Search Results (775)

The design of multitargeted drug candidates has recently emerged as a highly attractive area of research. Numerous heterometallic compounds have been developed to enhance both the biological efficacy and physicochemical properties of monometallic metallodrugs. Combining classical transition metals with established antitumor activity, such as Pt, Ru, and Au, with other metal-based fragments offers the potential to generate complex compounds with improved pharmacokinetic and pharmacodynamic profiles. Incorporating different bioactive metal cations within a single molecular framework may enhance anticancer activity through metal-specific interactions with distinct biological targets or through improved physicochemical characteristics of the resulting heteronuclear complexes. Recent studies have underscored the significant progress and promising impact of this multitargeted strategy, particularly in systems that combine ruthenium with other biologically active metal centers. This approach may enable selective biological targeting and help overcome drug resistance. This review compiles and analyzes reported ruthenium-based heteronuclear complexes, offering a comprehensive and critical assessment of recent advances in the rational design and synthesis of novel multinuclear compounds as potential chemotherapeutic agents. Particular emphasis is placed on understanding structure–activity relationships, mechanistic pathways, and the role of metal–metal and metal–ligand interactions in modulating biological responses. The findings summarized herein highlight the remarkable efficacy of a wide range of multinuclear ruthenium anticancer complexes and support the hypothesis that synergistic and/or cooperative interactions between distinct metal-based fragments can significantly enhance pharmacological performance, including improved selectivity, stability, and cellular uptake. Furthermore, emerging insights into their modes of action, resistance profiles, and potential for targeted delivery underscore their promise as viable alternatives to conventional therapies. Overall, this dynamic and rapidly evolving field is poised to inspire continued interdisciplinary research and drive the development of next-generation metallodrugs with improved therapeutic indices and clinical potential. Full article

Background/Objectives: Chimeric antigen receptor (CAR) T cell therapy has profoundly transformed the cancer treatment landscape, achieving unprecedented clinical success in patients with hematological malignancies. However, challenges such as cytokine release syndrome, neurotoxicity, antigen escape, and limited efficacy in solid tumors remain, underscoring the need for robust preclinical modeling to evaluate novel CAR T cell products. Methods: This review provides a comprehensive overview of in vitro and in vivo preclinical modeling for CAR T cell functionality and toxicity assessment. We examine traditional experimental approaches and their limitations, discuss emerging technologies, and highlight how these strategies can be integrated to advance future CAR T cell therapies. Results: In vitro assays provide insights into efficacy but fail to model trafficking, dynamic immune cell interactions, and complex tumor microenvironments. In vivo mouse models allow for more complex physiological evaluation but are limited by species differences. Next generation platforms, such as patient-derived tumor organoids and organ- or multi-organ-on-a-chip microfluidics are emerging as potential tools to model CAR T cell therapy in physiologically relevant contexts and computational approaches are being increasingly used to develop novel CAR designs and predict patient responses. Conclusions: By integrating traditional experimental approaches with innovative technologies, the CAR T cell field is poised to generate more clinically relevant and predictive data thereby accelerating the development of safer, more effective, and personalized CAR T cell therapies. Full article

The integration of artificial intelligence (AI) and big data is poised to significantly augment drug research and development, offering the potential to address persistent challenges such as lengthy timelines and high failure rates. This review provides a critical overview of AI applications across the preclinical drug discovery pipeline (the 2020–2026 literature), covering drug–target interaction prediction, structure prediction, de novo design, virtual screening, drug repurposing, and ADMET forecasting. Beyond surveying technical developments, we critically discuss key translational hurdles, including data quality, model interpretability, patient heterogeneity, and regulatory adaptation, and provide structured summaries of representative models. We conclude by outlining future directions, such as multimodal AI, digital twins, and closed-loop automation, that aim to bridge the gap between computational prediction and clinical application. This review aims to inform researchers and accelerate the delivery of safe and effective therapies. Full article

