Search Results (1,581)

Background: Abdominal wall hernias represent a significant global surgical burden, with over 20 million repairs performed annually. The convergence of rising obesity and diabetes rates with complex hernia management has necessitated innovative preoperative optimization strategies that address both metabolic dysfunction and mechanical challenges. Objectives: This comprehensive review synthesizes current evidence on emerging pharmacologic and procedural optimization strategies for patients undergoing abdominal wall hernia repair, with particular emphasis on glucagon-like peptide-1 (GLP-1) receptor agonists, botulinum toxin A (BTA) injections, progressive preoperative pneumoperitoneum (PPP) and biomechanical calculated repair. Methods: We conducted an extensive literature review incorporating recent clinical trials, observational studies, and meta-analyses, focusing on metabolic optimization with GLP-1 receptor agonists, mechanical preparation techniques, and their comparative effectiveness in reducing perioperative complications and hernia recurrence. Results: GLP-1 and GLP-1/GIP agonists demonstrate substantial metabolic benefits including weight reduction (10–20%), improved glycemic control, reduced systemic inflammation, and decreased postoperative complications in surgical populations. Recent evidence suggests reduced surgical site infection, thromboembolic events, and wound dehiscence in GLP-1 receptor agonists users. However, concerns regarding delayed gastric emptying and aspiration risk require careful perioperative management. BTA and PPP remain valuable techniques for mechanical optimization in loss-of-domain hernias, though modern biomechanically calculated repair (BCR) approaches using cyclic load analysis may reduce their necessity in many cases. The GRIP/CRIP concept demonstrates superior outcomes with 5–7% five-year recurrence rates compared to 15% with conventional approaches. Emerging evidence highlights collagen metabolism dysfunction as a fundamental determinant of hernia recurrence, prompting development of collagen-focused prehabilitation programs incorporating nutritional supplementation, aquatic exercise, and targeted physical conditioning. Conclusions: A paradigm shift toward integrated, personalized preoperative optimization is emerging, combining metabolic conditioning with mechanical preparation based on individual patient phenotypes and hernia complexity. Future research should focus on comparative effectiveness trials, optimal timing protocols, and multimodal strategies to maximize surgical outcomes while minimizing complications. Full article

Background: Local analgesia administered through a wound catheter is widely used for postoperative pain control, yet its effects on wound healing remain incompletely understood. This study examined how levobupivacaine alone or combined with meloxicam or buprenorphine influences inflammatory markers, angiogenesis, apoptosis, and transforming growth factor β1 (TGF-β1) expression during wound healing in rats. Methods: Thirty Sprague Dawley rats were assigned to five groups: control, saline, levobupivacaine (L), levobupivacaine/meloxicam (L/MEL), and levobupivacaine/buprenorphine (L/BUP). Treatments were administered via a wound catheter for three days. Blood and skin samples were collected before surgery and on days 3, 10, and 21. Results: Levobupivacaine combined with meloxicam or buprenorphine caused fluctuations in white blood cell counts, while albumin levels remained stable. Angiogenesis in the L/MEL group was markedly increased compared with the control, saline, and levobupivacaine-only groups, but the newly formed vessels exhibited consistently narrow lumina during the early healing phase. Caspase-3–positive cells were most numerous in L/MEL during inflammatory and proliferative phases, whereas delayed caspase-3 activation was observed in L/BUP. TGF-β1 expression peaked in both adjuvant groups on days 3 and 10. Conclusions: Meloxicam and buprenorphine increased TGF-β1 expression, but their vascular effects differed considerably. Meloxicam induced a marked increase in angiogenesis, but the newly formed vessels were structurally immature, displaying uniformly narrow lumina and poor architectural organisation, which led to their subsequent regression. In contrast, buprenorphine supported the formation of more mature vascular structures, characterised by wider vessel lumina and a more organised vascular network. These findings demonstrate that adjuvants used with levobupivacaine can significantly modify angiogenic and apoptotic responses and should be carefully considered when selecting multimodal local analgesia strategies after surgery. Full article

