Saved Queries

This paper evaluates a Sustainable Energy Efficiency Business Model (SEEBM) for small and medium sized enterprises (SMEs) in the European industrial sector. The sustainability-oriented model, developed by the authors, combines Energy Performance Contracting (EPC), Energy Supply Contracting (ESC), and Energy Saving Insurance (ESI) within a unified framework to support industrial decarbonisation. The study identifies key performance indicators and translates them into a System Dynamics model using a Design-Based Research approach. The model is built from secondary data drawn from 45 SME case studies in the European SMEmPower project and is validated through extreme condition testing and behavioural sensitivity analysis. Results indicate that the integrated model significantly enhances financial performance, reducing the average payback period from average 36 months to 10 months. Sensitivity analysis highlights the influence of contract duration, energy saving rates, and energy prices on both payback and emissions reduction outcomes. This research introduces a novel dynamic framework integrating EPC, ESC, and ESI, enabling time-based evaluation of investment viability and environmental impact. It offers a replicable decision support tool for policymakers and market actors seeking scalable, low risk pathways to SME decarbonisation. Overall, the model provides practical insights for improving investment decisions while accelerating the transition toward sustainable industrial systems across Europe. Full article

(This article belongs to the Special Issue Simulation and Optimisation for Operational Decision-Making in Renewable Energy Supply Chains and Systems)

►▼ Show Figures

Figure 1

43 pages, 3631 KB

Open AccessArticle

LeadWinO Self-Assessment Model for Managers Activity: A Feed-Forward Neural Network-Based Indicator System

by Lidija Kraujalienė and Alytis Gruodis

Adm. Sci. 2026, 16(5), 197; https://doi.org/10.3390/admsci16050197 (registering DOI) - 23 Apr 2026

Abstract

This study addresses the growing need for structured, measurable organizational development (OD) models amid digital transformation, geopolitical uncertainty, and increasing managerial complexity. Contemporary middle- and top-level managers are expected to ensure productivity, strategic clarity, resilience, and data-driven decision-making; however, existing leadership methodologies are often examined separately and lack integrated evaluation frameworks. The research analyses two prominent approaches: the American Action Science methodology and the Scandinavian (particularly Finnish) consensus-based leadership concept. While Action Science emphasizes explicit reasoning, double-loop learning, accountability, and measurable performance outcomes, the Finnish consensus model prioritizes trust, participation, and relational cohesion. The aim of the study is to develop and empirically test the original digital model LeadWinO (LEADership for WINning Organizations) for evaluating the organizational development activities of middle- and top-level managers. The model was empirically tested on managers in Lithuania. The novelty of the research lies in combining management and informatics perspectives by embedding organizational development evaluation into a digital, indicator-based, and potentially predictive framework. The type of study is quantitative research integrating questionnaire analysis in the case of multi-profile sections. Analytical tool used for data simulation is Feedforward Neural Network for constructing sufficient gapless sets of digitalized data. Research results showed that the American Action Science methodology is most effectively used by managers working in very small and small enterprises in the service and maintenance sectors. The findings are expected to contribute to the operationalization of leadership effectiveness under uncertainty and provide organizations with an auditable structure linking managerial behaviour, decision-making processes, and organizational performance outcomes. Full article

►▼ Show Figures

Figure 1

46 pages, 3406 KB

Open AccessReview

IgA Nephropathy: Mechanisms, Risk Stratification, and Precision Therapy

by Sami Alobaidi

Diagnostics 2026, 16(9), 1259; https://doi.org/10.3390/diagnostics16091259 (registering DOI) - 22 Apr 2026

