Search Results (876)

Biomarker detection demands low cost, rapid turnaround, interference resistance, and wide dynamic range. However, traditional immunoassays and nucleic acid amplification methods remain constrained by complex matrices, batch stability, and portability limitations. Molecularly imprinted polymers (MIPs) exhibit “artificial antibody”-like specific recognition and high stability, while nanomaterials (NMs), depending on their composition, structure, and interfacial organization, can provide conductive pathways, catalytic activity, high-density loading sites, or mass-transfer-favorable architectures. Electrochemical biosensors synergistically constructed from these two components achieve complementary functions in recognition, mass transfer, and signal transduction. This paper systematically reviews key strategies and mechanisms for NM–MIP synergistic construction, focusing on six synergistic strategies that target key bottlenecks in mass transfer, signal generation, and interfacial stability: dynamic response regulation, hierarchical structural engineering, anti-fouling interfaces, multi-signal cross-validation, catalytic–recognition integration, and interfacial binding regulation. Representative biomarker cases are analyzed to illustrate how functional modules can coordinate across sample processing, signal generation, and recognition confirmation to improve analytical reliability and overall sensing performance. Finally, the review discusses challenges in clinical translation, including consistent manufacturing, matrix interference, long-term stability, and standardized validation, while outlining future directions toward mechanism-guided imprint design, intelligent data-assisted optimization, and integration with microfluidic and wearable platforms for multiplexed biomarker detection. Full article

Rapid urbanization and complex topography complicate Urban Heat Island (UHI) spatio-temporal dynamics. Traditional models and coarse-resolution imagery often fail to capture fine-scale, spatially non-stationary seasonal driving mechanisms. This study investigates the multi-dimensional drivers of surface thermal dynamics in Kunming, a typical low-latitude plateau city, using seasonal median LST composite (2018–2025). Integrating eXtreme Gradient Boosting (XGBoost) with eXplainable Artificial Intelligence (XAI) models decoupled the nonlinear impacts of these drivers. Results reveal a seasonal thermal dichotomy: Summer exhibits the most intense UHI effect with extreme peak temperatures, while Spring presents an anomaly where natural and vegetated Local Climate Zones (LCZs) show pronounced warming. SHapley Additive exPlanations (SHAP) analysis identified a seasonal rotation: anthropogenic and structural factors dominate Summer and Autumn warming, whereas natural and topographic regulators govern Spring and Winter. GeoShapley deconstruction demonstrated strong spatial non-stationarity. Building-density warming is amplified in poorly ventilated urban cores, and fragmented vegetation’s cooling is offset by anthropogenic heat during peak summer. This study provides new insights into the seasonal drivers of urban thermal environments in plateau cities. Full article

The Angiopoietin–Tie2 pathway is a key regulator of postnatal vascular maintenance and remodeling, regulating vascular barrier function and integrity. While the opposing roles of the ligands Angiopoietin-1 (Ang 1) and Angiopoietin-2 (Ang 2) have been recognized for decades, the structural mechanism governing their distinct signaling outputs has only recently been elucidated. As artificial intelligence and protein design continue to develop, emerging evidence suggests that ligand valency and receptor clustering are key determinants of Tie2 pathway activation and endothelial cell function; that is, “form follows function”. This review summarizes the latest discovery in the structural biology and signaling mechanism of the Tie2 pathway using protein design to decode the ligand–receptor interactions. Probing the underlying molecular basis of Tie2 offers new therapeutic opportunities for targeting diseases, featuring vascular dysfunctions such as sepsis, traumatic brain injury, acute respiratory diseases, chronic inflammation, and cancer. This also highlights the next generation of AI-designed protein therapeutics. Full article

Nowadays, the application of insect sex pheromones in pest control technology has reached a relatively advanced technological maturity stage. However, the traditional research and development of sex pheromones requires a “one pest, one strategy” approach, which has drawbacks such as being time-consuming and focused on a single control target. The insect sex pheromone synthesis pathway involves multiple molecular components that work together to promote the synthesis and release of sex pheromone from the pheromone gland. Elucidating the mechanisms underlying pheromone biosynthesis offers the potential to uncover universal strategies for pheromone development, thereby improving the efficiency and effectiveness of pest management. This study arranged knowledge of the upstream regulatory pathways and summarized the structure and function of the molecular components involved. We also investigated the divergence of neuropeptides and their receptors that regulate pheromone biosynthesis among different insect species from an evolutionary perspective. Future research should integrate multi-omics, bioinformatics, structural biology, and artificial intelligence technologies to elucidate the synthesis and regulatory processes of insect semiochemicals, develop specific dsRNA and small molecule inhibitors, and accelerate the transformation and application of related molecular targets into highly effective and green pesticides. Full article

