Search Results (1,166)

Diabetic neuropathy is a frequent and disabling complication of diabetes, encompassing distal symmetric polyneuropathy and cardiovascular autonomic neuropathy, both associated with reduced quality of life and increased cardiovascular risk. Beyond its traditional interpretation as a direct consequence of chronic hyperglycaemia, oxidative stress has emerged as a central integrative mechanism linking metabolic overload, inflammation, mitochondrial dysfunction, and microvascular injury to progressive neural damage. These processes converge within the neurovascular unit, promoting a self-perpetuating cycle of axonal degeneration, impaired nerve perfusion and altered neuronal excitability. This narrative review synthesises experimental and clinical evidence on oxidative stress-related pathways implicated in diabetic neuropathy, including hyperglycaemia-activated metabolic routes, mitochondrial dysfunction, endoplasmic reticulum stress, and chronic inflammatory signalling. Classical antioxidant and mitochondrial-supportive interventions are evaluated alongside pleiotropic glucose-lowering agents, with particular emphasis on sodium–glucose cotransporter-2 inhibitors and glucagon-like peptide-1 receptor agonists, integrating mechanistic insights with biomarker and clinical outcome data. Conventional antioxidant strategies, such as α-lipoic acid, acetyl-L-carnitine, coenzyme Q10 and N-acetylcysteine, show reproducible benefits on neuropathic symptoms and oxidative stress markers, but evidence for sustained structural or disease-modifying effects remains limited. In contrast, incretin-based therapies and sodium–glucose cotransporter-2 inhibitors exert broader pleiotropic actions by attenuating oxidative and inflammatory signalling, improving mitochondrial homeostasis and endothelial function, with emerging evidence for modest but consistent neurophysiological and autonomic benefits. Overall, oxidative stress emerges as a key mechanistic hub in diabetic neuropathy. Future progress will depend on mechanism-aligned, neuropathy-specific clinical trials incorporating multidimensional endpoints and validated biomarkers. Full article

This study examines why consumers intend to visit restaurants recommended by food influencers on social media. Grounded in the Theory of Planned Behavior (TPB) and social influence mechanisms, we test an extended TPB model in which trust in the influencer is incorporated as an additional antecedent of intention and as a mediating mechanism linking influencer–follower identification to visit intention. To obtain information, a structured questionnaire was administered to a sample of 474 Ecuadorian social media users who follow at least one gastronomic influencer. Hypotheses were assessed using partial least squares structural equation modeling (PLS-SEM) and predictive assessment (PLSpredict). The results show that attitude toward recommendations and perceived control exert a significant effect on intention, while subjective norms have a more moderate influence. Trust is projected as an additional facilitator in the transition from evaluation to intention, indicating that parasocial affinity translates into intended behavior only when it is accompanied by perceived credibility. The study contributes to TPB and influencer marketing by clarifying how influencer-mediated digital recommendation contexts reshape the classic TPB mechanism and by specifying trust as the key bridge between identification and behavioral intention in a high-uncertainty gastronomic decision. Full article

STT3A encodes the catalytic subunit of the oligosaccharyltransferase A (OST-A) complex and is classically linked to severe autosomal-recessive congenital disorder of glycosylation (CDG). To define the distinct autosomal-dominant disorder, we reviewed all published cases and integrated three previously unpublished individuals from the CDG natural history study. Across 21 individuals, abnormal transferrin glycosylation was present in nearly all individuals (20/21), and subtle facial dysmorphism was common (18/21). Neurodevelopmental involvement was frequent, including motor delay (13/21), learning difficulties (13/21), speech delay (12/21), and intellectual disability (10/21). Musculoskeletal manifestations were also common, including skeletal abnormalities (12/21), short stature (11/21), muscle cramps (8/21), and early-onset osteoarthritis in adults (6/21). Less frequent features included congenital heart defects (5/21) and coagulation factor deficiency (5/21). Importantly, the newly reported individuals expand dominant STT3A-CDG with previously unreported features, including anorectal malformation, morbid obesity, and clinically significant bleeding diathesis with von Willebrand factor and factor VIII deficiency. Biochemical signatures ranged from classic type I transferrin patterns to subtle or atypical abnormalities, emphasizing that near-normal transferrin testing does not exclude the diagnosis. Variants clustered in conserved catalytic regions, with recurrent p.Arg405 across de novo, inherited, and mosaic cases supporting a mutational hotspot and likely dominant-negative mechanism. Full article

