Abstract: This paper presents a Load-Dependent Multimodal Vibration Signal Enhancement and Fusion Framework (LD-MVSEFF) for load-specific condition monitoring, building on the Customised Load Adaptive Framework (CLAF). The proposed approach enhances the classification of CLAF load-dependent fault subclasses namely Healthy, Mild, Moderate, and Severe by integrating complementary information from raw vibration signals and signal-encoded representations. Three input channels are employed, combining time–frequency domain features with Continuous Wavelet Transform (CWT) and Gramian Angular Difference Field (GADF) image encodings, with each channel independently trained and evaluated to identify its most effective classifiers. To address the reduced separability of the Mild and Moderate fault subclasses under varying load conditions, a weighted decision fusion strategy is introduced, assigning classifier contributions according to their class-specific strengths. Experimental evaluation over five runs demonstrates high and stable performance, with the best configuration achieving an overall accuracy of 99.04% ± 0.22% and an average training time of 18 min and 30 s. The results confirm the effectiveness of LD-MVSEFF as a robust multimodal methodology for load-specific condition monitoring.

Abstract: Objective Although the baseline Prognostic Nutritional Index (PNI) is a well known prognostic factor in lung cancer, the clinical significance of its fluctuation during treatment remains unclear. This study aimed to evaluate the prognostic value of dynamic changes in PNI and to determine whether improvement in nutritional-immune status correlates with survival outcomes. Methods A total of 478 patients diagnosed with lung cancer were retrospectively analyzed. PNI was calculated based on serum albumin and total lymphocyte counts. The baseline value was defined as PNI1, and the post treatment value as PNI2. The dynamic change in PNIΔ was categorized as increased, stable, or decreased. The relationship between these dynamic parameters and Overall Survival. and Progression Free Survival was assessed using Kaplan Meier and Cox regression analyses. Results The median follow up was 19.9 months. Patients with higher PNI1 and PNI2 scores exhibited significantly longer OS and PFS. Notably, patients who demonstrated an increase in PNIΔ during the treatment course had significantly longer overall survival compared to those with stable or decreased scores (p=0.023). In multivariate analysis, while cancer type and post treatment PNI (PNI2) were identified as independent prognostic factors (p=0.007 for PNI2), the dynamic improvement in PNI emerged as a critical indicator of better clinical trajectory in univariate analysis. Conclusion This study demonstrates that PNI is not merely a static baseline marker but a dynamic biomarker that reflects the host's response to treatment and disease. An increase in PNI values during treatment is associated with improved survival, suggesting that dynamic monitoring of nutritional and immune status provides valuable prognostic information for patient management in lung cancer.

Abstract: Considerable efforts have been devoted to modifying gravity in order to elucidate the possible existence or nature of dark matter and dark energy, describe observational data effectively, and advance toward a theory of quantum gravity. In addition, despite its immense success, quantum field theory requires renormalization techniques and breaks down at high energies. Notably, the Planck 2018 legacy release confirmed the existence of an enhanced lensing amplitude in the cosmic microwave background power spectra, which suggests a positively curved early Universe with a confidence level more than 99%. In this study, we model the global curvature of the Universe as the curvature of a ‘4D conformal bulk’ — a geometric manifestation of vacuum energy, and regard celestial objects that induce localized curvature within the bulk as ‘4D relativistic cloud-worlds’. Employing a dual-action variational principle that incorporates both local and global curvatures, we derive interaction field equations that generalize general relativity and recover quantum behavior in the flat-bulk limit. Within this framework, gravity emerges as the local curvature of the bulk — an indicator of the field strength of vacuum energy acting on embedded quantum fields, which are described as localized geometric excitations embedded in a structured vacuum background. A visualization of the evolution of the 4D relativistic cloud-world over the conformal spacetime of the 4D bulk is presented. We apply the derived interaction field equations to model active galactic nuclei and outline testable predictions that could directly provide confirmations or falsify the framework.

