Search Results (2,223)

The extracellular matrix (ECM) is a dynamic, tissue-specific network that integrates biochemical and mechanical cues to regulate cell behavior and organ homeostasis. Increasing evidence indicates that dysregulated ECM remodeling is an upstream driver of chronic human diseases rather than a passive consequence of injury. This review summarizes principles of ECM organization, mechanotransduction, and pathological remodeling and highlights translational opportunities for ECM-targeted therapies, biomaterial platforms, and precision diagnostics. We conducted a narrative synthesis of foundational and recent literature covering ECM composition and turnover, stiffness-dependent signaling, and disease-associated remodeling across fibrosis/cardiovascular disease, cancer, and metabolic disorders, together with advances in ECM-based biomaterials, drug delivery, and ECMderived biomarkers and imaging. Across organs, a self-reinforcing cycle of altered matrix composition, excessive crosslinking, and stiffness-dependent mechanotransduction (including integrin–FAK and YAP/TAZ pathways) sustains fibroinflammation, myofibroblast persistence, and progressive tissue dysfunction. In tumors, aligned and crosslinked ECM promotes invasion, immune evasion, and therapy resistance while also shaping perfusion and drug penetration. Translational strategies increasingly focus on modulating ECM synthesis and crosslinking, normalizing rather than ablating matrix architecture, and targeting ECM–cell signaling axes in combination with anti-fibrotic, cytotoxic, or immunotherapeutic regimens. ECM biology provides a unifying framework linking pathogenesis, therapy, and precision diagnostics across chronic diseases. Clinical translation will benefit from standardized quantitative measures of matrix remodeling, mechanism-based biomarkers of ECM turnover, and integrative imaging–omics approaches for patient stratification and treatment monitoring. Full article

Background: With extensive research and development in the past decade, the affordability of Poly (ADP-ribose) polymerase (PARP) inhibitor therapy has drastically improved. Homologous recombination deficiency (HRD), a key biomarker, has been identified as an important guiding factor for PARP inhibitor therapeutic decisions in breast and ovarian cancer. However, identification of patients who will respond to Poly (ADP-ribose) polymerase (PARP) inhibitor therapy is challenging due to the lack of a unifying morphological phenotype. Current HRD testing via next-generation sequencing (NGS) is tissue-dependent, has high failure rates, misses relevant HRD genes, and involves longer turn-around times. Methods: To overcome these limitations, we developed a multimodal AI model, TRINITY, combining imaging, image-based transcriptome data, and clinico-molecular data, to examine whole-slide images (WSIs) obtained from hematoxylin and eosin (H&E)-stained samples to non-invasively predict HRD status. Results: The TRINITY model, tested on 316 TCGA breast and OV samples, presented a sensitivity of 0.77 and 0.91, NPV of 0.94 and 0.86, PPV of 0.63 and 0.58, specificity of 0.89 and 0.47, and AUC-ROC of 0.91 and 0.72, respectively. The model also yielded a similar outcome in a blind study of 74 samples, with a sensitivity of 81.2, NPV of 0.85, PPV of 0.77, specificity of 0.81, and high AUC-ROC value of 0.89, showing its promising preliminary evidence of predicting HRD status on external cohorts. Conclusions: These findings demonstrate TRINITY’s potential as a rapid, cost-effective, and tissue-sparing alternative to conventional NGS testing. While promising, further validation is needed to establish its generalizability across broader cancer types. Full article

Tympanic membrane (TM) perforations, arising from infections, injuries, or chronic otitis media, remain a frequent clinical finding and can lead to hearing problems when the tissue does not regenerate adequately. Although autologous grafts are still the standard option for repairing persistent defects, they come with well-known limitations. Beyond the need for additional harvesting procedures, these grafts rarely reproduce the intricate, fibrous layering of the native TM, which can compromise sound transmission after healing. In search of alternatives, fibre-based scaffolds have attracted considerable interest. The primary advantage of this material is the level of structural control it affords. The fibre orientation, porosity, and overall microarchitecture can be adjusted to replicate the organisation and mechanical behaviour of the natural membrane. A range of biocompatible polymers—among them silk fibroin, poly(ε-caprolactone), poly(lactic acid), and poly(vinyl alcohol) and their composites—provide options for tuning stiffness, degradation rates, and interactions with cells, making them suitable building blocks for TM repair constructs. This review provides a comprehensive overview of contemporary fabrication methodologies, namely electrospinning, additive manufacturing, melt electrowriting, and hybrid strategies. In addition, it offers a detailed discussion of the evaluation procedures employed for these scaffolds and discusses how scaffold structure affects later performance. Mechanical testing, microstructural imaging, and in vitro biocompatibility assays help to determine how closely a construct can approach the performance of the native tissue. Bringing these elements together may support the gradual translation of fibre-based TM scaffolds into clinical practice. Full article

