Search Results (260)

Frequent seasonal phase transitions in cold and arid lakes require different remote sensing indices for frozen and open-water periods, complicating the use of traditional empirical indices for automated monitoring. To address this challenge, this study proposes an intelligent indexing framework integrating the heuristic reasoning of Large Language Models (LLMs) with Random Forest (RF) feature selection. Leveraging the Google Earth Engine (GEE) and Landsat 8 data from Ulansuhai Lake, five LLMs such as Gemini and ERNIE were employed to generate candidate spectral indices based on typical sample spectra. Optimal band combinations were identified via RF importance, and Land Surface Temperature (LST) was incorporated as a physical constraint for unified cross-seasonal classification and determine the optimal threshold. Results show that the LLM-derived ERNIE-WISI and Gemini-WISI exhibit high robustness. During the freezing period, ERNIE-WISI significantly outperformed other indices, achieving an Overall Accuracy (OA) of 89% and a Kappa of 0.86. Spatially, it yielded snow and ice mapping with clear textures and low commission errors. During the non-freezing period, ERNIE-WISI achieved an OA of 95% with a Kappa of 0.84. While Gemini-WISI achieved an OA of 94% with a Kappa of 0.80, performing comparably to MNDWI. Notably, ERNIE-WISI effectively suppressed background interference in complex landscapes like narrow channels and aquaculture areas, maintaining high geometric fidelity and spatial continuity. A key advantage of ERNIE-WISI is its consistent performance without seasonal threshold adjustments. Aligned with the AI for Science paradigm, this methodology bridges AI-driven heuristic discovery and physical remote sensing, offering a robust, transferable solution for long-term dynamic lake monitoring in extreme environments, thereby facilitating sustainable water management. Full article

The construction industry is recognised globally as one of the most hazardous sectors. Effective hazard management necessitates identifying and communicating these risks early in the project lifecycle. Construction Hazard Prevention through Design (CHPtD) addresses this by incorporating safety information into the design phase that is often cumbersome and heavily reliant on reviewer expertise. The present work enhances hazard recognition and visualisation by automating the process using computational intelligence and building information modelling, aligning with the theoretical framework of CHPtD. The proposed tool provides detailed hazard information, including the nature of the hazard, its causes, and potential resolutions, empowering designers to make informed decisions and mitigate risks proactively. The tool’s performance is evaluated using a confusion matrix, demonstrating promising results with an overall accuracy of 84.77% and a Kappa coefficient of 0.83. While the tool shows strong performance in identifying several hazard classes, further refinement is needed to improve its ability to detect catastrophic events and manage traffic-related hazards. Full article

Environmental Impact Assessment (EIA) is intended to function as a predictive, spatially grounded decision-support mechanism. Yet in many developing contexts, its operationalization remains fragmented, descriptive, and weakly standardized. Thus, this study addresses limitations in conventional EIA systems related to transparency, reproducibility, and uncertainty integration by proposing a spatially explicit, digital rule-based decision-support framework that operationalizes hierarchical receptor-based structuring, lifecycle-sensitive modelling, risk classification, and uncertainty propagation within an integrated Geographic Information Systems (GISs) architecture. The academic objective is to advance computational environmental assessment methodologies by formalizing EIA logic into a structured computational workflow that translates spatial interactions (including land use, population density, ecological sensitivity, hydrological zones) and project attributes (including project type, activities and operational conditions) into quantified risk profiles and mitigation mappings. This necessitates combining receptor proximity, overlap intensity, contextual sensitivity, operational conditions, and receptor vulnerability. The framework was applied to three airport case studies in Egypt—representing urban, peri-urban/desert expansion, and coastal–ecological environmental contexts—using standardized spatial preprocessing and normalized analytical scales. Validation was conducted using Monte Carlo uncertainty simulation, sensitivity analysis, Spearman rank correlation, and Cohen’s Kappa agreement analysis. The results demonstrated stable comparative risk classification across receptor categories, lifecycle phases, and impact mechanisms under moderate parameter perturbation (±15%). Cohen’s Kappa agreement values ranging from 0.71 to 0.79 indicated substantial consistency between model-generated exceedance zones and regulatory environmental classifications. In sum, the results demonstrate that receptor proximity, operational intensity, and lifecycle stage function as primary determinants of differentiated environmental risk configurations, and that the proposed framework can support transparent, reproducible, and spatially explicit environmental assessment. Full article