Biometric authentication on smartphones has advanced rapidly in recent years, with face recognition becoming the dominant modality due to its convenience and easy integration with modern mobile hardware. However, despite these developments, smartphone-based facial recognition systems remain vulnerable to a broad spectrum of attacks. This survey provides an updated and comprehensive examination of the evolving attack landscape and corresponding defense mechanisms, incorporating recent advances up to 2025. A key contribution of this work is a structured taxonomy of attack types targeting smartphone facial recognition systems, encompassing (i) 2D and 3D presentation attacks; (ii) digital attacks; and (iii) dynamic attack patterns that exploit acquisition conditions. We analyze how these increasingly realistic and condition-dependent attacks challenge the robustness and generalization capabilities of modern face anti-spoofing (FAS) systems. On the defense side, the paper reviews recent progress in liveness detection, deep-learning- and transformer-based approaches, quality-aware and domain-generalizable models, and emerging unified frameworks capable of handling both physical and digital spoofing. Hardware-assisted methods and multi-modal techniques are also examined, with specific attention to their applicability in mobile environments. Furthermore, we provide a systematic overview of commonly used datasets, evaluation metrics, and cross-domain testing protocols, identifying limitations related to demographic bias, dataset variability, and controlled laboratory conditions. Finally, the survey outlines key research challenges and future directions, including the need for mobile-efficient anti-spoofing models, standardized in-the-wild evaluation protocols, and defenses robust to unseen and AI-generated spoof types. Collectively, this work offers an integrated view of current trends and emerging paradigms in smartphone-based face anti-spoofing, supporting the development of more secure and resilient biometric authentication systems. Full article

Background: Methamphetamine (Meth) addiction, with its high relapse rates, poses a significant global challenge. Conventional therapies remain inadequate, highlighting the need for effective adjunctive treatments. Objective: This review synthesises evidence to propose a novel ‘Exercise Modality–Neural Target–Rehabilitation Stage’ integration model, elucidating how aerobic, resistance, and mind–body exercises differentially target specific neural pathways to ameliorate cognitive deficits, emotional dysregulation, and craving in Meth use disorder. Methods: A narrative synthesis of 84 studies (up to March 2025) from PubMed, Web of Science, and CNKI was conducted, focusing on the neurobiological basis and clinical application of exercise interventions. Results: The analysis identifies a key overarching neurobiological pattern: different exercise modalities work complementarily to reverse Meth-induced imbalance in glutamate/gamma-aminobutyric acid (Glu/GABA) neurotransmitter homeostasis. Aerobic exercise upregulates prefrontal–striatal BDNF to enhance cognitive control, while resistance training modulates the amygdala–striatal dopamine system to improve emotional stability. Additionally, mind–body exercises help balance the autonomic nervous system, which in turn helps manage cravings. Building on this, we construct a standardised ‘screening–assessment–prescription’ framework to guide personalised interventions across the various stages of withdrawal. Conclusions: The primary contribution of this review is the integrative model that positions exercise as a precise, evidence-based rehabilitation strategy. The proposed framework provides a practical blueprint for clinical translation, with future research focusing on developing personalised intelligent rehabilitation systems by integrating multimodal exercise with advanced technologies. Full article

This study presents an integrated and reproducible framework for within-tier screening of potential healthcare accessibility in Bangkok. Facilities in three service tiers (primary 294 units, regular 75, referral 29) are analyzed using point-pattern diagnostics, Voronoi geometric partitions, population-weighted allocation from subdistrict controls, and cumulative network travel-time isochrones. Spatial diagnostics indicate clustering among primary care units, a near-random configuration for regular units, and modest dispersion for referral hospitals, summarized by observed-to-expected nearest-neighbor ratios of approximately 0.77, 1.05, and 1.19, respectively. Voronoi partitions translate these distributions into geometric units that enlarge with increasing inter-facility spacing, while population-weighted assignments reveal higher population-per-partition-area burdens in the outer east and southwest. Isochrone maps (5–60 min rings) show central corridors with short travel times and peripheral areas where potential access declines. Interpreted against statutory planning intent, the maps indicate broad consistency of siting with high-intensity zones, alongside residual gaps at residential fringes. Framed as repeatable indicators of access and coverage, the workflow contributes to measuring and monitoring urban health sustainability under universal health coverage and routine planning cycles. The framework yields transparent indicators that support monitoring, priority setting, and incremental adjustments within each tier. Limitations include planar proximity assumptions, uniform areal weighting, single-mode modeled travel times without temporal variation, and the absence of capacity measures, motivating future work on capacity-weighted partitions, minimal dasymetric refinements, and time-dependent multimodal scenarios. Full article