This study addresses the challenge of consistently transferring atomistic parameters of the C–C bond into phenomenological material characteristics within the framework of continuum mechanics. Particular attention is given to determining the effective transverse diameter of the covalent C–C bond in carbon nanostructures. The dependence of this diameter on Poisson’s ratio ν is examined, and the influence of the interatomic stiffness constants ${\mathrm{k}}_{\mathrm{r}},{\mathrm{k}}_{\mathsf{\theta }}\mathrm{and}{\mathrm{k}}_{\tau }$ is systematically analyzed. Classical representative-volume models of the C–C bond based on the Euler–Bernoulli beam hypothesis violate thermodynamic stability conditions and lead to nonphysical Poisson’s ratio values exceeding 0.5, due to the neglect of shear deformation effects. To overcome this limitation, an approach based on Timoshenko beam theory is proposed, accounting for both bending and shear deformations. This approach enables estimation of energetically equivalent states between the phenomenological representative volume and the corresponding atomistic C–C bond model. As a result, a sixth-order algebraic equation is derived linking the effective bond diameter, the Poisson’s ratio, and the molecular mechanics force constants. Analysis of this equation reveals a narrow range of effective bond diameters and Poisson’s ratios for which thermodynamic stability conditions are satisfied. Within this range, physically consistent macroscopic material parameters can be directly expressed in terms of atomistic force constants. Full article

Steroid hormones exert diverse and tissue-specific biological effects despite sharing a conserved tetracyclic scaffold. Among these, anabolic–androgenic steroids (AAS) present a longstanding paradox: structurally related compounds can elicit markedly different anabolic, androgenic, and cardiovascular outcomes. This narrative review integrates advances in steroid structural chemistry, androgen receptor (AR) biology, and intracellular signaling to elucidate the molecular mechanisms underlying anabolic–androgenic dissociation. We summarize classical genomic and emerging non-genomic modes of steroid action, emphasizing how receptor conformation, ligand-binding domain architecture, co-regulator recruitment, and signaling bias shape downstream biological responses. Particular focus is placed on the structure–activity relationships of endogenous and synthetic androgens, with C17-substitution chemistry highlighted as a central determinant of receptor affinity, metabolic stability, pharmacokinetics, and tissue selectivity. By linking molecular structure to receptor-level mechanisms, we contextualize the physiological and pathophysiological effects of major AAS classes used clinically and non-medically, including testosterone esters, 19-nor derivatives, 17α-alkylated steroids, heterocyclic compounds, and halogenated compounds. While much of the mechanistic evidence derives from preclinical models, the integrated framework presented here provides a coherent basis for interpreting divergent anabolic, androgenic, and cardiovascular effects observed in humans. Collectively, this review bridges fundamental steroid biology with applied physiology and sports medicine, offering mechanistic insight relevant to therapeutic development, anti-doping science, and risk assessment of supraphysiological androgen exposure. Full article

Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) infection induces heterogeneous immune responses that influence both acute disease severity and long-term immune remodeling. A key question in the context of infection and vaccination is whether SARS-CoV-2 exerts direct oncogenic effects or instead acts as a transient immunological stressor capable of reinforcing tumor-permissive pathways. Current evidence does not support classical viral oncogenesis. Rather, severe infection is characterized by early interferon (IFN) imbalance followed by NF-κB-dominant inflammatory amplification, promoting sustained IL-6/JAK–STAT3 and MAPK signaling, chronic cytokine production, metabolic reprogramming, and impaired antitumor immune surveillance. At the molecular level, viral structural proteins modulate host signaling networks. The spike (S1) protein engages TLR2/TLR4–MyD88 pathways, activating NF-κB and MAPK cascades, while the membrane (M) protein reinforces NF-κB–STAT3 circuits linked to epithelial–mesenchymal transition and inflammatory gene expression. These mechanisms intensify pre-existing oncogenic signaling without initiating malignant transformation. Tissue-specific responses are further shaped by IFN competence, renin–angiotensin system balance, and metabolic context. In parallel, immune evasion programs shared by chronic viral infection and cancer, including checkpoint upregulation, impaired antigen presentation, and suppressive myeloid expansion, may be transiently reinforced following severe infection. In contrast, SARS-CoV-2 vaccination induces spatially restricted, self-limited innate activation without sustained inflammatory signaling or persistent antigen exposure. By preventing severe disease and chronic immune dysregulation, vaccination interrupts pathways hypothesized to intersect with cancer biology, with no evidence of increased cancer incidence. Ongoing longitudinal studies are required to clarify the long-term oncologic implications of post-infectious immune remodeling. Full article

