Search Results (11,572)

In vitro neurovascular unit (NVU) models are essential for reproducing blood–brain barrier (BBB) transport and neurovascular cell interactions. However, the literature remains fragmented: biomaterial chemistry, fabrication parameters and organ-on-a-chip architecture are commonly evaluated in isolation, while inconsistent reporting of matrix properties, processing history, cell source, flow and barrier readouts prevents head-to-head comparison and the extraction of transferable design rules. To address this gap, this review integrates biomaterials, manufacturing technologies and organ-on-a-chip engineering within a unified material–process–structure–function framework. We translate endothelial junctions, basement-membrane components and perivascular cells into experimentally actionable material requirements; compare natural, synthetic, semisynthetic and decellularized extracellular-matrix hydrogels; and examine crosslinking, peptide functionalization, stimuli responsiveness, composite-network formation and preparation methods. Findings from Transwell, microfluidic, tubular, self-assembled and 3D-bioprinted BBB systems are used to relate matrix stiffness, degradability, ligand density, permeability, device-body material and fabrication route to barrier maturation, analytical access and reproducibility. By defining matched controls and minimum reporting requirements for chemistry, mechanics, transport and processing, this review provides a practical basis for next-generation BBB models that can improve permeability and efficacy screening in drug discovery, reproduce disease- and patient-specific barrier dysfunction, and support individualized response testing with iPSC- or patient-derived cells. Full article

Background: Patient-specific targeted radionuclide therapy (TRT) requires consideration not only of administered activity but also of the spatial distribution of radiopharmaceutical uptake and radionuclide-specific energy deposition. This study developed a voxel-based computational workplan to compare ¹⁷⁷Lu, ¹⁶¹Tb and ⁹⁰Y, together with hybrid radionuclide models, using patient-specific PSMA PET-derived tumour activity distributions. Methods: PSMA PET/CT data from 20 patients with prostate cancer, comprising 10 ¹⁸F-PSMA and 10 ⁶⁸Ga-PSMA examinations, were processed to obtain 2285 quality-filtered lesions. Radionuclide-specific dose-point kernels (DPKs) were generated in water using OpenGATE and applied to voxel-wise lesion activity distributions to reconstruct absorbed-dose maps. Kernel characteristics were evaluated using radial energy-containment metrics, and ¹⁷⁷Lu, representing ¹⁶¹Tb simulations, was subjected to grid-convergence testing and external comparison with a published DPK. Lesion dosimetry was assessed using Dmean, D90, D95, equivalent uniform dose (EUD) and tumour control probability (TCP), with uncertainty quantified using patient-cluster bootstrap confidence intervals. Kinetic sensitivity and diagnostic tracer subgroup analyses were additionally performed. Results: The study showed that ¹⁶¹Tb produced the highest median lesion-level Dmean, D90, D95 and EUD at 182.79, 136.28, 132.07 and 148.53 Gy, respectively, with a median TCP of 0.981. Corresponding values for ¹⁷⁷Lu were 141.33, 105.42, 102.10 and 114.78 Gy (TCP 0.930), while ⁹⁰Y produced lower local dose metrics but the broadest radial dose distribution, consistent with its longer-range β-particle crossfire. ¹⁶¹Tb remained the highest-ranking radionuclide across the investigated kinetic cases and within both diagnostic tracer subgroups. Hybrid ¹⁶¹Tb/⁹⁰Y kernels provided a controllable compromise between localised energy deposition and extended crossfire; a 70:30 model increased central dose localisation while retaining an R90 and R95 of 6 and 7 mm, respectively. Grid-convergence and published-DPK comparisons supported the numerical adequacy of the kernel methodology. Radionuclide emission characteristics substantially influence the transformation of heterogeneous tumour uptake into spatial absorbed-dose distributions. Within this model, ¹⁶¹Tb provided the strongest overall lesion-level dosimetric performance, whereas the extended range of ⁹⁰Y may offer complementary crossfire for selected bulky or heterogeneous lesions. Conclusions: The findings support phenotype-informed radionuclide comparison and provide a computational basis for investigating hybrid strategies. However, the absolute dose estimates and proposed radionuclide combinations remain model-based and require validation using serial therapeutic imaging, heterogeneous patient-specific dosimetry and normal-organ dose constraints before clinical translation. Full article