Abstract: Background: Epilepsy is network disorder and network-based approaches to its diagnostics and therapies attract growing attention. Identification of prognostic markers of epilepsy, allowing selecting patients with risk of epilepsy development, is urgent unresolved problem. We examined whether intracortical connectivity patterns reflect early epileptogenic changes in the cortex. Methods: We used audiogenic kindling model, in which cortical epileptogenesis is initiated by repetition of reflex subcortically-driven seizures. Two measures of functional connectivity - mutual information and mean phase coherence – were applied to electrocorticographic recordings obtained from homotopical sites of parietal cortex in awake rats during interictal and immediate postictal periods. Interhemispheric connectivity and synchrony in non-kindled and slightly kindled rats were compared. Cortical spreading depolarization (SD), the first manifestation of growing cortical excitability in the model, was used as electrographic marker of earliest kindling stage. Results: In kindled animals, baseline levels of hemispheric connectivity and gamma band synchrony were significantly lower compared to seizure-naive rats. Before kindling, subcortical seizures were followed by mild postictal depression of cortical gamma oscillations without changes in interhemispheric functional connectivity. Early in kindling, seizures produced wideband depression of cortical activity and striking drop of hemispheric connectivity. Conclusion: Thus, primary network alterations during epileptogenesis are reduced synchronization and decoupling of hemispheres, both sustained (between seizures) and transient (postictal). Breakdown of long-range communication may reflect homeostatic plastic changes and active attempt to restrict epileptogenic reorganization of neural networks early in epileptogenesis. We think that resting-state hemispheric disconnection may be used as an early marker of epileptogenesis. Seizure-induced SD contributes to generation of postictal events.

Abstract: Emerging hospitality markets confront a two-speed ecosystem where operational digitalization outpaces strategic AI readiness, creating a benefit-feasibility gap. Providers recognize substantial technology value yet face implementation constraints from costs, integration complexity, and skills shortages, while guests demonstrate acceptance conditional on trust with privacy concerns suppressing willingness to pay. Drawing on dual-perspective empirical evidence derived from Albania's accommodation sector, integrating a national provider readiness assessment and a guest acceptance study, this Design Science Research study develops a segment-differentiated technological blueprint through systematic integration of Design Thinking, service blueprinting, and systems thinking methodologies. Integrated TAM-TOE-DOI framework analysis reveals three distinct provider segments requiring differentiated implementation pathways: Tech Leaders positioned for AI capabilities, Selective Adopters benefiting from smart modules, and Skeptics requiring foundational capabilities. Empirical evidence establishes that regional ecosystem characteristics outweigh organizational scale in determining adoption feasibility, trust operates as gating condition moderating acceptance and financial commitment, and supply-demand misalignment creates bottlenecks invisible to single-perspective assessments. Theoretical contributions extend TAM-TOE-DOI frameworks from explanatory constructs to design requirements, conceptualize supply-demand alignment as adoption mechanism, and generate two generalizable design principles: dual-constraint satisfaction requiring simultaneous provider feasibility and guest acceptance, and trust-as-architecture embedding trust mechanisms as structural properties. Practically, the blueprint provides differentiated guidance for policymakers, technology vendors, education providers, and accommodation providers, with transferability to Western Balkans, Mediterranean, and post-transition economies facing comparable heterogeneous readiness and resource constraints.

Abstract: Wind farms are known to trigger avoidance behaviour leading to habitat loss in some raptors. The recovery of the Spanish imperial eagle, Aquila adalberti, in Cadiz, a Spanish province with a high density of wind farms, is of concern. Macro-displacement was studied by comparing juvenile density between wind farms and control areas. Meso-displacement was studied comparing actual density in each 200 m interval of distance around turbines against a random distribution, assuming no-avoidance, controlling for the influence of other environmental factors. We found no evidence of avoidance at macro scale. At meso scale, using density method, we did not find any evidence supporting eagle avoidance behaviour. The study of avoidance behaviours is an ongoing topic that can help to improve conservation and management decisions, especially for species sensitive to the presence of wind farms and other threatening infrastructures in their habitats.

Abstract: This study examined the relationship between adherence to the Mediterranean diet (MD), dietary and vitamin intake, physical activity, and body composition in young adults. A total of 145 Spanish university students (34 women and 111 men) were included in this cross-sectional study, with a mean body mass index (BMI) of 23 kg/m2. MD adherence was assessed using the Mediterranean Diet Adherence Screener (MEDAS). Dietary intake was evaluated through a three-day food record, physical activity using the International Physical Activity Questionnaire (IPAQ), and body composition by bioelectrical impedance analysis. Overall adherence to the MD was moderate. Participants with high MD adherence showed significantly lower body weight (p < 0.05), BMI (p < 0.01), fat mass (p < 0.05), and fat mass percentage (p < 0.05) compared with those with low adherence. Energy, protein, and carbohydrate intake per kilogram of body weight were higher (p < 0.05) in the high-adherence group. Fiber intake was greater (p < 0.001) among those with higher MD adherence. Adherence to the MD was also associated with higher intakes of vitamins C (p < 0.05), E (p < 0.05), retinol equivalents (p < 0.05), and carotenoids (p < 0.001). MD adherence was inversely correlated (r = −0.24, p < 0.01) with body weight and BMI. Fiber intake showed positive correlations with several water-soluble vitamins, particularly folate (p < 0.001). In conclusion, higher adherence to the MD among university students was associated with healthier body composition and improved vitamin intake adequacy, independently of physical activity. These findings support the promotion of the MD as an effective nutritional strategy to enhance micronutrient intake and overall diet quality in young adults.