Siderophores are classically understood as microbial iron-acquisition metabolites: low-molecular-weight ligands secreted by bacteria to solubilize and transport Fe(III) under iron-limited conditions. In this review, we expand that paradigm by highlighting an emerging and underappreciated chemical axis—boron coordination by siderophores—that links terrestrial (soil/rhizosphere) and marine microbiomes. Across diverse bacterial taxa, siderophore production is widespread and central to competitive fitness because Fe(III) is poorly soluble and frequently sequestered in environmental or host matrices. Yet in boron-rich settings (seawater and borate-enriched soils), the same oxygen-donor architectures that support Fe(III) chelation can also engage boron chemistry. We synthesize evidence that carboxylate/α-hydroxyacid (dicitrate-type) and catecholate siderophores can form tetrahedral borate/boronate complexes, whereas hydroxamate siderophores generally lack the vicinal dianionic O,O motif required for stable boron binding. Structurally characterized examples—including vibrioferrin, rhizoferrin, and petrobactin—demonstrate that boron complexation is experimentally observable by ESI-MS and multinuclear NMR and can be modulated by pH and microenvironment. Integrating these findings with datasets on boron-tolerant bacteria, we propose that when iron is scarce and boron is available, boron–siderophore complexation becomes chemically feasible and may influence microbial physiology by altering ligand conformation, metal selectivity, and potentially extracellular signaling behavior—especially in marine systems where borate is abundant at oceanic pH. Overall, this review frames boron-binding siderophores as a cross-ecosystem phenomenon and a promising conceptual bridge between environmental boron geochemistry, microbial metal economy, and metalloid-mediated signaling. Full article

Exercise-induced muscle damage (EIMD) has classically been attributed to localized mechanical disruption following eccentric contractions. Emerging evidence, however, indicates that EIMD represents a systems-level failure of stress integration within skeletal muscle rather than a purely mechanical lesion. Mechanical loading initiates disturbances in intracellular Ca²⁺ homeostasis, which interact with metabolic stress, redox imbalance, and immune activation to form self-reinforcing feedback loops. When compensatory capacity is exceeded, transient injury may shift toward maladaptive remodeling marked by mitochondrial dysfunction, ferroptosis, chronic inflammation, and impaired regeneration. Recent studies identify reactive oxygen species accumulation, iron-dependent lipid peroxidation, dysregulated energy sensing, and aberrant immune polarization as key molecular tipping points governing injury reversibility. Beyond their regenerative role, satellite cells act as integrators of metabolic history and epigenetic memory, linking repetitive injury to reduced muscle adaptability, age-related sarcopenia, and heightened metabolic disease risk. Here, we synthesize evidence from animal models, clinical studies, and multi-omics analyses to establish a systems biology framework for EIMD. We delineate the spatiotemporal interactions among mechanical, metabolic, oxidative, immune, and regenerative modules; identify regulatory nodes that determine adaptive repair versus pathological outcomes; and critically evaluate current nutritional, physical, pharmacological, and regenerative interventions from a mechanism-oriented perspective. Finally, we discuss how multi-omics, digital monitoring, and individualized rehabilitation may enable precision management of EIMD and advance understanding of muscle stress resilience and adaptive limits. Full article

Background: Evidence linking adenoid hypertrophy (AH) and atopy is conflicting. We examined whether Cassano-graded AH severity is more closely associated with inflammatory markers than with IgE-mediated sensitization. Methods: We retrospectively included children aged 3–12 years diagnosed with AH between December 2022 and December 2025. AH was graded according to the Cassano classification and dichotomized as advanced AH (Stage III–IV). Atopic features were evaluated separately as clinical atopy, IgE-mediated sensitization, elevated total IgE, and eosinophilia. Multivariable logistic regression analyses were performed to assess factors associated with clinical atopy, sensitization, and advanced AH. Results: Among 426 children, clinical atopy was present in 28.2%, sensitization in 23.0%, elevated total IgE in 16.4%, and eosinophilia in 27.7%; 39.2% had advanced AH. In multivariable analysis, clinical atopy was independently associated with family history of atopy (aOR 13.9; 95% CI 7.9–24.4), elevated total IgE (aOR 3.86; 95% CI 2.10–7.08), and passive smoking exposure (aOR 1.73; 95% CI 1.07–2.79). Sensitization was independently associated only with family history of atopy (aOR 4.99; 95% CI 1.99–12.53). Advanced AH was independently associated only with eosinophilia (aOR 2.07; 95% CI 1.30–3.29). Conclusions: AH severity was associated with eosinophilia rather than classical IgE-mediated sensitization. Assessment of eosinophilia may aid routine severity evaluation in children with AH. Full article