Microalgae are among the most efficient photosynthetic organisms on Earth, and their capacity for CO₂ fixation directly links the global carbon cycle with green energy conversion, positioning them as strategic biological platforms for achieving carbon neutrality. This review provides a comprehensive and multiscale synthesis of the engineering and biological mechanisms underlying microalgal CO₂ fixation, integrating perspectives from gas–liquid mass transfer, CO₂ assimilation pathways, key enzymatic systems, metabolic regulation, and environmental control. From an engineering standpoint, we analyze the limitations governing CO₂ transfer from the gas phase to the aqueous phase and critically evaluate intensification strategies aimed at enhancing inorganic carbon availability in cultivation systems. At the biological and biochemical levels, we dissect carbon concentrating mechanisms (CCMs), including C₄-like pathways, and elucidate the structural organization, regulatory properties, and functional coordination of Rubisco and carbonic anhydrase systems. Particular emphasis is placed on the coupling between enzyme-level regulation and metabolic flux redistribution, supported by insights from metabolic flux analysis and systems-level modeling, to establish theoretical and engineering foundations for improving carboxylation efficiency. Finally, we propose an integrated roadmap for the future development of microalgal CO₂ fixation technologies, highlighting the convergence of synthetic biology, artificial intelligence, and systems engineering to achieve end-to-end optimization from molecular mechanisms to reactor-scale performance, while enabling the valorization of waste gas streams and circular carbon utilization. This review aims to provide a coherent theoretical framework and forward looking perspective for the development of efficient, intelligent, and sustainable microalgal CO₂ fixation systems. Full article

Background: Neurodegenerative diseases (NDs) are mainly considered disorders marked by severe immunometabolic imbalance, characterized by ongoing neuroinflammation and glial activation. While mitochondrial dysfunction and oxidative stress are well-known features, the upstream metabolic factors linking these pathological processes remain poorly understood. Methods: In this review, we examined recent preclinical and clinical studies exploring the connections between lipid metabolism, glial immunometabolism, and regulated cell death pathways. Our focus was on how long-chain fatty acids (LCFAs) facilitate communication among mitochondria, reactive oxygen species (ROS), and ferroptosis in Alzheimer’s disease (AD), Parkinson’s disease (PD), and amyotrophic lateral sclerosis (ALS). Results: New evidence shifts LCFAs from merely being passive indicators of cellular damage to active, upstream regulators of the neuroimmune response. Existing research shows that excess LCFA intake can overload astrocytic mitochondrial oxidative phosphorylation, leading to abnormal lipid droplet buildup and reactive astrogliosis. This lipid-driven reactivity promotes microglial polarization toward a persistent pro-inflammatory state. Notably, high levels of specific LCFAs, especially arachidonic acid, increase ROS production and lipid peroxidation. This lipotoxic environment ultimately triggers ferroptosis, an iron-dependent form of cell death shared across multiple NDs. Conclusions: The harmful interaction among mitochondrial dysfunction, lipid peroxidation, and ferroptosis is driven by an imbalance in LCFA levels. Addressing current challenges, such as the complex effects of polyunsaturated fatty acid supplementation, requires advanced techniques like single-cell multi-omics and artificial intelligence. Understanding this intricate lipidomic-transcriptomic crosstalk is crucial for moving toward personalized neuroimmunometabolism and developing new treatments to prevent ferroptosis. Full article

Globalization refers to the increasing density of economic, social, and technological interconnections on a global scale. In the healthcare industry, it simultaneously accelerates innovation and increases systemic vulnerabilities. This study aims to review and conceptually synthesise the main channels of impact: (1) pharmaceuticals, clinical development, and regulation; (2) supply chains and resilience; (3) service mobility (health tourism); (4) human resources and competencies; (5) digitalization, artificial intelligence (AI), and data governance; (6) ethics, law, and public policy; and (7) sustainability and climate change. The COVID-19 pandemic highlighted the risks associated with global interdependencies, particularly in supply chains, while also demonstrating the innovation-accelerating effects of knowledge sharing and international cooperation. Particular attention is given to artificial intelligence and digital health, which open up new potential for efficiency and quality improvement from research and development through diagnostics to healthcare organization, while simultaneously intensifying concerns related to data protection, cyber security, and liability. Telemedicine, platform-based systems, and real-world data may contribute to addressing the care needs of ageing societies, but only when supported by appropriate competencies and sound data governance. As global data flows intensify, the importance of data protection, bias mitigation, transparency, and accountability correspondingly increases. Through the cultural channels of globalization, health-conscious lifestyles and complementary approaches are also spreading, which we address in a brief, separate subsection. The guidelines of international organizations foster standardization; however, due to differences in local capacities and institutional environments, the effects are not homogeneous. In conclusion, the study emphasises the dual nature of globalization; it expands access and accelerates innovation, while at the same time creating new vulnerabilities—in supply chains, labour mobility, and data security—and, together with climate-related risks, generating complex adaptive pressures for the healthcare industry. Full article