Abstract: Modern information systems suffer from a fundamental architectural flaw: data coherence depends on external validation layers, creating systemic entropy and computational waste. We present the G Model, a mathematical framework that redefines information as points in a ge- ometric space where incoherence is mathematically impossible. Through a triaxial formalization (Meaning, Location, Connection) and an intrinsic coherence operator (Φ), the system guaran- tees that only valid data can exist within the managed universe (Ω). We formalize this with four fundamental axioms ensuring coherence, uniqueness, acyclicity, and deterministic propaga- tion. The model extends Codd’s normalization through five semantic normal forms addressing temporal and semantic coherence. We provide formal proofs of key theorems, including optimal propagation complexity and impossibility of inconsistency. This work represents a paradigm shift from “data storage systems” to “coherent information spaces,” providing a foundation for trustworthy AI training data and critical infrastructure where error is inadmissible.

Abstract:

Background: Food waste contains abundant (+)-catechin, but its efficient recovery remains challenging. This study aimed to prepare ionic liquid (IL)-modified sorbents and establish an efficient method for (+)-catechin recovery from chocolate waste via solid-phase extraction (SPE); Methods: Three serious of IL-modified sorbents (Sil-IL, ZIF67-IL, Sil@ZIF67-IL) were synthesized. Their adsorption performance was evaluated under different conditions; adsorption isotherms and kinetics were fitted to Langmuir/Freundlich and pseudo-first/second-order models, respectively. Sorbent stability and (+)-catechin recovery from chocolate waste extracts were tested; Results: Sil@ZIF67-Hmim showed the highest adsorption capacity (154.4 mg/g) at 25 °C within 120 min. Adsorption followed the Langmuir model (R²=0.99), indicating chemical adsorption. Sil@ZIF67-Hmim was subjected to repeated solid phase extraction (SPE) for five consecutive days, the recovery rate ranged from 98.1%-99.2%, and the relative standard deviation (RSD) was 3.2%-4.4%; Conclusion: Sil@ZIF67-Hmim is a high-efficiency sorbent for (+)-catechin recovery from chocolate waste, providing a novel approach for food waste valorization and highlighting the application potential of IL-modified MOF-silica composites.

Abstract:

Cardiovascular diseases, particularly atherosclerotic coronary artery disease (CAD), remain among the leading causes of mortality worldwide. Although traditional risk factors—such as arterial hypertension, dyslipidemia, diabetes mellitus, obesity, smoking, and physical inactivity—are well established, accumulating evidence highlights the significant role of psychosocial factors in modulating cardiovascular risk. Among these, occupational stress—conceptualized through models such as job strain (high job demands combined with low control) and effort–reward imbalance—has been consistently associated with an increased risk of coronary events. The interaction between occupational stress and classical cardiovascular risk factors remains insufficiently elucidated and challenging to quantify. This review examines the current scientific evidence regarding the relationship between occupational stress and CAD, synthesizing findings from major epidemiological studies and relevant meta-analyses. Chronic exposure to work-related stress activates neuroendocrine pathways, including the hypothalamic–pituitary–adrenal axis and the sympathetic nervous system, promotes a state of low-grade systemic inflammation, and facilitates the adoption of unhealthy behaviors such as smoking, poor dietary habits, physical inactivity, and excessive alcohol consumption. These mechanisms contribute to endothelial dysfunction, hypercoagulability, and acceleration of the atherosclerotic process. Landmark investigations, including the INTERHEART study, meta-analyses conducted by Kivimäki and colleagues, and prospective studies by Chandola on the metabolic syndrome, support both the cumulative and independent impact of occupational stress on cardiovascular risk. Although the proportion of risk attributable to occupational stress is lower than that associated with traditional risk factors, its modifiable nature underscores a substantial potential for targeted preventive interventions. Strategies aimed at reducing occupational stress encompass individual-level approaches (stress management programs, lifestyle modification, psychological support), organizational interventions (optimizing the balance between job demands and employee control, enhancing social support in the workplace), and public health policies (occupational health promotion programs, regulatory measures addressing work-related stress, and screening for occupational stress). Recognizing occupational stress as a modifiable risk factor for CAD has important implications for both clinical practice and public health. Future research should focus on large-scale longitudinal studies, the identification of stress-related biomarkers, and the cost-effectiveness of stress-reduction interventions in the prevention and management of coronary artery disease.