Iron deficiency (ID) has emerged as a pivotal yet underrecognized factor in the pathogenesis of immune-mediated inflammatory skin diseases (IMISDs) such as psoriasis, atopic dermatitis, and hidradenitis suppurativa. Beyond its classical role in erythropoiesis, iron acts as a key modulator of immune cell activity, redox balance, and overall metabolic homeostasis. This review synthesises the latest evidence on the intricate relationship between systemic inflammation, disturbances of iron metabolism, and immunometabolic imbalances that underline the pathogenesis of IMISDs. Findings indicate that chronic inflammation drives functional iron deficiency through IL-6–hepcidin-mediated sequestration of iron, resulting in reduced bioavailability and altered mitochondrial activity in immune and epithelial cells. This imbalance is associated with excessive and chronically enhanced oxidative and inflammatory responses of these cells, further advancing inflammation, anaemia of chronic disease, and disturbances of tissue repair. Moreover, emerging evidence supports an “iron-skin axis,” and suggests that skin cells, particularly epidermal keratinocytes, are actively involved in the regulation of iron pathways. Collectively, these insights position iron homeostasis as a missing link between systemic inflammation, immunometabolic imbalance, and disease burden in IMISDs. Full article

Background/Objectives: The gut–liver axis plays a central role in cholesterol homeostasis, linking intestinal absorption, microbial metabolites, and hepatic lipid regulation. Dysregulation of this axis contributes to hypercholesterolemia and cardiometabolic risk, beyond classical cholesterol synthesis pathways. This study evaluated a novel multi-botanical formulation (MIX) that combines Gastrodia elata, Black Garlic, Primula veris, and Emblica officinalis (AMLA) to integrate modulation of cholesterol metabolism through intestinal and hepatic mechanisms. Methods: Individual extracts were chemically characterised for polyphenols, flavonoids, polysaccharides, S-allyl-L-cysteine (SAC), and tannins. Caco-2 cells were treated with varying doses to determine optimal concentrations and for viability, transepithelial electrical resistance, and permeability analysis. Supernatants post-intestinal passage were applied to HepG2 cells under high-glucose conditions to assess viability, oxidative stress, SRC/ERK-MAPK signalling, cholesterol synthesis (HMGR), LDL uptake, PCSK9–LDLR–SREBP-2 axis, and bile acid production. Results: MIX enhanced intestinal barrier integrity (TEER, tight junctions, permeability) and preserved cell viability compared with single extracts. In HepG2 cells, MIX demonstrated synergistic effects: it reduced HMGR expression by 83–90% relative to individual extracts, increased LDLR expression by 43–97%, suppressed PCSK9 by up to 92%, and lowered total cholesterol and LDL uptake more effectively than RYRF. MIX also amplified bile acid production and free cholesterol excretion, indicating improved hepatic clearance pathways. SRC and ERK-MAPK signalling were favourably modulated, supporting hepatocyte survival under metabolic stress. Conclusions: The multi-botanical formulation exerts complementary and synergistic effects on intestinal absorption and hepatic cholesterol regulation, integrating suppression of cholesterol synthesis, enhanced LDL clearance, and stimulated elimination via bile acids. These findings highlight the potential of the MIX formulation to modulate metabolically induced cholesterol dysregulation, supporting further in vivo and clinical investigation. Full article

Quantiles are among the most fundamental constructs in probability theory and statistics, intrinsically linked to order structures, stochastic dominance, and the principles of robust statistical inference. Although the univariate theory of quantiles is by now classical and well developed, their generalization to multivariate settings remains mathematically subtle and methodologically demanding. In particular, extending the notion of “location within a distribution” beyond one dimension raises delicate questions of geometry, ordering, and equivariance. Within this landscape, the spatial—or geometric—formulation of multivariate quantiles has emerged as a rigorous and conceptually unifying framework capable of reconciling these issues. In this work we advance this paradigm by introducing a kernel-based estimation procedure for nonparametric conditional geometric quantiles of a multivariate response $Y\in {\mathbb{R}}^q$ ($q\ge 2$) given a functional covariate X that takes values in an infinite-dimensional space. The data are assumed to form a strictly stationary and ergodic process, while the responses may be subject to a missing-at-random mechanism, a feature of substantial practical relevance. Our analysis establishes strong consistency of the proposed estimator, characterizes its optimal convergence rate, and derives its asymptotic distribution. These limit theorems, in turn, provide the theoretical foundation for constructing asymptotically valid confidence regions and for performing inference in multivariate quantile regression with functional covariates. The theoretical developments rest on natural complexity conditions for the involved functional classes together with mild smoothness and regularity assumptions. This balance between generality and mathematical precision ensures that the resulting methodology is not only robust in a rigorous probabilistic sense but also widely applicable to contemporary problems in high-dimensional and functional data analysis. The proposed methodology is numerically investigated through simulations and is implemented in a real data application. Full article

