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Financial time series forecasting remains a challenging task due to the high non-stationarity and concept drift inherent to market data. Existing deep learning models, such as LSTMs and transformers, typically employ static weights after training, limiting their ability to adapt to rapid market regime shifts (e.g., from trends to reversals). To bridge this gap between static parameters and dynamic environments, we propose a novel framework named Game-Theoretic HyperNetwork (GTH-Net), which introduces a context-aware meta-learning mechanism to achieve adaptive forecasting. Specifically, we first introduce an Evolutionary Game-Theoretic Correction Module (E-GTCM) to explicitly extract latent buying and selling pressure based on market microstructure priors through an iterative gated evolution process. Subsequently, we propose a HyperNetwork-based fusion mechanism that treats the extracted game state as a meta-context to dynamically generate the weights of the forecasting head. This allows the model to automatically switch its prediction rules in response to shifting market regimes. Extensive experiments on real-world stock datasets demonstrate that GTH-Net significantly outperforms baselines in terms of machine learning predictive accuracy and simulated financial profitability. Furthermore, ablation studies and parameter analysis confirm that the dynamic weight generation mechanism effectively captures market reversals caused by overcrowded trades. Full article

27 pages, 4695 KB

Open AccessArticle

A Novel Weighted Ensemble Framework of Transformer and Deep Q-Network for ATP-Binding Site Prediction Using Protein Language Model Features

by Jiazhi Song, Jingqing Jiang, Chenrui Zhang and Shuni Guo

Int. J. Mol. Sci. 2026, 27(7), 3097; https://doi.org/10.3390/ijms27073097 (registering DOI) - 28 Mar 2026

Abstract

Adenosine triphosphate (ATP) serves as a central energy currency and signaling molecule in cellular processes, with ATP-binding sites in proteins playing critical roles in enzymatic catalysis, signal transduction, and gene regulation. The accurate identification of ATP-binding sites is essential for understanding protein function mechanisms and facilitating drug discovery, enzyme engineering, and disease pathway analysis. In this study, we present a novel hybrid deep learning framework that synergizes heterogeneous learning paradigms based on protein sequence information for accurate ATP-binding site prediction. Our approach integrates two complementary base classifiers. One is a Transformer-based model, which leverages high-level contextual embeddings generated by Evolutionary Scale Modeling 2 (ESM-2), a state-of-the-art protein language model, combined with a local–global dual-attention mechanism that enables the model to simultaneously characterize short-segment and long-range contextual dependencies across the entire protein sequence. The other is a deep Q-network (DQN)-inspired classifier that achieves residue-level prediction as a sequential decision-making process. The final predictions are generated using a weighted ensemble strategy, where optimal weights are determined via cross-validations to leverage the strengths of both models. The prediction results on benchmark independent testing sets indicate that our method achieves satisfactory performance on key metrics. Beyond predictive efficacy, this work uncovers the intrinsic biological mechanisms underlying protein–ATP interactions, including the synergistic roles of local structural motifs and global conformational constraints, as well as family-specific binding patterns, endowing the research with substantial biological significance. The research in this work offers a deeper understanding of the protein–ligand recognition mechanisms and supportive efforts on large-scale functional annotations that are critical for system biology and drug target discovery. Full article

(This article belongs to the Section Molecular Informatics)

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25 pages, 3134 KB

Open AccessArticle

Heritage of Hybrid Temples at the Margins as Tourist Attractions: Insights from a Thai–Chinese Temple on Malaysia–Thai Borderland

by Punya Tepsing, Kiran Shinde and Thaenphan Senaphan Buamai

Heritage 2026, 9(4), 137; https://doi.org/10.3390/heritage9040137 (registering DOI) - 28 Mar 2026

Abstract

This paper investigates how hybrid temples are created and transformed into tourist attractions, focusing on Wat Phothikyan Phutthathum—a Thai–Chinese temple located in Kelantan, close to Malaysia’s border with Thailand. This study aims to understand how both the local Thai minority and Chinese majority contribute to temple hybridisation, examine the influence of such temples on community identity, and explore their growing importance as tourist attractions. It highlights the negotiation and cultural exchange that shape new heritage spaces for borderland communities. Using visual analysis and interviews, the research shows that, since there are no Chinese temples nearby, Chinese Buddhists and Taoists adapt Thai temples by incorporating their own rituals and art. This results in blended iconography and practices, guided by an open-minded Thai monk. Features like large Buddha statues, staircases featuring naga-dragon designs, and murals combining different traditions reveal this fusion. The temple’s unique artwork and spiritual environment attract visitors from Muslim-majority areas and various countries like Thailand, Taiwan, and Singapore. As tourism becomes central to the temple’s role, the local authorities emphasise its religious significance and multicultural symbolism, aligning with economic interests and daily interactions among minority groups. This study offers valuable empirical and theoretical perspectives on the blending of religious heritage sites in border regions. Full article