Background: The development of robust artificial intelligence (AI) models for diagnosing Temporomandibular Disorders (TMDs) is severely constrained by data scarcity and patient privacy regulations. Cone-beam computed tomography (CBCT), the gold standard for assessing osseous changes in the temporomandibular joint (TMJ), inherently contains sensitive biometric facial features, making de-identification difficult without losing critical anatomical information. This study aims to develop and evaluate TMJCTGenerator, a specialized latent diffusion model (LDM) framework designed to synthesize high-fidelity, diverse, and anonymous TMJ CBCT images. We hypothesize that this LDM approach can achieve superior anatomical fidelity and diversity compared to traditional generative adversarial network (GAN)- and variational autoencoder (VAE)-based methods, specifically in capturing fine osseous details within sagittal and coronal views of the mandibular condyle. Methods: A training dataset comprising 348 anonymized CBCT volumes was obtained in this retrospective comparative study to extract high-resolution sagittal and coronal regions of interest of the mandibular condyle. An independent test set of 39 anonymized CBCT volumes was further included. We developed a class-conditional LDM that integrates a pre-trained VAE for perceptual compression with a conditional U-Net for iterative denoising in the latent space. Performance was evaluated via qualitative anatomical fidelity assessment, Fréchet Inception Distance (FID), and a blinded Visual Turing test conducted by experienced clinicians to determine the distinguishability of synthetic images from real data. Results: Qualitative analysis revealed that TMJCTGenerator produced images with superior sharpness and anatomical consistency compared to baseline models, successfully reconstructing fine bone structures essential for diagnosing degenerative joint disease. TMJCTGenerator achieved lower FID scores than both VAE and GAN baselines. In the visual Turing test, clinicians were unable to reliably distinguish the generated images from real scans, and non-inferiority analysis confirmed that the synthetic data were statistically non-inferior to real data. Furthermore, TMJCTGenerator demonstrated the capability to generate diverse pathological conditions, ranging from normal anatomy to severe osteoarthritic changes. Conclusions: The proposed LDM framework effectively addresses the data scarcity and privacy bottlenecks in TMJ AI research by generating realistic, fully anonymous medical imaging data. TMJCTGenerator outperforms traditional generative methods in both visual fidelity and diversity, offering a viable solution for training downstream diagnostic algorithms. The source code and pre-trained models of TMJCTGenerator have been made open-source. Full article