Abstract

IgA nephropathy is the most common primary glomerulonephritis worldwide and a leading cause of chronic kidney disease and kidney failure, with geographic and ancestral variation and a course ranging from asymptomatic urinary abnormalities to progressive loss of kidney function. This narrative review links the multi-hit model to risk stratification, biomarkers, current management, and emerging therapies, and highlights implementation gaps. Risk assessment is longitudinal, prioritizing proteinuria and estimated glomerular filtration rate trajectories and integrating Oxford MEST-C, prediction tools, and biomarker and multi-omics approaches, while recognizing limitations in histologic reproducibility and model calibration. Current management is anchored in optimized supportive care aimed at sustained proteinuria reduction and kidney protection, including intensive blood pressure control with maximal tolerated renin–angiotensin system blockade, dietary sodium restriction and lifestyle measures, and sodium–glucose co-transporter 2 inhibitors for eligible patients. For selected higher-risk patients with persistent proteinuria despite optimized supportive care, immunomodulatory strategies are discussed, including systemic corticosteroids and targeted-release budesonide (Nefecon), emphasizing structured toxicity risk mitigation and cautioning against assuming interchangeability among alternative oral budesonide formulations. Emerging therapies are organized around mechanism-aligned targets across the BAFF/APRIL axis, complement pathways, and endothelin-based approaches, with growing interest in sequencing and combination regimens layered on supportive care. Key gaps include reliance on surrogate endpoints, limited long-term durability and safety data, and uneven evidence for special populations. Full article

(This article belongs to the Special Issue Advances in Diagnostics of Chronic Kidney Disease)

20 pages, 6455 KB

Open AccessArticle

Lightweight Deep Learning Framework for Real-Time PRPD-Based Insulation Defect Classification in Medium-Voltage Cable Testing

by Paweł Kluge, Jacek Starzyński, Wojciech Kołtunowicz, Tomasz Bednarczyk and Łukasz Kolimas

Energies 2026, 19(9), 2029; https://doi.org/10.3390/en19092029 (registering DOI) - 22 Apr 2026

Abstract

Partial discharge (PD) measurements are crucial for evaluating the condition of the insulation systems of medium-voltage (MV) cables and their accessories. However, identifying PD defect types from phase-resolved partial discharge (PRPD) patterns still largely relies on expert knowledge. In this paper, the authors critically evaluate lightweight deep neural network architectures for automated classification of insulation defects from PRPD patterns: YOLOv8n, the MobileNetV2–YOLO hybrid network, and a compact SqueezeNet-based model. PD measurements were performed in a controlled environment in a factory laboratory for MV power cables in order to better evaluate the capability of the investigated models. The results demonstrate that lightweight deep neural architectures can effectively classify PRPD patterns and be deployed in a real measurement environment. The proposed approach has been integrated with the OMICRON MPD Suite measurement system, enabling automated defect recognition and visualisation during routine testing of MV cable. Full article

24 pages, 8083 KB

Open AccessArticle

From Biological Baselines to Community Fisheries Agreements: A Participatory Model for Sustainable Amazonian Fisheries

by Fernando Sánchez-Orellana, Rafael Yunda, Jonathan Valdiviezo-Rivera, Daysi Gualavisi-Cajas, Tarsicio Granizo and Gabriela Echevarría

Sustainability 2026, 18(9), 4180; https://doi.org/10.3390/su18094180 (registering DOI) - 22 Apr 2026

Abstract

Small-scale inland fisheries in the Amazon are critical for food security, yet their sustainability is increasingly threatened by overexploitation and environmental degradation. In data-limited contexts such as the northern Ecuadorian Amazon, the absence of continuous monitoring constrains the development of adaptive management strategies. This study develops an integrated socio-ecological baseline to support the establishment of fisheries agreements in five Indigenous communities of the Napo and Aguarico rivers. Through a participatory monitoring approach, we generated reproductive parameters (gonadosomatic index, fecundity, size at first maturity), population structure metrics, and length–weight relationships for key subsistence species across three hydrological phases. Reproductive investment exhibited marked seasonality, with peak gonadosomatic indices during rising waters in most species, identifying a critical period for protection. Life-history strategies ranged from high-fecundity periodic strategists to low-fecundity equilibrium species, implying differentiated vulnerability to harvesting. Community perceptions prioritized large migratory catfish and floodplain habitats, aligning with biological indicators of vulnerability. High performance in technical training demonstrated the feasibility of long-term local monitoring systems. By linking biological indicators with local ecological knowledge, this study proposes a pathway from baseline assessment to adaptive co-management. The framework presented here provides a transferable model for strengthening sustainability, governance, and food security in tropical small-scale fisheries facing persistent data limitations. Full article