(This article belongs to the Special Issue Cultural Landscape and Sustainable Heritage Tourism)

22 pages, 312 KB

Open AccessArticle

The Impact of New Quality Productive Forces on High-Quality Development of Higher Education: Evidence from China

by Changkui Liu

Sustainability 2026, 18(7), 3308; https://doi.org/10.3390/su18073308 (registering DOI) - 28 Mar 2026

Abstract

Amid accelerating technological change and structural transformation, advanced productivity regimes characterized by technological innovation, digital transformation, and green upgrading have become key drivers of economic restructuring. In China, this transformation is conceptualized as new quality productive forces (NQPFs). However, their implications for higher education systems remain insufficiently explored. This study examines how NQPFs influence the high-quality development of higher education. Using panel data from 30 Chinese provinces from 2015 to 2022, composite indices constructed with the entropy method are used to measure NQPFs and the high-quality development of higher education, and mediation effect models, together with a Spatial Durbin Model, are employed to analyze the underlying mechanisms and spatial interactions. The results show that NQPFs significantly promote the high-quality development of higher education. This effect operates mainly through industrial collaborative agglomeration and digital infrastructure development and also generates positive spatial spillover effects across regions. These findings highlight the role of productivity transformation in shaping the structural foundations of higher education development in the digital era. Full article

21 pages, 2277 KB

Open AccessArticle

Stray Currents Beyond the Fundamental in the Swedish BT Railway Power System

by Tommy Hjertberg and Sarah Karolina Rönnberg

Energies 2026, 19(7), 1670; https://doi.org/10.3390/en19071670 (registering DOI) - 28 Mar 2026

Abstract

Sweden has a very high and variable soil resistivity, making it difficult to ensure a consistently good connection to earth along the track. Booster Transformers (BTs) have been used to ensure that the current returns through the intended path and that stray currents are limited. The ability of BTs to control return currents is limited by their series impedance and by imperfect coupling. In this article, we make a detailed model of the BT system between two feed-in points and evaluate how well the BT system can contain stray currents at harmonic frequencies. The main contribution is that we demonstrate that harmonic currents are significantly less well contained by the BT system, and that the practice of allowing local grid connections to the railway earth system risks creating significant stray currents in the local grid, particularly at harmonic frequencies, but also that electrical safety may be compromised by the transmission of touch voltages to locations with a different soil resistivity than the rail bed. Full article

(This article belongs to the Section F1: Electrical Power System)

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34 pages, 393 KB

Open AccessArticle

Symmetry-Aware Dual-Encoder Architecture for Context-Aware Grammatical Error Correction in Chinese Learner English: Toward a Spaced-Repetition Instructional Structure Sensitive to Individual Differences

by Jun Tian

Symmetry 2026, 18(4), 579; https://doi.org/10.3390/sym18040579 (registering DOI) - 28 Mar 2026

Abstract

Grammatical error correction (GEC) for Chinese learner English is still dominated by sentence-level modeling, which limits discourse-level consistency and weakens adaptation to learner-specific error profiles. From an instructional perspective, these limitations also reduce the value of automated feedback as a basis for spaced-repetition instructional structures sensitive to individual differences. This study proposes a symmetry-aware dual-encoder architecture for context-aware GEC in Chinese learner English. A context encoder captures preceding-sentence information, while a source encoder integrates BERT-based semantic representations with Bi-GRU-based syntactic features for the current sentence. A gated decoder performs asymmetric fusion of local and contextual evidence. To better reflect corpus-level tendencies in Chinese learner English, a CLEC-informed augmentation strategy generates synthetic errors using empirical category frequencies as a coarse sampling prior. Experiments on CoNLL-2014, JFLEG, and CLEC show consistent improvements over strong neural baselines in F_0.5 and GLEU under the current desktop-oriented implementation setting. Nevertheless, the integration of BERT, dual encoders, and gated decoding introduces non-negligible computational overhead, and the present system is therefore better suited to desktop writing-support scenarios than to strict real-time or large-scale online deployment. The proposed framework thus provides a practical technical basis for personalized grammar feedback and for future spaced-repetition instructional designs in ESL writing support. Full article