Background/Objectives: Artificial intelligence (AI) has gained substantial prominence in pediatric dentistry, offering new opportunities to enhance diagnostic precision and clinical decision-making. AI-based systems are increasingly applied in caries detection, early childhood caries (ECC) risk prediction, tooth development assessment, mesiodens identification, and other key diagnostic tasks. This systematic review and meta-analysis aimed to synthesize evidence on the diagnostic performance of AI models developed specifically for pediatric dental applications. Methods: A systematic search was conducted in PubMed, Scopus, Web of Science, and Embase following PRISMA-DTA guidelines. Studies evaluating AI-based diagnostic or predictive models in pediatric populations (≤18 years) were included. Reference screening, data extraction, and quality assessment were performed independently by two reviewers. Pooled sensitivity, specificity, and area under the receiver operating characteristic curve (AUC) were calculated using random-effects models. Sources of heterogeneity related to imaging modality, annotation strategy, and dataset characteristics were examined. Results: Thirty-two studies met the inclusion criteria for qualitative synthesis, and fifteen were eligible for quantitative analysis. For radiographic caries detection, pooled sensitivity, specificity, and AUC were 0.91, 0.97, and 0.98, respectively. Prediction models demonstrated good diagnostic performance, with pooled sensitivity of 0.86, specificity of 0.82, and AUC of 0.89. Deep learning architectures, particularly convolutional neural networks, consistently outperformed traditional machine learning approaches. Considerable heterogeneity was identified across studies, primarily driven by differences in imaging protocols, dataset balance, and annotation procedures. Beyond quantitative accuracy estimates, this review critically evaluates whether current evidence supports meaningful clinical translation and identifies pediatric domains that remain underrepresented in AI-driven diagnostic innovation. Conclusions: AI technologies exhibit strong potential to improve diagnostic accuracy in pediatric dentistry. However, limited external validation, methodological variability, and the scarcity of prospective real-world studies restrict immediate clinical implementation. Future research should prioritize the development of multicenter pediatric datasets, harmonized annotation workflows, and transparent, explainable AI (XAI) models to support safe and effective clinical translation. Full article

The artistic exchange during Buddhism’s early transmission represents a vital field within Silk Road art studies. When Buddhist art first entered China during the Eastern Han Dynasty (25–220), many artistic elements originating from Indian and Central Asian traditions manifested via a highly fragmentary mode of dissemination. As a result, prior scholarship on Buddhist art in the Han Dynasty has predominantly focused on case studies of individual motifs such as Buddha images, lotus patterns, lions, and elephants. These studies form an essential foundation for the present research. This paper observes that Buddha images from the Han period were not always disseminated as isolated icons but were frequently closely associated with octagonal columns and arches/lintels. Tracing their origins reveals a connection to the “column–arch–Buddha” narrative motif found in the architectural art of Indian and Central Asian Buddhism. This motif extended eastward through the Western Regions (Xiyu 西域, present-day Xinjiang 新疆) and ultimately reached the core territories of the Han Empire, undergoing various transformations—including deconstruction, reassembly, and translation—in the process. Understanding these combinatory modes and their underlying intent is crucial for comprehending the essential nature of the early interaction and fusion between Buddhist art and Han Chinese civilization. Full article

Carbon dots (CDs) have emerged as a distinct class of fluorescent nanomaterials distinguished by their tunable physicochemical properties, ultrasmall size, exceptional photoluminescence, versatile surface chemistry, high biocompatibility, and chemical stability, positioning them as promising candidates for biomedical applications ranging from sensing and imaging to drug delivery and theranostics. As CDs increasingly transition toward biological and clinical use, a fundamental understanding of their interactions with biological membranes becomes essential, as cellular membranes govern nanoparticle uptake, intracellular transport, and therapeutic performance. Model membrane systems, such as phospholipid vesicles and liposomes, offer controllable platforms to elucidate CD-membrane interactions by isolating key physicochemical variables otherwise obscured in complex biological environments. Recent studies demonstrate that CD surface chemistry, charge, heteroatom doping, size, and hydrophobicity, together with membrane composition, packing density, and phase behavior, dictate nanoparticle adsorption, insertion, diffusion, and membrane perturbation. In addition, CD-liposome hybrid systems have gained momentum as multifunctional nanoplatforms that couple the fluorescence and traceability of CDs with the encapsulation capacity and biocompatibility of lipid vesicles, enabling imaging-guided drug delivery and responsive theranostic systems. This review consolidates current insights into the mechanistic principles governing CD interactions with model membranes and highlights advances in CD-liposome hybrid nanostructures. By bridging fundamental nanoscale interactions with translational nanomedicine strategies, this work provides a framework for the rational design of next-generation CD-based biointerfaces with optimized structural, optical, and biological performance. Full article

