Search Results (40)

The current understanding of cellular protein distribution in clinical samples is limited. This is partially due to the complexity and heterogeneity of tissues combined with the qualitative nature of analysis by immunohistochemistry (IHC). The common use of manual assessment in the clinic is time-consuming and restricts both the complexity of scoring and the scale of patient tissue analysis. This has limited the transfer of biological observations into pathology and their integration into diagnostics. Immunofluorescence (IF) techniques allow detailed and high-throughput investigation of proteins in cell models, but their application to tissues has been hindered by poor antibody penetration, autofluorescence artefacts, and weak signals. With a growing focus on precision medicine, scalable techniques to investigate and analyse proteins are critically important. To address this, we generated a high-throughput ImmunoHistoFluorescence (IHF) approach, applying IF to tissue samples followed by automated acquisition and artificial intelligence (AI)-based analysis of sub-nuclear protein distribution to enable precise investigation of complex protein localization patterns. This advancement offers a method to transfer in vitro findings into human tissues to analyse protein localization patterns in physiologically relevant contexts for improved understanding of disease-driving mechanisms in patients, identification of new biomarkers, and acceleration of translational research. Full article

Breast cancer (BC) is a highly heterogeneous disease with distinct molecular subtypes, each exhibiting unique metabolic adaptations that drive tumor progression and therapy resistance. Metabolomics has emerged as a powerful tool for understanding cancer metabolism and identifying clinically relevant biomarkers guiding personalized therapeutic strategies. Advances in analytical techniques such as mass spectrometry (MS) and nuclear magnetic resonance (NMR) spectroscopy have enabled the identification of metabolic alterations associated with BC initiation, progression, and treatment response (dysregulated glycolysis, lipid metabolism, amino acid utilization, and redox homeostasis). This review aims to provide a comprehensive overview of the role of metabolomics in BC research, focusing on its applications in identifying metabolic biomarkers for early diagnosis, prognosis, and treatment response. It underscores how metabolomic profiling can unravel the metabolic adaptations of different BC subtypes, offering insights into tumor biology and mechanisms of therapy resistance. Ultimately, it highlights the promise of metabolomics in driving biomarker-guided diagnostics and the development of metabolically informed, personalized therapeutic strategies in the era of precision medicine. Full article

Chronic kidney disease (CKD) is a progressive condition affecting over 800 million people worldwide (more than 10% of the general population) and is a major contributor to morbidity and mortality. Early detection is critical, yet current diagnostic methods (e.g., computed tomography or magnetic resonance imaging) do not focus on functional impairments, which begin long before structural damage becomes evident, limiting timely and accurate assessment. Nuclear medicine imaging, particularly planar scintigraphy and single-photon emission computed tomography (SPECT), offers a non-invasive evaluation of renal function, but its clinical use is hindered by interpretive complexity and variability. Machine learning (ML) holds promise for enhancing image analysis and supporting early CKD diagnosis. This study presents a scoping review of ML applications in CKD detection and monitoring using renal scintigraphy. Following the PRISMA framework, the literature was systematically identified and screened in two phases: one targeting ML methods applied specifically to renal scintigraphy, and another encompassing broader ML use in scintigraphic imaging. The results reveal a notable lack of studies integrating advanced ML techniques, especially deep learning, with renal scintigraphy, despite their potential. Key challenges include limited annotated datasets, inconsistent imaging protocols, and insufficient validation. This review synthesizes current trends, identifies methodological gaps, and highlights opportunities for developing reliable, interpretable ML tools to improve nuclear imaging-based diagnostics and support personalized management of CKD. Full article