(This article belongs to the Special Issue Symmetry and Asymmetry in Natural Language Processing)

19 pages, 1666 KB

Open AccessArticle

MTLL: A Novel Multi-Task Learning Approach for Lymphocytic Leukemia Classification and Nucleus Segmentation

by Cuisi Ou, Zhigang Hu, Xinzheng Wang, Kaiwen Cao and Yipei Wang

Electronics 2026, 15(7), 1419; https://doi.org/10.3390/electronics15071419 (registering DOI) - 28 Mar 2026

Abstract

Bone marrow cell classification and nucleus segmentation in microscopic images are fundamental tasks for computer-aided diagnosis of lymphocytic leukemia. However, bone marrow cells from different subtypes exhibit high morphological similarity, and structural information is often constrained under optical microscopic imaging, posing challenges for stable and effective feature representation. To address this issue, we propose MTLL (Multitask Model on Lymphocytic Leukemia), a novel multitask approach that performs cell classification and nucleus segmentation within a unified network to exploit their complementary information. The model constructs a hybrid backbone for shared feature representation based on a CNN-Transformer architecture, in which Fuse-MBConv modules are tightly integrated with multilayer multi-scale transformers to enable deep fusion of local texture and global semantic information. For the segmentation branch, we design an AM (Atrous Multilayer Perceptron) decoder that combines atrous spatial pyramid pooling with multilayer perceptrons to fuse multi-scale information and accurately delineate nucleus boundaries. The classification branch incorporates prior knowledge of cell nuclei structures to capture subtle variations in cellular morphology and texture, thereby enhancing the model’s ability to distinguish between leukemia subtypes. Experimental results demonstrate that the MTLL model significantly outperforms existing advanced single-task and multi-task models in both lymphocytic leukemia classification and cell nucleus segmentation. These results validate the effectiveness of the multi-task feature-sharing strategy for lymphocytic leukemia diagnosis using bone marrow microscopic images. Full article

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30 pages, 2984 KB

Open AccessReview

Protein Engineering and Immobilization of Imine Reductases for Pharmaceutical Synthesis: Recent Advances and Applications

by Nevena Kaličanin, Nikolina Popović Kokar, Milica Spasojević Savković, Anja Stošić, Olivera Prodanović, Nevena Surudžić and Radivoje Prodanović

Chemistry 2026, 8(4), 40; https://doi.org/10.3390/chemistry8040040 (registering DOI) - 28 Mar 2026

Abstract

Imine reductases (IREDs) have emerged as valuable biocatalysts for the asymmetric synthesis of chiral amines, key intermediates in numerous active pharmaceutical ingredients. Their ability to operate under mild reaction conditions with high chemo- and stereoselectivity provides an attractive alternative to conventional metal-catalyzed or chemical reduction processes. However, the broader industrial application of wild-type IREDs is often constrained by their limited substrate scope and moderate catalytic efficiency. Recent advances in biocatalysis have demonstrated that engineered IREDs can catalyze the reduction of a wide range of natural and non-natural imines, significantly expanding their applicability in pharmaceutical and fine chemical synthesis. In parallel, enzyme immobilization strategies have proven highly effective for improving operational stability, facilitating enzyme reuse, and enabling continuous flow biocatalytic processes. Efficient cofactor regeneration systems have further enhanced the practical implementation of IRED-based transformations. Advances in protein engineering, including structure-guided design, semi-rational mutagenesis, and directed evolution, have generated enzyme variants with improved catalytic activity, stereoselectivity, and substrate tolerance. The integration of high-throughput screening technologies and machine-learning-assisted enzyme design has further accelerated the discovery and optimization of efficient IRED biocatalysts. This review summarizes recent progress in the protein engineering and immobilization of IREDs and discusses future perspectives for their industrial application. Full article

(This article belongs to the Section Medicinal Chemistry)

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35 pages, 2535 KB

Open AccessReview

Next-Generation Redox Mediators: Itaconate, Nitro-Fatty Acids, Reactive Sulfur Species and Succinate as Emerging Switches in Predictive Redox Medicine

by Luca Gammeri, Alessandro Allegra, Fabio Stagno and Sebastiano Gangemi

Antioxidants 2026, 15(4), 427; https://doi.org/10.3390/antiox15040427 (registering DOI) - 28 Mar 2026

