Search Results (5,486)

Multicomponent mixed lanthanide oxide (MMLO) nanoparticles possess considerable potential as multimodal imaging agents because they integrate diverse excellent optical and magnetic properties within a single nanoparticle. Herein, we present triply and quadruply mixed lanthanide oxide nanoparticles, namely, gadolinium (Gd)/dysprosium (Dy)/europium (Eu) oxide (GDEO), Gd/Dy/terbium (Tb) oxide (GDTO), and Gd/Dy/Eu/Tb oxide (GDETO) nanoparticles. Gd³⁺ can strongly induce positive (T₁) contrast in magnetic resonance imaging (MRI), Dy³⁺ and Tb³⁺ can generate negative (T₂) contrast in MRI, and Eu³⁺ and Tb³⁺ emit visible photons that are applicable to fluorescence imaging (FI). All the nanoparticles were grafted with hydrophilic, biocompatible polyacrylic acid (PAA) to enhance colloidal stability and biocompatibility and further grafted with small amounts of an organic photosensitizer, 2,6-pyridinedicarboxylic acid (PDA), to obtain a high absolute fluorescent quantum yield (QY) with an extended fluorescent lifetime (τ). All PAA-MMLO and PAA/PDA-MMLO nanoparticles exhibited nearly monodispersed particle-size distributions with average particle diameters of ~2 nm and displayed considerably higher longitudinal (r₁) and transverse (r₂) water proton spin relaxivities than commercial molecular MRI contrast agents. The PAA/PDA-GDEO, PAA/PDA-GDTO, and PAA/PDA-GDETO nanoparticles exhibited high absolute QYs of 45, 29, and 61%, respectively, and long τ values of 1–2 ms, making them suitable for time-delayed noise-free fluorescence signal detection. These findings confirm the high potential of PAA-MMLO nanoparticles as T₁ and/or T₂ MRI contrast agents and PAA/PDA-MMLO nanoparticles as both T₁ and/or T₂ MRI and FI agents. Full article

Obesity is a systemic metabolic disorder characterized by chronic low-grade inflammation and insulin resistance, with growing evidence indicating that the brain represents a primary and particularly vulnerable target organ. Beyond peripheral metabolic consequences, obesity induces region-specific structural, functional, and biochemical alterations within the central nervous system, contributing to cognitive impairment, dysregulated energy homeostasis, and increased susceptibility to neurodegenerative diseases. This narrative review examines key neurometabolic and neuroinflammatory mechanisms underlying obesity-related brain vulnerability, including downstream neuroinflammation, impaired insulin signaling, mitochondrial dysfunction, oxidative stress, blood–brain barrier disruption, and impaired brain clearance mechanisms. These processes preferentially affect frontal and limbic networks involved in executive control, reward processing, salience detection, and appetite regulation. Advanced neuroimaging has substantially refined our understanding of these mechanisms. Magnetic resonance spectroscopy provides unique in vivo insight into early neurometabolic alterations that may precede irreversible structural damage and is complemented by diffusion imaging, volumetric MRI, functional MRI, cerebral perfusion imaging, and positron emission tomography. Together, these complementary modalities reveal microstructural, network-level, structural, hemodynamic, and molecular alterations associated with obesity-related brain vulnerability and support the concept that such brain dysfunction is dynamic and potentially modifiable. Integrating neurometabolic and multimodal neuroimaging biomarkers with metabolic and clinical profiling may improve early risk stratification and guide preventive and therapeutic strategies aimed at preserving long-term brain health in obesity. Full article