Currently, single-modal tumor therapy has significant limitations, while multi-modal combination therapy can overcome this bottleneck and open up new pathways for enhancing the efficacy of tumor therapy. However, it is still difficult to design a functionalized nanocarrier that can simultaneously mediate multiple therapeutic approaches. To tackle this challenge, we developed a multifunctional nano-codelivery system with glucose oxidase (GOx) loaded inside iron-doped zeolitic imidazolate framework-8 (Fe/ZIF-8), abbreviated as GFZ. This system effectively integrates the synergy and complementarity between ferroptosis therapy and starvation therapy (STT). Herein, GFZ innovatively combines the pH sensitivity of the ZIF-8 skeleton with the EPR effect of nanoparticles to achieve on-demand triggered release, significantly improving the accuracy of tumor targeting. Furthermore, GOx-mediated STT effectively alleviates the insufficiency of endogenous H₂O₂ during the ferroptosis process, thereby enhancing and synergizing with ferroptosis therapy. Experiments demonstrated both in vitro and in vivo that GFZ activates antitumor cascade reactions, inhibits tumor recurrence and metastasis, and exhibits excellent biocompatibility. Consequently, given its remarkable potential, GFZ is poised to emerge as a new mode of nano-delivery platform. Full article

Background/Objectives: Hypertensive disorders of pregnancy (HDPs) affect approximately one in seven hospital deliveries in the United States and increase the risk of pregnancy-associated mortality. Home blood pressure monitoring (HBPM) for patients with HDPs has emerged as a model of care poised to improve ascertainment of blood pressure and triage of care during pregnancy and postpartum periods. However, the strength of evidence supporting HBPM approaches has been variable. This rapid review aimed to understand how HBPM approaches for pregnant and postpartum populations with HDPs have been evaluated in order to strengthen future research. Methods: Search criteria included peer-reviewed literature in English and French published during 2018–2024 that assessed HBPM approaches for pregnant and postpartum populations in high-income countries. A total of 370 records were screened and reviewed to identify 52 eligible articles. Key study characteristics, methodologies, and outcome measures were extracted. Identified outcome measures were mapped by outcome type (implementation, health service, and client) to assess gaps in evaluation of HBPM approaches. Results: A range of study designs were employed to evaluate HBPM approaches: experimental (17%), observational (52%), qualitative (10%), mixed method (10%), and economic (11%) designs. Over a third employed a comparison group, most of which compared HBPM approaches to usual antepartum or postpartum care. Only 11 studies reported on impact outcomes (long-term blood pressure control, adverse maternal and perinatal outcomes). Significant gaps were identified among the implementation outcomes examined. While patient engagement measures were common, assessment of provider adherence and engagement was limited. Hospital admissions and emergency department visits were often employed as proxies to measure HBPM effectiveness, efficiency, and safety. However, no studies adequately reported effectiveness measures for remote patient triage. Conclusions: Our results call for improved HBPM metrics to ensure patients are receiving high-quality care responsive to their clinical condition. Future studies on HBPM approaches should prioritize more transparent reporting on health actor engagement. A composite measure including both patient and provider adherence to monitoring and triage processes will provide stronger evidence on the effectiveness of HBPM for pregnant and postpartum patients and share impactful learning for health systems interested in adopting HBPM approaches. Full article

This review systematically analyzes the technological progress, structural characteristics, and performance disparities among various diamond grinding wheel bond systems, aiming to establish a unified performance evaluation framework. This framework clarifies material selection criteria and highlights promising research directions. Eight prevalent bond systems are encompassed: resin, metal, ceramic, brazing, electroplating, composite, additive manufacturing, and glass-ceramics. A comparative analysis of these systems is conducted across multiple dimensions. Key evaluation metrics primarily include bond strength, thermal stability, self-sharpening capability, thermal conductivity, and formability. Considerable variations in these indicators are observed across the different bonding agents. Each system presents distinct advantages alongside inherent limitations. Within the constructed multi-metric framework, glass-ceramic bonding agents demonstrate high comprehensive potential in critical aspects such as bond strength and thermal stability, underscoring their research value as a novel high-performance bond system. Current primary challenges focus on the regulation of crystallization kinetics, the design of interfacial reaction layers, and multiscale performance prediction. Future research may advance along several paths. Synergistic design of material composition and microstructure is essential, while in-depth investigation into multiphysics coupling mechanisms remains necessary. Furthermore, data-driven material optimization methods are poised to unlock new possibilities for bond development. These approaches are expected to facilitate the precise design and application of high-performance diamond grinding wheel bonds. Full article

