Search Results (650)

Background: The in vivo accumulation of Advanced Glycation End products (AGEs) is associated with the development of several chronic aging-related and degenerative diseases, as they alter protein structures and activate oxidative and inflammatory processes through interactions with the receptor for AGEs (RAGE). Plant secondary metabolites play a key role in counteracting the glycation process through various mechanisms of action. Therefore, Aloysia citrodora leaf polyphenolic extract could represent a source of anti-glycative compounds. Methods: The methanolic extract was characterized by RP-HPLC-DAD-MSⁿ, and its anti-glycative properties were investigated using several in vitro assays mimicking the different steps of the glycation reaction. In parallel, molecular docking studies were carried out to evaluate potential interactions between the identified metabolites and RAGE. Furthermore, A. citrodora metabolites’ stability under simulated in vitro digestion was assessed, and the anti-glycative activity of the bioaccessible fraction was investigated. Results:A. citrodora extract, rich in iridoid glycosides, phenylethanoid glycosides, and flavones, strongly inhibited AGE formation (from 10% to 100%) in both the middle and end step of the reaction and had high methylglyoxal and glyoxal trapping capacity. However, the digestion process affected extract stability, particularly under intestinal conditions, yielding an overall bioaccessibility of about 40% and leading to a subsequent reduction in anti-glycative properties. Finally, molecular modeling analysis highlighted the ability of the studied metabolites to bind RAGE. Conclusions:A. citrodora represents a promising source of natural anti-glycative agents with potential applications as food ingredients. However, it is essential to improve the extract bioaccessibility and to preserve its anti-glycative properties by developing a suitable formulation. Full article

Soil salinization–alkalization is a critical abiotic constraint on global agriculture, threatening agroecosystem sustainability. Leymus chinensis, a high–quality perennial forage with strong stress resilience, is an ideal model for studying saline–alkali tolerance in graminaceous crops. We integrated physiological, transcriptomic, and metabolomic profiling to dissect its responses under moderate vs. severe carbonate stress, mimicking natural saline–alkali soils rather than single salt stress treatments. Multi–omics analysis revealed drastic reprogramming of energy metabolism, carbohydrate homeostasis, water transport, and secondary metabolism. Our novel finding reveals that L. chinensis uses stress–severity–dependent mechanisms, with flavonoid biosynthesis as a central “regulatory hub”: moderate saline–alkali stress acts as a stimulus for “Adaptive Activation” (energy + antioxidants), promoting growth, while severe stress exceeds tolerance thresholds, causing “systemic imbalance” (oxidative damage + metabolic disruption) and growth retardation. Via WGCNA and metabolome–transcriptome modeling, 22 transcription factors linked to key flavonoid metabolites were identified, functioning as molecular switches for stress tolerance. Our integrated approach provides novel insights into L. chinensis’ tolerance networks, and the flavonoid biosynthesis pathways and regulatory genes offer targets for precision molecular breeding to enhance forage stress resistance and mitigate yield losses from salinization–alkalization. Full article

The global energy transition faces a chasm between current policy commitments (IEA’s STEPS) and the deep, rapid transformation required to realize all national net zero pledges (IEA’s APC). This perspective addresses the critical innovation and policy gap blocking the APC pathway, where many high-impact, clean technologies remain at low-to-medium Technology Readiness Levels (TRLs 3–6) and lack formal policy support. The insufficient nature of current climate policy nomenclature is highlighted, which often limits Nature-based Solutions (NbS) to incremental projects rather than driving systemic technological change (Bio-inspiration). Then, we propose that a deliberate shift from simple biomimetics (mimicking form) to biomimicry (emulating life cycle sustainability) is the essential proxy for acceleration. Biomimicry inherently targets the grand challenges of resilience, resource efficiency, and multi-functionality that carbon-centric metrics fail to capture. To institutionalize this change, we advocate for the mandatory integration of bio-inspired design into National Determined Contributions (NDCs) by reframing NbS as Nature-based Innovation (NbI) and introducing novel quantitative metrics. Finally, a three-step roadmap to guide this systemic shift is presented, from deployment of prototypes (2025–2028), to scaling evidence and standardization (2029–2035), to consolidation and regenerative integration (2036–2050). Formalizing these principles through policy will de-risk investment, mandate greater R&D rigor, and ensure that the next generation of energy infrastructure is not just carbon-neutral, but truly regenerative, aligning technology deployment with the necessary speed and depth of the APC scenario. Full article