Attention-deficit/hyperactivity disorder (ADHD) is a common neurodevelopmental disorder in childhood, characterized by persistent inattention, hyperactivity, and impulsivity. This narrative review aims to synthesize and critically evaluate recent large-scale magnetic resonance imaging (MRI) studies to clarify the neuroanatomical and functional brain alterations associated with ADHD in children. By addressing current gaps in understanding, this work seeks to identify reliable neurobiological markers that could improve diagnostic accuracy and guide personalized interventions. The literature reveals that large-scale structural MRI studies consistently report abnormal development in total cortical volume and surface area, prefrontal cortex volume, and basal ganglia volume in children with ADHD. Moreover, gray matter alterations show significant age-dependent effects, with the degree of impairment potentially serving as neurobiological markers. Diffusion magnetic resonance imaging studies reveal disrupted white matter microstructures in regions such as the left uncinate fasciculus, superior and inferior longitudinal fasciculi, corpus callosum, cingulum, and internal capsule. Importantly, these white matter abnormalities often persist into adulthood, highlighting their clinical relevance. Functional MRI findings indicate reduced global connectivity within core hubs of the default mode network in children with ADHD. Furthermore, deficits in inhibitory control identified via fMRI may represent one of the neurofunctional signatures that differentiates ADHD from typically developing controls. By consolidating evidence from large-scale multimodal MRI studies, this review provides a comprehensive understanding of the neurodevelopmental alterations in ADHD and underscores their potential utility for improving diagnosis and treatment. Full article

Background: Sarcopenia is a clinically important complication of chronic liver disease (CLD), but its underlying mechanisms may differ according to disease etiology. Quantitative MRI biomarkers, including proton density fat fraction (PDFF) and magnetic resonance elastography (MRE), may help characterize etiology-specific patterns of muscle loss. This study aimed to explore etiology-specific associations between MRI-derived biomarkers and sarcopenia, with a particular focus on metabolic dysfunction-associated steatohepatitis (MASH) and viral hepatitis. Methods: This retrospective single-center study included 131 CLD patients (77 with MASH, 54 with viral hepatitis) who underwent MRI, including PDFF and MRE. Sarcopenia was defined by L2 skeletal muscle index thresholds (<42 cm²/m² for men, <38 cm²/m² for women). Muscle identification was performed by automatic threshold-based segmentation by a single observer. Multivariable logistic regression analyses incorporating interaction terms were performed to evaluate whether associations between MRI biomarkers and sarcopenia differed by etiology. Results: Sarcopenia was present in 56% of patients. In the overall cohort, older age (OR = 1.05, p = 0.01), lower PDFF (OR = 0.93, p = 0.03), and lower liver stiffness (OR = 0.51, p = 0.006) were independently associated with sarcopenia. A significant interaction between BMI and disease etiology was observed (p = 0.02). Subgroup analyses suggested that in MASH, sarcopenia was associated with aging, hepatic fat depletion, and lower stiffness. In contrast, in viral hepatitis, it tended to be associated with higher stiffness and lower BMI. Conclusion: MRI-derived hepatic fat and stiffness reflect distinct etiologic patterns of sarcopenia in CLD—metabolically depleted in MASH and fibrosis-related in viral hepatitis. These findings suggest that sarcopenia in MASH and viral hepatitis may reflect different underlying phenotypic patterns, highlighting the importance of considering disease etiology in imaging-based sarcopenia assessment. The results should be interpreted as hypothesis-generating and warrant validation in prospective studies. Full article

Background/Objectives: To date, no studies have examined radiologic findings by histologic patterns of primary hepatic angiosarcoma; this study clarified radiologic findings of primary hepatic angiosarcoma according to distinct histologic patterns. Methods: From January 2010 to October 2024, 17 individuals (mean age, 69 years ± 11; 11 men) with pathologically confirmed primary hepatic angiosarcoma underwent computed tomography (CT) with or without magnetic resonance imaging (MRI). Histologic patterns were classified as mass-forming, subdivided into vasoformative and non-vasoformative (epithelioid and spindled) patterns, or non-mass-forming, subdivided into sinusoidal and peliotic patterns. Two radiologists independently reviewed CT and MRI images, classifying lesions as non-mass-forming or mass-forming. Hypervascular portions and targetoid patterns were also assessed. Associations between histologic patterns and radiologic findings were evaluated using Fisher’s exact test. Results: Mass-forming tumors were observed in 13 individuals (76.5%), and non-mass-forming tumors in 4 individuals (23.5%). Significant correlation (p < 0.05) was found between radiologic classification (non-mass-forming or mass-forming) and corresponding pathologic patterns. Pathologic subdivision into vasoformative and non-vasoformative patterns did not correlate with hypervascular portions on imaging. Conclusions: Pathological classification into mass-forming and non-mass-forming patterns corresponds closely to radiologic classification of mass-forming and non-mass-forming lesions, indicative of strong pathologic features in imaging. Full article