Drosophila melanogaster shares approximately 75% of human disease-related genes and possesses sophisticated genetic toolkits, including GAL4/UAS, CRISPR-Cas9, and RNA interference (RNAi), making it a rapid, cost-effective, and genetically tractable in vivo platform for natural products (NPs) discovery. This review systematically summarizes the modeling strategies, pathological mechanisms, and therapeutic applications of Drosophila models for six major human diseases, including type 2 diabetes, nephrolithiasis, inflammatory bowel disease, cancer, Alzheimer’s disease, and Parkinson’s disease. Cross-disease analysis identifies five evolutionarily conserved signaling networks—IIS/PI3K/Akt/FOXO, JNK/JAK/STAT, Nrf2/Keap1, mTOR/TORC1, and IMD/Toll—as common molecular targets of bioactive NPs, providing a unified mechanistic framework for understanding their multi-target pharmacological activities and broad therapeutic potential. Critically, we propose a generalizable integrated stepwise pipeline: high-throughput fly screening of crude extracts, bioassay-guided isolation of active monomers, genetic mechanistic dissection via RNAi and mutant rescue, and layered validation in human cells and selective mammalian models. This pipeline addresses key challenges in NPs research, including the identification of bioactive constituents and mechanistic validation, while improving screening efficiency and translational potential. Overall, this review establishes a multi-disease-applicable framework linking disease modeling, conserved signaling mechanisms, and translational pharmacology, providing practical guidance for future mechanism-driven NP discovery and preclinical development using Drosophila. By leveraging Drosophila genetics to bridge evolutionary conservation and human pathology, this framework offers a powerful, paradigm-shifting strategy to accelerate mechanism-driven NP discovery and preclinical development. Full article

Cardiovascular diseases are increasingly recognized as immune-inflammatory disorders in which adaptive immunity shapes tissue injury, repair, and long-term remodeling. T cells are central to these processes because they integrate antigen recognition, lineage-defining transcriptional programs, tissue trafficking, cytokine production, and immunological memory. In this Review, we synthesize current evidence on the Krüppel-like factor (KLF) family as a transcriptional framework linking T-cell biology to cardiovascular disease. KLF2 primarily regulates T-cell quiescence and trafficking, KLF10 supports regulatory T-cell suppressive function and immune-metabolic fitness, KLF4 contributes to inflammatory effector differentiation, and KLF13 regulates delayed inflammatory chemokine expression and, in thymocyte models, exerts a survival-restraining effect through apoptosis-related pathways. Across atherosclerosis, myocardial infarction, myocarditis, hypertension, and heart failure, these KLF-dependent programs may influence the balance between pathogenic effector responses and protective regulatory mechanisms. The strongest direct disease-specific evidence currently supports a role for KLF10 within the CD4⁺ T-cell lineage in experimental atherosclerosis, with complementary functional evidence implicating Treg–macrophage interactions, whereas the roles of KLF-dependent T-cell programs in other cardiovascular settings remain mechanistically compelling but less fully validated. Future progress will require disease-specific T-cell-restricted models, spatially resolved immune analyses, and cell-selective translational strategies to define the therapeutic relevance of the KLF–T-cell axis. Full article

