Search Results (47)

Tissue-engineered scaffolds, particularly composite scaffolds composed of polymers combined with ceramics, bioactive glasses, or nanomaterials, play a vital role in regenerative medicine by providing structural and biological support for tissue repair. As scaffold designs grow increasingly complex, the need for non-invasive imaging modalities capable of monitoring scaffold integration, degradation, and tissue regeneration in real-time has become critical. This review summarizes current non-invasive imaging techniques used to evaluate tissue-engineered constructs, including optical methods such as near-infrared fluorescence imaging (NIR), optical coherence tomography (OCT), and photoacoustic imaging (PAI); magnetic resonance imaging (MRI); X-ray-based approaches like computed tomography (CT); and ultrasound-based modalities. It discusses the unique advantages and limitations of each modality. Finally, the review identifies major challenges—including limited imaging depth, resolution trade-offs, and regulatory hurdles—and proposes future directions to enhance translational readiness and clinical adoption of imaging-guided tissue engineering (TE). Emerging prospects such as multimodal platforms and artificial intelligence (AI) assisted image analysis hold promise for improving precision, scalability, and clinical relevance in scaffold monitoring. Full article

Although intracranial hypertension (ICH) has traditionally been framed as simply a numerical escalation of intracranial pressure (ICP) and usually dealt with in its clinical form and not in terms of its complex underlying pathophysiology, an emerging body of evidence indicates that ICH is not simply an elevated ICP process but a complex process of molecular dysregulation, glymphatic dysfunction, and neurovascular insufficiency. Our aim in this paper is to provide a complete synthesis of all the new thinking that is occurring in this space, primarily on the intersection of glymphatic dysfunction and cerebral vein physiology. The aspiration is to review how glymphatic dysfunction, largely secondary to aquaporin-4 (AQP4) dysfunction, can lead to delayed cerebrospinal fluid (CSF) clearance and thus the accumulation of extravascular fluid resulting in elevated ICP. A range of other factors such as oxidative stress, endothelin-1, and neuroinflammation seem to significantly impair cerebral autoregulation, making ICH challenging to manage. Combining recent studies, we intend to provide a revised conceptualization of ICH that recognizes the nuance and complexity of ICH that is understated by previous models. We wish to also address novel diagnostics aimed at better capturing the dynamic nature of ICH. Recent advances in non-invasive imaging (i.e., 4D flow MRI and dynamic contrast-enhanced MRI; DCE-MRI) allow for better visualization of dynamic changes to the glymphatic and cerebral blood flow (CBF) system. Finally, wearable ICP monitors and AI-assisted diagnostics will create opportunities for these continuous and real-time assessments, especially in limited resource settings. Our goal is to provide examples of opportunities that exist that might augment early recognition and improve personalized care while ensuring we realize practical challenges and limitations. We also consider what may be therapeutically possible now and in the future. Therapeutic opportunities discussed include CRISPR-based gene editing aimed at restoring AQP4 function, nano-robotics aimed at drug targeting, and bioelectronic devices purposed for ICP modulation. Certainly, these proposals are innovative in nature but will require ethically responsible confirmation of long-term safety and availability, particularly to low- and middle-income countries (LMICs), where the burdens of secondary ICH remain preeminent. Throughout the review, we will be restrained to a balanced pursuit of innovative ideas and ethical considerations to attain global health equity. It is not our intent to provide unequivocal answers, but instead to encourage informed discussions at the intersections of research, clinical practice, and the public health field. We hope this review may stimulate further discussion about ICH and highlight research opportunities to conduct translational research in modern neuroscience with real, approachable, and patient-centered care. Full article