Background: Artificial intelligence (AI) and deep learning are increasingly applied in cardiovascular imaging. However, the “black box” nature of these models raises challenges for clinical trust and integration. Explainable Artificial Intelligence (XAI) seeks to address these concerns by providing insights into model decision-making. This systematic review synthesizes current research on the use of XAI methods in radiological cardiovascular imaging. Methods: A systematic literature search was conducted in PubMed, Scopus, and Web of Science to identify peer-reviewed original research articles published between January 2015 and March 2025. Studies were included if they applied XAI techniques—such as Gradient-Weighted Class Activation Mapping (Grad-CAM), Shapley Additive Explanations (SHAPs), Local Interpretable Model-Agnostic Explanations (LIMEs), or saliency maps—to cardiovascular imaging modalities, including cardiac computed tomography (CT), magnetic resonance imaging (MRI), echocardiography and other ultrasound examinations, and chest X-ray (CXR). Studies focusing on nuclear medicine, structured/tabular data without imaging, or lacking concrete explainability features were excluded. Screening and data extraction followed PRISMA guidelines. Results: A total of 28 studies met the inclusion criteria. Ultrasound examinations (n = 9) and CT (n = 9) were the most common imaging modalities, followed by MRI (n = 6) and chest X-rays (n = 4). Clinical applications included disease classification (e.g., coronary artery disease and valvular heart disease) and the detection of myocardial or congenital abnormalities. Grad-CAM was the most frequently employed XAI method, followed by SHAP. Most studies used saliency-based techniques to generate visual explanations of model predictions. Conclusions: XAI holds considerable promise for improving the transparency and clinical acceptance of deep learning models in cardiovascular imaging. However, the evaluation of XAI methods remains largely qualitative, and standardization is lacking. Future research should focus on the robust, quantitative assessment of explainability, prospective clinical validation, and the development of more advanced XAI techniques beyond saliency-based methods. Strengthening the interpretability of AI models will be crucial to ensuring their safe, ethical, and effective integration into cardiovascular care. Full article

The integration of artificial intelligence (AI) and virtual reality (VR) in high-risk training simulations represents a significant advance in preparing professionals for critical situations. This study presents an exhaustive bibliometric review of the scientific literature published between 2015 and 2025, analyzing the trends, impact, and evolution of these technologies in various high-risk fields. The methodology employed included systematic searches in databases, such as Web of Science and Scopus, using keywords related to AI, VR, and high-risk simulation. Here, 700 articles were analyzed, applying co-citation analysis and scientific mapping techniques. The results reveal an exponential growth in publications on this topic, with an average annual increase of 5.54%. The following main thematic clusters were identified: emergency medicine, aviation, nuclear industry, and disaster response. The co-authorship analysis showed strong international collaboration, with the United States, China, and Germany standing out as leaders in research. This study provides a comprehensive view of the current state of research, identifying the main areas, gaps, and opportunities in the application of AI and VR in high-risk training. Full article

This article explores the foundational principles of ionizing radiation and provides a comprehensive overview of its impact on thermoplastic polymers. Ionizing radiation, encompassing gamma rays, X-rays, and electron beams, has been extensively studied due to its capacity to alter the molecular structure of polymers. These changes enable advancements in various applications by promoting molecular crosslinking, controlled degradation, molecular grafting, and crystallinity adjustments. The article delves into the fundamental mechanisms of radiation thermoplastic polymer interactions, including ionization, electronic excitation, and free radical formation. It highlights how these processes lead to structural transformations that enhance the physical, thermal, and mechanical properties of thermoplastic polymers. Factors such as radiation type, absorbed doses, temperature, and environmental conditions are discussed in the context of their role in controlling these modifications. Key practical applications are identified across fields such as medicine, food packaging, aerospace, and industry. Examples include the production of sterilizable medical devices, enhanced food packaging for longer shelf life, and radiation-resistant materials for the aerospace and nuclear sectors. Despite its many advantages, the article also emphasizes challenges such as process variability, polymer sensitivity to radiation, and standardization difficulties. The review underscores emerging research directions, including optimizing irradiation parameters and integrating advanced characterization techniques like Fourier Transform Infrared Spectroscopy (FT-IR) and X-ray diffraction (XRD). The development of new polymer blends and composites, designed for irradiation-induced property enhancement, represents a promising area of innovation. Full article