Background: Ovarian neuroendocrine neoplasms (O-NENs) are extremely rare, representing less than 1% of all ovarian neoplasms and under 5% of all neuroendocrine tumors (NETs). They encompass two primary histological subtypes: well-differentiated carcinoids and poorly differentiated neuroendocrine carcinomas, which display distinct biological behaviors and prognoses. The ovary can also be a site of metastasis from extra-ovarian NETs. Owing to their rarity, clinical management lacks standardization, and diagnosis is often incidental following surgery for presumed epithelial ovarian neoplasms. Objectives: This review aims to provide an updated synthesis of current evidence on the epidemiology, pathogenesis, clinical presentation, diagnosis, treatment strategies, and prognosis of O-NENs, highlighting unmet clinical needs. Methods: A literature search was performed on PubMed for the years 2014–2024 using the keywords: “ovarian neuroendocrine tumor”, “ovarian neuroendocrine neoplasm”, “ovarian neuroendocrine carcinoma”, and “ovarian carcinoid”. Only articles published in English were considered. Given the rarity of the disease, in addition to meta-analyses and systematic reviews, relevant case reports and case series were also included to provide a comprehensive clinical picture, yielding 32 eligible articles. Results: Evidence indicates that O-NENs remain understudied, with most data derived from case reports and small series. Clinical presentations vary from asymptomatic masses to hormone-related syndromes, often mimicking other ovarian pathologies. Diagnostic work-up typically follows the same protocol as epithelial ovarian cancer, with the neuroendocrine nature only recognized postoperatively. Treatment strategies are empirical and largely extrapolated from extra-ovarian NETs due to the absence of specific guidelines. Prognosis varies widely depending on histotype, stage, and secretory activity. Conclusions: O-NENs pose significant diagnostic and therapeutic challenges due to their rarity and heterogeneity. Greater clinical awareness, multidisciplinary management, and multicenter research are essential to establish evidence-based protocols and improve patient outcomes. Full article

In response to the personalized and precise rehabilitation needs for motor injuries and stroke associated with population aging, this study proposes a design method for an intelligent rehabilitation trainer that integrates Bayesian information gain (BIG) and axis matching techniques. Grounded in the biomechanical characteristics of the human ankle joint, the design fully draws upon biomimetic principles, constructing a 3-PUU-R hybrid serial–parallel bionic mechanism. By mimicking the dynamic variation of the ankle’s instantaneous motion axis and its balance between stiffness and compliance, a three-dimensional digital model was developed, and multi-posture human factor simulations were conducted, thereby achieving a rehabilitation process more consistent with natural human movement patterns. Natural randomized disability grade experimental data were collected for 100 people to verify the validity of the design results. On this basis, a Bayesian information gain framework was established by quantifying the reduction of uncertainty in rehabilitation outcomes through characteristic parameters, enabling the dynamic optimization of training strategies for personalized and precise ankle rehabilitation. The rehabilitation process was modeled as a problem of uncertainty quantification and information gain optimization. Prior distributions were constructed using surface EMG (electromyography) signals and motion trajectory errors, and mutual information was used to drive the dynamic adjustment of training strategies, ultimately forming a closed-loop control architecture of “demand perception–strategy optimization–execution adaptation.” This innovative integration of probabilistic modeling and cross-joint bionic design overcomes the limitations of single-joint rehabilitation and provides a new paradigm for the development of intelligent rehabilitation devices. The deep integration mechanism-based dynamic axis matching and Bayesian information gain holds significant theoretical value and engineering application prospects for enhancing the effectiveness of neural plasticity training. Full article

Small interfering RNAs (siRNAs) have emerged as a powerful tool in the treatment of aggressive cancers. By exploiting and mimicking the natural gene regulation mechanism of RNA interference (RNAi), they allow for sequence-specific silencing of aberrant genes. siRNA-mediated knockdown of the inflammatory cytokine tumour necrosis factor-alpha (TNF-α) presents a novel therapy for triple-negative breast cancer (TNBC). This study investigated the potential of novel biomimetic glutathione-synthesised gold nanoclusters (AuNCs) as siRNA delivery vehicles. AuNCs were functionalized with biocompatible chitosan and polyethene glycol, and their interactions with siRNAs were investigated through binding studies. In vitro cytotoxicity and cellular uptake were conducted in the human breast cancer (MCF-7), TNBC (MDA-MB-231), and embryonic kidney (HEK293) cells, while the effect of anti-TNF-α siRNA nanocomplexes on biological processes, such as oxidative stress, apoptosis, and cell cycle distribution, was investigated using flow cytometry. UV–visible and Fourier transform infrared spectroscopy, as well as transmission electron microscopy, confirmed the synthesis and functionalization of the AuNCs. Functionalized AuNCs (FAuNC) effectively bound and condensed siRNA and protected against nuclease degradation. AuNCs facilitated efficient cellular uptake and were well-tolerated in vitro. Anti-TNF-α siRNA treatment of the MDA-MB-231 cells increased apoptosis and oxidative stress levels, and affected cell cycle distribution. Although the overall knockdown was low, these FAuNCs exhibited favorable physicochemical characteristics, low cytotoxicity and good cellular uptake in vitro, warranting further optimisation for improved delivery of therapeutic siRNAs. Full article