Brain magnetic resonance imaging (MRI) is highly susceptible to motion artifacts that degrade fine structural details and undermine quantitative analysis. Conventional U-Net-based deep learning approaches for motion artifact reduction typically operate only in the image domain and are often trained on data with simplified motion patterns, thereby limiting physical plausibility and generalization. We propose Sim-DDNet, a simulation-data-based dual-domain network that combines k-space-based motion simulation with a joint image-k-space reconstruction architecture. Motion-corrupted data were generated from T2-weighted Alzheimer’s Disease Neuroimaging Initiative brain MR scans using a k-space replacement scheme with three to five random rotational and translational events per volume, yielding 69,283 paired samples (49,852/6969/12,462 for training/validation/testing). Sim-DDNet integrates a real-valued U-Net-like image branch and a complex-valued k-space branch using cross attention, FiLM-based feature modulation, soft data consistency, and composite loss comprising L1, structural similarity index measure (SSIM), perceptual, and k-space-weighted terms. On the independent test set, Sim-DDNet achieved a peak signal-to-noise ratio of 31.05 dB, SSIM of 0.85, and gradient magnitude similarity deviation of 0.077, consistently outperforming U-Net and U-Net++ across all three metrics while producing less blurring, fewer residual ghost/streak artifacts, and reduced hallucination of non-existent structures. These results indicate that dual-domain, data-consistency-aware learning, which explicitly exploits k-space information, is a promising approach for physically plausible motion artifact correction in brain MRI. Full article

Actinium-225 (²²⁵Ac) has emerged as a pivotal alpha-emitter in modern radiopharmaceutical therapy, offering potent cytotoxicity with the potential for precise tumour targeting. Accurate, patient-specific image-based dosimetry for ²²⁵Ac is essential to optimize therapeutic efficacy while minimizing radiation-induced toxicity. Establishing a robust dosimetry workflow is particularly challenging due to the complex decay chain, low administered activity, limited count statistics, and the indirect measurement of daughter gamma emissions. Clinical single-photon emission computed tomography/computed tomography protocols with harmonized acquisition parameters, combined with robust volume-of-interest segmentation, artificial intelligence (AI)-driven image processing, and voxel-level analysis, enable reliable time-activity curve generation and absorbed-dose calculation, while reduced mixed-model approaches improve workflow efficiency, reproducibility, and patient-centred implementation. Cadmium zinc telluride-based gamma cameras further enhance quantitative accuracy, enabling rapid whole-body imaging and precise activity measurement, supporting patient-friendly dosimetry. Complementing these advances, the cerium-134/lanthanum-134 positron emission tomography in vivo generator provides a unique theranostic platform to noninvasively monitor ²²⁵Ac progeny redistribution, evaluate alpha-decay recoil, and study tracer internalization, particularly for internalizing vectors. Together, these technological and methodological innovations establish a mechanistically informed framework for individualized ²²⁵Ac dosimetry in targeted alpha therapy, supporting optimized treatment planning and precise response assessment. Continued standardization and validation of imaging, reconstruction, and dosimetry workflows will be critical to translate these approaches into reproducible, patient-specific clinical care. Full article

Background/Objectives: Despite significant advances in imaging technology, real-time intra-fraction monitoring of moving targets remains a challenge in markerless radiotherapy. This retrospective study investigates the use of ExacTrac Dynamic by Brainlab as an intra-fraction monitoring tool for stereotactic body radiotherapy (SBRT) in both early-stage NSCLC and oligometastatic disease. Methods: A total of 63 X-ray pairs from 21 patients were analyzed to evaluate tumor visualization with and without a surrogate approach. Statistical analysis was conducted to determine whether failures could be attributed to tumor size or localization using the Mann–Whitney U-test and Fisher’s exact test. The accuracy of the X-ray/digitally reconstructed radiograph (DRR) surrogate-based fusion was assessed by calculating and comparing the corresponding 3D vectors according to the linear mixed effects model, with a random slope effect for size of surrogate and a random intercept per patient. Results: Surrogates enhanced tumor visualization on X-ray/DRR fusions from 14.3% to 75.5%. Tumor size and lung affected (left or right) did not predict visualization success. Tumor location, however, tended to influence visibility, with lesions in the upper lobes being more readily visualized (88%) than those in the lower lobes (48.1%), although no statistical significance was reported (p > 0.05). Regarding geometric accuracy, 76% of the analyzed data points deviated less than 5 mm in the 3D vector measurements, the mean values were around 4 mm (±3 mm), and the medians were within 3 mm across all conditions. No statistically significant differences (p > 0.05) were found based on the surrogate size or the triggering time of the X-ray during the breathing cycle. Conclusions: Surrogate-based DRRs, referred to as Correlation Objects, demonstrate consistent geometric accuracy across multiple surrogate sizes and X-ray acquisitions, supporting the clinical translation of markerless lung targeting workflows for lung SBRT. Full article