Primary hyperparathyroidism is commonly caused by parathyroid adenomas, hyperplasia, or, rarely, carcinoma. In up to 20% of cases, parathyroid tissue may be ectopic, often located in the mediastinum due to aberrant embryologic migration. Ectopic parathyroid glands pose a diagnostic and therapeutic challenge, and an accurate preoperative localization is essential for an effective and safe resection. Imaging modalities such as CT scan, TC-sestamibi scintigraphy, PET/CT, ultrasonography and MRI are routinely employed, whereas combined techniques offer improved diagnostic accuracy. Emerging approaches, however, including PET/CT with choline tracers, have shown promise in enhancing sensitivity in complex or recurrent cases. When ectopic glands are in the mediastinum, thoracic surgical intervention is required. Traditional open approaches, such as sternotomy or thoracotomy, are associated with significant morbidity. The development and evolution of minimally invasive surgery (MIS) has become the preferred approach in selected cases. When MIS is performed, intraoperative assessment and parathyroid identification are crucial to ensure complete gland removal. Intraoperative parathyroid hormone (ioPTH) monitoring provides real-time confirmation of surgical success. The integration of advanced imaging, intraoperative monitoring, and minimally invasive techniques significantly improves surgical outcomes while minimizing complications and accelerating patient recovery. Ultimately, the effective treatment of ectopic parathyroid glands relies on a personalized approach, adapting both diagnostic and surgical strategies to the unique anatomical and clinical context of each patient. Integration of advanced imaging, intraoperative monitoring, and minimally invasive techniques, combined with a multidisciplinary team involving endocrinologists, radiologists, and thoracic surgeons, is key to optimizing outcomes and reducing patient morbidity. Full article

Musculoskeletal (MSK) disorders remain a major global cause of disability, with diagnostic complexity arising from their heterogeneous presentation and multifactorial pathophysiology. Recent advances across imaging modalities, molecular biomarkers, artificial intelligence applications, and point-of-care technologies are fundamentally reshaping musculoskeletal diagnostics. This review offers a novel synthesis by unifying recent innovations across multiple diagnostic imaging modalities, such as CT, MRI, and ultrasound, with emerging biochemical, genetic, and digital technologies. While existing reviews typically focus on advances within a single modality or for specific MSK conditions, this paper integrates a broad spectrum of developments to highlight how use of multimodal diagnostic strategies in combination can improve disease detection, stratification, and clinical decision-making in real-world settings. Technological developments in imaging, including photon-counting detector computed tomography, quantitative magnetic resonance imaging, and four-dimensional computed tomography, have enhanced the ability to visualize structural and dynamic musculoskeletal abnormalities with greater precision. Molecular imaging and biochemical markers such as CTX-II (C-terminal cross-linked telopeptides of type II collagen) and PINP (procollagen type I N-propeptide) provide early, objective indicators of tissue degeneration and bone turnover, while genetic and epigenetic profiling can elucidate individual patterns of susceptibility. Point-of-care ultrasound and portable diagnostic devices have expanded real-time imaging and functional assessment capabilities across diverse clinical settings. Artificial intelligence and machine learning algorithms now automate image interpretation, predict clinical outcomes, and enhance clinical decision support, complementing conventional clinical evaluations. Wearable sensors and mobile health technologies extend continuous monitoring beyond traditional healthcare environments, generating real-world data critical for dynamic disease management. However, standardization of diagnostic protocols, rigorous validation of novel methodologies, and thoughtful integration of multimodal data remain essential for translating technological advances into improved patient outcomes. Despite these advances, several key limitations constrain widespread clinical adoption. Imaging modalities lack standardized acquisition protocols and reference values, making cross-site comparison and clinical interpretation difficult. AI-driven diagnostic tools often suffer from limited external validation and transparency (“black-box” models), impacting clinicians’ trust and hindering regulatory approval. Molecular markers like CTX-II and PINP, though promising, show variability due to diurnal fluctuations and comorbid conditions, complicating their use in routine monitoring. Integration of multimodal data, especially across imaging, omics, and wearable devices, remains technically and logistically complex, requiring robust data infrastructure and informatics expertise not yet widely available in MSK clinical practice. Furthermore, reimbursement models have not caught up with many of these innovations, limiting access in resource-constrained healthcare settings. As these fields converge, musculoskeletal diagnostics methods are poised to evolve into a more precise, personalized, and patient-centered discipline, driving meaningful improvements in musculoskeletal health worldwide. Full article