Tissue engineering and regenerative medicine are shifting from passive tissue replacement toward instructive platforms that regulate cellular behavior, immune responses, vascularization, and extracellular matrix remodeling. This review examines recent advances in natural, synthetic, composite, and stimuli-responsive biomaterials, biofabrication and 3D bioprinting, stem and progenitor cell therapies, extracellular vesicles and other cell-free products, immunomodulatory scaffolds, skin organoids and organ-on-a-chip systems, nanotechnology, and artificial intelligence-assisted design. Particular emphasis is placed on plastic, reconstructive, and aesthetic applications, including skin and wound repair, craniofacial bone and cartilage regeneration, peripheral nerve reconstruction, vascularization, and dental and periodontal repair. The review also considers biomodulators and skinboosters as emerging regenerative-aesthetic interventions that aim to improve dermal hydration, fibroblast activity, collagen remodeling, and skin quality rather than provide volume replacement alone. Importantly, these technologies differ substantially in translational maturity, ranging from in vitro and preclinical platforms to early clinical interventions, established clinical products, and commercially available treatments for which durable regenerative efficacy remains incompletely validated. Throughout this review, biological plausibility and preclinical efficacy are therefore distinguished from human clinical evidence, regulatory or established clinical use, and commercial availability. Progress will require standardized characterization, mechanism-linked potency assays, clinically relevant models, and outcome measures that capture functional integration, durability, safety, and aesthetic performance. Full article

Plant diseases destroy 20–40% of global food production annually, posing a critical threat to food security for a projected population of 9.7 billion by 2050. Conventional diagnostic approaches relying on expert visual assessment are slow, costly, and unsuitable for modern agricultural scales. While deep convolutional neural networks demonstrated early promise, single-modality, image-centric systems consistently fail under real-world field conditions characterized by variable lighting, co-occurring infections, and cultivar diversity. This review synthesizes a decade of progress across four interconnected frontiers: the evolution of deep learning architectures for plant disease detection; the adaptation of foundation models including CLIP, SAM, and DINOv2 to agricultural contexts; the development of multimodal fusion frameworks integrating imagery, environmental, genomic, and hyperspectral data; and the transition from static disease diagnosis to descriptive comparison of reported metrics, which suggested that multimodal approaches frequently reported improved diagnostic performance relative to corresponding single-modality baselines, although direct cross-study comparison was limited by methodological heterogeneity. A systematic review following PRISMA guidelines identifies eligible comparative studies. Descriptive comparison of reported performance metrics across these studies indicated that multimodal approaches generally achieved higher accuracy and sensitivity than single-modality models, particularly for pre-symptomatic disease detection. Eight critical research gaps are identified, including the absence of a unified agricultural foundation model and limited climate-aware forecasting under non-stationary climate projections. A structured research agenda is proposed to accelerate translation from laboratory performance to globally equitable, field-deployable crop protection systems. Full article

Background/Objectives: Dental age estimation is important in clinical and forensic practice, particularly when skeletal indicators are unavailable or compromised. Machine-learning models often achieve lower regression errors than conventional dental methods; however, whether this translates into better classification performance at legally relevant age thresholds remains unclear. This study compared the Demirjian and Willems methods with several machine learning models for overall accuracy and threshold-specific performance at the jurisdiction-specific ages of 12 and 15 years. Methods: A total of 1384 panoramic radiographs from individuals aged 8.00–15.99 years were retrospectively evaluated. The developmental stages of the seven left mandibular permanent teeth and sex were used as model inputs. Linear Regression, Decision Tree, Random Forest, Support Vector Regression, Multilayer Perceptron, Gradient Boosting, and XGBoost were trained using cross-validation and evaluated on an internal holdout set. Performance was assessed using regression errors, age-group-specific bias, sensitivity, specificity, balanced accuracy, and likelihood ratios. Results: Machine-learning models generally produced lower errors than conventional methods. In the holdout set, the lowest mean absolute error was 0.512 years for Support Vector Regression and Gradient Boosting, followed by 0.519 years for XGBoost, compared with 0.649 and 0.654 years for the Willems and Demirjian methods. However, lower regression error did not consistently improve threshold-specific performance. At 12 years, machine learning models increased sensitivity but reduced specificity and positive likelihood ratios relative to Willems. At 15 years, Linear Regression and Random Forest produced no positive predictions, whereas the better-performing models showed results similar to Willems. Conclusions: Lower regression error does not necessarily indicate better forensic threshold-specific classification performance. Dental age-estimation models should therefore be validated using threshold-specific likelihood ratios, classification metrics, and age-group-specific bias in addition to overall prediction errors. Full article