Tissue engineering offers a promising solution by developing scaffolds that mimic the extracellular matrix and guide cellular growth and differentiation. Recent evidence suggests that scaffolds must provide not only biocompatibility and appropriate mechanical properties, but also the structural complexity and heterogeneity characteristic of natural tissues. Particle-based scaffolds represent an emerging paradigm in regenerative medicine, wherein micro- and nanoparticles serve as primary building blocks rather than minor additives. This approach offers exceptional control over scaffold properties through precise selection and combination of particles with varying composition, size, rigidity, and surface characteristics. The presented review examines the fundamental principles, fabrication methods, and properties of particle-based scaffolds. It discusses how interparticle connectivity is achieved through techniques such as selective laser sintering, colloidal gel formation, and chemical cross-linking, while scaffold architecture is controlled via molding, templating, cryogelation, electrospinning, and 3D printing. The resulting materials exhibit tunable mechanical properties ranging from soft injectable gels to rigid load-bearing structures, with highly interconnected porosity that is essential for cell infiltration and vascularization. Importantly, particle-based scaffolds enable sophisticated pharmacological functionality through controlled delivery of growth factors, drugs, and bioactive molecules, while their modular nature facilitates the creation of spatial gradients mimicking native tissue complexity. Overall, the versatility of particle-based approaches positions them as prospective tools for tissue engineering applications spanning bone, cartilage, and soft tissue regeneration, offering solutions that integrate structural support with biological instruction and therapeutic delivery on a single platform. Full article

Portfolio optimization is fundamental to modern finance, enabling investors to construct allocations that balance risk and return while satisfying practical constraints. When transaction costs and cardinality limits are incorporated, the problem becomes a computationally demanding mixed-integer quadratic program. This work demonstrates how principles from biomimetics—specifically, the computational strategies employed by biological neural systems—can inspire efficient algorithms for complex optimization problems. We demonstrate that this problem can be reformulated as a constrained quadratic program and solved using dynamics inspired by spiking neural networks. Building on recent theoretical work showing that leaky integrate-and-fire dynamics naturally implement projected gradient descent for convex optimization, we develop a solver that alternates between continuous gradient flow and discrete constraint projections. By mimicking the event-driven, energy-efficient computation observed in biological neurons, our approach offers a biomimetic pathway to solving computationally intensive financial optimization problems. We implement the approach in Python and evaluate it on portfolios of 5 to 50 assets using five years of market data, comparing solution quality against mixed-integer solvers (ECOS_BB), convex relaxations (OSQP), and particle swarm optimization. Experimental results demonstrate that the SNN solver achieves the highest expected return (0.261% daily) among all evaluated methods on the 50-asset portfolio, outperforming exact MIQP (0.225%) and PSO (0.092%), with runtimes ranging from 0.5 s for small portfolios to 8.4 s for high-quality schedules on large portfolios. While current Python runtimes are comparable to existing approaches, the key contribution is establishing a path to neuromorphic hardware deployment: specialized SNN processors could execute these dynamics orders of magnitude faster than conventional architectures, enabling real-time portfolio rebalancing at institutional scale. Full article