Planar microwave (MW) sensors offer high-resolution, non-invasive technology for monitoring critical soil properties, serving as a support for modern precision agriculture. While laboratory studies confirm their exceptional sensitivity, the widespread adoption of these sensors is severely impeded by critical translational challenges that constitute a defining “lab-to-field gap”. These barriers include high sensor-to-sensor variability, debilitating thermal cross-sensitivity, soil heterogeneity necessitating unique site-specific calibration, and the enduring tension between high-performance and cost-effective scaling. This review systematically synthesizes the current state of planar permittivity MW technology, moving beyond technical mechanisms to critically assess these operational limitations. We detail advanced architectural strategies designed to bridge this gap, focusing particularly on the transition toward more robust solutions. The key strategies analyzed include the adoption of differential sensor designs using microstrip patch antennas to mitigate common-mode environmental errors, the integration of ultra-compact metamaterial structures such as split-ring resonators (SRRs) and complementary split-ring resonators (CSRRs) for enhanced field robustness and deep soil sensing, and the necessity of multi-parameter sensing capabilities (moisture, pH, and salinity). By establishing a comprehensive roadmap that prioritizes field stability, cost efficiency, and seamless IoT integration, this review demonstrates that planar MW sensors are poised to become reliable and scalable tools. Addressing these critical translational hurdles will ensure optimal resource management, significantly enhance crop productivity, and enable sustainable practices within smart farming ecosystems. Full article

Natural products (NPs) have historically provided the foundational scaffolds for drug development, yet traditional bioprospecting faces critical limitations: high rediscovery rates, laborious isolation workflows, and substantial attrition during clinical translation. The emergence of big data technologies is fundamentally transforming this landscape, enabling a shift from serendipity-based discovery toward systematic, data-driven approaches. This review examines how the integration of artificial intelligence (AI), machine learning (ML), and multi-omics datasets is accelerating natural product research across three key domains: (1) genome mining for biosynthetic gene cluster identification using platforms such as antiSMASH, (2) cheminformatics-driven prediction of structure–activity relationships and ADMET properties, and (3) metabolomics-guided dereplication to prioritize novel bioactive scaffolds. We evaluate the convergence of genomics, metabolomics, and computational chemistry in enabling in silico lead optimization and the discovery of cryptic metabolites from previously inaccessible microbial taxa. While challenges in data standardization and scalability persist, the synergy between big data and NP research is accelerating clinical translation. Despite persistent challenges in data standardization, scalability, and equitable benefit-sharing, the convergence of big data and NP research is poised to redefine drug development. These advances position computational NP research as a cornerstone of next-generation drug development. Full article

The synergistic control of multiple pollutants is critically challenged by complex nonlinear interactions, strong spatiotemporal heterogeneity, and the difficulty of tracing causal drivers. Deep learning offers high predictive power but suffers from the “black-box” problem, limiting its acceptance in environmental decision-making. Explainable Deep Learning (XDL) integrates physical mechanisms with interpretable algorithms, achieving both prediction accuracy and explanatory transparency. This review systematically evaluates the effectiveness and limitations of XDL in analyzing multi-pollutant interactions, with a comparative focus on atmospheric and aquatic environments. Key techniques, including SHAP, attention mechanisms, and physics-informed neural networks, are examined for their roles in synergistic monitoring, source apportionment, and regulatory optimization. The main findings reveal that: (1) XDL, particularly the “tree model + SHAP” paradigm, has become a dominant tool for quantifying driving factors, yet most attributions remain correlational rather than causal; (2) physics-informed fusion (soft vs. hard constraints) improves physical consistency but faces unresolved conflicts between data and physical laws, with current models lacking a conflict detection mechanism; (3) cross-media comparison shows a unified technical logic of “physical mechanism guidance + post hoc feature attribution”, but atmospheric applications lead in embedding advection–diffusion constraints, while aquatic research excels in spatial topology modeling via graph neural networks; (4) critical bottlenecks include the lack of causal inference, uncertainty-unaware interpretations, and data scarcity. Future directions demand a shift from correlation-only to causal-aware attribution, from blind fusion to conflict-detecting systems, and from no evaluation standards to domain-specific validation benchmarks. XDL is poised to transform multi-pollutant governance from experience-driven to intelligence-driven approaches, provided that verifiable interpretability and physical consistency become core design principles. Full article