Optical Coherence Tomography (OCT) is a relatively new medical imaging device that provides high-resolution and real-time visualization of biological tissues. Initially designed for ophthalmology, OCT is now being applied in other types of pathologies, like cancer diagnosis. This review highlights its impact on disease diagnosis, biopsy guidance, and treatment monitoring. Despite its advantages, OCT has limitations, particularly in tissue penetration and differentiating between malignant and benign lesions. To overcome these challenges, the integration of nanoparticles has emerged as a transformative approach, which significantly enhances contrast and tumor vascularization at the molecular level. Gold and superparamagnetic iron oxide nanoparticles, for instance, have demonstrated great potential in increasing OCT’s diagnostic accuracy through enhanced optical scattering and targeted biomarker detection. Beyond these innovations, integrating OCT with multimodal imaging methods, including magnetic resonance imaging (MRI), positron emission tomography (PET), and ultrasound, offers a more comprehensive approach to disease assessment, particularly in oncology. Additionally, advances in artificial intelligence (AI) and biosensors have further expanded OCT’s capabilities, enabling real-time tumor characterization and optimizing surgical precision. However, despite these advancements, clinical adoption still faces several hurdles. Issues related to nanoparticle biocompatibility, regulatory approvals, and standardization need to be addressed. Moving forward, research should focus on refining nanoparticle technology, improving AI-driven image analysis, and ensuring broader accessibility to OCT-guided diagnostics. By tackling these challenges, OCT could become an essential tool in precision medicine, facilitating early disease detection, real-time monitoring, and personalized treatment for improved patient outcomes. Full article

Gastro-entero-pancreatic neuroendocrine tumors (GEP-NETs) represent a rare and varied class of neoplasms, characterized by diverse clinical presentations and prognostic trajectories. Accurate and prompt diagnosis is vital to inform and optimize therapeutic decisions. Ultrasound, including standard B-mode imaging and advanced methods such as contrast-enhanced ultrasound (CEUS) and endoscopic ultrasound (EUS), serves as a key component in the diagnostic evaluation of these tumors. B-mode US and CEUS provide non-invasive, accessible methods for early detection and characterization. On B-mode imaging, GEP-NETs typically present as well-defined, hyperechoic, or iso-echoic lesions, while CEUS highlights their characteristic vascularity, marked by arterial-phase hyperenhancement and venous-phase washout. Compared to CT and MRI, ultrasound offers real-time, dynamic imaging without ionizing radiation or nephrotoxic contrast agents, making it particularly advantageous for patients requiring frequent monitoring or with contraindications to other imaging modalities. CT and MRI are widely regarded as the preferred methods for staging and surgical planning due to their detailed anatomical visualization. However, ultrasound, especially CEUS, provides a significant adjunctive role in both early detection and the follow-up on GEP-NETs. This analysis delves into the strengths, challenges, and innovations in ultrasound technology for diagnosing pancreatic NETs, focusing on its contribution to comprehensive imaging strategies and its impact on patient care decisions. Full article

Background/Objectives: Accurate assessment of tumor response to neoadjuvant chemotherapy (NAC) in breast cancer is critical for optimizing treatment strategies. While magnetic resonance imaging (MRI) and mammography are commonly used for response evaluation, they have inherent limitations. Ultrasound (US) has emerged as a promising, cost-effective, and real-time alternative. This study aimed to evaluate the effectiveness of US in tracking tumor regression during NAC and its correlation with pathologic tumor regression grade (TRG). Methods: This study included 282 breast cancer patients undergoing NAC. Tumor size was measured using ultrasound at three key time points: pre-chemotherapy, after four cycles, and post-chemotherapy. Spearman’s correlation was used to assess the relationship between US-measured tumor changes and TRG. Multinomial logistic regression and receiver operating characteristic (ROC) curve analyses were performed to determine the predictive accuracy of the measurements from our US in identifying pathologic complete response (pCR). Conclusions: Ultrasound is a reliable, real-time imaging tool for monitoring NAC response in breast cancer patients. Its ability to predict pCR and track tumor shrinkage highlights its potential for treatment adaptation. Standardization of US protocols and integration with AI-based analysis may further improve its clinical utility, making it a valuable adjunct in breast cancer treatment monitoring. Full article