Biotherapeutics are medicinal agents derived from or related to naturally occurring molecules in the body and are designed to modulate specific immune targets. This review examines clinically established biologics and targeted small molecules that affect cytokine-driven transcriptional programs, principally JAK/STAT and canonical and non-canonical NF-κB signaling, across autoimmune, neoplastic, hematological, dermatological, and rheumatic diseases. Periodontitis is used as a translational model because dysbiotic mucosal inflammation, cytokine signaling, and osteoclastogenesis may also intersect with systemic autoimmunity. In particular, Porphyromonas gingivalis-associated protein citrullination provides a plausible link to anti-citrullinated protein antibody-positive rheumatoid arthritis in genetically susceptible individuals, although causality remains unproven. The review also evaluates plant-derived modulators of JAK/STAT and related transcriptional pathways. Curcumin and resveratrol have entered small rheumatoid arthritis studies, whereas evidence for catechins, celastrol, and artemisinin derivatives in autoimmune disease remains predominantly preclinical. These compounds may inform adjunctive or locally delivered strategies, but clinical translation requires better target selectivity, bioavailability, dose standardization, and safety data. Full article

Arabic-speaking populations face disproportionate cybersecurity risks due to the predominantly English-centric design of existing awareness materials, which fail to accommodate Arabic dialectal diversity, script complexity, and culturally embedded communication patterns. These deficiencies impair users’ ability to interpret phishing messages, authentication requests, and security alerts, increasing susceptibility to social engineering, identity theft, and data breaches. This paper presents SECURE-A²RC, a rubric-constrained, Arabic-aware large language model framework designed to deliver scalable, interpretable, and culturally relevant cybersecurity education. The framework comprises two coupled components. The first, the Arabic-Aware Secure Communication Encoder (A-SCE), employs an instruction-tuned LLM to produce multidimensional encodings that capture three learner competencies: security intent comprehension; linguistic deception cue recognition encompassing urgency, authority impersonation, and incentive framing; and action-critical translation fidelity across Arabic dialectal registers and Arabic–English bilingual contexts. The second, the Rubric-Constrained Adaptive Feedback Generator (RCAFG), translates A-SCE encodings into personalized, expert-aligned instructional feedback and proficiency-calibrated adaptive tasks, ensuring pedagogical consistency, security correctness, and dialect awareness throughout the learning cycle. The framework is evaluated on three domain-relevant corpora: the English–Arabic Parallel Phishing Email Corpus, the Open MalSec dataset, and the Arabic Spam and Ham Tweets dataset. SECURE-A²RC achieves a 31% improvement in phishing identification accuracy and a 26% reduction in action-critical translation errors compared to conventional awareness materials. A comparative evaluation against SERENA, a Multi-Agent LLM, and the Arabic Multitask Learning Model confirms consistent superiority across detection accuracy, F1-score, dialectal robustness, and educational effectiveness metrics, affirming rubric-constrained LLM integration as a viable approach to equitable multilingual cybersecurity education. Full article

Monocular camera relocalization recovers a six-degree-of-freedom pose from one RGB image, but direct absolute pose regression typically predicts translation and rotation from one terminal representation. We propose Decoupled SwinPose, a hierarchical Swin-Tiny regressor that instead reads translation from a shallow, higher-resolution Stage 1 map and rotation from the deep, contextual Stage 3 representation, testing this asymmetric-readout hypothesis through three falsifiable predictions. Across three TUM RGB-D motion regimes and all seven Microsoft 7-Scenes environments, against constant-pose, retrieval, and matched shared-terminal controls, it achieves the lowest three-seed mean translation and rotation error on all ten evaluated sequences within the matched reimplemented cohort. Relative to the strongest alternative learned model within this cohort, translation error falls by up to 29.6% on TUM RGB-D and 52.6% on 7-Scenes, and rotation error by up to 27.2% and 54.4%, respectively. Routing ablations, including a capacity-matched control, support T1-R3 as the strongest overall trade-off among six tested configurations, and matched profiling shows the readout adds only 0.29% parameters with essentially identical FLOPs and FP32 cost relative to a shared-terminal baseline on an NVIDIA L4. These results support asymmetric multilevel readouts as an effective, low-cost architectural prior for transformer-based monocular pose regression in the evaluated indoor setting. Full article