Background: Sézary syndrome (SS) is an aggressive leukemic variant of cutaneous T-cell lymphoma (CTCL) with distinct clinical and biological features compared to rarer entities such as primary cutaneous CD8⁺ aggressive epidermotropic cytotoxic T-cell lymphoma (PCAECTCL). Although recurrent genomic alterations in CTCL have been described, comparative analyses at the pathway level across biologically divergent subtypes remain limited. Here, we leveraged a conversational artificial intelligence (AI) platform for precision oncology to enable rapid, integrative, and hypothesis-driven interrogation of publicly available genomic datasets. Methods: We conducted a secondary analysis of somatic mutation and clinical data from the Columbia University CTCL cohort accessed via cBioPortal. Cases were stratified into SS (n = 26) and PCAECTCL (n = 13). High-confidence coding variants were curated and mapped to biologically relevant signaling pathways and functional gene categories implicated in CTCL pathogenesis. Pathway-level mutation frequencies were compared using Fisher’s exact tests, with effect sizes quantified as odds ratios. Tumor mutational burden (TMB) was compared using the Wilcoxon rank-sum test. Subtype-specific co-mutation patterns were evaluated using pairwise association analyses and visualized through oncoplots and network heatmaps. A conversational AI agent, AI-HOPE, was used to iteratively refine cohort definitions, prioritize pathway-level signals, and contextualize findings. Results: TMB was comparable between SS and PCAECTCL (p = 0.96), indicating no significant difference in global mutational load. In contrast, pathway-centric analyses revealed marked qualitative differences. SS demonstrated enrichment of alterations in epigenetic regulators, tumor suppressor and cell-cycle control pathways, NFAT signaling, and DNA damage response mechanisms, consistent with transcriptional dysregulation and immune modulation. PCAECTCL exhibited relatively higher frequencies of alterations involving epigenetic regulators and MAPK pathway signaling, suggesting distinct oncogenic dependencies. Co-mutation analysis revealed a more constrained and focused interaction landscape in SS, whereas PCAECTCL displayed broader and more heterogeneous co-mutation networks, indicative of divergent evolutionary trajectories. Notably, ERBB2 mutations were significantly enriched between subtypes (p = 0.031), highlighting a potential subtype-specific therapeutic vulnerability. Conclusions: This study demonstrates that SS is distinguished from PCAECTCL not by increased mutational burden but by distinct pathway-level architectures, particularly involving epigenetic regulation, immune signaling, and transcriptional control. These findings generate biologically grounded, testable hypotheses for subtype-specific therapeutic targeting and underscore the value of conversational AI as a scalable framework for accelerating discovery in translational cancer genomics. Full article

Over the past three decades, artificial intelligence (AI) has substantially reshaped marketing research and practice, yet the discipline has not established a systematic understanding of its evolutionary trajectory and intellectual structure. A bibliometric analysis of 1923 Scopus publications (1992–2025) was conducted using CiteSpace to explore collaboration patterns, conceptual development, and thematic organization. It identified six evolutionary stages with accelerating innovation cycles, starting with neural networks (1992–2000) and ending with generative AI (2024–2025), with research attention per stage compressing from approximately 9 years to just 2 years. The analysis of the collaboration network shows that the key contributors are India, China, the USA, and the UK. Co-citation analysis indicates that there are three thematic dimensions with seven clusters, namely: (i) AI technological foundations and capabilities, (ii) AI marketing applications and transformation, and (iii) responsible AI governance and ethics. It suggests a Three-Force Evolutionary Framework, which combines technology-push, market-pull, and governance-moderator forces to describe the dynamics of the field. This framework shows that the Regulatory Awakening of 2018 (e.g., GDPR and the Cambridge Analytica incident) guided, not limited, innovation, and highlighted the critical personalization–privacy paradox on which modern developments are based. It identifies three priority research directions: generative AI in creative marketing, consumer trust in the personalization–privacy paradox, and organizational adaptation to fast innovation cycles. This study provides scholars with a comprehensive knowledge map, practitioners with strategic imperatives for responsible AI adoption, and policymakers with evidence that well-designed regulation accelerates innovation by balancing commercial value with societal concerns. Full article

Whether industrial artificial intelligence (industrial AI) contributes to environmental sustainability remains an open empirical and theoretical question. While digital and intelligent technologies are widely promoted as drivers of green transformation, their net impact on carbon emissions is ambiguous due to potentially offsetting efficiency gains and rebound effects. This study examines how industrial AI influences urban carbon emissions using panel data for 260 Chinese cities from 2005 to 2019. We construct a novel city-level industrial AI development index by integrating information on data infrastructure, AI-related talent supply and intelligent manufacturing services using the entropy weight method. Employing two-way fixed-effects models, instrumental-variable estimations, lag structures, and multiple robustness checks, we identify the causal impact of industrial AI on carbon emissions. The results indicate that industrial AI significantly reduces urban carbon emissions. Mechanism analyses suggest that this effect operates primarily through improvements in energy efficiency and green technological innovation, while being partially offset by scale expansion. Furthermore, a higher share of secondary industry mitigates the emission-reducing effect of industrial AI. Heterogeneity analysis further indicates stronger emission-reduction effects in eastern regions, large cities, and areas with higher human capital and stronger environmental regulation. The findings suggest that intelligent industrial upgrading can simultaneously enhance productivity and support climate mitigation, but this effect is highly context-dependent, offering policy insights for achieving sustainable industrial modernization and carbon neutrality in emerging economies. Full article