Background: Bone marrow (BM) adipocytes play a critical role in the progression of both solid tumor metastases and expansion of hematological malignancies across a spectrum of ages, from pediatric to aging populations. Single-point biopsies remain the gold standard for monitoring BM diseases, including hematologic malignancies, but these are limited in capturing the full complexity of loco-regional and global BM microenvironments. Non-invasive imaging techniques such as Magnetic Resonance Imaging (MRI), Computed Tomography (CT), and Positron Emission Tomography (PET) could provide valuable alternatives for real-time evaluation in both preclinical translational and clinical studies. Methods: We developed a preclinical proton density fat fraction (PDFF) MRI technique for the quantitative assessment of BM composition, focusing on the fat fraction (FF) within mouse femurs. We validated this method using aging mice and young mice subjected to 10 Gy X-ray irradiation, compared to young control mice. Water–fat phantoms with varying fat percentages (0% to 100%) were used to optimize the imaging sequence, and immunohistochemical (IHC) staining with H&E validated equivalent adipose content in the femur BM region. Results: Significant differences in FF were observed across age groups (p = 0.001 for histology and p < 0.001 for PDFF) and between irradiated and control mice (p = 0.005 for histology and p = 0.002 for PDFF). A strong correlation (R²~0.84) between FF values from PDFF-MRI and histology validated the accuracy of the technique. Conclusions: These findings highlight PDFF-MRI’s potential as a non-invasive, real-time, in vivo biomarker for quantitatively assessing the BM fat fraction in preclinical studies, particularly in studies evaluating the effects of aging, disease progression, and cytotoxic cancer therapies, including chemotherapy and radiation. Full article

Bladder cancer, a highly heterogeneous disease, necessitates precise diagnostic and therapeutic strategies to enhance patient outcomes. Metabolomics, through comprehensive small-molecule analysis, provides valuable insights into cancer-associated metabolic alterations at the cellular, tissue, and systemic levels. Concurrently, molecular imaging modalities like PET, MRI, and CT enable the non-invasive, real-time visualization of tumor biology, facilitating the spatial and functional assessment of biomarkers. Key findings highlight the identification of metabolomic profiles correlated with cancer progression, recurrence, and treatment responses across serum, urine, and tissue samples. Advanced analytical platforms, such as LC-MS and NMR, uncover distinct metabolic signatures and pathway alterations in glycolysis, amino acid metabolism, and lipid biosynthesis. Molecular imaging further enhances staging accuracy and treatment monitoring by visualizing metabolic activity and receptor expression. The integration of these technologies addresses the limitations of invasive diagnostic methods and paves the way for precision oncology. Future advancements should focus on multi-omics integration, AI-driven analysis, and large-scale clinical validation to ensure broad accessibility and transformative impacts on bladder cancer management. Full article

The reliability of the “last mile” of cold-chain logistics is crucial for food safety. This study investigated the effect of different packaging treatments on the quality of anhydrously preserved live Ruditapes philippinarum (R. philippinarum) in “last mile” cold chain disruption. The temperature profiles of three packaging treatments at ambient temperature (25 °C) were monitored. Quality assessment was conducted based on sensory scoring, survival rate, total viable count (TVC), water-holding capacity (WHC), pH, total volatile basic nitrogen (TVB-N), thiobarbituric acid-reactive substances (TBA), color, and texture. Low-frequency nuclear magnetic resonance (LF-NMR) and magnetic resonance imaging (MRI) were utilized to characterize the water state profile. The findings demonstrated a progressive increase in internal package temperature throughout the “last mile”, with packages containing additional ice packs more effectively maintaining lower temperature and restricting the migration of “hot spots” towards the center. Specifically, the package with three ice packs maintained a markedly lower temperature, which effectively inhibited microbial activity, lipid oxidation, and the production of alkaline substances, resulting in higher survival rates, water-holding capacity, texture, sensory acceptability, and immobilized water fraction. Furthermore, LF-NMR relaxation parameters showed strong correlations with various physicochemical indices, suggesting a potential approach for real-time quality monitoring. This study provides insights for maintaining live R. philippinarum quality during the “last mile”. Full article