Three-dimensional semantic segmentation plays a crucial role in advancing scene understanding in fields such as autonomous driving, drones, and robotic applications. Existing studies usually improve prediction accuracy by fusing data from vehicle-mounted cameras and vehicle-mounted LiDAR. However, current semantic segmentation methods face two main challenges: first, they often directly fuse 2D and 3D features, leading to the problem of information redundancy in the fusion process; second, there are often issues of image feature loss and missing point cloud geometric information in the feature extraction stage. From the perspective of multimodal fusion, this paper proposes a point cloud semantic segmentation method based on a multimodal gated attention mechanism. The method comprises a feature extraction network and a gated attention fusion and segmentation network. The feature extraction network utilizes a 2D image feature extraction structure and a 3D point cloud feature extraction structure to extract RGB image features and point cloud features, respectively. Through feature extraction and global feature supplementation, it effectively mitigates the issues of fine-grained image feature loss and point cloud geometric structure deficiency. The gated attention fusion and segmentation network increases the network’s attention to important categories such as vehicles and pedestrians through an attention mechanism and then uses a dynamic gated attention mechanism to control the respective weights of 2D and 3D features in the fusion process, enabling it to solve the problem of information redundancy in feature fusion. Finally, a 3D decoder is used for point cloud semantic segmentation. In this paper, tests will be conducted on the SemanticKITTI and nuScenes large-scene point cloud datasets. Full article

Background/Objectives: Tinnitus causes significant cognitive and emotional distress; however, its clinical assessment mostly relies on subjective measures without evaluation of objective indices. In this narrative review, we examined the potential of electroencephalography (EEG)-based neurophysiological markers as objective biomarkers in tinnitus assessment. Methods: The Web of Science, PubMed, EMBASE, and MEDLINE databases were searched to identify research articles on EEG-based analysis of individuals with tinnitus. Studies in which treatment and control groups were compared across four analytical domains (spectral power analysis, functional connectivity, microstate analysis, and entropy measures) were included. Qualitative synthesis was conducted to elucidate neurophysiological mechanisms, methodological characteristics, and clinical implications. Results: Analysis of 18 studies (n = 1188 participants) revealed that tinnitus is characterized by distributed neural dysfunction that extends beyond the auditory system. Spectral power analyses revealed sex-dependent, frequency-specific abnormalities across distributed brain regions. Connectivity analyses demonstrated elevated long-range coupling in high-frequency bands concurrent with diminished low-frequency synchronization. Microstate analyses revealed alterations in spatial configuration and transition probabilities. Entropy quantification indicated elevated complexity, particularly in the frontal and auditory cortices. Conclusions: EEG-derived neurophysiological markers demonstrate associations with tinnitus in group analyses and show potential for elucidating pathophysiological mechanisms. However, significant limitations, including low spatial resolution, small sample sizes, methodological heterogeneity, and lack of validation for individual-level diagnosis or treatment prediction, highlight the need for cautious interpretation. Standardized analytical protocols, larger validation studies, multimodal neuroimaging integration, and demonstration of clinical utility in prospective trials are required before EEG markers can be established as biomarkers for tinnitus diagnosis and management. Full article

Background/Objectives: Rapid postural reactions are critical for preventing falls, yet the neural systems supporting these responses are not fully understood, particularly with respect to aging and neurological disorders. Understanding how the brain detects, interprets, and responds to balance disturbances is essential for developing new interventions. This scoping review aimed to synthesize evidence from neuroimaging studies to identify the cortical and subcortical mechanisms underlying reactive balance and to characterize how these mechanisms are altered by aging and pathology. Methods: A structured search of EMBASE, PubMed, and CINAHL (7 November 2024) identified studies examining neural activity during experimentally induced balance perturbations. Sixty-one studies met inclusion criteria (EEG n = 45; MRI n = 9; fNIRS n = 8; PET n = 1) and were analyzed for patterns of regional activation and age- or disease-related differences. Results: Evidence converges on a distributed network supporting reactive balance. Sensorimotor, premotor, supplementary motor, and prefrontal cortices show consistent involvement, while cerebellar, brainstem, and basal ganglia structures contribute to rapid, automatic responses. Aging and neurological conditions commonly heighten cortical activation, suggesting reduced automaticity and increased reliance on compensatory control. Conclusions: Reactive balance emerges from coordinated activity across cortico-subcortical systems that are altered by aging and pathology. Further research incorporating multimodal imaging approaches and more ecologically realistic perturbation paradigms is needed to clarify mechanistic pathways and inform precision-based fall-prevention strategies. Full article