Metabolomics approaches, such as Fourier transform infrared (FTIR) spectroscopy, nuclear magnetic resonance (NMR) spectroscopy, and mass spectrometry (MS), have emerged as powerful tools for studying cardiovascular diseases (CVD), including hypertension. The use of biological fluids, like plasma and serum, has garnered significant interest due to their accessibility and potential in elucidating disease mechanisms. This review aims to summarize the current literature on the application of metabolomics techniques (FTIR, NMR, and MS) in the study of hypertension, focusing on their contributions to understanding disease pathophysiology, biomarker discovery, and therapeutic advancements. A comprehensive analysis of metabolomic studies was performed, with a particular emphasis on the diversity of altered metabolites associated with systolic blood pressure (SBP), diastolic blood pressure (DBP), and sex-related differences. Metabolomics techniques, including FTIR, NMR, and MS, provide comprehensive insights into the biochemical alterations underlying hypertension, such as amino acid and fatty acid metabolism impairment or inflammation and oxidative stress processes. This review underscores their role in advancing biomarker identification, deepening our understanding of disease mechanisms, and supporting the development of targeted therapeutic strategies. The integration of these tools highlights their potential in personalized medicine and their capacity to improve clinical outcomes. Full article

Background and Objectives: Single-photon emission computed tomography (SPECT) myocardial perfusion imaging (MPI) is a well-established technique for evaluating myocardial perfusion and function in patients with suspected or known coronary artery disease. While conventional dual-detector SPECT scanners have limitations in spatial resolution and photon detection sensitivity, recent advancements, including full-ring solid-state cadmium zinc telluride (CZT) detectors, offer enhanced image quality and improved diagnostic accuracy. This study aimed to compare the performance of Veriton-CT, a full-ring CZT SPECT system, with GE Discovery 530c, a dedicated cardiac fixed-angle gamma camera, in myocardial perfusion imaging and their correlation with coronary angiography findings. Materials and Methods: This was a prospective study that analyzed 21 patients who underwent MPI at the Department of Nuclear Medicine, Lithuanian University of Health Sciences, Kauno Klinikos. A one-day stress–rest protocol using 99mTc-Sestamibi was employed, with stress testing performed via bicycle ergometry or pharmacological induction. MPI was first conducted using GE Discovery 530c (GE Health Care, Boston, MA, USA), followed by imaging on Veriton-CT, which included low-dose CT for attenuation correction. The summed stress score (SSS), summed rest score (SRS), and summed difference score (SDS) were analyzed and compared between both imaging modalities. Coronary angiography results were retrospectively collected, and lesion-based analysis was performed to assess the correlation between imaging results and the presence of significant coronary artery stenosis (≥35% and ≥70% narrowing). Image quality and the certainty of distinguishing the inferior myocardial wall from extracardiac structures were also evaluated by two independent researchers with differing levels of experience. Results: Among the 14 patients included in the final analysis, Veriton-CT was more likely to classify MPI scans as normal (64.3%) compared to GE Discovery 530c (28.6%). Additionally, Veriton-CT provided a better assessment of the right coronary artery (RCA) basin, showing greater agreement with coronary angiography findings than GE Discovery 530c, although the difference was not statistically significant. No significant differences in lesion overlap were observed for the left anterior descending artery (LAD) or left circumflex artery (LCx) basins. Furthermore, the image quality assessment revealed slightly better delineation of extracardiac structures using Veriton-CT (Spectrum Dynamics Medical, Caesarea, Israel), particularly when evaluated by an experienced researcher. However, no significant difference was observed when assessed by a less experienced observer. Conclusions: Our findings suggest that Veriton-CT, with its full-ring CZT detector system, may offer advantages over fixed-angle gamma cameras in improving image quality and reducing attenuation artifacts in MPI. Although the difference in correlations with coronary angiography findings was not statistically significant, Veriton-CT showed a trend toward better agreement, particularly in the RCA basin. These results indicate that full-ring SPECT imaging could improve the diagnostic accuracy of non-invasive MPI, potentially reducing the need for unnecessary invasive angiography. Further studies with larger patient cohorts are required to confirm these findings and evaluate the clinical impact of full-ring SPECT technology in myocardial perfusion imaging. Full article