Background: Accurate preoperative staging is the cornerstone of therapeutic decision-making in gastric cancer (GC), yet standard modalities often fail to capture the full extent of disease, particularly in diffuse and poorly cohesive histotypes. This review aims to provide a comprehensive update on diagnostic imaging for GC, evaluating the established roles of CT, EUS, and PET/CT alongside the emerging capabilities of Magnetic Resonance Imaging (MRI) and Artificial Intelligence (AI). Methods: A structured narrative review was conducted by searching indexed biomedical databases for studies published between 2015 and 2024. A structured literature search screening process identified 410 relevant studies focusing on T, N, and M staging accuracy, quantitative imaging biomarkers, and radiomics. Results: While Multidetector CT remains the universal first-line modality, its sensitivity declines in infiltrative tumors and low-volume peritoneal carcinomatosis. EUS retains superiority for early (T1-T2) lesions but may offer limited value in advanced stages. Conversely, MRI (leveraging diffusion-weighted imaging (DWI) and multiparametric protocols) indicates superior soft-tissue contrast, potentially outperforming CT in the assessment of serosal invasion, nodal involvement, and occult peritoneal metastases. Furthermore, emerging fibroblast activation protein inhibitor (FAPI) PET tracers show promise in overcoming the limitations of FDG in mucinous and diffuse GC. Finally, radiomics and deep learning models are providing novel quantitative biomarkers for non-invasive risk stratification. Conclusions: Contemporary GC staging requires a tailored, multimodality approach. Evidence supports the increasing integration of MRI and quantitative imaging into clinical workflows to overcome the limitations of conventional techniques and support precision oncology. Full article

Alzheimer’s Disease (AD) is an advanced brain illness that affects millions of individuals across the world. It causes gradual damage to the brain cells, leading to memory loss and cognitive dysfunction. Although Magnetic Resonance Imaging (MRI) is widely used in AD diagnosis, the existing studies rely solely on the visual representations, leaving alternative features unexplored. The objective of this study is to explore whether MRI sonification can provide complementary diagnostic information when combined with conventional image-based methods. In this study, we propose a novel dual-stream multimodal framework that integrates 2D MRI slices with their corresponding audio representations. MRI images are transformed into audio signals using a multi-scale, multi-orientation Gabor filtering, followed by a Hilbert space-filling curve to preserve spatial locality. The image and sound modalities are processed using a lightweight CNN and YAMNet, respectively, then fused via logistic regression. The experimental results of the multimodal achieved the highest accuracy in distinguishing AD from Cognitively Normal (CN) subjects at 98.2%, 94% for AD vs. Mild Cognitive Impairment (MCI), and 93.2% for MCI vs. CN. This work provides a new perspective and highlights the potential of audio transformation of imaging data for feature extraction and classification. Full article