Arrhythmogenic cardiomyopathy (ACM) is a genetic myocardial disorder marked by progressive cardiomyocyte loss, fibro-fatty replacement, ventricular arrhythmias, and risk of sudden cardiac death. Traditionally considered a structural and electrical disease driven by desmosomal dysfunction, emerging evidence redefines ACM as an inflammatory cardiomyopathy in which immune activation plays a central role. This review integrates genetic, molecular, experimental, and clinical data to highlight inflammation as a unifying feature of ACM. Desmosomal gene variants impair cell adhesion and also activate cardiomyocyte-intrinsic inflammatory pathways, including nuclear factor of kappa B (NFκB) and glycogen synthase kinase 3β (GSK3β) signaling, promoting cytokine release, immune cell recruitment, and fibrotic remodeling. Preclinical studies suggest inflammation precedes structural changes, indicating it may be an initiating event rather than a secondary response. Clinical and pathological findings support this model, with inflammatory infiltrates, circulating cytokines, and autoantibodies observed across disease stages. These processes often present as episodic “hot phases” resembling myocarditis, thus complicating diagnosis. The inflammatory landscape involves both innate and adaptive immunity, along with stromal and neuronal remodeling, contributing to arrhythmogenesis through gap junction disruption, calcium-handling abnormalities, and fibrosis. Environmental factors such as exercise, stress, and metabolic disturbances further modulate inflammatory pathways and disease expression. Therapeutically, this evolving perspective supports immunomodulatory approaches, including inhibition of NFκB, GSK3β, and cytokine signaling. Early clinical data on immunosuppressive and cytokine-directed therapies are promising, especially during active inflammatory phases, while gene-based strategies specifically address the underlying genetic defects. In conclusion, ACM should be recognized as an inflammatory cardiomyopathy shaped by interactions between genetic susceptibility and immune dysregulation. Integrating genetic and immunologic profiling may improve diagnosis, risk stratification, and treatment, ultimately leading to refined personalized therapeutic strategies. Full article

Aging is associated with chronic, low-grade inflammation (“inflammaging”), which contributes to neuropsychiatric and neurodegenerative disorders such as depression, Alzheimer’s disease, and Parkinson’s disease. Conventional pharmacotherapies often provide limited benefit in older adults and are further complicated by polypharmacy and drug–drug interactions. Psilocybin, a serotonergic psychedelic acting primarily as a partial agonist at the 5-HT_2A receptor and currently undergoing accelerated clinical development, has emerged as a potential multimodal therapeutic agent addressing these challenges. Acting via its active metabolite psilocin, 5-HT_2A receptor-mediated signaling modulates cortical glutamatergic transmission, enhances tropomyosin receptor kinase B/brain-derived neurotrophic factor (TrkB/BDNF) pathways, and modulates neuroimmune cascades (includingnuclear factor kappa B (NF-κB), with convergent systems-level effects such as reorganization of the default mode network. Human studies report acute reductions in TNF-α with variable effects on IL-6 and CRP, consistent with an immunomodulatory profile. Pharmacokinetically, psilocybin shows properties advantageous in geriatric care: rapid onset, short half-life, and predominant phase-II glucuronidation, reducing interaction risk. Controlled studies demonstrate rapid antidepressant and anxiolytic effects in major depressive disorder, treatment-resistant depression, and existential distress, with emerging feasibility signals in neurodegeneration. Together, these findings support the hypothesis that a time-limited, mechanism-based intervention may improve mood and cognition while attenuating inflammation. This review integrates current evidence on psilocybin’s neuroimmune and pharmacokinetic mechanisms relevant to aging, outlining its potential role in inflammation-related disorders and highlighting the need for targeted studies in older adults, who remain underrepresented in psychedelic research. Full article