This narrative review provides an overview of the various diagnostic tools used to assess cartilage health, with a focus on early detection, nutrition intervention, and management of osteoarthritis. Early detection of cartilage damage is crucial for effective patient management. Traditional diagnostic tools like radiography and conventional magnetic resonance imaging (MRI) sequences are more suited to detecting late-stage structural changes. This paper highlights advanced imaging techniques, including sodium MRI, T2 mapping, T1ρ imaging, and delayed gadolinium-enhanced MRI of cartilage, which provide valuable biochemical information about cartilage composition, particularly the glycosaminoglycan content and its potential links to nutrition-related factors influencing cartilage health. Cartilage degradation is often linked with inflammation and measurable via markers like CRP and IL-6 which, although not specific to cartilage breakdown, offer insights into the inflammation affecting cartilage. In addition to imaging techniques, biochemical markers, such as collagen breakdown products and aggrecan fragments, which reflect metabolic changes in cartilage, are discussed. Emerging tools like optical coherence tomography and hybrid positron emission tomography–magnetic resonance imaging (PET-MRI) are also explored, offering high-resolution imaging and combined metabolic and structural insights, respectively. Finally, wearable technology and biosensors for real-time monitoring of osteoarthritis progression, as well as the role of artificial intelligence in enhancing diagnostic accuracy through pattern recognition in imaging data are addressed. While these advanced diagnostic tools hold great potential for early detection and monitoring of osteoarthritis, challenges remain in clinical translation, including validation in larger populations and integration into existing clinical workflows and personalized treatment strategies for cartilage-related diseases. Full article

Background/Objective: This study explores the use of AI-powered radiomics to classify and monitor brain metastasis progression and regression following Gamma Knife radiosurgery (GKRS) based on MRI imaging. A clinical decision support application was developed using Streamlit to provide real-time, AI-driven predictions for treatment monitoring. Methods: MRI scans from 60 patients (3194 images) were analyzed using a transfer learning-enhanced AlexNet deep learning model. Class imbalance was mitigated through dynamic class weighting and data augmentation to ensure equitable performance across all classes. Optimized preprocessing pipelines ensured dataset standardization. Model performance was evaluated using accuracy, precision, recall, F1-scores, and AUC, with 95% confidence intervals. Additionally, a comparative analysis of Gamma Knife radiosurgery (GKRS) outcomes and predictive modeling demonstrated strong correlations between tumor volume evolution and treatment response. The AI predictions and visualizations were integrated into a Streamlit-based application to ensure clinical usability and ease of access. The AI-driven approach effectively classified progression and regression patterns, reinforcing its potential for clinical integration. Results: The transfer learning model achieved flawless classification accuracy (100%; 95% CI: 100–100%) along with perfect precision, recall, and F1-scores. The AUC score of 1.0000 (95% CI: 1.0000–1.0000) indicated excellent discrimination between progression and regression cases. Compared to the baseline AlexNet model (99.53% accuracy; 95% CI: 98.90–100.00%), the TL-enhanced model resolved all misclassifications. Tumor volume analysis identified the baseline size as a key predictor of progression (Pearson r = 0.795, r = 0.795, r = 0.795, p < 0.0001, p < 0.0001, and p < 0.0001). The training time (420.12 s) was faster than ResNet-50 (443.38 s) and EfficientNet-B0 (439.87 s), while achieving equivalent metrics. Despite 100% accuracy, the model requires multi-center validation for generalizability. Conclusions: This study demonstrates that transfer learning with dynamic class weighting provides a highly accurate and reliable framework for monitoring brain metastases post-GKRS. The Streamlit-based AI application enhances clinical decision-making by improving diagnostic precision and reducing variability. Explainable AI techniques, such as Grad-CAM visualizations, improve interpretability and support clinical adoption. These findings emphasize the transformative potential of AI in personalized treatment strategies, extending applications to genomic profiling, survival modeling, and longitudinal follow-ups for brain metastasis management. Full article