(This article belongs to the Special Issue Sustainable Fisheries Management and Ecological Protection)

►▼ Show Figures

Figure 1

20 pages, 3437 KB

Open AccessArticle

Deep Reinforcement Learning-Guided Bio-Inspired Active Flow Control of a Flapping-Wing Drone for Real-Time Disturbance Suppression

by Saddam Hussain, Mohammed Messaoudi, Nouman Abbasi and Dajun Xu

Actuators 2026, 15(5), 231; https://doi.org/10.3390/act15050231 (registering DOI) - 22 Apr 2026

Abstract

Flapping-wing drones (FWDs), owing to their compact size and operation in cluttered and unsteady airflow environments, encounter significant aerodynamic and stability challenges. Studies of avian flight reveal that falcons and other raptors actively deflect their covert feathers to mitigate gusts and maintain stable flight. Drawing inspiration from this mechanism, this study presents a peregrine falcon-inspired Active Flow Control Unit (AFCU) integrated with a Deep Deterministic Policy Gradient (DDPG)-based deep reinforcement learning (DRL) controller for real-time disturbance attenuation. The AFCU employs mechanical covert feathers (MCFs) that actuate to dissipate gust loads during high wind conditions. A reduced-order bond graph model that encapsulates the nonlinear interaction between the primary wing and the feather-based active flow control surfaces is created which is used as a dynamic training environment for the DDPG agent. Utilizing closed-loop interactions, the successfully obtained learned policy produces optimal actuator forces to reduce feather-displacement error and aerodynamic load variations. The designed controller stabilizes the internally unstable open-loop AFCU, attaining near-zero steady-state error and settling times under 1.6 s for gust magnitudes ranging from 12.5 to 20 m/s. Simulations further illustrate a reduction of up to 50% in gust-induced loads compared to traditional approaches. This integration of bio-inspired design with learning-based active flow control offers a viable avenue for the development of highly adaptive and gust-resilient flapping-wing aerial systems. Full article

(This article belongs to the Special Issue Active Flow Control: Recent Advances in Fundamentals and Applications—3rd Edition)

►▼ Show Figures

Figure 1

18 pages, 880 KB

Open AccessArticle

Comparative Evaluation of Five Multimodal Large Language Models for Medical Laboratory Image Recognition: Impact of Prompting Strategies on Diagnostic Accuracy

by Hui-Ru Yang, Kuei-Ying Lin, Ping-Chang Lin, Jih-Jin Tsai and Po-Chih Chen

Diagnostics 2026, 16(9), 1258; https://doi.org/10.3390/diagnostics16091258 (registering DOI) - 22 Apr 2026

Abstract

Background: Multimodal large language models (MLLMs) show promise in medical imaging, but their performance is highly dependent on prompt engineering. This study systematically evaluates how different prompting strategies affect diagnostic accuracy in clinical laboratory image interpretation. Methods: We evaluated five MLLMs (ChatGPT-4o, Gemini 2.0 Flash, Claude 3.5 Sonnet, Grok-2, and Perplexity Pro (Claude 3.5 Sonnet)) using 177 proficiency testing images across three domains: blood smears (n = 78), urinalysis (n = 50), and parasitology (n = 49). Three prompting approaches were compared: (1) complex multi-choice prompts with 20 diagnostic options, (2) zero-shot open-ended prompts, and (3) two-step descriptive-reasoning prompts. Images were sourced from the Taiwan Society of Laboratory Medicine external quality assurance archives with expert consensus diagnoses. Results: Zero-shot prompting significantly outperformed complex multi-choice prompts across all models and domains (p < 0.001). With zero-shot prompts, Gemini achieved 78.5% overall accuracy (urinalysis: 92.0%; parasitology: 75.5%; blood smears: 64.1%), representing a 17% improvement over complex prompts. Two-step descriptive-reasoning prompts further improved blood smear accuracy by 8–12% for top-performing models, but showed minimal benefit in urinalysis and parasitology. The re-query mechanism (“please reconsider”) improved urinalysis accuracy by 7.6% but had a negligible effect on blood smears and parasitology. Conclusions: Prompting strategy critically determines MLLM diagnostic performance. Zero-shot approaches with minimal constraints consistently outperform complex multi-choice formats. The remarkable performance of general-purpose models in structured domains like urinalysis (>90% accuracy) demonstrates the considerable progress of multimodal AI. However, complex morphological tasks like blood smear interpretation require either specialized prompting techniques or domain-specific fine-tuning. These findings provide evidence-based guidance for optimizing AI integration in clinical laboratories. Full article