The design of differentiated fiscal instruments for industrial sustainability requires robust, data-driven tools capable of capturing the heterogeneity of environmental performance across manufacturing units—a challenge that conventional econometric approaches address only partially, given the non-linear nature of operational–environmental interactions in reconfigurable production systems. This study introduces a two-phase computational framework that integrates unsupervised machine learning and supervised classification to generate evidence-based sustainability profiles for fiscal policy targeting. Its principal contribution is the combination of K-Means clustering with a binary artificial neural network (ANN) classifier, operationalized through an accessible decision-support interface that enables differentiated incentive allocation without requiring programming expertise from policymakers. A dataset of 1000 manufacturing records comprising seven operational and technological input variables—material usage, production capacity, reconfiguration time, downtime, AI optimization, IoT connectivity, and predictive maintenance—and three environmental output indicators—energy consumption, carbon emissions, and waste generation—was analyzed. In Phase One, K-Means segmentation with k = 6, selected through multi-criteria convergence (Silhouette = 0.102; Elbow, Davies–Bouldin, and Calinski–Harabasz indices), identified six distinct sustainability profiles with marked environmental differentiation. In Phase Two, a binary ANN classifier (architecture: 7 → 64 → 32 → 1 neurons; ReLU and sigmoid activations) was trained to distinguish the reference cluster C0 (low environmental impact: energy 145.1 kWh, emissions 45.2 CO₂-eq) from the high-impact cluster C1 (emissions 67.8 CO₂-eq, waste 41.5 kg). The trained classifier achieved an overall accuracy of 75.4% and an AUC-ROC of 0.774 on the held-out test set, with a macro-averaged F1-score of 0.753 and a Cohen’s kappa coefficient of 0.508, indicating moderate-to-substantial agreement beyond chance. Class C1 (high-impact establishments) achieved a precision of 0.794 and a recall of 0.730, supporting reliable identification of manufacturing units that would most benefit from targeted fiscal support. The framework is deployed through a Gradio-based graphical interface incorporating a traffic-light sustainability classification (green/yellow/red), enabling direct and interactive application by tax authorities and industrial policymakers. The modular architecture supports adaptation to larger or sector-specific datasets, making it transferable across industrial policy contexts. Full article

Recent Phase 3 clinical trials of selective kappa-opioid (KOP) receptor antagonists aticaprant and navacaprant failed to demonstrate sufficient clinical efficacy in treatment-resistant depression (TRD). This highlights a critical gap in current strategies that target opioid-mediated hedonic suppression. We propose two hypotheses to explain these setbacks: (1) neutral antagonists are inherently ineffective in blocking constitutively active KOP receptor hyperactivation and (2) the nociceptin opioid (NOP) receptor provides functional redundancy that compensates for KOP receptor blockade. Gaining insights from paralogous compensation in drug-resistant tumors, we argue for shifting from selective opioid antagonists to dual KOP/NOP receptor blockers to meaningfully improve reward function. This concept provides a theoretical framework for overcoming clinical resistance where selective KOP targeting with neutral antagonists has failed. Thus, we advocate for the development of opioid inverse agonists (such as nor-BNI, CAS: 105618-26-6), pan-antagonists (such as AT-076, CAS: 1657028-64-2), and combinations of selective blockers. Full article

Motivation: In modern organizations where remote and hybrid work has become normalized, fostering trust without frequent face-to-face interaction is a critical management challenge. This study aims to explore how non-verbal digital dynamics associate with trust formation within hybrid product development teams from a teamwork science perspective, integrating Big Five traits and established trust scales. Methods: The empirical study observed twelve product development teams (N = 40) participating in a major innovation competition over an eight-month period. Dynamic behavioral data, including speaking time, nodding, smiling, and silence, were extracted from online workshop recordings using synchronized behavioral coding validated by high inter-rater reliability (Cohen’s Kappa k ≥ 0.78). These were integrated with Big Five personality traits, mutual trust scales, and idea value metrics into a Bayesian Network (BN) to model probabilistic dependencies. The structural model was validated using the Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC) to ensure predictive robustness. Furthermore, we performed sensitivity analysis on the BN to quantify how specific shifts in non-verbal cues—particularly nodding and the functional categories of silence—disproportionately affect the “Mutual Trust” node. While this exploratory study utilizes a sample of “digital native” student teams, it provides a critical baseline for “high digital fluency” collaboration, which we contextualize against the “asymmetric cues” found in multi-generational corporate environments. Results: Sensitivity analysis identified specific probabilistic associations suggesting that effective role fulfillment is the strongest predictor of idea originality. Crucially, nodding was identified as a behavioral ‘digital reward’ that enhances psychological safety, facilitating divergent thinking. Smiling showed a strong association with feasibility and consensus-building during convergent phases. The model further identifies distinct behavioral ‘fingerprints’: high-trust sequences are characterized by frequent non-verbal backchanneling and deliberate “thinking silences,” whereas low-trust sequences exhibit a disproportionate increase in unproductive lapses (e.g., a 10% increase in lapses correlating with an 18% decrease in trust probability). Furthermore, a probabilistic pathway was identified where teams with highly open members and frequent non-verbal validation exhibit higher mutual support behaviors. Conclusions: This research offers empirical insights into how trust can be modeled in hybrid environments through specific combinations of behavioral and personality traits. Practically, this study proposes “Hybrid Team Protocols”—such as intentional backchanneling and the normalization of deliberative silence—as actionable Organizational Development (OD) interventions. These provide managers with data-driven guidelines to visualize and monitor the quality of digital collaboration while emphasizing the ethical necessity of transparent implementation to prevent “digital performance” and ensure psychological safety across diverse organizational structures. Full article