Artificial intelligence (AI) has rapidly evolved from an experimental tool in spine research to a multi-domain framework that has significantly influenced imaging analysis, surgical decision-making, and individualized outcome prediction. Recent advances have expanded beyond isolated applications, enabling automated image interpretation, patient-specific risk stratification, discovery of qualitative phenotypes, and integration of heterogeneous clinical and biomechanical data. These developments signal a shift toward more comprehensive, context-aware analytic systems capable of supporting complex clinical workflows in spine care. Despite these gains, widespread clinical adoption remains limited. High internal performance metrics do not consistently translate into reliable generalizability, interpretability, or real-world clinical readiness. Persistent challenges, which include dataset heterogeneity, transportability across institutions, alignment with clinical decision-making processes, and appropriate validation strategies, continue to constrain widespread implementation. In this perspective, we synthesize post-2019 advances in spine AI across key application domains: imaging analysis, predictive modeling and decision support, qualitative phenotyping, and emerging hybrid and language-based frameworks through a unified clinical-readiness lens. By examining how methodological progress aligns with clinical context, validation rigor, and interpretability, we highlight both the transformative potential of AI in spine research and the critical steps required for responsible, effective integration into routine clinical practice. Full article

Background/Objectives: [¹⁸F]Fluproxadine (formerly [¹⁸F]AF78) is a PET radiotracer targeting the norepinephrine transporter (NET) with potential applications in cardiac, neurological, and oncological imaging. Its guanidine moiety, while essential for NET binding, presents major radiosynthetic challenges due to high basicity and the harsh deprotection conditions required for protected precursors. Previous methods relied on multistep procedures, strong acids, and complex purification, limiting clinical translation. This study aimed to develop a practical one-step radiosynthesis suitable for routine and automated production. Methods: A direct SN2-type nucleophilic [¹⁸F]fluorination was performed using an unprotected guanidine precursor to eliminate deprotection steps. Reaction parameters, including the base system, solvent composition, precursor concentration, and temperature, were optimized under conventional and microwave heating. Radiochemical conversion (RCC) and operational robustness were evaluated, and purification strategies were assessed for automation compatibility. Results: Direct [¹⁸F]fluorination using the unprotected precursor reduced the total synthesis time to 60–70 min. Optimal conditions employed a tert-butanol/acetonitrile (4:1) solvent system with K₂CO₃/Kryptofix222, affording RCC up to 33% under conventional heating. Microwave irradiation further improved efficiency, achieving RCC of up to 64% within 1.5 min at 140 °C. The method showed broad tolerance to variations in the base molar ratio and precursor concentration and enabled isocratic HPLC purification. Conclusions: This one-step radiosynthesis overcomes longstanding challenges in [¹⁸F]fluproxadine production by eliminating harsh deprotection and enabling high-yield, automation-ready synthesis, thereby improving clinical feasibility. Full article

HPV-positive OPSCC shows a favourable prognosis, prompting evaluation of de-escalated and adaptive strategies. Quantitative imaging may provide scalable biomarkers to individualise care. Quantitative imaging can support baseline risk stratification, early on-treatment decision-making, and posttreatment surveillance in HPV-positive OPSCC. Real-world translation requires standardised reporting, calibration/harmonisation across centres, rigorous model validation, and workflow integration with radiotherapy planning. Quantitative MRI, CT, and PET, augmented by radiomics and AI, show convergent promise as non-invasive biomarkers to enable safe individualisation of therapy in HPV-positive OPSCC, contingent on methodological rigour and prospective, externally validated studies. Despite this promise, clinical translation faces substantial barriers, including limited external validation, heterogeneous methodologies, and the need for standardised, prospectively validated pipelines. Full article

