2425; https://doi.org/10.3390/w17162425 (registering DOI) - 16 Aug 2025

Abstract

With the expansion of offshore and subsea infrastructure in Arctic and sub-Arctic regions, concerns are rising, driven by climate change and global warming, over the risk of drifting icebergs colliding with these structures in cold waters. Traditional methods for estimating iceberg underwater height and assessing subgouge soil properties, such as costly and time-consuming underwater surveys or centrifuge tests, are still used, but the industry continues to seek faster and more cost-efficient solutions. In this study, the extra tree regression (ETR) algorithm was employed for the first time to simultaneously model iceberg drafts and subgouge soil properties in both sandy and clay seabeds. The ETR approach first predicted the iceberg draft, then simulated subgouge soil reaction forces and deformations. A total of 22 ETR models were developed, incorporating parameters relevant to both iceberg draft estimation and subgouge soil characterization. The best-performing ETR models, along with the most influential input variables, were identified through a combination of sensitivity, error, discrepancy, and uncertainty analyses. The ETR model predicted iceberg draft with a high level of accuracy (R = 0.920, RMSE = 1.081), while the superior model for vertical reaction force in sand achieved an RMSE of 43.95 with 70% of predictions within 16% error. The methodology demonstrated improved prediction capacity over traditional techniques and can serve early-stage iceberg risk management. Full article

(This article belongs to the Special Issue Next-Generation Iceberg Behavior Modeling: Integrating Machine Learning, Computational Fluid Dynamics, and Environmental Dynamics)

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16 pages, 485 KiB

Open AccessSystematic Review

Effects of Nicotine-Free E-Cigarettes on Gastrointestinal System: A Systematic Review

by Ivana Jukic, Ivona Matulic and Jonatan Vukovic

Biomedicines 2025, 13(8), 1998; https://doi.org/10.3390/biomedicines13081998 (registering DOI) - 16 Aug 2025

Abstract

Background/Objectives: Nicotine-free electronic cigarettes (NFECs) are becoming increasingly popular, especially among youth and non-smokers, yet their effects on the gastrointestinal tract (GIT) remain poorly understood. This systematic review synthesizes available in vitro, in vivo, and limited human evidence on NFEC-associated changes in gastrointestinal health and function. Methods: Literature searches were conducted in Medline, Web of Science, Cochrane, and Scopus in July 2025, following PRISMA guidelines. Eligible studies examined NFEC effects on any GIT segment, including the oral cavity, liver, intestines, and microbiome. Data on study design, exposure characteristics, and main outcomes were extracted and narratively synthesized. Results: Of 111 identified records, 94 full-text articles were retrieved, and 21 studies met the inclusion criteria. Most were preclinical, with only one human pilot study. Evidence from oral cell and microbial models suggests that NFEC aerosols can induce pro-inflammatory cytokine production, impair cell viability, and disrupt microbial metabolism through their base constituents (propylene glycol, vegetable glycerine, and flavourings). Animal studies indicate possible hepatic oxidative stress, altered lipid metabolism, and gut barrier dysfunction, with some data suggesting more pronounced steatosis in nicotine-free exposures compared to nicotine-containing counterparts. Microbiome studies report reduced tight junction expression and altered neutrophil function. Conclusions: Current evidence is limited and predominantly preclinical but indicates that NFEC exposure can affect multiple aspects of gastrointestinal health. Robust longitudinal and interventional human studies are urgently needed to determine the clinical relevance of these findings and to inform regulation and public health policy. Full article

(This article belongs to the Special Issue Cellular and Molecular Mechanisms in Gastrointestinal Tract Disease)

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