Background/Objectives: The tumor microenvironment (TME) of pancreatic ductal adenocarcinoma (PDAC) is characterized by an enriched stroma, hampering the effectiveness of therapy. This co-clinical study aimed to (1) provide insight into early post-treatment changes in the TME using multiparametric magnetic resonance imaging (mpMRI)-derived quantitative imaging biomarkers (QIBs) in a preclinical PDAC model treated with radiotherapy and correlate these QIBs with histology; (2) evaluate the feasibility of obtaining these QIBs in patients with PDAC using clinically approved mpMRI data acquisitions. Methods: Athymic mice (n = 12) at pre- and post-treatment as well as patients with PDAC (n = 11) at pre-treatment underwent mpMRI including diffusion-weighted (DW) and dynamic contrast-enhanced (DCE) data acquisition sequences. DW and DCE data were analyzed using monoexponential and extended Tofts models, respectively. DeepLIIF quantified the total percentage (%) of tumor cells in hematoxylin and eosin (H&E)-stained tissues from athymic mice. Spearman correlation and Wilcoxon signed rank tests were performed for statistical analysis. Results: In the preclinical PDAC model, mean pre- and post-treatment ADC and K^trans values differed significantly (p < 0.01), changing by 20.50% and 20.41%, respectively, and the median total tumor cells quantified by DeepLIIF was 24% (range: 15–53%). Post-treatment ADC values and relative change in v_e (rΔv_e) showed a significant negative correlation with total tumor cells (ρ = −0.77, p < 0.014 for ADC and ρ = −0.77, p = 0.009 for rΔv_e). In patients with PDAC, pre-treatment mean ADC and K^trans values were 1.76 × 10⁻³ (mm²/s) and 0.24 (min⁻¹), respectively. Conclusions: QIBs in both preclinical and clinical settings underscore their potential for future co-clinical research to evaluate emerging drug combinations targeting both tumor and stroma. Full article

In light of the growing prevalence of the autoimmune disease multiple sclerosis (MS), accurate detection of MS lesions in brain magnetic resonance imaging (MRI) images plays a critical role in assisting neurologists with timely diagnosis. The high similarity between MS lesions and normal brain tissues, however, makes this task particularly challenging. Although numerous deep-learning-based approaches have been proposed for the automatic segmentation of MS lesions, the method presented in this study has achieved superior results. ZechariahNet is a U-Net-based architecture that integrates transition down blocks, squeeze-attention (SA) blocks, dense blocks, and Convolutional LSTM (C-LSTM) blocks within a 3D CNN framework. By jointly exploiting spatial–temporal information from three consecutive MRI slices (previous, current, and subsequent) and strategically applying C-LSTM modules across the encoder and decoder paths, the proposed model effectively captures the neighborhood dependencies for enhanced feature extraction and reconstruction. These architectural innovations significantly improve segmentation accuracy, enabling ZechariahNet to achieve a dice similarity coefficient (DSC) of 84.72%, outperforming existing state-of-the-art methods. Full article

Metabolic dysfunction-associated steatotic liver disease (MASLD) is strongly linked to systemic metabolic disturbances and features a lipid-driven cascade that promotes hepatic inflammation and fibrosis. Choline insufficiency contributes to disease advancement by altering phospholipid turnover and redox homeostasis; however, its spatial and temporal regulatory roles throughout MASLD progression remain insufficiently defined. A 10-week high-fat, choline-deficient (HFCD) mouse model was established, and liver pathology was evaluated at weeks 6, 8, and 10. Time-resolved assessments combined untargeted metabolomics, magnetic resonance imaging–proton density fat fraction (MRI-PDFF), serum biochemistry, histological staining, immunofluorescence, and transmission electron microscopy to characterize dynamic alterations in lipid metabolism, redox status, inflammation, and fibrogenesis. The HFCD diet produced a clear temporal sequence of liver injury. Steatosis, phosphatidylcholine depletion, and early antioxidant loss appeared by week 6. By week 8, mitochondrial structural damage and pronounced cytokine elevation were evident. At week 10, collagen deposition and α-SMA activation signaled fibrotic progression. Metabolomics indicated significant disruptions in pathways related to ATP-binding cassette (ABC) transporters, one-carbon metabolism, and the tricarboxylic acid (TCA) cycle. Using integrated analytical strategies, this study suggests that choline deficiency may be associated with a time-dependent pathological cascade in MASLD, beginning with phospholipid destabilization and extending to altered mitochondria–endoplasmic reticulum crosstalk at mitochondria-associated membranes, alongside amplified oxidative–inflammatory responses, which collectively may contribute to progressive fibrogenesis as the disease advances. Full article