(This article belongs to the Special Issue The Application of Large Language Models (LLMs) and Vision-Language Models (VLMs) in Healthcare)

17 pages, 973 KB

Open AccessReview

Integrating Advanced Endoscopic Techniques and Confocal Microscopy for Early Detection of Extrahepatic Cholangiocarcinoma

by Barbara Lattanzi, Francesco Covotta, Anna Crescenzi, Antonietta Lamazza, Francesco Maria Di Matteo, Domenico Alvaro and Vincenzo Cardinale

Cancers 2026, 18(9), 1334; https://doi.org/10.3390/cancers18091334 (registering DOI) - 22 Apr 2026

Abstract

Extrahepatic cholangiocarcinoma (eCCA) is a highly aggressive malignancy arising from the biliary epithelium, with surgical resection representing the only potentially curative treatment. The predominant periductal infiltrating growth pattern, characterized by subepithelial tumor spread and desmoplastic stromal reaction, severely limits the diagnostic sensitivity of conventional endoscopic sampling techniques, which primarily assess the luminal mucosal surface. This review provides a histomorphology-oriented diagnostic framework for indeterminate extrahepatic biliary strictures, integrating advanced endoscopic technologies with emerging optical diagnostic approaches. ERCP combined with cholangioscopy demonstrates superior sensitivity for perihilar strictures, while EUS-guided tissue acquisition shows higher diagnostic yield in distal cholangiocarcinoma, also providing locoregional staging. Advanced EUS technologies—including elastography, contrast harmonic EUS, and Detective Flow Imaging—further improve characterization of indeterminate strictures by evaluating tissue stiffness, microvascular architecture, and periductal infiltration. Ex vivo fluorescence confocal laser microscopy (FCM) enables real-time microscopic evaluation of biopsy specimens, reducing diagnostic turnaround time and minimizing inadequate sampling. A location-adapted diagnostic algorithm integrating cross-sectional imaging, ERCP, cholangioscopy, and EUS is proposed. An integrated, biology-informed endoscopic approach tailored to tumor location and ductal wall involvement may significantly improve early eCCA detection and guide patient selection for curative treatment. Full article

(This article belongs to the Special Issue Neoadjuvant Therapy in Hepatopancreatobiliary Malignancies: Current Trends and Future Directions)

30 pages, 12170 KB

Open AccessArticle

“Urban Sprawl” or “Urban Compactness”? Differentiated Impacts of Urban Growth Patterns on the Coupling Coordination Between Pollution and Carbon Emissions

by Jiuyan Zhou, Jianbin Xu and Yuyi Zhao

Land 2026, 15(5), 701; https://doi.org/10.3390/land15050701 (registering DOI) - 22 Apr 2026

Abstract

Rapid urbanization in China has reshaped the coupling coordination between pollution and carbon emissions. However, existing studies largely rely on linear approaches and lack multidimensional and nonlinear assessments of urban growth patterns. Using panel data for 289 prefecture-level cities from 2010 to 2023, including built-up land, nighttime lights, CO₂ emissions, and PM_2.5 concentrations, this study develops three indicators: Urban Expansion Intensity (UEI), Urban Sprawl Index (USI), and Urban Compactness (UC). By integrating a coupling coordination model, K-means clustering, Geographically and Temporally Weighted Regression (GTWR), and interpretable XGBoost-SHAP analysis, four urban growth patterns are identified: High-Speed Low-Efficiency Expansion (HLE), Low-Speed Low-Efficiency Expansion (LLE), High-Speed High-Efficiency Compact (HHC), and Low-Speed High-Efficiency Compact (LHC). Results indicate that: (1) USI and UC exhibit significant nonlinear threshold effects on CCD; moderate expansion and higher compactness enhance synergy, whereas excessive dispersion or over-compactness weakens coordination. (2) UEI plays a relatively indirect and spatially heterogeneous role. (3) HHC and LHC cities achieve the highest CCD levels, while HLE cities perform the lowest. (4) Urban expansion shows an overall contraction trend, yet substantial regional disparities persist. These findings highlight nonlinear and spatially heterogeneous mechanisms linking urban growth patterns and pollution–carbon coupling coordination, providing implications for differentiated spatial governance. Full article