Abstract

Oxidative stress is no longer viewed as a random imbalance between reactive oxygen species and antioxidants, but as a failure of an integrated redox network that connects metabolism, immunity, and metal homeostasis. Classical markers such as malondialdehyde and 4-hydroxynonenal define oxidative damage, yet they cannot explain how redox adaptation occurs or fails. Over the past decade, the discovery of regulated cell-death pathways (ferroptosis, cuproptosis) and emerging metabolic signals has revealed a new generation of adaptive redox mediators—including itaconate, nitro-fatty acids, reactive sulfur species and succinate—that act as electrophilic or persulfidating regulators rather than passive by-products of oxidation. This review integrates mechanistic, biochemical and clinical evidence to define how these mediators remodel the nuclear factor erythroid 2-related factor 2/Kelch-like ECH-associated protein 1, nuclear factor kappa-light-chain-enhancer of activated B cells, and hypoxia-inducible factor 1-alpha axes, coordinate lipid–metal–sulfur cross-talk, and shape vulnerability or resistance to ferroptosis and cuproptosis. By combining deep molecular research with translational perspectives, we propose a unifying framework for predictive redox medicine based on composite biomarker panels and AI-assisted phenotyping. Understanding and quantifying these next-generation mediators will open new avenues for precision nutrition, drug development, and disease prevention—transforming oxidative-stress biology from a descriptive field into an actionable platform for human health. Full article

(This article belongs to the Section ROS, RNS and RSS)

21 pages, 2741 KB

Open AccessReview

Research Progress of Methane Membrane Separation Technology

by Xiujuan Feng, Haoyu Zhang, Haotong Guo, Chuhao Huang, Yiwen Fu, Shuqi Wang, Jing Yang, Jie Li and Yankun Ma

Membranes 2026, 16(4), 119; https://doi.org/10.3390/membranes16040119 (registering DOI) - 28 Mar 2026

Abstract

Membrane technology demonstrates broad prospects in the field of methane capture and purification due to its high efficiency and low energy consumption characteristics. This paper systematically reviews the research progress in membrane technology for methane separation in recent years, focusing on the design and optimization of membrane material systems, in-depth analysis of mass transfer mechanisms, and practical applications in areas such as biogas upgrading and natural gas decarbonization. Researchers have significantly enhanced membrane separation performance for CO₂/CH₄, CH₄/N₂, and other systems by developing novel material systems such as polymer membranes, inorganic membranes, and mixed matrix membranes (MMMs), combined with strategies like pore structure regulation, interface optimization, and functionalization. Although membrane technology has shown good economic feasibility and application potential in some scenarios, challenges such as long-term material stability, anti-plasticization capability, and large-scale manufacturing remain the main current obstacles. Future research should further focus on the development of novel membrane materials, process integration optimization, and intelligent process control to promote a greater role for membrane technology in the efficient utilization of methane resources and energy structure transformation. Full article

(This article belongs to the Special Issue Advanced Membrane Technologies for Gas Capture and Clean Energy: Multiscale Insights and Applications)

23 pages, 989 KB

Open AccessArticle

AI-Driven Corruption Risk Indicator Detection: A Comparative Evaluation of Transformer-Based NLP Models in Unstructured Procurement Data

by Nikolaos Peppes, Theodoros Alexakis, Emmanouil Daskalakis and Evgenia Adamopoulou

Information 2026, 17(4), 329; https://doi.org/10.3390/info17040329 (registering DOI) - 28 Mar 2026

Abstract

The detection of corruption-related indicators within unstructured, textual procurement data remains a complex task due to linguistic ambiguity, contextual variation and domain-specific terminology. This study presents a comparative evaluation of three transformer-based Natural Language Processing (NLP) architectures (BERT-base-uncased, RoBERTa-base and DeBERTa-v3-base) for automated corruption risk indicator detection in procurement texts coming from heterogeneous sources. A unified dataset is constructed by linking unstructured technical documentation with structured procurement outcomes, enabling an outcome-driven risk labeling strategy. Performance evaluation is conducted through different metrics, including precision, recall, F1-score and ROC-AUC, complemented by explainability analysis using Integrated Gradients. The results demonstrate a clear performance progression and highlight the comparative strengths of the evaluated architectures. Overall, this study highlights the potential of contextual transformer models to support scalable, transparent and operational anti-corruption monitoring systems. Full article

(This article belongs to the Collection Natural Language Processing and Applications: Challenges and Perspectives)