Quercus pyrenaica (Willd.), a sub-Mediterranean oak, is expected to experience substantial distribution shifts under climate change, with some populations in Portugal at risk. Beyond climate-driven pressures, long-standing anthropogenic pressures have likely contributed to the species’ current vulnerability. This work aims to characterize the current status of closed-canopy Q. pyrenaica forests by providing a spatio-temporal assessment of forest fragmentation and its recent evolution. Using multispectral bands from Sentinel-2 time-series data, vegetation indices, embedding vectors generated by Google’s AlphaEarth foundational model, and topographic variables, we applied a machine learning Random Forest classifier to map Q. pyrenaica forests in 2019 and 2024 and to analyze their spatial configuration patterns. The findings indicate robust predictive performance (spatial cross-validation OA of 95.1%, Kappa of 83.7%, and F1 of 86.9%) and reveal the prominent role of AlphaEarth embedding features in the RF classifier, suggesting that these features are well-suited for classifying forest habitats of conservation importance. Quercus pyrenaica occurs predominantly at mid-elevations (~820 m a.s.l.), on gentle slopes (~9°), topographically neutral terrain, and northwestern-facing aspects, consistently across both years. Between 2019 and 2024, the Q. pyrenaica forest area showed an increasing signal. However, the results point to a landscape in an initial phase of forest recovery, constrained by land-use legacies, with cover increasing predominantly through the sprawl of small, geometrically complex, and poorly connected patches. Together, these results provide a baseline to track recent changes in Q. pyrenaica distribution and fragmentation, highlighting a contrast between apparent area expansion and declining overall structural integrity. In the future, patch connectivity and full recovery of secondary succession should be a priority for policymakers and forest owners. Full article

Introduction: Nerve-sparing (NS) during robot-assisted radical prostatectomy (RARP) plays a critical role in postoperative functional recovery, particularly urinary continence and erectile function. Despite the importance of precise neurovascular bundle (NVB) preservation, intraoperative assessment of NS quality remains largely subjective and lacks standardized evaluation tools. The aim of this study was to develop and preliminarily evaluate a structured intraoperative scoring system designed specifically for assessing NS quality during RARP. Methods: A novel 10-point intraoperative NS scoring system (NSQ Score) based on five domains was developed: dissection plane, bleeding control, bundle manipulation, continuity of dissection, and symmetry. Each parameter was rated on a 0–2 scale. Thirty robot-assisted radical prostatectomy (RARP) procedures performed in 2024 were randomly selected from a prospectively maintained institutional surgical video archive. Cases were not pre-filtered based on tumor stage, surgical difficulty, or intraoperative complexity. High-definition video recordings of the nerve-sparing phase were anonymized and independently evaluated by three experienced observers blinded to patient outcomes and to each other’s assessments. Inter-rater agreement was analyzed using weighted Cohen’s kappa statistics with quadratic weights, complemented by exact and near-agreement proportions. Cluster bootstrap resampling was applied to account for bilateral observations. Results: A total of 48 evaluable observations were analyzed. The overall inter-rater agreement demonstrated a weighted kappa of 0.41 (95% CI 0.36–0.48), indicating fair-to-moderate agreement among reviewers. Exact agreement occurred in 43% of observations, while near-agreement (allowing one ordinal level difference) reached 98%. Among individual parameters, symmetry demonstrated the highest reliability with substantial agreement (κ = 0.70; 95% CI 0.58–0.81). Other domains showed fair agreement, including intraoperative bleeding (κ = 0.36), continuity of dissection (κ = 0.39), bundle manipulation (κ = 0.34), and dissection plane (κ = 0.27). Agreement levels were comparable between left- and right-sided dissections. Conclusions: We propose a novel structured intraoperative scoring system for evaluating nerve-sparing quality during RARP. The scale is simple, procedure-specific, and feasible for structured postoperative or video-based assessment. Preliminary results demonstrate fair-to-moderate inter-rater reliability with very high near-agreement, supporting the feasibility of this tool for clinical use. The proposed scoring system may facilitate standardized training, objective performance assessment, and future studies correlating intraoperative NS quality with functional outcomes. Full article