(This article belongs to the Section Urban Contexts and Urban-Rural Interactions)

►▼ Show Figures

Graphical abstract

16 pages, 605 KB

Open AccessArticle

Escape into Social Media? A 4-Week Tracking Study on Nomophobia and Smartphone Coping

by Jiahao Li, Yang Chu, Shan Liu, Yanfang Liu and Jie Xu

Healthcare 2026, 14(9), 1125; https://doi.org/10.3390/healthcare14091125 (registering DOI) - 22 Apr 2026

Abstract

Background: Nomophobia, the fear of being without a mobile phone, has become an increasing public health concern. While existing theories suggest that smartphones often serve as tools for emotional regulation, the situational mechanisms driving these compensatory behaviors remain under-explored. This study investigated how nomophobia levels interact with daily emotional fluctuations and busyness to influence smartphone-based coping patterns. Methods: We employed an intensive longitudinal approach combining objective smartphone tracking with a 4-week daily diary design. Thirty-seven participants were monitored, yielding 837 daily observations. Smartphone use was categorized into Instant Messaging (IM), Social Media Use (SMU), and Non-social Use (NSU). Multilevel linear regression analyzed the interaction effects on usage metrics. Results: Nomophobia significantly correlated with the duration and frequency of SMU, but not IM or NSU. A significant three-way interaction was observed: individuals with high levels of nomophobia exhibited a significantly increased frequency of overall usage, SMU and NSU when experiencing negative emotions during periods of low busyness. In contrast, low-nomophobia individuals maintained stable usage patterns regardless of situational stressors. Conclusions: By conceptualizing smartphone usage as a behavioral proxy for the coping process, this study provides preliminary evidence that nomophobia is associated with a situation-dependent coping pattern, primarily involving increased social media usage. These findings underscore the importance of integrating situational contexts and underlying coping processes to better understand and manage problematic smartphone use. Full article

(This article belongs to the Special Issue Addressing Behavioral Addictions and Promoting Emotional Well-Being: Insight, Challenges, and Solutions)

►▼ Show Figures

Figure 1

27 pages, 3040 KB

Open AccessSystematic Review

Stakeholder-Centred Value Creation Framework for Advancing Circular Economy Practices in the Construction Industry: A Systematic Review

by Thilini Liyanawatta and Melissa Teo

Buildings 2026, 16(9), 1652; https://doi.org/10.3390/buildings16091652 - 22 Apr 2026

Abstract

Despite increasing emphasis on circular economic practices, the construction sector remains slow to adopt circular approaches, largely due to limited stakeholder engagement. In this context, understanding how value is perceived by stakeholders is critical for motivating their participation in circular economy practices. This study presents a systematic literature review conducted in accordance with PRISMA guidelines to examine value creation models and frameworks across multiple disciplines. A total of 49 studies were identified and analysed through a structured screening and qualitative content analysis process. The review clarifies the conceptual underpinnings of “value” in a circular system, examines how value can be created and delivered, and identifies the essential elements required for a value creation framework in construction to motivate stakeholders toward circular practices. The findings highlight that a circular value creation framework needs to collectively generate, deliver, and capture economic, environmental, and social value for multiple stakeholders. Based on these insights, the study develops a stakeholder-centred conceptual framework for value creation in construction waste management. The originality of the framework lies in its integration of stakeholder value perceptions with circular economic implementation, explicitly addressing the challenges of the complex and project-based construction environment. Full article