Abstract:

Background: Heart failure (HF) is a progressive, multisystem syndrome characterized by recurrent decompensation, high hospitalization rates, and substantial mortality. Conventional HF management is mainly episodic and often fails to detect worsening conditions in advanced disease. Digital medicine and remote patient monitoring (RPM) hold promise for moving HF care toward earlier detection, proactive action, and personalized care. Methods: We conduct a narrative review to summarize evidence from randomized clinical trials, real-world registries, and emerging digital health technologies regarding the present and future utility of digital medicine in HF care. There is greater emphasis on pathophysiology-based surveillance, personalized care models, and integration into planned health care pathways. Results: Integrated digital interventions, such as implantable hemodynamic monitoring, organized telemedicine programs, or device-based diagnostic technologies, can minimize HF hospitalizations, prolong life, improve quality of life, and optimize resource utilization in health care systems when incorporated into coordinated care. Crucially, trials emphasize that clinical benefit depends not on technology but on a prompt clinical response, multidisciplinary cooperation, and ongoing interaction between the patient and the doctor. New technologies—including voice-based biomarkers, smartphone-derived photoplethysmography, ballistocardiography, and artificial intelligence–driven data integration—may help transition RPM from a hardware-based system to a scalable, “deviceless” approach. Conclusions: Digital medicine is a game-changer for reimagining HF care, involving not only continuous monitoring of physiological changes but also personalized, proactive clinical decision-making. To implement truly patient-centered, predictive HF management in the years to come, technological innovation must be combined with human connection, ethical governance, and health-system readiness.

Abstract: This study develops an innovative method for the attribution and visual reconstruction of hand-woven fabrics using artificial intelligence, employing Chinese Hong'an Homespun as a case study. The paper proposes a comprehensive algorithm integrating microscopic analysis, physical micro-model creation, and bimodal prompt engineering. The semantic differential method with a five-point scale was applied for objective evaluation of visual replica of historical fabrics. Comparative testing of AI models (Midjourney, ChatGPT, Qwen3, DouBaoAI, HailuoAI) revealed significant differences in their ability to reproduce characteristic features of hand weaving. The results demonstrate the superiority of detailed prompts with precise quantitative parameters and confirm the effectiveness of micro-models as visual anchors. The research establishes new standards in the digital documentation of cultural heritage and opens prospects for preserving traditional textile techniques. The most successful AI are Midjourney and ChatGPT have achieved an average score of 0.88 on the semantic scale, confirming the practical applicability of the method.

Abstract:

This study aimed to evaluate FOXC1-mediated regulatory mechanisms on gene and protein expression profiles in primary human limbal epithelial cells (pLECs), via siRNA knockdown; under basal and lipopolysaccharide (LPS) and interleukin-1β (IL-1β) induced inflammatory conditions. Gene expression was analysed for markers related to inflammation (CCL2, IL-6, IL-8, TNF-α, TGF-β), epithelial differentiation (KRT3, KRT12, KRT13, PAX6, FOXC1), cell proliferation and remodelling (FOSL2, MKi67, MMP2, VEGFA) and retinoic acid metabolism (ALDH3A1, CRABP2, CYP1B1, FABP5, RDH10, RBP1, STRA6). FOXC1 siRNA silencing in human pLECs significantly altered mRNA expression across multiple functional pathways, including inflammatory signaling (CCL2, IL-6, IL-8, IL-1α, VEGFA; p≤0.030), epithelial differentiation (KRT12, KRT13, PAX6; p≤0.045), cell proliferation and stress response (FOSL2, MKi67, VEGFA; p≤0.048) and retinoic acid metabolism (ALDH3A1, CRABP2, CYP1B1, FABP5, RDH10, STRA6; p≤0.037). Corresponding protein levels, evaluated by Western blotting and ELISA, were significantly modulated for the FABP5–CRABP2 axis, IL-6, IL-8, IL-1α, KRT12, KRT13, TGF-β, and RDH10 under different treatment conditions; (p≤0.045). FOXC1 maintains an anti-inflammatory, immune-quiescent state and coordinates TGF-β–mediated signaling, keratin expression, and retinoic acid metabolism to preserve corneal epithelial identity and homeostasis. Disruption of FOXC1 expression perturbs these pathways, potentially predisposing the ocular surface to fibrosis, lineage instability, and impaired regenerative capacity.