Using high-resolution field data from the Yunnan Provincial Grassland Pest Survey and an optimized MaxEnt model, we compared the climate-driven habitat dynamics of two invasive Asteraceae weeds (Chromolaena odorata, Ageratina adenophora) and a native weed (Cirsium japonicum). We assessed whether invasive and native weeds differ in environmental responses, future range dynamics, and management strategies, and three novel patterns were revealed. First, the invasive Chromolaena odorata exhibits a sustained positive response to mean annual temperature (contribution 67.6%), while the native Cirsium japonicum shows a strictly unimodal response with a narrow optimum (0–10 °C, contribution 46.4%) and high-temperature sensitivity, projecting over 50% habitat loss by the 2050s under high emissions. Second, the invasive Ageratina adenophora displays a southern contraction versus northern expansion pattern under high emissions (current highly suitable area ~9.12 × 10⁴ km²), suggesting that extreme warming may enable it to breach high-altitude barriers. Third, all three species show unimodal responses to human disturbance with species-specific optima. Overall, the invasive species, leveraging broad ecological amplitudes and strong adaptability, are poised for continued expansion of their potential suitable habitat, while the native species, constrained by a narrow niche and limited dispersal capacity, faces systemic habitat loss. These findings provide a mechanistic basis for differentiating management strategies between invasive and native problematic weeds in Yunnan grasslands. Full article

Medicine–food homology (MFH) substances, which possess both medicinal and edible properties, have garnered widespread attention in the global health context of the new era. The MFH industry has experienced explosive growth and has gradually become a key supporting aspect of TCM modernization. However, due to the pollution of the modern environment, the content of pollutants in MFH products has been increasing, raising concerns regarding quality, safety, and efficacy control. Traditional quality-analysis technologies struggle to meet the needs of rapid on-site detection because of their dependence on large instruments and the complexity of operation. This dilemma has propelled advances in sensor technology. With its advantages of high sensitivity, real-time detection, and portability, sensor technology has become a key technical support for quality control and supervision in the field of MFH. In this review, we comprehensively categorize the mainstream sensor types used for analysis in the field of MFH, including intelligent sensors, optics, electrochemistry, biosensors, etc. This review outlines their research status, elaborates on their primary application directions and corresponding core technologies, discusses current challenges (including stability, interference, and cost), and presents future perspectives. Overall, sensor-based technologies offer a promising and scalable solution for the quality control of MFH products, addressing critical challenges such as stability, interference, and cost. With ongoing advances in intelligent sensing, optics, electrochemistry, and biosensing platforms, these methods are poised to play an increasingly vital role in ensuring the safety, efficacy, and quality consistency of MFH products amid growing environmental pressures. Full article

Industrialization has led to the substantial release of heavy metals and organic pollutants into soil and groundwater, resulting in severe contaminated site issues that pose significant threats to ecosystems and human health. This review aims to systematically review the current development status and challenges of contaminated site remediation technologies, and explore the potential of artificial intelligence (AI) applications in site remediation, to provide a theoretical reference for advancing intelligent remediation. Conventional remediation technologies mainly include physical methods (e.g., solidification/stabilization (S/S), soil vapor extraction (SVE), thermal desorption, pump and treat (P&T), groundwater circulation wells (GCWs)), chemical methods (e.g., chemical oxidation/reduction, electrokinetic remediation (EKR), soil washing), and biological methods (phytoremediation, microbial remediation), along with combined strategies that integrate multiple approaches. Although these technologies have achieved certain successes in engineering practice, they still face common challenges such as risks of secondary pollution, long remediation periods, high costs, poor adaptability to complex hydrogeological conditions, and insufficient long-term stability, making it difficult to fully meet the remediation demands of complex contaminated sites. Subsequently, the potential of emerging technologies—including nanomaterial-based remediation, bioelectrochemical systems, and molecular biology-assisted remediation—is introduced. On this basis, the forefront applications of AI in contaminated site remediation are discussed, covering site monitoring and characterization, risk assessment, remedial strategy selection, process prediction and parameter optimization, material design, and post-remediation intelligent stewardship. Machine learning (ML), explainable AI (XAI), and hybrid modeling approaches have markedly improved remediation efficiency and decision-making. Looking forward, with advancements in XAI, mechanism-data fusion models, and environmental foundation models, AI is poised to drive a paradigm shift toward intelligent and precision remediation. However, challenges related to data quality, model interpretability, and interdisciplinary expertise remain key barriers to overcome. Full article