Background: Orthodontic treatment is increasingly recognised as a complex, patient-centred intervention whose impact extends beyond occlusal correction to include physical comfort, psychosocial well-being, and self-perceived esthetics. Oral health-related quality of life (OHRQoL) has therefore become a key outcome for evaluating orthodontic care across all treatment stages. Aim: This narrative review of 140 studies synthesises current evidence on OHRQoL changes in orthodontic patients before treatment, during active therapy, and after treatment completion, with particular emphasis on temporal patterns and appliance-related differences. Methods: A comprehensive narrative review of 140 studies was conducted using PubMed, Scopus, Web of Science, Cochrane Library, and Google Scholar (search period: inception to December 2025). Studies assessing OHRQoL or patient-reported outcomes in orthodontic patients of any age were included. Only studies employing validated instruments, such as OHIP, CPQ, OIDP, and PIDAQ, were considered. Dual-reviewer agreement was assessed using Cohen’s kappa (κ = 0.82). Formal risk-of-bias assessment was conducted using ROBINS-I for non-randomised studies and the Cochrane Risk of Bias tool for RCTs. Sensitivity analyses were performed comparing high-quality studies (low risk of bias, n = 52) versus all included studies. Results: The reviewed evidence consistently demonstrates that malocclusion is associated with impaired baseline OHRQoL, particularly affecting psychosocial and esthetic domains. The early phase of orthodontic treatment is marked by a transient deterioration in OHRQoL due to pain, discomfort, speech disturbances, and functional limitations (87% of studies report pain peaks within 24–48 h; 79% report resolution by 4–7 days). These effects typically diminish as patients adapt to the appliance. Progressive improvement is observed during mid-treatment, while treatment completion is associated with substantial long-term gains in self-esteem, social functioning, and overall quality of life. Appliance type influences short-term outcomes, with clear aligners generally associated with better early OHRQoL than fixed and lingual systems (65–75% of studies favour aligners for early comfort; 78% favour lingual systems for esthetic satisfaction). Conclusions: Orthodontic treatment follows a dynamic, time-dependent OHRQoL trajectory characterised by short-term impairment and significant long-term psychosocial benefits. Systematic integration of validated OHRQoL measures into orthodontic care may enhance patient-centred decision-making and optimise clinical outcomes. Full article

Background: Gadoxetic acid-enhanced MRI is essential for detecting and characterizing focal liver lesions. However, transient severe motion artifacts in the arterial phase can degrade image quality. Gadoxetic acid dilution has been proposed to mitigate these artifacts, but its impact on multiple arterial phase acquisition remains unclear. Objective: To evaluate the effect of gadoxetic acid dilution on image quality across multiple arterial phases in liver MRI, incorporating a phase-by-phase analysis. Methods: This retrospective study included 81 patients (52 men, 29 women; mean age 70.1 years) who underwent serial gadoxetic acid-enhanced MRI with undiluted and diluted contrast (1:1 saline dilution). MRI was performed on 1.5 T and 3.0 T scanners with a standardized injection rate of 1.0 mL/s. Two radiologists independently rated anatomic conspicuity, respiratory motion artifacts, and overall image quality using a Likert scale (1 to 5 with higher scores indicating better quality). A phase-by-phase analysis was conducted after a three-month washout period. Wilcoxon signed-rank tests were used for statistical comparisons, and inter-rater agreement was assessed with quadratic kappa coefficients. Results: Inter-observer agreement was substantial (ƙ = 0.602–0.702). Phase-by-phase analysis revealed significant improvement in image quality for the first three arterial phases (p = 0.003, 0.005, 0.050). Although the diluted method showed higher scores, the differences were not statistically significant in anatomic conspicuity (3.73 vs. 3.59, p = 0.110), respiratory artifacts (3.54 vs. 3.41, p = 0.291), and overall image quality (3.67 vs. 3.51, p = 0.083). Conclusions: Gadoxetic acid dilution improves image quality in early arterial phases of liver MRI, suggesting its potential to reduce motion artifacts. Full article