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38 pages, 2279 KB

Open AccessArticle

Universal Comparison Methodology for Hough Transform Approaches

by Danil Kazimirov, Vitalii Gulevskii, Alexey Kroshnin, Ekaterina Rybakova, Arseniy Terekhin, Elena Limonova and Dmitry Nikolaev

Mathematics 2026, 14(7), 1136; https://doi.org/10.3390/math14071136 (registering DOI) - 28 Mar 2026

Abstract

The Hough transform (HT) is widely used in computer vision, tomography, and neural networks. Numerous algorithms for HT computation have been proposed, making their systematic comparison essential. However, existing comparative methodologies are either non-universal and limited to certain HT formulations or task-oriented, relying on application-specific criteria that do not fully capture algorithmic properties. This paper introduces a novel unified methodology for the systematic comparison of HT algorithms. It evaluates key characteristics, including computational complexity, accuracy, and auxiliary space complexity, while explicitly accounting for the property of self-adjointness. The methodology integrates both implementation-level and theoretical considerations related to the interpretation of HT as a discrete approximation of the Radon transform. A set of mathematically justified evaluation functions, not previously described in the literature, is proposed to support our methodology. Importantly, the methodology is universal, applicable across diverse HT paradigms, encompasses pattern-based and Fourier-based fast HT (FHT) algorithms, and offers a comprehensive alternative to existing task-specific methodologies. Its application to several state-of-the-art FHT algorithms (

F H T 2 D T

F H T 2 S P

A S D 2

K H M

, and Fast Slant Stack) yields new experimentally confirmed theoretical insights, identifies

A S D 2

as the most balanced algorithm, and provides practical guidelines for algorithm selection. In particular, the methodology reveals that for image sizes up to 3000, the maximum normalized computational complexity increases as follows:

F H T 2 D T

(1.1),

A S D 2

(15.3), and

K H M

(30.6), while the remaining algorithms exhibit at least 1.1 times higher values. The maximum orthotropic approximation error equals 0.5 for

A S D 2

K H M

, and Fast Slant Stack; lies between 0.5 and 1.5 for

F H T 2 S P

; and reaches 2.1 for

F H T 2 D T

. In terms of worst-case normalized auxiliary space complexity, the lowest values are achieved by

F H T 2 D T

(2.0), Fast Slant Stack (4.0, lower bound), and

A S D 2

(6.8), with all other algorithms requiring at least 8.2 times more memory. Full article

(This article belongs to the Special Issue Applications of Mathematical Models in Image Processing and Algorithm Theory)

18 pages, 1643 KB

Open AccessArticle

Root-Derived Flammulina velutipes Polysaccharides Improve Myofibrillar Protein Stability and Maintain Catfish Surimi Quality During Freeze–Thaw Cycling

by Ruiying Chen, Ning He, Xiaodong Li, Yu Zhan, Xin Zhang and Yingchun Zhu

Gels 2026, 12(4), 285; https://doi.org/10.3390/gels12040285 (registering DOI) - 28 Mar 2026

Abstract

Frozen surimi, a commonly used raw material in processed aquatic products, is vulnerable to repeated freeze–thaw fluctuations that accelerate protein denaturation and quality loss. In this study, root-derived Flammulina velutipes polysaccharides (FVPs) were extracted from the root-like portion of enoki mushroom, and surimi supplemented with 2% FVP and a blank control (CK) were stored at −18 °C and subjected to a total of five freeze–thaw cycles. The effects of FVP on myofibrillar protein (MP) characteristics and the storage quality of catfish surimi during the freeze–thaw cycles were analyzed. Compared with CK, FVP markedly alleviated the deterioration of water-holding capacity, gel strength, and MP solubility throughout freeze–thaw cycling. It also effectively inhibited the increase in thiobarbituric acid reactive substance (TBARS) values and MP aggregation and delayed the rate of decrease in the storage modulus (G′) and loss modulus (G″) of surimi. Additionally, low-field nuclear magnetic resonance (LF-NMR) further showed that FVP limited the conversion of immobilized water to free water, indicating enhanced water retention under repeated freeze–thaw stress. Fourier transform infrared spectroscopy (FTIR) and scanning electron microscopy (SEM) analyses revealed that FVP stabilized the secondary structure of MPs, making the microstructure of surimi more uniform and compact. The results of this study indicate that FVP exhibited significant cryoprotective effects during freeze–thaw cycles of surimi relative to the untreated control group, providing a theoretical basis for its potential application in aquatic product storage. Full article