Continuous and personalized monitoring are beneficial for patients suffering from neurodegenerative diseases such as Alzheimer’s disease, Parkinson’s disease and multiple sclerosis. However, such levels of monitoring are seldom ensured by traditional models of care. This paper presents NeuroPredict, a secure edge–cloud Internet of Medical Things (IoMT) platform that addresses this problem by integrating commercial wearables and in-house sensors with cognitive and behavioral evaluations. The NeuroPredict platform links high-frequency physiological signals with periodic cognitive tests through the use of a modular architecture with lightweight device connectivity, a semantic integration layer for timestamp alignment and feature harmonization across heterogeneous streams, and multi-timescale data fusion. Its use of encrypted transport and storage, role-based access control, token-based authentication, identifier separation, and GDPR-aligned governance addresses security and privacy concerns. Moreover, the platform’s user interface was built by considering human-centered design principles and includes role-specific dashboards, alerts, and patient-facing summaries that are meant to encourage engagement and decision-making for patients and healthcare providers. Experimental evaluation demonstrated the NeuroPredict platform’s data acquisition reliability, coherence in multimodal synchronization, and correctness in role-based personalization and reporting. The NeuroPredict platform provides a smart system infrastructure for eHealth and remote monitoring in neurodegenerative care, aligned with priorities on wearables/IoMT integration, data security and privacy, interoperability, and human-centered design. Full article

Background/Objectives: Traditional dietary monitoring methods such as 24 h recalls rely on self-report, leading to recall bias and underreporting. Similarly, dietary control approaches, including portion control and calorie restriction, depend on user accuracy and consistency. Augmented reality (AR) offers a promising alternative for improving dietary monitoring and control by enhancing engagement, feedback accuracy, and user learning. This systematic review aimed to examine how AR technologies are implemented to support dietary monitoring and control and to evaluate their usability and effectiveness among adults. Methods: A systematic search of PubMed, CINAHL, and Embase identified studies published between 2000 and 2025 that evaluated augmented reality for dietary monitoring and control among adults. Eligible studies included peer-reviewed and gray literature in English. Data extraction focused on study design, AR system type, usability, and effectiveness outcomes. Risk of bias was assessed using the Cochrane RoB 2 tool for randomized controlled trials and ROBINS-I for non-randomized studies. Results: Thirteen studies met inclusion criteria. Since the evidence based was heterogeneous in design, outcomes, and measurement, findings were synthesized qualitatively rather than pooled. Most studies utilized smartphone-based AR systems for portion size estimation, nutrition education, and behavior modification. Usability and satisfaction varied by study: One study found that 80% of participants (N = 15) were satisfied or extremely satisfied with the AR tool. Another reported that 100% of users (N = 26) rated the app easy to use, and a separate study observed a 72.5% agreement rate on ease of use among participants (N = 40). Several studies also examined portion size estimation, with one reporting a 12.2% improvement in estimation accuracy and another showing −6% estimation, though a 12.7% overestimation in energy intake persisted. Additional outcomes related to behavior, dietary knowledge, and physiological or psychological effects were also identified across the review. Common limitations included difficulty aligning markers, overestimation of amorphous foods, and short intervention durations. Despite these promising findings, the existing evidence is limited by small sample sizes, heterogeneity in intervention and device design, short study durations, and variability in usability and accuracy measures. The limitations of this review warrant cautious interpretation of findings. Conclusions: AR technologies show promise for improving dietary monitoring and control by enhancing accuracy, engagement, and behavior change. Future research should focus on longitudinal designs, diverse populations, and integration with multimodal sensors and artificial intelligence. Full article