As a unique cultural heritage of Chinese ethnic minorities, Dong architecture provides rich historical and cultural information. Rapid and accurate extraction of ethnic building information from remote sensing images in complex terrain and high-density settlement environments is highly important for the protection of architectural heritage and the management of rural space. Huanggang Dong Village in Liping County, Guizhou Province, China, is taken as a case study. This paper develops a multifeature fusion machine learning framework for the automatic recognition of Dong ethnic architecture based on centimeter-level visible images captured by UAV. First, the vegetation index, HSI color features and texture features based on the gray level co-occurrence matrix are extracted from the UAV visible light orthophoto image. Through the random forest feature importance ranking and correlation test, six key features, namely, the VDVI, HSI-S, HSI-I, mean, variance and contrast, are selected to construct a multifeature space. This step constitutes the feature construction stage of the proposed methodology and provides the basis for subsequent classification. Second, on the basis of a support vector machine (SVM) and random forest (RF), classification models are constructed. The effects of different feature combinations and different algorithms on classification accuracy are systematically compared, and the results are evaluated in terms of overall accuracy (OA), the kappa coefficient, user accuracy (UA) and producer accuracy (PA). This second part highlights the classification phase of the methodology, which tests the feature space using different algorithms and evaluates the performance of the models. The experimental data fully show that under the condition of a single feature, the SVM model dominated by texture features performs best, with an OA of 85.33% and a kappa of 0.799; under the condition of multifeature fusion, the RF algorithm has a stronger ability to integrate multisource features. The accuracy of building category recognition based on the total feature and dimensionality reduction feature space is particularly prominent. The total feature and overall accuracy reach 89.00%, and the kappa coefficient is 0.850. The UA and PA reached 89.66% and 94.55%, respectively. Through in-depth comparative analysis, the vegetation index–color–texture multifeature fusion and machine learning classification framework based on UAV visible light images can achieve high-precision extraction of Dong architecture without relying on high-cost sensors. It can effectively alleviate the confusion between water bodies and shadows and between dark roofs and vegetation and effectively separate traditional Dong architecture from roads, vegetation and other elements. It provides a low-cost and feasible way for digital archiving, dynamic monitoring and protection management of the traditional village architectural heritage of ethnic minorities. Full article

Autophagy is a critical cellular mechanism that regulates the degradation of misfolded and aggregated proteins and non-functional intracellular organelles. Based on the fundamental qualities of the substrates targeted for degradation and the distinct molecular mechanisms involved, autophagy can be classified into three major types: macroautophagy, microautophagy, and chaperone-mediated autophagy (CMA). Sequestosome 1 (SQSTM1)/p62, which functions as a signaling hub integrating nuclear factor kappa B (NF-κB), the mechanistic target of rapamycin complex 1 (mTORC1), and Kelch-like ECH-associated protein 1 (Keap1)–nuclear factor erythroid 2–related factor 2 (NRF2) pathways, serves as a selective macroautophagy/autophagy receptor that binds ubiquitinated cargo proteins and recruits them to the autophagosome for subsequent degradation in the autolysosome. Furthermore, the phase separation of p62 is an important regulatory process in the autophagy mechanism, but recent studies have demonstrated that impaired or excessive autophagy mediated by p62 is associated with cancer development. This review summarizes the role of autophagy—including its types, mechanisms, and the pathway related to the ubiquitin-dependent selective autophagy receptor p62—in cancer progression. Full article

In recent years, insect-derived peptides have attracted attention for their potential biological activities, particularly antioxidant properties. This study assessed the antioxidant activity of two widely consumed edible insects, T. molitor and A. diaperinus larvae, using cell-free and cell-based approaches. Whole lyophilized larvae, digestion products from the oral, gastric, and intestinal phases, as well as the <3 kDa permeate fraction (D-P3) derived from the intestinal digestion phase, were evaluated using biochemical antioxidant assays. Overall, digested samples exhibited higher antioxidant capacity than their undigested counterparts. At the cellular level, treatment of LPS-stimulated, PMA-differentiated THP-1 macrophages with A. diaperinus D-P3 was associated with increased mRNA expression of genes related to antioxidant defense, including NFE2-like bZIP transcription factor 2 (NFE2L2, also known as Nrf2), glutathione-disulfide reductase (GSR), superoxide dismutase 1 (SOD1), and catalase (CAT), whereas T. molitor D-P3 preferentially modulated nuclear factor kappa B p50 subunit (NFKB1) and nuclear factor kappa B p65 subunit (RELA). Overall, these findings indicate that gastrointestinal digestion enhances the bioaccessibility of antioxidant components in both edible insect species while revealing species-specific transcriptional responses under in vitro inflammatory conditions. This multilevel assessment provides mechanistic insight into the antioxidant-related biological activity of digestion-derived insect peptides and supports their further investigation as functional ingredients in food and feed systems. Full article