The co-culture technique, mimicking natural microbial interactions, has proven to be successful at activating silent biosynthetic gene clusters (BGCs) to produce novel metabolites or enhance the yield of specific metabolites. To effectively decode induction processes, it is critical to have a comprehensive understanding of intermicrobial interactions across both volatile and non-volatile metabolomes. As part of our attempt to uncover structurally unique and biologically active natural products from mangrove endophytic fungi, Phomopsis asparagi DHS-48 was co-cultured with another mangrove fungal strain, Pestalotiopsis sp. HHL-101. The competition interaction of the two strains was investigated using morphology and scanning electron microscopy (SEM), and it was discovered that the mycelia of the DHS-48 and HHL-101 compressed and tangled with each other in the co-culture system, forming an interwoven pattern. To profile volatile-mediated chemical interactions during fungal co-culture, headspace solid-phase microextraction gas chromatography mass spectrometry (HS-SPME-GC-MS) coupled with orthogonal partial least squares-discriminant analysis (OPLS-DA) was adopted. Meanwhile, non-volatile metabolites from both liquid and solid small-scale co-cultures were profiled via HPLC. Two new polyketides, named phaseolorin K (1) and pestaphthalide C (7), together with 11 known compounds (2–6, 8–13), were characterized from solid-state co-cultivation extracts of these two titled strains. Their planar structures were established by analysis of HRMS, MS/MS, and NMR spectroscopic data, while absolute configurations were assigned using ECD calculations. Co-culture feeding experiments demonstrated that DHS-48 exerts antagonistic activity against HHL-101 through altering its hyphal morphology, which mediated enhanced biosynthesis of non-volatile antimicrobial metabolites 5 and 6. Biological assays revealed that compounds 4–6 exhibited potent in vitro cytotoxicity against human cancer cell lines HeLa and HepG2, compared to the positive controls adriamycin and fluorouracil. Compound 2 moderately inhibited the proliferation of ConA-induced T and LPS-induced B murine spleen lymphocytes. Full article

To reduce the time of postoperative recovery and to prevent post-surgical complications, biocompatible synthetic materials with osteoconductive and osteoinductive properties are used as bone substitutes in large bone defect management. A simplified biomimetic approach to similar materials is based on the use of an inorganic filler, a polymer matrix, and a compatibilizer, mimicking the composition of the natural bone. Based on plate-like micro-sized carbonated hydroxyapatite (pCAp), we prepared compression-molded samples optionally containing an additional polyester component (poly(ε-caprolactone) PCL, poly(L-lactide) PLLA, or poly(L-methylglycolide) PLMG); syntheticblock copolymers comprising fragments of the corresponding polyester and poly(ethylene phosphoric acid) (PEPA) were also prepared and studied asa ‘two-in-one’ polymer matrix/compatibilizer. Bone regeneration experiments involving a three-month rat tibial defect model were conducted with 250–500 μm granules of the composites. Comparative studies of the introduction of the polyester-b-PEPA copolymer into composites revealed a positive effect, which manifests itself in accelerated bone regeneration, which further intensified for pCAp/PEPA-b-PLMG. The latter composite formulation was used to study the results of the introduction of cerium into the filler. One-month experiments with pCAp, CePO₄-doped pCAp, and composites of these inorganic fillers with PEPA-b-PLMG were conducted. For the first time, a positive synergistic effect of the presence of cerium and PEPA in the composite, which appeared in substitution of the implant material by two-thirds of newly formed partly matured bone, was observed four weeks after surgery. Full article

The ocular surface is a structure crucial to maintaining eye health and proper vision. Unfortunately, ocular surface diseases functioning as chronic epithelial defects, inflammation, impaired healing, require immediate regenerative repair treatment that can restore tissue integrity and function. Conventional therapies, such as artificial tears and topical anti-inflammatory agents, principally provide symptomatic relief without addressing the underlying biological deficits, thus leading to incomplete or delayed recovery. Therefore, blood derivatives have emerged as a promising bioactive therapy that not only lubricates but also actively promotes regeneration through the delivery of cytokines, growth factors, and vitamins naturally present in blood. Due to their properties mimicking the components of natural tears, autologous origin, biocompatibility and capacity to enhance tissue repair, they have emerged as a cornerstone in regenerative medicine. Therefore, the purpose of this review was to compare the evolution, positive aspects, and drawbacks, in order to demonstrate the molecular mechanism of action and the therapeutic efficacy of different blood derivates at treating ocular surface disease. Over time, these biologic preparations have evolved from the use of simple traditional serum-based derivatives to more advanced platelet-rich products, underscoring the evolving understanding of platelet-driven molecular and cellular mechanisms in tissue regeneration. Despite their widespread use, we would like to highlight the current limitations related to the lack of standardized preparation protocols, variability in composition, and evidence-based integration into clinical practice. Finally, this review highlights contemporary research trends and depicts future directions advancing the field. Key priorities include the establishment of standardized, reproducible preparation protocols; the development of next-generation platelet-derived concentrates and biomaterials; and the integration of multi-omics technologies to achieve comprehensive profiling of their biological and therapeutic activity. Moving toward methodological standardization and the execution of well-designed, high-quality comparative clinical trials will be essential to reinforce the scientific foundation, enhance translational potential, and ensure the clinical reliability of blood-derived therapies in modern regenerative medicine. Full article