Background: Retrieval-augmented generation (RAG) aims to reduce hallucinations and outdated knowledge by grounding LLM outputs in retrieved evidence, but empirical results are scattered across tasks, systems, and metrics, limiting cumulative insight. Objective: We aimed to synthesise empirical evidence on RAG effectiveness versus parametric-only baselines, map datasets/architectures/evaluation practices, and surface limitations and research gaps. Methods: This systematic review was conducted and reported in accordance with PRISMA 2020. We searched the ACM Digital Library, IEEE Xplore, Scopus, ScienceDirect, and DBLP; all sources were last searched on 13 May 2025. This included studies from January 2020–May 2025 that addressed RAG or similar retrieval-supported systems producing text output, met citation thresholds (≥15 for 2025; ≥30 for 2024 or earlier), and offered original contributions; excluded non-English items, irrelevant works, duplicates, and records without accessible full text. Bias was appraised with a brief checklist; screening used one reviewer with an independent check and discussion. LLM suggestions were advisory only; 2025 citation thresholds were adjusted to limit citation-lag. We used a descriptive approach to synthesise the results, organising studies by themes aligned to RQ1–RQ4 and reporting summary counts/frequencies; no meta-analysis was undertaken due to heterogeneity of designs and metrics. Results: We included 128 studies spanning knowledge-intensive tasks (35/128; 27.3%), open-domain QA (20/128; 15.6%), software engineering (13/128; 10.2%), and medical domains (11/128; 8.6%). Methods have shifted from DPR + seq2seq baselines to modular, policy-driven RAG with hybrid/structure-aware retrieval, uncertainty-triggered loops, memory, and emerging multimodality. Evaluation remains overlap-heavy (EM/$F_1$), with increasing use of retrieval diagnostics (e.g., Recall@k, MRR@k), human judgements, and LLM-as-judge protocols. Efficiency and security (poisoning, leakage, jailbreaks) are growing concerns. Discussion: Evidence supports a shift to modular, policy-driven RAG, combining hybrid/structure-aware retrieval, uncertainty-aware control, memory, and multimodality, to improve grounding and efficiency. To advance from prototypes to dependable systems, we recommend: (i) holistic benchmarks pairing quality with cost/latency and safety, (ii) budget-aware retrieval/tool-use policies, and (iii) provenance-aware pipelines that expose uncertainty and deliver traceable evidence. We note the evidence base may be affected by citation-lag from the inclusion thresholds and by English-only, five-library coverage. Funding: Advanced Research and Engineering Centre. Registration: Not registered. Full article

Signs of aging in the upper face arise from multimodal changes in facial anatomy, contributing to concerns such as eyebrow ptosis and forehead lines. While neurotoxin injections are widely used to address these lines, the anatomical variability of the frontalis muscle presents procedural challenges. This retrospective analysis aimed to introduce and preliminarily evaluate a structured injection pattern for forehead treatment, developed with attention to the biomechanics of upper facial musculature. A total of 24 patients (mean age 42.5 ± 9.1 years) treated with a standardized injection scheme using letibotulinumtoxinA were included. All subjects also received concomitant glabellar treatment. The protocol incorporated identification of the line of convergence and targeted injections at defined points to balance elevation, optimize muscular activity, and minimize the risk of eyebrow descent. Forehead line severity was assessed at rest and during animation, and three-dimensional surface imaging was used to quantify vertical skin displacement. At baseline, 79.2% of patients presented with severe dynamic forehead lines, and 29.1% exhibited severe static lines. After two weeks, 62.5% showed no dynamic lines and 41.7% showed no static lines. All subjects demonstrated a ≥1-point improvement in dynamic line severity, with 87.5% achieving a ≥2-point improvement. For static lines, 95.8% achieved a ≥1-point improvement and 20.8% showed a ≥2-point improvement after two weeks. The mean dosage was 17.8 ± 0.7 U. Two patients (8.3%) required a touch-up, and no adverse events were observed. These findings suggest that this structured injection approach may offer a consistent method for addressing forehead lines; however, the results should be interpreted within the limitations of a small, uncontrolled retrospective series. Prospective controlled studies with larger populations are needed to further validate the technique. Full article