Managing the transition risks of carbon-intensive firms is essential for reconciling climate governance with the stable operation of the real economy; nevertheless, existing scholarship has yet to fully elucidate how climate policy uncertainty contributes to the formation of these risks. In this paper, we develop a firm-specific measure of climate policy uncertainty exposure by integrating China’s aggregate climate policy uncertainty index with climate-risk-related textual data retrieved from listed companies’ annual reports. Drawing on a panel dataset of A-share listed companies in nine carbon-intensive sectors over 2010–2023, we employ a partial-linear double/debiased machine-learning methodology to investigate how climate policy uncertainty exposure influences multidimensional firm transition risk. Our baseline estimations indicate that greater climate policy uncertainty exposure is associated with a statistically significant rise in transition risk among high-carbon firms, with the preferred model producing a coefficient estimate of 0.0243. These findings remain robust to an array of sensitivity checks and endogeneity-correction procedures. Mechanism analysis provides evidence consistent with four potential channels involving weaker intra-industry competition, lower corporate risk-taking, tighter financing constraints, and higher agency costs. Heterogeneity examinations reveal that the detrimental impact is particularly evident among larger enterprises, high-technology companies, and firms characterized by comparatively lower pollution levels. Further analysis based on conditional average treatment effects and best linear predictors reveals that media supervision and the presence of long-term institutional investors substantially reduce the extent to which climate policy uncertainty translates into firm transition risk. This study provides firm-level empirical evidence elucidating how climate policy uncertainty shapes multidimensional transition risk in the low-carbon transformation of high-carbon industries. Full article

Background/Objectives: The health-protective properties of olive oil phenolics, including their potential chemopreventive and anticancer effects, have attracted considerable scientific interest. However, their in vivo efficacy and mechanisms of action remain insufficiently understood. Recent advances in extraction and purification technologies have enabled large-scale production of highly purified olive oil phenols and phenolic-rich extracts, facilitating translational research. Methods: Herein, the antitumor efficacy of isolated olive oil phenols and phenolic-rich formulations was investigated in an MDA-MB-231 triple-negative breast cancer (TNBC) xenograft model. Results: Intraperitoneal administration of oleocanthal (OLC), oleuropein aglycone (OleA) or their combination reduced endpoint tumor burden, with OLC exhibiting the most pronounced effect. Oral administration of total olive oil phenolics (OOPs) achieved comparable efficacy. Pre-treatment initiated before tumor cell implantation conferred the greatest protection, consistent with a prophylactic mode of action. In contrast, delayed intervention displayed diminished or no antitumor benefit. Phenolic-rich extra virgin olive oil likewise showed an inhibition trend in tumor progression. Mechanistically, OOPs attenuated plasma protein oxidation, modulated proteasome mediated proteolysis, and reduced γH2AX levels in vivo. Furthermore, OOPs negatively affected the MDA-MB-231 cell migration in a concentration-dependent manner in vitro. Conclusions: Collectively, these findings are consistent with antitumor activities of olive oil phenolics via multiple mechanisms and support their further investigation as prophylactic agents in TNBC and as nutraceuticals. Full article