(This article belongs to the Special Issue Research on the Environmental Impact Throughout the Life Cycle of Buildings)

►▼ Show Figures

Figure 1

31 pages, 2271 KB

Open AccessArticle

An MDAO Method for Assessing Benefits of Variable Cycle Engines in the Conceptual Design of Supersonic Civil Aircraft

by Chao Yang and Xiongqing Yu

Aerospace 2026, 13(5), 399; https://doi.org/10.3390/aerospace13050399 - 22 Apr 2026

Abstract

The Variable Cycle Engine (VCE) is a key enabling technology for addressing the economic and environmental challenges of next-generation supersonic civil aircraft. This paper presents a multidisciplinary design analysis and optimization (MDAO) approach to quantitatively assess the potential benefits of Variable Cycle Engines (VCE) in the conceptual design of supersonic civil aircraft. In this approach, component-level models of a conventional Mixed-Flow Turbofan (MFTF) and a double-bypass VCE with a Core Driven Fan Stage (CDFS) are integrated into the MDAO process. Employing a multi-point optimization strategy, the engine design parameters and off-design control schedules are first determined. Subsequently, for each given engine design (MFTF and CDFS VCE), the airframe geometry parameters are optimized to minimize the aircraft Maximum Take-off Weight (MTOW). The application of this approach is illustrated through a case study of a medium-sized supersonic civil transport. The results indicate that, under the assumption of identical weights for the VCE and the MFTF, the design with the VCE reduces the MTOW by 2.8%, block fuel consumption by 5.7%, and total mission Nitrogen Oxides (NOx) emissions by 24.2% compared to the design with the MFTF. Additionally, lateral noise and flyover noise during the take-off phase are decreased by 2.2 EPNdB and 1.9 EPNdB, respectively. To account for the potential weight increase caused by the structural complexity of the VCE, a parametric weight sensitivity analysis is conducted. Results show that the VCE retains its advantages in MTOW, fuel efficiency, noise, and emissions for weight penalty factors up to 1.15. Full article

(This article belongs to the Special Issue Overall Aircraft Design for New Airframe Technologies, New Energy Systems, and New Operations—2nd Edition)

►▼ Show Figures

Figure 1

23 pages, 1876 KB

Open AccessArticle

Retrieval-Augmented Few-Shot Malware Detection via Binary Visualization and Vision–Language Embeddings

by Woo Jin Jung, Nae-Joung Kwak and Byoung-Yup Lee

Appl. Sci. 2026, 16(9), 4100; https://doi.org/10.3390/app16094100 - 22 Apr 2026

Abstract

The rapid evolution of malware families poses significant challenges for cybersecurity systems, particularly when newly emerging threats lack sufficient labeled data. Although image-based deep learning approaches have achieved strong performance under fully supervised conditions, their dependence on retraining limits adaptability in dynamic environments. To address this issue, we propose a Retrieval-Augmented Few-Shot Malware Detection Framework that integrates binary-to-image visualization, multimodal embedding using a frozen Vision–Language Model (Qwen2.5-VL), and similarity-based external memory retrieval. Malware binaries are converted into grayscale images and embedded into a semantic vector space without task-specific fine-tuning. During inference, query samples retrieve similar support embeddings from a vector database, and predictions are generated through similarity-weighted aggregation, enabling adaptation without parameter updates. Evaluated on the MalImg dataset with 25 malware families under 1-shot to 10-shot settings, the framework achieves 0.886 accuracy in the 10-shot configuration. Ablation results demonstrate that combining VLM embeddings with retrieval mechanisms provides consistent improvements over individual components. These findings highlight the effectiveness of decoupling representation learning from adaptation for scalable few-shot malware detection. Full article

(This article belongs to the Special Issue Security, Privacy and Application in New Intelligence Techniques: 2nd Edition)

►▼ Show Figures

Figure 1

23 pages, 6049 KB

Open AccessArticle

Seamless Inter-Domain Mobility with Hybrid SDN-LISP

by Kuljaree Tantayakul, Adisak Intana, Aung Aung and Riadh Dhaou

Future Internet 2026, 18(5), 227; https://doi.org/10.3390/fi18050227 - 22 Apr 2026