Breast cancer remains a leading cause of cancer-related deaths among women, emphasizing the critical need for early detection and monitoring techniques. Conventional imaging modalities such as mammography, MRI, and ultrasound have face sensitivity, specificity, cost, and patient comfort limitations. This study introduces a handheld Near-Infrared Diffuse Optical Tomography (NIR DOT) probe for breast cancer imaging. The NIRscan probe utilizes multi-wavelength light-emitting diodes (LEDs) and a linear charge-coupled device (CCD) sensor to acquire real-time optical data, reconstructing cross-sectional images of breast tissue based on scattering and absorption coefficients. With wavelengths optimized for the differential optical properties of tissue components, the probe enables functional imaging, distinguishing between healthy and malignant tissues. Clinical evaluations have demonstrated its potential for precise tumor localization and monitoring therapeutic responses, achieving a sensitivity of 94.7% and specificity of 84.2%. By incorporating machine learning algorithms and a modified diffusion equation (MDE), the system enhances the accuracy and speed of image reconstruction, supporting rapid, non-invasive diagnostics. This development represents a significant step forward in portable, cost-effective solutions for breast cancer detection, with potential applications in low-resource settings and diverse clinical environments. Full article

From learning environments to battlefields to marketing teams, the desire to measure cognition and cognitive fatigue in real time has been a grand challenge in optimizing human performance. Near-infrared spectroscopy (NIRS) is an effective optical technique for measuring changes in subdermal hemodynamics, and it has been championed as a more practical method for monitoring brain function compared to MRI. This study reports on an innovative functional NIRS (fNIRS) sensor that integrates the entire system into a compact and wearable device, enabling long-term monitoring of patients. The device provides unrestricted mobility to the user with a Bluetooth connection for settings configuration and data transmission. A connected device, such as a smartphone or laptop equipped with the appropriate interface software, collects raw data, then stores and generates real-time analyses. Tests confirm the sensor is sensitive to oxy- and deoxy-hemoglobin changes on the forehead region, which indicate neuronal activity and provide information for brain activity monitoring studies. Full article

Background/Objectives: Combining radiation therapy with mild hyperthermia, especially via magnetic resonance-guided focused ultrasound (MRgFUS), holds promise for enhancing tumor control and alleviating symptoms in cancer patients. However, current clinical applications of MRgFUS focus primarily on ablative treatments, and using MRI guidance for each radiation session increases treatment costs and logistical demands. This study aimed to test a streamlined workflow for repeated hyperthermia treatments that reduces the need for continuous MRI monitoring, using an approach based on impulse response function (Green’s function) to optimize acoustic power settings in advance. Methods: We implemented the Green’s function approach in a perfused, tissue-mimicking phantom, conducting 30 experiments to simulate hyperthermia delivery via MRgFUS. Pre-calculated acoustic power settings were applied to maintain a stable hyperthermia target without the need for real-time feedback control from MRI thermometry. Additionally, a retrospective analysis of patient thermometry data from MRgFUS sonications was performed to assess feasibility in clinical contexts. Results: Our experiments demonstrated consistent, stable hyperthermia (+7 °C) for 15 min across varying perfusion rates, outperforming conventional closed-loop MRI feedback methods in maintaining temperature stability. The retrospective analysis confirmed that this method is noise-robust and clinically applicable. Conclusions: This off-line approach to hyperthermia control could simplify the integration of MRgFUS hyperthermia in cancer treatment, reducing costs and logistical barriers. These findings suggest that our method may enable the broader adoption of hyperthermia in radiation therapy, supporting its role as a viable adjuvant treatment in oncology. Full article