Background/Objectives: Breast cancer is the most frequently diagnosed cancer among women worldwide and a leading cause of cancer-related death. Despite the use of bisphosphonates, RANKL inhibitors, chemotherapy, and available endocrine therapies, the five-year survival rate for patients with bone metastases remains approximately 20–30%, underscoring the urgent need for novel, metastasis-specific therapeutic strategies. Recent studies demonstrated that miR-10b can serve as an attractive therapeutic target for the treatment of metastatic breast cancer, particularly in the context of bone metastasis. This study aimed to test antimir-10b therapeutics in a mouse model of breast cancer bone metastasis. Methods: We utilized a previously developed dextran-coated iron oxide nanoparticle-based platform for delivery of antisense anti-miR-10b oligonucleotides to bone metastases. The magnetic properties of the nanoparticles allowed for in vivo imaging of therapeutic delivery to metastatic tumors. Results: We showed the delivery of the therapeutics in the bone colonization model by in vivo imaging as well as significant survival benefits in injected animals. There was a significant inhibition of miR-10b following treatment that resulted in significant upregulation of the downstream target HOXD10 in vitro and a similar trend in vivo. Repeated dosing of the therapeutics was well tolerated, and no systemic toxicity was observed, supporting the safety profile of this approach. Conclusions: Collectively, these studies demonstrated that targeting miR-10b using an image-guided anti-miR-10b nanotherapeutic represents a promising and translatable strategy for targeting breast cancer bone metastases. Full article

Hepatocellular carcinoma (HCC) is one of the malignant tumors with high incidence and mortality rates worldwide. Given the poor prognosis of patients with HCC, it is crucial to explore the molecular mechanisms underlying HCC development and to evaluate prognostic markers. Differential expression analysis followed by univariate Cox, LASSO, and multivariate Cox regression identified four genes (EPO, SOCS2, IL18RAP, and KPNA2), and a Cox-based risk score was evaluated in the TCGA-LIHC cohort and externally in GSE14520 using Kaplan–Meier and time-dependent ROC analyses. Bulk, single-cell, and protein resources provided convergent expression context. Survival machine-learning analysis using observed overall-survival time and censoring status identified Cox–Ridge as the best-performing model in TCGA-LIHC, with more modest performance in GSE14520, and immune profiling revealed risk-group-associated differences in estimated immune and stromal components, immune-cell composition, and immune-checkpoint expression. The oncoPredict/GDSC2 screen highlighted five potential drug candidates for experimental prioritization. Because the drug screen is based on computationally predicted sensitivities, these findings should be regarded as hypothesis-generating and require validation in prospective cohorts and experimental systems before clinical translation. Full article

Background/Objectives: Chimeric antigen receptor (CAR)-T cell therapy has achieved substantial clinical success in haematological malignancies but has shown limited efficacy against solid tumours. Key barriers include inadequate tumour trafficking, immunosuppressive tumour microenvironments, poor selectivity and heterogeneity of antigen expression, and challenges related to safety and manufacturing. Biomaterial-based technologies have emerged as a potential strategy to address many of these limitations. This review aims to critically evaluate biomaterial approaches designed to enhance CAR-T cell therapy of solid tumours and assess their translational potential. Methods: A narrative review of recent pre-clinical translational studies was conducted, focussing on biomaterial platforms developed to improve CAR-T cell delivery, persistence, functionality, safety control, and manufacturing efficiency in solid-tumour settings. Approaches were analysed according to their mechanisms of action, therapeutic benefits, and stage of translational readiness. Results: Biomaterial strategies, including nanoparticles, injectable and implantable hydrogels, scaffolds, and hybrid delivery systems, have improved CAR-T infiltration, survival, and therapeutic efficacy in several solid-tumour models. Localised delivery of cytokines and other immunomodulatory cues enabled improved spatio-temporal control of CAR-T activation, reducing systemic toxicity, and increasing persistence. Additional applications include amplified ex vivo CAR-T expansion and support for non-viral or in vivo CAR-T generation. However, increased material complexity was frequently associated with challenges in scalability, regulatory approval, and long-term safety. Conclusions: Biomaterial-enabled approaches offer a versatile toolkit to address key biological and translational barriers limiting CAR-T cell therapy of solid tumours. Strategies based on clinically familiar materials and simplified designs appear most suitable for near-term clinical translation, emphasising the need to balance engineering innovation with safety, scalability, and integration into existing clinical workflows. Full article