Abstract:

Major depressive disorder (MDD) represents a heterogeneous condition lacking reliable neurobiological biomarkers and mechanistic understanding. Time-resolved characterisation of brain dynamics reveals that mental health is associated with a characteristic dynamical regime, exhibiting spontaneous switching between a repertoire of ghost attractor states forming resting-state networks. Analysing resting-state fMRI data from 848 MDD patients and 794 healthy controls across 17 sites in China (REST-meta-MDD) using Leading Eigenvector Dynamics Analysis (LEiDA), we found MDD patients exhibit significantly reduced default mode network (DMN) occupancy (p < 0.001; Hedges' g = −0.51) and increased occipito-parieto-temporal state occupancy (p < 0.001; Hedges' g = 0.42), suggesting compensatory dynamical rebalancing. These findings extend prior observations of disrupted DMN in MDD, aligning with the emerging dynamical systems framework for mental health to advance mechanistic understanding of MDD pathophysiology.

Abstract: This work compares two common approaches for classifying schizophrenia from EEG data—EEGNet, a compact convolutional neural network, and a Random Forest trained on spectral features—with an emphasis on how well they generalize across datasets. The models were trained on the ASZED-153 dataset using subject-level stratified cross-validation and then evaluated on a completely separate Kaggle EEG dataset collected under different recording conditions. While internal validation appeared reasonably encouraging (70.7% accuracy for EEGNet and 66.8% for Random Forest), performance dropped sharply on the external dataset (54.6% and 45.4%, respectively). This 16–21 percentage point decline was consistent with Maximum Mean Discrepancy results (MMD=0.0914), indicating meaningful distribution differences between datasets. A simple domain adaptation attempt (correlation alignment) provided only a modest improvement (about +1.2 percentage points) and did not recover internal performance levels. Overall, these findings highlight the practical challenge of developing EEG-based classifiers that remain reliable across recording sites and underscore the importance of external validation and more robust cross-site training strategies before considering any clinical deployment.

Abstract: The accumulation of polyethylene (PE) waste presents significant environmental and economic challenges, particularly in developing regions where plastic valorisation infrastructure remains limited. In this work, waste polyethylene was upgraded through coordination-catalyzed oxidative functionalization using earth-abundant Schiff base metal complexes of iron, cobalt, manganese, and copper with salen and salophen ligands. The process enables selective incorporation of oxygen-containing functional groups while largely preserving polymer molecular integrity, offering a material-oriented alternative to fuel-focused plastic recycling. Fourier transform infrared spectroscopy confirmed the formation of carbonyl and hydroxyl functionalities, with the carbonyl index (CI) increasing from 0.02 ± 0.01 for untreated polyethylene to 0.48 ± 0.04 and 0.42 ± 0.03 for Fe(salen)Cl and Co(salen) catalysts, respectively, under identical conditions. Salophen-based complexes consistently exhibited slightly higher oxidation efficiencies than their salen analogues. Gel permeation chromatography revealed controlled molecular weight reduction, with number-average molecular weight (Mₙ) decreasing from 62.4 × 10³ g•mol⁻¹ (untreated PE) to 56.8 × 10³ and 54.9 × 10³ g•mol⁻¹ for Fe- and Co-based systems, while dispersity remained within polymer-grade ranges. Differential scanning calorimetry and thermogravimetric analysis showed only minor changes in melting temperature and thermal stability. Surface-sensitive X-ray photoelectron spectroscopy confirmed oxidation localized primarily at the polymer surface, while atomic absorption spectroscopy indicated residual metal contents below 10 ppm. Catalyst reusability studies demonstrated sustained activity over multiple cycles. Overall, this coordination-catalyzed strategy provides a scalable and industrially relevant pathway for upgrading polyethylene waste into value-added functional polymers, with strong potential for integration into emerging circular polymer economies in Nigeria and other African regions.