Continuous estimation of joint angles based on surface electromyography (sEMG) signals is a promising method for naturally controlling prosthetic limbs and assistive devices. However, conventional methods based on neural networks have limitations such as long training times and calibration burdens. This study investigates the use of an echo state network (ESN), which enables fast training, to estimate wrist joint angles from sEMG. Five participants mimicked the motion of a 1-degree-of-freedom robotic arm by flexing and extending their wrist, while sEMG signals from the wrist flexor and extensor muscles and the robotic arm’s angle were recorded. The ESN was trained to take two sEMG channels as input and the robotic joint angle as output. High-accuracy estimation with a median coefficient of determination R² = 0.835 was achieved for representative ESN parameters. Additionally, the effects of the reservoir size, spectral radius, and time constant on estimation accuracy were evaluated using data from a single participant. Furthermore, online estimation of joint angles based on sEMG signals enabled successful control of the robotic arm. These results suggest that sEMG-based ESN estimation offers fast, accurate joint control and could be useful for prosthetics and fundamental studies on body perception. Full article

Breast cancer remains one of the leading causes of cancer morbidity and mortality among women worldwide. Conventional two-dimensional (2D) cell culture models and animal studies often fail to accurately recapitulate the complex tumor microenvironment and heterogeneous nature of breast cancer. Recent advancements in tissue engineering have enabled the development of more physiologically relevant models using three-dimensional (3D) bioprinting and organoid technology. This study focuses on integrating 3D bioprinting with patient-derived organoid models to replicate breast cancer tissue architecture, cellular heterogeneity, and tumor-stroma interactions. Utilizing biomimetic bioinks and customized bioprinting protocols, we successfully fabricated breast cancer tissue constructs embedded with stromal and immune components. These engineered models demonstrated high fidelity in mimicking in vivo tumor pathophysiology, including angiogenesis, epithelial–mesenchymal transition, and extracellular matrix remodeling. Furthermore, the platform allowed for high-throughput drug screening and evaluation of therapeutic responses, revealing differential sensitivities to chemotherapeutics and targeted therapies. Our findings highlight the potential of bioprinted organoid models as powerful tools for personalized medicine, enabling more predictive and reliable cancer research and drug development. Full article

Natural gas pipeline leak monitoring suffers from severe environmental noise, non-stationary signals, and complex multi-source variable couplings, limiting prediction accuracy and robustness. Inspired by biological perceptual systems, particularly their multimodal integration and dynamic attention allocation, we propose GL-TransLSTM, a biomimetic hybrid deep learning model. It synergistically combines Transformer’s global self-attention (emulating selective focus) and LSTM’s gated memory (mimicking neural temporal retention). The architecture incorporates a multimodal fusion pipeline; raw sensor data are first decomposed via CEEMDAN to extract multi-scale features, then processed by an enhanced LSTM-Transformer backbone. A novel physics-informed gated attention mechanism embeds gas diffusion dynamics into attention weights, while an adaptive sliding window adjusts temporal granularity. This study makes evaluations on an industrial dataset with methane concentration, temperature, and pressure, GL-TransLSTM achieves 99.93% accuracy, 99.86% recall, and 99.89% F1-score, thereby significantly outperforming conventional LSTM and Transformer-LSTM baselines. Experimental results demonstrate that the proposed biomimetic framework substantially enhances modeling capacity and generalization for non-stationary signals in noisy and complex industrial environments through multi-scale fusion, physics-guided learning, and bio-inspired architectural synergy. Full article

High-efficiency diesel and lean-burn engines produce lower exhaust temperatures, which can delay the activation of after-treatment catalysts such as Diesel Oxidation Catalysts (DOCs). This study explores ion beam sputtering as a post-synthesis strategy to enhance the low-temperature activity of commercial Pt/CeO₂–ZrO₂ catalysts. Low-energy ions (0.5–1.5 keV) were applied with controlled variations in treatment number, beam current, and exposure time to selectively generate oxygen vacancies and improve Pt dispersion. Structural and chemical effects were characterized using X-ray diffraction (XRD), BET surface area measurements, X-ray photoelectron spectroscopy (XPS) and extended X-ray absorption fine structure (EXAFS). Catalytic performance was evaluated through CO and C₃H₆ oxidation under conditions mimicking lean-burn engine exhaust. Increasing the number of ion treatments progressively lowered light-off temperatures, correlating with enhanced Pt–Ce³⁺ interactions and improved surface reducibility. Variations in beam current and exposure time further modulated these surface effects, confirming the tunable nature of the approach. The results demonstrate that ion beam sputtering selectively modifies the catalyst surface without altering the bulk structure, directly linking atomic-scale modifications to improved low-temperature activity. This strategy offers a promising route to overcome delayed light-off issues in modern high-efficiency engines, providing a precise, controllable method to optimize emission control catalysts. Full article