Abstract

One of the challenges in managing mobility in a heterogeneous network domain remains a significant challenge in Software-Defined Networking (SDN). While SDN has effectively facilitated intra-domain mobility, inter-domain mobility has been a major issue, leading to service interruptions, packet loss, and unstable communication sessions. This article presents a new concept in mobility management: a hybrid SDN-LISP network that facilitates inter-domain communication by integrating SDN with the Locator/Identifier Separation Protocol (LISP). The main idea is to introduce a new event-based orchestration model that uses OpenFlow Packet-In messages to provide instantaneous updates to Endpoint Identifiers-to-Routing Locators (EID-to-RLOC) mappings, unlike traditional LISP, which relies on timers for updates. The proposed framework has been implemented and evaluated on a Mininet-WiFi testbed under various mobility conditions. The results obtained from the experimental evaluation reveal that packet loss is reduced by 92.32% when using the proposed framework over the conventional SDN Mobility approach. Although there is a slight increase in jitter overhead due to LISP encapsulation of 0.628 ms, the framework does not compromise Transmission Control Protocol (TCP) session continuity. In addition, the control plane synchronization time is also minimized to 277.5 ms. This reveals that the proposed framework is a stable mobility solution that does not depend on any conventional IP mobility solutions and can be used in future network environments requiring seamless inter-domain connectivity. Full article

(This article belongs to the Section Network Virtualization and Edge/Fog Computing)

31 pages, 1941 KB

Open AccessArticle

Integrative Multi-Omics Analysis and Computational Modeling Identifying Shared Inflammatory Pathways and JAK Inhibitor Targets in PG and IBD

by Hui Yao, Yi Wu and Ruzhi Zhang

Int. J. Mol. Sci. 2026, 27(9), 3733; https://doi.org/10.3390/ijms27093733 - 22 Apr 2026

Abstract

This study investigates shared molecular mechanisms between pyoderma gangrenosum (PG) and inflammatory bowel disease (IBD) and systematically evaluates the therapeutic potential of JAK inhibitors targeting this pathway. Despite the clear clinical comorbidity, the core inflammatory pathways driving cross-tissue associations between the two diseases remain unclear. Furthermore, systematic mechanistic evidence is lacking regarding whether JAK inhibitors act by regulating shared pathological pathways in patients with comorbidities. To address this, this study integrated PG skin and IBD intestinal transcriptome data, single-cell transcriptomic data, and genome-wide association study (GWAS) meta-data from public databases. It employed a multi-level computational biology approach combining Mendelian randomization, weighted gene co-expression network analysis, protein interaction network construction, molecular docking simulations, and system dynamics modeling. The results revealed that genetic analysis confirmed IBD as a causal risk factor for PG, precisely identifying six shared genetic loci. Transcriptomic analysis identified a cross-tissue conserved inflammatory module centered on the JAK-STAT pathway, with JAK2 and STAT3 identified as network hubs. Molecular docking predicted high affinity of baricitinib for both JAK1 and JAK2, while system dynamics modeling demonstrated that its intervention effectively suppresses signaling in the shared inflammatory network. This study reveals the molecular basis of the “gut–skin axis” comorbidity between PG and IBD from a multi-omics integration perspective. It provides predictive computational evidence for the use of JAK inhibitors in targeted comorbidity therapy. Baricitinib is identified as a particularly promising candidate. These findings advance the transition from empirical drug use to mechanism-guided precision treatment strategies. Although this study provides multiscale computational simulation evidence, the lack of direct experimental validation of these predicted results necessitates further confirmation through in vitro and in vivo experiments. Full article

(This article belongs to the Special Issue Mathematical Computation and Modeling in Biology)

Show export options Show export options

Select all

Export citation of selected articles as:

Error

Oops... you haven't selected anything for export.

Displaying article 1-50 on page 1 of 1325.

Go to page 1 2 3 4 5

Search Results (66,232)

Further Information

Guidelines

MDPI Initiatives

Follow MDPI