Mitochondria play a crucial role in cellular bioenergetics, signaling, and metabolism; yet, many fundamental mechanisms such as the proton transfer along the membranes, the link between membrane curvature and oxidative phosphorylation, and the nanoscale organization of enzyme supercomplexes remain poorly understood due to the limitations of classical biochemical approaches. This review addresses this gap by systematically analyzing the contemporary physical methods used to investigate the mitochondrial structure and function from the micro to nano scale. It covers advanced fluorescence and super-resolution microscopy, electron and volume electron microscopy, and scanning probe techniques, as well as cryo-electron tomography for resolving supramolecular assemblies in near-native conditions. The review highlights the applications of the modern fluorescent probes, expansion and phase microscopy, and machine-learning-based image analysis for a quantitative assessment of the mitochondrial morphology, membrane potential, and dynamics in living cells and tissues. Complementary spectroscopic and scattering methods, including Raman spectroscopy, NMR, and X-ray and neutron scattering, are discussed as tools for probing the redox state, metabolite composition, and membrane organization. Emphasis is placed on integrating high-resolution experimental data with advanced computational frameworks to test competing models of mitochondrial function and pathology, and to guide the development of biomimetic and biomedical technologies. Full article

This review develops a unified geometric framework for synthesizing global asymptotic laws, termed classical entanglement. The central tool is the entanglement operator, a Minkowski–$L_a$ metric blend that couples asymptotic regimes through an index $a>1$, producing a nonlinear global state whose intermediate region is metrically non-separable and cannot be written as a linear combination of its limits. The framework reveals a universal transition knee whose curvature scales linearly with a, independent of amplitudes or local scales. We show that this geometric mechanism encompasses Orlicz norms, weighted Hölder metrics, and iterated Hölder constructions, the latter being structurally isomorphic to self-similar root approximants. A conceptual “Rosetta Stone” links practitioner terminology, geometric meta-language, and functional-analytic structures, clarifying how classical entanglement unifies empirical blending, metric curvature, and Calderón-type interpolation. Applications to turbulence (Darcy friction factor), fractional dynamics, and scale-dependent diffusion illustrate how classical entanglement provides stable, asymptotically consistent global states across multi-scale systems. Full article

Traditional mean–variance portfolio optimization proves inadequate for cryptocurrency markets, where extreme volatility, fat-tailed return distributions, and unstable correlation structures undermine the validity of variance as a comprehensive risk measure. To address these limitations, this paper proposes a unified entropy-based portfolio optimization framework grounded in the Maximum Entropy Principle (MaxEnt). Within this setting, Shannon entropy, Tsallis entropy, and Weighted Shannon Entropy (WSE) are formally derived as particular specifications of a common constrained optimization problem solved via the method of Lagrange multipliers, ensuring analytical coherence and mathematical transparency. Moreover, the proposed MaxEnt formulation provides an information-theoretic interpretation of portfolio diversification as an inference problem under uncertainty, where optimal allocations correspond to the least informative distributions consistent with prescribed moment constraints. In this perspective, entropy acts as a structural regularizer that governs the geometry of diversification rather than as a direct proxy for risk. This interpretation strengthens the conceptual link between entropy, uncertainty quantification, and decision-making in complex financial systems, offering a robust and distribution-free alternative to classical variance-based portfolio optimization. The proposed framework is empirically illustrated using a portfolio composed of major cryptocurrencies—Bitcoin (BTC), Ethereum (ETH), Solana (SOL), and Binance Coin (BNB)—based on weekly return data. The results reveal systematic differences in the diversification behavior induced by each entropy measure: Shannon entropy favors near-uniform allocations, Tsallis entropy imposes stronger penalties on concentration and enhances robustness to tail risk, while WSE enables the incorporation of asset-specific informational weights reflecting heterogeneous market characteristics. From a theoretical perspective, the paper contributes a coherent MaxEnt formulation that unifies several entropy measures within a single information-theoretic optimization framework, clarifying the role of entropy as a structural regularizer of diversification. From an applied standpoint, the results indicate that entropy-based criteria yield stable and interpretable allocations across turbulent market regimes, offering a flexible alternative to classical risk-based portfolio construction. The framework naturally extends to dynamic multi-period settings and alternative entropy formulations, providing a foundation for future research on robust portfolio optimization under uncertainty. Full article