Each year, around 23 million miscarriages occur worldwide, which have a substantial emotional impact on parents, and impose significant societal costs. While medical care accounts for most expenses, work productivity loss contributes significantly. Addressing underlying causes of miscarriage could improve parents’ mental health and potentially their economic impact. In most countries, investigations into miscarriage causes are only recommended after recurrent cases, focusing mainly on maternal factors. Fetal and placental tissue are rarely examined, as current guidelines do not advise routine genetic analyses of pregnancy tissue, because the impact of further clinical decision making and individual prognosis is unclear. However, this leaves over 90% of all miscarriage cases unexplained and highlights the need for alternative methods. We therefore conducted a narrative review on genetic analysis, autopsy, and imaging of products of conception (POC). Karyotyping, QF-PCR, SNP array, and aCGH were reviewed in different research settings, with QF-PCR being the most cost-effective, while obtaining the highest technical success rate. Karyotyping, historically being considered the gold standard for POC examination, was the least promising. Post-mortem imaging techniques including post-mortem ultrasound (PMUS), ultra-high-field magnetic resonance imaging (UHF-MRI), and microfocus computed tomography (micro-CT) show promising diagnostic capabilities in miscarriages, with micro-CT achieving the highest cost-effective performance. In conclusion, current guidelines do not recommend diagnostic testing for most cases, leaving the majority unexplained. Although genetic and imaging techniques show promising diagnostic potential, they should not yet be implemented in routine clinical care and require thorough evaluation within research settings—assessing not only diagnostic and psychosocial outcomes but also economic implications. Full article

This work presents the development and evaluation of Artificial Intelligence (AI) models for the automatic classification of brain tumors in Magnetic Resonance Imaging (MRI) scans. Several deep learning architectures were implemented and compared, including VGG-19, ResNet50, EfficientNetB3, Xception, MobileNetV2, DenseNet201, InceptionV3, Vision Transformer (ViT), and an Ensemble model. The models were developed in Python (version 3.12.4) using the Keras and TensorFlow frameworks and trained on a public Brain Tumor MRI dataset containing 7023 images. Data augmentation and hyperparameter optimization techniques were applied to improve model generalization. The results showed high classification performance, with accuracies ranging from 89.47% to 98.17%. The Vision Transformer achieved the best performance, reaching 98.17% accuracy, outperforming traditional Convolutional Neural Network (CNN) architectures. Explainable AI (XAI) methods Grad-CAM, LIME, and Occlusion Sensitivity were employed to assess model interpretability, showing that the models predominantly focused on tumor regions. The proposed approach demonstrated the effectiveness of AI-based systems in supporting early diagnosis of brain tumors, reducing analysis time and assisting healthcare professionals. Full article

Quercus texana seeds are recalcitrant and thus highly sensitive to desiccation, which makes storage difficult. For practical seed handling, it is important to define their safe water content and to understand how water is distributed during dehydration. The present study utilized magnetic resonance imaging (MRI) and nuclear magnetic resonance (NMR) technologies to investigate the migration and phases of water, respectively, revealing the underlying reasons for the recalcitrance of Q. texana seeds. The water content of fresh Q. texana seeds was found to be 39.6% and the germination percentage was 93.3%. As the water content decreased, the germination percentage decreased continuously, reaching 0% at a water content of 13.0%. At 20.0% water content, the germination percentage was 71.7%. MRI showed that water was primarily stored in the embryo axis and cotyledon center in fresh Q. texana seeds. Water loss occurs in the following order during seed dehydration: embryo axis, cotyledon center, cotyledon periphery, and cotyledon end. However, water in the radicle region persisted until seed water content decreased to 15.0%, at which point no signal was detected. The NMR T₂ relaxation spectrum indicated the presence of bound water (T₂₁ = 0.01–5.44 ms) and free water (T₂₂ = 7.19–1401.93 ms) in the seeds. During the dehydration process, most of the water was lost as free water, and the T₂₂ shifted to longer times. Concurrently, the bound water shifted to shorter T₂₁ times. Overall, for practical purposes, seed water should be maintained at or above 20.0%. MRI further showed that water loss from the radicle plays a decisive role in the decline of seed germination, and that protecting the region of radicle and the cupule scar can effectively retard water loss. Furthermore, the bound-water content is positively correlated with seed germination. Full article