Cardiovascular disease (CVD) is a leading cause of death, accounting for over 30% of annual global fatalities. Ischemic heart disease, in turn, is a frontrunner of worldwide CVD mortality. With the burden of coronary disease rapidly growing, understanding the nuances of cardiac imaging and risk prognostication becomes paramount. Myocardial perfusion imaging (MPI) is a frequently utilized and well established testing modality due to its significant clinical impact in disease diagnosis and risk assessment. Recently, nuclear cardiology has witnessed major advancements, driven by innovations in novel imaging technologies and improved understanding of cardiovascular pathophysiology. Applications of artificial intelligence (AI) to MPI have enhanced diagnostic accuracy, risk stratification, and therapeutic decision-making in patients with coronary artery disease (CAD). AI techniques such as machine learning (ML) and deep learning (DL) neural networks offer new interpretations of immense data fields, acquired through cardiovascular imaging modalities such as nuclear medicine (NM). Recently, AI algorithms have been employed to enhance image reconstruction, reduce noise, and assist in the interpretation of complex datasets. The rise of AI in nuclear medicine (AI-NM) has proven itself groundbreaking in the efficiency of image acquisition, post-processing time, diagnostic ability, consistency, and even in risk-stratification and outcome prognostication. To that end, this narrative review will explore these latest advances in AI in nuclear medicine and its rapid transformation of the cardiac diagnostics landscape. This paper will examine the evolution of AI-NM, review novel AI techniques and applications in nuclear cardiac imaging, summarize recent AI-NM clinical trials, and explore the technical and clinical challenges in its implementation of artificial intelligence. Full article

Breast cancer, a prevalent and significant cause of cancer-related mortality in women, often necessitates precise detection through nuclear medicine techniques. Despite the utility of computer-aided navigation in thoracoscopic surgeries like mastectomy, challenges persist in accurately locating and tracking target tissues amidst intricate surgical scenarios. This study introduces a novel system employing a channel R-CNN model to automatically segment target regions in thoracoscopic images and provide precise cutting curve indications for surgeons. By integrating a Detection Network Head and Thorax Network Head, this multi-channel framework outperforms existing single-task models, marking a pioneering effort in cutting curve indication for thoracoscopic procedures. Utilizing a specialized dataset, the model achieves a notable region segmentation mIOU of 79.4% and OPA of 83.2%. In cutting path planning, it attains an mIOU of 68.6% and OPA of 77.5%. The system operates at an average speed of 23.6 frames per second in videos, meeting the real-time response needs of surgical navigation systems. This research underscores the potential of advanced imaging and AI-driven solutions in enhancing precision and efficacy in thoracoscopic surgeries. Full article

The gut microbiota, a complex community of microorganisms, plays a crucial role in gastrointestinal (GI) health, influencing digestion, metabolism, immune function, and the gut–brain axis. Dysbiosis, or an imbalance in microbiota composition, is associated with GI disorders, including irritable bowel syndrome (IBS), inflammatory bowel disease (IBD), and colorectal cancer (CRC). Conventional microbiota analysis methods, such as next-generation sequencing (NGS) and nuclear magnetic resonance (NMR), provide valuable insights but are often expensive, time-consuming, and destructive. Raman spectroscopy (RS) is a non-invasive, cost-effective, and highly sensitive alternative. This analytical technique relies on inelastic light scattering to generate molecular “fingerprints”, enabling real-time, marker-free analysis of microbiota composition and metabolic activity. This review explores the principles, sample preparation techniques, and advancements in RS, including surface-enhanced Raman spectroscopy (SERS), for microbiota research. RS facilitates identifying microbial species, analysing key metabolites like short-chain fatty acids (SCFA), and monitoring microbiota responses to dietary and therapeutic interventions. The comparative analysis highlights RS’s advantages over conventional techniques, such as the minimal sample preparation, real-time capabilities, and non-destructive nature. The integration of RS with machine learning enhances its diagnostic potential, enabling biomarker discovery and personalised treatment strategies for GI disorders. Challenges, including weak Raman signals and spectral complexity, are discussed alongside emerging solutions. As RS technology advances, mainly through portable spectrometers and AI integration, its clinical application in microbiota diagnostics and personalised medicine is poised to transform GI healthcare, bridging microbiota research with practical therapeutic strategies. Full article