Background/Objectives: Long COVID (LC) is characterized by persistent symptoms such as fatigue, pain, and reduced quality of life, often lasting months after acute infection. Exercise-based interventions have shown promise, but evidence for non-aerobic programs remains limited. This study aimed to evaluate the effects of a 12-week motor control exercise program on body composition and fatigue in women with LC and to explore associations with physical activity and psychosocial factors. Methods: An exploratory pre–post non-controlled intervention study was conducted in 17 women with LC symptoms persisting for over one year. Participants completed 24 individualized sessions of a non-aerobic therapeutic exercise program focused on trunk stabilization. Outcomes included body composition (bioimpedance analysis), fatigue (Modified Fatigue Impact Scale), health-related quality of life (EQ-5D-5L), physical activity (IPAQ), and kinesiophobia (TSK-11). Paired t-tests, effect sizes, correlations, and regression models were applied. Results: The intervention significantly reduced total body fat (37.09% to 35.41%, p < 0.001) and trunk fat (35.82% to 33.82%, p < 0.001), with large effect sizes. Physical and psychosocial fatigue improved markedly (MFIS physical: 29.71 to 21.06, p < 0.001; psychosocial: 6.00 to 4.29, p = 0.001), while cognitive fatigue showed non-significant change. Pain/discomfort scores decreased substantially (2.86 to 1.79, p < 0.001). Vigorous activity and walking time increased, and sedentary time decreased. No significant changes were observed in muscle mass or kinesiophobia. Conclusions: A structured, non-aerobic exercise program can effectively reduce body fat, alleviate fatigue, and improve pain perception in women with LC, supporting its role in rehabilitation. Multimodal strategies may be required to address cognitive symptoms and fear of movement. Full article

Accessing operational information across industrial systems (ERP, MES, SCADA, PLC) in concrete plants requires 15–30 min and specialized knowledge. This work addresses this accessibility gap by developing a conversational AI system that democratizes industrial information access through natural language. A five-layer cognitive architecture was implemented integrating the Mistral-7B model quantized in GGUF Q4_0 format (3.82 GB) on a Raspberry Pi 5, Spanish speech recognition/synthesis, and heterogeneous industrial protocols (OPC UA, MQTT, REST API) across all automation pyramid levels. Experimental validation at Frumecar S.L. (Murcia, Spain) characterized performance, thermal stability, and reliability. Results show response times of 14.19 s (simple queries, SD = 7.56 s), 16.45 s (moderate, SD = 6.40 s), and 23.24 s (complex multilevel, SD = 6.59 s), representing 26–77× improvement over manual methods. The system maintained average temperature of 69.3 °C (peak 79.6 °C), preserving 5.4 °C margin below throttling threshold. Communication latencies averaged 8.93 ms across 10,163 readings (<1% of total latency). During 30 min of autonomous operation, 100% reliability was achieved with 39 successful queries. These findings demonstrate the viability of deploying quantized LLMs on low-cost edge hardware, enabling cognitive democratization of industrial information while ensuring data privacy and cloud independence. Full article

Background: The current definition of metabolic dysfunction-associated steatotic liver disease (MASLD) relies on a classical assessment of steatosis via liver biopsy, with grades S0–S3 (5–100% fat) potentially underestimating low-grade steatosis. We propose a new, more sensitive classification based on magnetic resonance imaging–proton-density fat fraction (MRI-PDFF), splitting the existing S0 and S1 grades into three classes: new-S0, very early S1 (S1A), and later S1 (S1B). We aimed to determine whether these early S1A/S1B phenotypes differed clinically or biologically from the new-S0 grade using large population cohorts. Methods: We assessed the prevalence of the new MRI-PDFF—based grades in 29,252 healthy participants from the UK Biobank discovery cohort, 286 outpatients with type 2 diabetes, and in six previously published databases (N = 149,212) using SteatoTest-2 or a proxy. We performed a multimodal assessment of steatosis using longitudinal MRI-PDFF and liver biopsy data (N = 286). Models were used to adjust for phenotypes and overall mortality, controlling for age, sex, and cardiometabolic factors. Results: In the UK Biobank cohort, the prevalences of the new-S0, S1A, and S1B grades were 54%, 26%, and 17%, respectively. Grades S1A and new-S0 were most discriminated by triglycerides (odds ratio [OR]: 2.40, 95% confidence interval [CI]: 2.07–2.77, p < 0.00001) and body mass index (BMI; OR: 1.30, 95% CI: 1.27–1.33, p < 0.00001), and grades S1A and S1B were most discriminated by triglycerides, BMI, systolic blood pressure (SBP), and glycated hemoglobin (HbA1c). Adjusting for age, sex, SBP, BMI, HbA1c, triglycerides, and high-density lipoprotein–cholesterol) revealed significantly lower 15-year survival in the high-risk group (97.2%, 95% CI: 96.9–97.7) versus the low-risk (99.4%, 95% CI: 99.2–99.6) group (p < 0.00001). Conclusions: The early trajectory of liver steatosis is undetectable in 26% of middle-aged adults. This early steatosis phenotype differs clinically and biologically from the new-S0 grade in large population cohorts. Full article