Background/Objectives: Magnetic resonance imaging (MRI) can be used to assess muscle function while performing a motion task within the scanner. Quantitative measures such as contraction velocity and strain can be derived from the images. Cine phase contrast (PC) MRI for time-resolved imaging of muscle function relies on the consistently repeated execution of the motion task for several minutes until data acquisition is complete. This may be difficult for patients with neuromuscular dysfunctions. To date, this approach has been applied only in adults, but not pediatric populations. The aim of this pilot study was to investigate the feasibility of PC MRI for assessing calf muscle function during electrically evoked and voluntary motion in children with cerebral palsy (CP) using open-source hardware and software. Methods: Cine PC MRI was performed at 3T in ambulatory pediatric patients with CP and typically developing children under electrical muscle stimulation (EMS) (n = 14/13) and during voluntary plantarflexion (n = 4/4) using a home-built pedal with a force sensor. A visual feedback software was developed to enable synchronized imaging of voluntary muscle contractions. Muscle contraction velocity and strain were calculated from the MRI data. Data quality was rated by two readers. Results: During EMS, the velocity data quality was rated as sufficient in 21% of scans in patients compared with 82% of scans in controls. During the voluntary task, all patients demonstrated increased compliance and greater generated force output than during EMS. Voluntary motion imaging was successful in all controls but none of the patients, as motion periodicity in patients was worse during voluntary than during stimulated contraction. Conclusions: Cine phase-contrast MRI combined with EMS or voluntary motion proved challenging in pediatric patients with CP, particularly in those with more severe baseline muscle dysfunction or reduced tolerance to stimulation. In contrast, the approach was successfully implemented in typically developing children. Although the scope of the patient-based findings is limited by data heterogeneity, the method demonstrates considerable potential as a tool for monitoring treatment-related changes in muscle function, particularly in less severely affected patients. Further refinement of the EMS and voluntary motion protocols, together with a reduction in MRI acquisition time, is required to improve motion periodicity, tolerability, and consequently the overall success rate in the intended pediatric patient cohort. Full article

Background: Autism Spectrum Disorders (ASD) are neurodevelopmental disorders characterized by repetitive behaviors and social interaction deficits. While the severity of ASD is classified into levels (1–3) by the DSM-5, reliable circulating biomarkers to differentiate these levels are lacking. This retrospective pilot study examines plasma amino acid levels in children with ASD to identify the potential biomarkers of disease severity. Methods: Plasma samples from 30 children diagnosed with ASD (24 males, 6 females, aged 3–12 years) were analyzed. Participants were stratified into two groups based on the Autism Diagnostic Observation Schedule Calibrated Severity Score (ADOS CSS): Group 1, presenting with mild symptoms (Level 1, n = 11), and Group 2, characterized by moderate-to-severe symptoms (Levels 2–3, n = 19). This was further confirmed by the identification of electroencephalogram (EEG) anomalies (21.1%) and magnetic resonance imaging (MRI) abnormalities (5.3%), which were detected exclusively in Group 2 and absent in Group 1. Amino acid levels were measured by ion-exchange chromatography. Statistical analyses (Mann–Whitney U test and chi-square test) were used to compare AA levels between groups. Results: Statistically significant differences were observed in the levels of phosphoethanolamine, aspartic acid, and glutamic acid between the two groups. These amino acids (AA) were significantly higher in the moderate-to-severe symptoms group (Levels 2–3) compared to the mild symptoms group (Level 1) (p < 0.05). All AA values remained within age-appropriate reference ranges. Conclusions: Plasma levels of phosphoethanolamine, aspartic acid, and glutamic acid may serve as potential biomarkers for ASD severity in children. Results from this exploratory analysis suggest that AA profiling could differentiate ASD severity and identify specific metabolic pathways, such as excitatory neurotransmission and phospholipid turnover. Further studies with larger cohorts are necessary to validate these findings and explore the role of AAs in ASD pathophysiology. Full article