(This article belongs to the Special Issue Research and Application of Edible Gels)

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24 pages, 4811 KB

Open AccessArticle

Lightweight Power Line Defect Detection Based on Improved YOLOv8n

by Yuhan Yin, Xiaoyi Liu, Kunxiao Wu, Ruilin Xu, Jianyong Zheng and Fei Mei

Sensors 2026, 26(7), 2112; https://doi.org/10.3390/s26072112 (registering DOI) - 28 Mar 2026

Abstract

To address the challenges of small targets, severe background clutter, and high deployment cost in UAV-based power-line defect detection, this paper proposes a lightweight defect detection model based on an improved YOLOv8n. In the downsampling stage, we design an improved lightweight adaptive downsampling module (ADownPro) to replace part of conventional convolutions, which uses a dual-branch parallel structure for stronger feature interaction and depthwise separable convolutions (DSConv) for complexity reduction. In the feature extraction stage, an integration of cross-stage partial connections and partial convolution (CSPPC) is proposed to replace the C2F module for efficient multi-scale feature fusion. In the detection head, mixed local channel attention (MLCA), which combines channel-spatial information and local–global contextual features, is introduced to strengthen defect-focused representations under complex backgrounds. For the loss function, a scale-annealed mixed-quality EIoU loss (SAMQ-EIoU) is proposed by combining iso-center scale transformation, scale factor annealing and focal-style quality reweighting to improve localization accuracy at high IoU thresholds. Experiments on a constructed dataset covering six typical defect categories show that the improved YOLOv8n achieves 91.4% mAP@0.50 and 64.5% mAP@0.50:0.95, with only 1.59 M parameters and 4.9 GFLOPs. Compared with mainstream detectors, the proposed model achieves a better balance between detection accuracy and lightweight design. In particular, compared with the recently proposed YOLOv8n-DSN and IDD-YOLO, it improves mAP@0.50 by 0.6% and 0.8%, and mAP@0.50:0.95 by 1.2% and 4.8%, respectively, while further reducing the parameter count by 1.00 M and 1.26 M, and the FLOPs by 1.7 G and 0.2 G. Moreover, the cross-dataset evaluation on the public UPID and SFID datasets further demonstrate the robustness and generalization ability of the proposed method. Full article

(This article belongs to the Section Fault Diagnosis & Sensors)

18 pages, 679 KB

Open AccessArticle

Becoming a Different Person: Living with Hepatic Encephalopathy as a Condition in Everyday Life—A Qualitative Explorative Study

by Marie Louise S. Hamberg, Rikke Parsberg Werge, Susanne Vahr Lauridsen and Thora Skodshøj Thomsen

Healthcare 2026, 14(7), 874; https://doi.org/10.3390/healthcare14070874 (registering DOI) - 28 Mar 2026

Abstract

Background/Objectives: Patients with liver cirrhosis experience a high symptom burden and low Health-Related Quality of Life (HR-QoL). Hepatic encephalopathy (HE) occurs in 75% of patients with cirrhosis but is sparsely described from the patient’s perspective. Due to recurrent cognitive impairment, a marginalized diagnosis, and a healthcare discourse emphasizing involvement and self-responsibility, these patients appear vulnerable when navigating a complex healthcare system. This study aims to explore how patients with chronic liver disease experience living with HE as a recurring condition, and how these patients are met by healthcare professionals (HCPs). Methods: Eight semi-structured interviews were conducted with four patients and four HCPs. Data were analyzed thematically following Braun and Clarke’s six-step analysis within the framework of Interpretive Description. The study was reported according to COREQ Guidelines. Results: The overarching theme “Becoming a different person” captured the profound identity changes experienced by patients. Three main themes emerged: 1. change and loss—in identity and self-understanding, in relationships, in relation to losing control, and in relation to experiencing isolation; 2. new paths—mental and practical alternative strategies; 3. HE in clinical encounters—requiring empathy, flexibility, and continuity. Stigma related to cirrhosis and its association with alcohol further intensified patients’ vulnerability. Conclusions: HE is experienced as a transformative and isolating condition, deeply affecting patients’ autonomy and social roles through vulnerability. The clinical encounter is shaped by the cognitive impairment due to HE, requiring tailored and sensitive care. Full article

(This article belongs to the Special Issue New Advances in Liver Disease Treatment and Care—a Holistic and Patient-Centered Approach)

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