The liver’s anatomic position and immune specialization make it both a major target and a major filter for systemically delivered therapeutics. Because portal venous inflow exposes the liver early to gut-derived molecules and exogenous compounds, many intravenously administered agents, including gene-based medicines and their viral and non-viral delivery systems, preferentially enter and accumulate in hepatic tissue. This review synthesizes how core liver physiology and immunobiology influence the performance, safety, and clinical translation of genomic medicines in hepatology, and outlines near-term practice and research shifts likely to define a genomics-driven future in liver disease care. We review the hepatic microarchitecture relevant to therapeutic trafficking, including sinusoidal transit, the space of Disse, hepatocyte uptake, and hepatobiliary elimination, and highlight the gatekeeping roles of liver sinusoidal endothelial cells and Kupffer cells in clearing particulate material and shaping inflammatory signaling. We then discuss how these same features create both opportunities, such as efficient hepatic targeting, and constraints, including innate immune activation, vector clearance, and variable intrahepatic distribution, for DNA- and RNA-based platforms. Finally, we propose five actionable developments poised to move genomics from a niche tool to a routine component of hepatology practice: earlier genomic testing in unexplained liver disease, multidisciplinary hepatology genome rounds, a centralized liver-specific gene resource, genetics-aware clinical trial design, and expansion of genetic therapies. Integrating liver biology with genomic medicine is essential to improve diagnostic yield, personalize therapy, and accelerate translation of gene-based treatments while mitigating immunologic and delivery-related barriers. Full article

NANOG overexpression has been reported to reverse aging-associated decline in mesenchymal stem/stromal cell (MSC) function, but the molecular machinery engaged by NANOG in MSCs remains incompletely defined. Here, we applied APEX proximity labeling coupled with quantitative mass spectrometry to define the NANOG proximity interactome (proxeome) in human MSCs. Of 1040 quantified proteins, 828 were significantly enriched in the APEX-NANOG (H₂O₂ labeling) samples, consistent with a broad NANOG-centered neighborhood rather than a single stoichiometric complex. Enriched proteins encompass RNA-processing pathways (including splicing/RNP factors and selected m6A-related proteins), transcriptional coactivation and elongation control (Mediator and 7SK/P-TEFb regulators), chromatin repression/poising modules (Polycomb and HDAC/NuRD/CoREST/SIN3), ATP-dependent chromatin remodeling (BAF/SWI-SNF), three-dimensional genome organization and replication-coupled chromatin maintenance (CTCF/cohesin, CHAF1A, RIF1, UHRF1), and regulators of MSC identity and signal integration (Hippo/mechanotransduction and TGFβ-linked transcriptional circuits). Together, these data provide a spatial proteomic map of NANOG-associated nuclear neighborhoods in MSCs and a foundation for mechanistic hypotheses for how NANOG may stabilize stem-like programs. Full article

Collagen, the most abundant extracellular matrix (ECM) protein, has emerged as a cornerstone biomaterial in drug delivery and regenerative medicine due to its intrinsic biocompatibility, biodegradability, and low immunogenicity. Engineering collagen into microspheres transforms its functionality beyond bulk scaffolds by increasing surface area, enabling minimally invasive delivery, and providing precise control over degradation, mechanical properties, and therapeutic release. This review provides a comprehensive analysis of collagen-based microspheres, with a particular focus on their dual role as biomimetic microenvironments and delivery systems. Recent advances in fabrication strategies, including emulsification, microfluidics, spray-drying, and electrospraying, are discussed in the context of scalability, size control, and payload encapsulation. Composite approaches that incorporate bioactive minerals, polysaccharides, or synthetic polymers are highlighted for their ability to enhance mechanical performance and biological function. We further examine characterization frameworks that link microscale structure and physicochemical properties to biological outcomes, with emphasis on how collagen microspheres replicate key structural, mechanical, and signaling features of native tissue microenvironments. Collagen microspheres have demonstrated broad utility as controlled delivery platforms, cell-instructive microcarriers, and injectable systems for tissue regeneration, including applications in bone, cartilage, skin, and nerve repair, as well as advanced wound care and localized cancer therapy. Finally, we critically assess current challenges related to scalable manufacturing, sterilization compatibility, and batch reproducibility, and outline emerging solutions such as recombinant collagen, advanced biofabrication, and stimuli-responsive systems. Collectively, collagen microspheres represent a powerful and adaptable platform poised to advance next-generation regenerative and therapeutic technologies. Full article