Generative artificial intelligence (AI)-based music generation has the potential to create new opportunities for music therapy; however, integrated examinations of generative AI and music therapy remain limited. This paper provides a focused survey of recent studies that apply generative AI within music therapy-related contexts, examining how such approaches have been explored in relation to therapeutic considerations, including emotional and physiological regulation. Rather than offering an exhaustive historical review, we analyze generative AI-augmented music therapy systems from a system-level perspective, focusing on their overall design and implementation. Based on this survey, we discuss open research challenges at the intersection of generative music, adaptive systems, and digital health, and outline future research directions toward scalable and personalized generative AI-based music therapy. Full article

The increasing emphasis on sustainable and decentralized energy has elevated microgrids as a central element of modern power systems. By integrating renewable energy sources, advanced energy storage technologies, and intelligent control strategies, microgrids enhance efficiency, stability, and flexibility and play a vital role in creating resilient and adaptable energy networks. This review provides a comprehensive analysis of Energy Management Systems (EMSs) in microgrids, distinguishing between planning-oriented tools for techno-economic evaluation and control-oriented platforms for real-time operation and optimization. Hierarchical control architectures spanning primary, secondary, and tertiary levels are examined, highlighting their roles in frequency and voltage regulation, load sharing, and economic dispatch. Optimization techniques for EMSs are analyzed across deterministic, stochastic, metaheuristic, and artificial intelligence/machine learning methods, addressing objectives, constraints, uncertainties, and multi-timeframe decision-making. AI-based methods, including supervised learning, deep learning, and reinforcement learning, are highlighted for their ability to enhance predictive control, system autonomy, and operational efficiency, despite their computational demands. Future trends emphasize AI-based predictive control, deep learning for energy forecasting, multi-microgrid coordination, hybrid energy storage management, and cybersecurity enhancements. Overall, an intelligent EMS, combined with innovative technologies, is critical for developing resilient, scalable, and sustainable microgrid solutions that meet the evolving demands of modern energy systems. Full article

A phage is a virus that targets bacteria with high precision. While phage therapy provides a targeted alternative to broad-spectrum antibiotics, it is not completely free from the challenges of antimicrobial resistance, as phages can facilitate the horizontal transfer of resistance genes through transduction and promote the growth of phage-resistant strains. Nonetheless, within the One Health framework, the strategic use of phages remains a vital and promising tool for addressing the global antimicrobial resistance crisis. This paper reviews current research on phage therapy for gastrointestinal diseases such as cirrhosis, enteritis, and Helicobacter pylori infection. It also details how phages help regulate gut microecological balance and discusses how phage dysbiosis can lead to innate immune dysfunction and worsen conditions like inflammatory bowel disease. The review summarizes both the therapeutic potential and limitations observed in clinical trials and fundamental studies. Transitioning from laboratory research to clinical practice is hindered by multiple complex challenges, including the stomach’s extreme acidity, physical entrapment by the intestinal mucus layer, the rapid co-evolution of bacterial resistance, and ecological risks associated with temperate phages. To overcome challenges like gastrointestinal barrier tolerance and address ethical, technical, and practical hurdles for clinical use, the paper outlines treatment strategies for specific conditions and highlights future directions, providing guidance for employing phages in digestive system disease management. These future innovations focus on integrating artificial intelligence-driven precision matching, advanced bioengineering for durable delivery systems, and multimodal combination therapies to safely modulate the intestinal microecology. Full article

Resveratrol (RSV), a bioactive polyphenol, has emerged as a pleiotropic modulator within the integrated pathophysiology of cardiovascular disease (CVD) across the life course. Effective CVD management requires a transition from organ-centric frameworks to systems-level models that acknowledge dynamic crosstalk among metabolic, renal, and cardiovascular networks. Oxidative stress constitutes a central unifying axis in this interconnected biology, propagating cross-organ injury from early developmental stages onward. Mechanistically, RSV acts as a redox-responsive gene regulator by activating the Nrf2–ARE pathway, restoring nitric oxide bioavailability, and orchestrating SIRT1, AMPK, and NF-κB signaling to recalibrate mitochondrial function, inflammatory tone, and endothelial integrity. Within the Developmental Origins of Health and Disease (DOHaD) paradigm, RSV exhibits reprogramming potential that attenuates the intergenerational transmission of hypertension, kidney disease, and metabolic dysfunction. Although clinical translation is constrained by limited bioavailability and rapid metabolism, advanced delivery systems and artificial intelligence-enabled optimization strategies provide promising avenues to enhance therapeutic precision and scalability. This narrative review integrates mechanistic and translational insights to position RSV as a systems-oriented life-course intervention with sustained and intergenerational relevance in CVD. Full article