The “Neuroimaging and Pathology Biomarkers in Parkinson’s Disease” course held on 12–13 September 2025 in Milan, Italy, convened an international faculty to review state-of-the-art biomarkers spanning neurotransmitter dysfunction, protein pathology and clinical translation. Here, we synthesize the four themed sessions and highlights convergent messages for diagnosis, stratification and trial design. The first session focused on neuroimaging markers of neurotransmitter dysfunction, highlighting how positron emission tomography (PET), single photon emission computed tomography (SPECT), and magnetic resonance imaging (MRI) provided complementary insights into dopaminergic, noradrenergic, cholinergic and serotonergic dysfunction. The second session addressed in vivo imaging of protein pathology, presenting recent advances in PET ligands targeting α-synuclein, progress in four-repeat tau imaging for progressive supranuclear palsy and corticobasal syndromes, and the prognostic relevance of amyloid imaging in the context of mixed pathologies. Imaging of neuroinflammation captures inflammatory processes in vivo and helps study pathophysiological effects. The third session bridged pathology and disease mechanisms, covering the biology of α-synuclein and emerging therapeutic strategies, the clinical potential of seed amplification assays and skin biopsy, the impact of co-pathologies on disease expression, and the “brain-first” versus “body-first” model of pathological spread. Finally, the fourth session addressed disease progression and clinical translation, focusing on imaging predictors of phenoconversion from prodromal to clinically overt stages of synucleinopathies, concepts of neural reserve and compensation, imaging correlates of cognitive impairment, and MRI approaches for atypical parkinsonism. Biomarker-informed pharmacological, infusion-based, and surgical strategies, including network-guided and adaptive deep brain stimulation, were discussed as examples of how multimodal biomarkers may inform personalized management. Across all sessions, the need for harmonization, longitudinal validation, and pathology-confirmed outcome measures was consistently emphasized as essential for advancing biomarker qualification in multicentre research and clinical practice. Full article

The Secret of the Golden Flower (Taiyi Jinhua Zongzhi 太乙金華宗旨), a Qing dynasty spirit-writing (fuji扶乩) text, is widely known through the Wilhelm–Jung translation lineage, where jinhua 金華 is rendered as “Golden Flower” and read as mandala-like symbolism. Based on a close reading of the Daozang Jiyao 道藏輯要version, this article argues that in the Chinese text jinhua is not primarily a floral image but a technical and experiential term for luminosity in Daoist inner-alchemical cultivation. Hua 華 is resemanticized from botanical “flower/flourishing” into “radiance,” and the work explicitly defines the key term as “jinhua is light”. The text further organizes cultivation into a three-stage trajectory—“sudden emergence”, “circulation”, and “great condensation”, through which qi 氣 is refined into light and luminosity stabilizes as spirit (shen 神). Finally, the analysis situates this luminous grammar within the work’s explicit Three Teachings (sanjiao 三教) framing: Confucian “illuminating virtue” (mingde 明德) and Buddhist idioms of luminous mind-nature (xin-xing guangming 心性光明) and dharma-body language function as a shared vocabulary for describing non-grasping awareness and embodied realization. On this basis, jinhua is best understood not as a decorative metaphor or a purely psychological symbol but as a practice-oriented mechanism of ontological luminosity, clarifying both the inner-alchemical logic of The Secret and the stakes of its modern reception. Full article

Glioblastoma (GBM) remains one of the most treatment-resistant human malignancies, largely due to the interplay between disrupted fluid dynamics, immune evasion, and the structural complexity of the tumor microenvironment; in addition to these, treatment resistance is also driven by intratumoral heterogeneity, glioma stem cell persistence, hypoxia-induced metabolic and epigenetic plasticity, adaptive oncogenic signaling, and profound immunosuppression within the tumor microenvironment. Emerging evidence shows that dysfunction of the glymphatic system, mislocalization of aquaporin-4, and increased intracranial pressure compromise cerebrospinal fluid–interstitial fluid exchange and impair antigen drainage to meningeal lymphatics, thereby weakening immunosurveillance. GBM simultaneously remodels the blood–brain barrier into a heterogeneous and permeable blood–tumor barrier that restricts uniform drug penetration yet enables tumor progression. These alterations intersect with profound immunosuppression mediated by pericytes, tumor-associated macrophages, and hypoxic niches. Advances in imaging, including DCE-MRI, DTI-ALPS, CSF-tracing PET, and elastography, now allow in vivo characterization of glymphatic function and interstitial flow. Therapeutic strategies targeting the fluid-immune interface are rapidly expanding, including convection-enhanced delivery, intrathecal and intranasal approaches, focused ultrasound, nanoparticle systems, and lymphatic-modulating immunotherapies such as VEGF-C and STING agonists. Integrating barrier modulation with immunotherapy and nanomedicine holds promise for overcoming treatment resistance. Our review synthesizes the mechanistic, microenvironmental, and translational advances that position the glymphatic–immune axis as a new frontier in glioblastoma research. Full article