Marfan syndrome (MFS) is a genetic disorder affecting connective tissue, often leading to cardiovascular complications such as aortic aneurysms and mitral valve prolapse. Cardiovascular multimodality imaging plays a crucial role in the diagnosis, monitoring, and management of MFS patients. This review explores the advancements in echocardiography, cardiovascular magnetic resonance (CMR), cardiac computed tomography (CCT), and nuclear medicine techniques in MFS. Echocardiography remains the first-line tool, essential for assessing aortic root, mitral valve abnormalities, and cardiac function. CMR provides detailed anatomical and functional assessments without radiation exposure, making it ideal for long-term follow-up. CT offers high-resolution imaging of the aorta, crucial for surgical planning, despite its ionizing radiation. Emerging nuclear medicine techniques, though less common, show promise in evaluating myocardial involvement and inflammatory conditions. This review underscores the importance of a comprehensive imaging approach to improve outcomes and guide interventions in MFS patients. It also introduces novel aspects of multimodality approaches, emphasizing their impact on early detection and management of cardiovascular complications in MFS. Full article

Nonalcoholic fatty liver disease (NAFLD) has rapidly emerged as the most prevalent chronic liver disease globally, representing a significant and escalating public health challenge. Meconopsis integrifolia (Maxim.) Franch, a traditional Tibetan medicinal herb used for treating hepatitis, remains largely unexplored regarding its therapeutic potential and active components in combating NAFLD. This study first evaluated the in vitro lipid accumulation inhibitory activity of different extraction fractions of M. integrifolia using a HepG2 cell steatosis model. The ethyl acetate fraction was found to significantly reduce triglyceride (TG) and low-density lipoprotein (LDL) levels, inhibit lipid droplet deposition in HepG2 cells, and promote lipid metabolism balance through modulation of the AMPK/SREPB-1c/PPAR-α signaling pathway. Further analysis utilizing chromatographic techniques and nuclear magnetic resonance spectroscopy (NMR) led to the isolation of 13 compounds from the active ethyl acetate fraction. Notably, compounds 6, 9, 10, 11, 12, and 13 were identified for the first time from this Tibetan herb. In vitro activity assays and molecular docking analyses further confirmed that the compounds Luteolin (1), Quercetin 3-O-[2‴, 6‴-O-diacetyl-β-d-glucopyranosyl-(1→6)-β-d-glucopyranoside] (6), and Quercetin 3-O-[2‴-O-acetyl-β-d-glucopyranosyl-(1→6)-β-d-glucopyranoside] (8) are potential key components responsible for the NAFLD-ameliorating effects of M. integrifolia. This study highlights the therapeutic potential of M. integrifolia in treating NAFLD and provides a foundation for its further development and application, underscoring its significance in the advanced utilization of traditional Tibetan medicine. Full article

Various imaging methods assist in diagnosing periprosthetic joint infection (PJI). These include radiological techniques such as radiography, computed tomography (CT), magnetic resonance imaging (MRI), and ultrasound (US); as well as advanced nuclear medicine techniques including bone scintigraphy (BS), anti-granulocyte antibody imaging (AGS), leukocyte scintigraphy (LS), and fluorodeoxyglucose positron emission tomography (FDG-PET and FDG-PET/CT). Each imaging technique and radiopharmaceutical has been extensively studied, with unique diagnostic accuracy, limitations, and benefits for PJI diagnosis. This review aims to detail and describe the most commonly used imaging techniques and radiopharmaceuticals for evaluating PJI, focusing particularly on knee and hip arthroplasties. Full article

Background: Renal cell carcinoma is one of the most aggressive urogenital malignancies, with an increasing number of cases worldwide. The majority of cases are diagnosed at an advanced stage, as this form of growth is typically silent. An accurate evaluation of the extent of the disease is crucial for selecting the most appropriate treatment approach. Nuclear medicine imaging is increasingly being applied in oncological diagnostics, prompting ongoing research into renal cell carcinoma markers that could serve as a foundation for theranostic approaches in this disease. Positron emission tomography/computed tomography imaging with prostate-specific membrane antigen (PSMA) ligands has already demonstrated successful utility in diagnosis of other cancers, including prostate cancer and gliomas. Emerging evidence of high sensitivity and specificity in detecting renal cell carcinoma lesions provides a suitable foundation for its application in both the diagnosis and subsequent management of this malignancy. Methods: This systematic review synthesizes the current scientific evidence on the molecular imaging of renal cell carcinoma using PSMA ligands, emphasizing the potential future applications of this imaging marker in theranostic approaches. Results and Conclusions: Based on a systematic review of the literature, it appears that PET/CT with PSMA ligands has the potential to surpass traditional imaging techniques in diagnostic accuracy while also providing valuable prognostic information. Full article