Abstract: This study introduces a novel neural network-based symbolic computation algorithm (NNSCA) for obtaining exact solutions to the (3+1)dimension Jimbo-Miwa equation. By integrating neural networks with symbolic computation, NNSCA addresses the limitations of conventional approaches, enabling the derivation and visualization of exact solutions. The neural network architecture is meticulously designed, and the partial differential equation is transformed into algebraic constraints via Maple, establishing a closed-loop solution framework. NNSCA offers a generalized paradigm for investigating high-dimensional nonlinear partial differential equations, highlighting its substantial application prospects.

Abstract: Background: Immune checkpoint inhibitors (ICIs) have revolutionized the treatment of advanced non-small cell lung cancer (aNSCLC). However, immune-related adverse events (irAEs) remain a clinical challenge in this context. Genetic variants acting as cis-eQTLs may predict toxicity risk, thereby enabling personalized treatment. We investigated the association between the IL7 rs16906115 polymorphism, adverse events (AEs), and survival outcomes in patients with aNSCLC receiving ICIs. Methods: This retrospective cohort study analyzed 153 patients with aNSCLC treated with ICIs (2018–2023) at two centers in Spain. The final analytical cohort included 124 patients with complete clinical follow-up. IL7 rs16906115 genotyping was performed using TaqMan assays. Associations between genotypes/alleles, AEs, and survival (PFS/OS) were evaluated using logistic regression and Kaplan-Meier analysis. A clinical-genetic predictive model was developed. Results: The minor A allele frequency was 8.5%. Carriers of the A allele (AG/AA genotypes) had significantly higher adverse event rates compared to GG homozygotes (OR = 3.77, 95% CI: 1.16–12.6, p = 0.0081). The as-sociation remained significant after multivariable adjustment (OR = 4.64, 95% CI: 1.50–17.2, p = 0.0203). Crucially, A-allele carriers exhibited significantly shorter Pro-gression-Free Survival compared to non-carriers (median 6.6 vs. 10 months, p = 0.0029). The combined clinical-genetic model achieved superior predictive perfor-mance for toxicity (AUC = 0.67) compared to clinical-only models (AUC = 0.57), suc-cessfully stratifying patients into moderate- and high-risk groups, respectively. Conclusions: IL7 rs16906115 polymorphism represents a potential pharmacogenetic bi-omarker for predicting adverse events and identifying patients with poor prognosis in aNSCLC immunotherapy. Incorporating genetic profiling into clinical practice may enable personalized toxicity monitoring and enhance treatment safety using precision medicine.

Abstract: Patient satisfaction is crucial to aesthetic surgery, yet measuring how well outcomes meet patient expectations has always been challenging. Rather than relying on the surgeon’s impression, we’ve synthesized research on Patient-Reported Outcome Measures (PROMs) in facial aesthetics. Our work zeroes in on the FACE-Q instrument and explores newer technological applications. We conducted a comprehensive literature review of studies on facelifts (563 patients across 10 studies), injectable treatments (2292 patients in 23 studies), and rhinoplasty (937 patients across 10 studies). Our original data came from a Dutch cohort of Clinique Rebelle in Amsterdam—259 patients undergoing facial procedures, supplemented by Computerized Adaptive Testing (CAT) simulation research. The FACE-Q scales demonstrated strong psychometric properties—Cronbach’s alpha between 0.885 and 0.951—and successfully captured differences between patients that traditional photos miss. CAT methods reduced questionnaire length by roughly 71% without sacrificing measurement accuracy (r = 0.98 with complete surveys). Looking ahead, machine learning shows real potential for forecasting patient satisfaction outcomes. Implementing routine PROM collection in aesthetic practice makes sense on multiple fronts: better patient selection, benchmarking quality across surgeons, protecting against medicolegal concerns, and aligning with value-based healthcare models. We also discuss how AI and 3D imaging might reshape outcome assessment going forward.

Abstract:

The optimal dietary balance between n‑6 and n‑3 polyunsaturated fatty acids (PUFAs), the safe upper intake of n‑6 PUFAs—particularly linoleic acid—and the physiological consequences of their metabolic competition remain unresolved in the context of the Western diet. Since the 1980s, Bill Lands and colleagues have argued that high n‑6 PUFA intake can shift the balance of n‑3–derived pathways and eicosanoid signaling, potentially influencing processes relevant to non‑communicable diseases. Despite its potential public‑health implications, this hypothesis has received limited systematic attention. In this narrative review, we synthesize key aspects of Lands’ work, evaluate supportive and contradictory evidence, and highlight mechanistic insights into lipid competition and inflammatory regulation. We conclude that these unresolved but testable hypotheses warrant renewed investigation, as their corroboration could reshape dietary guidelines and strategies for chronic disease prevention.