Recent Advances In Imaging Research Articles

DeepCervix: enhancing cervical cancer detection through transfer learning with VGG-16 architecture

Cervical cancer remains a significant global health concern, emphasizing the urgent need for improved detection methods to ensure timely treatment. This research introduces a sophisticated methodology leveraging recent advances in medical imaging and deep learning algorithms to enhance the accuracy and efficiency of cervical cancer detection. The proposed approach comprises meticulous data preprocessing to ensure the integrity of input images, followed by the training of deep learning models including ResNet-50, AlexNet, and VGG-16, renowned for their performance in computer vision tasks. Evaluation metrics such as accuracy, precision, recall, and F1-score demonstrate the efficacy of the methodology, with an outstanding accuracy rate of 98% achieved. The model’s proficiency in accurately distinguishing healthy cervical tissue from cancerous tissue is underscored by precision, recall, and F1-score values. The primary strength of this deep learning-based approach lies in its potential for early detection, promising significant impact on cervical cancer diagnosis and treatment outcomes. This methodology contributes to advancements in medical imaging techniques, facilitating improved outcomes in cervical cancer detection and treatment.

The Human Cornea: Unravelling Its Structural, Chemical and Biochemical Complexities.

The cornea, the transparent part of the anterior eye, is vital for light refraction and vision. This review examines the intricate chemical and biochemical interactions essential for maintaining corneal transparency and highlights significant advancements in corneal biology. The cornea comprises five layers: the epithelium, Bowman's layer, stroma, Descemet's membrane, and endothelium, each contributing uniquely to its structure and function. The epithelium, maintained by limbal stem cells, serves as a barrier and interacts with the tear film to maintain ocular surface health. The stroma, abundant in organized collagen fibrils and regulated by proteoglycans, is crucial for corneal clarity and biomechanical integrity, while the endothelium regulates corneal hydration and nutrition. Recent imaging advances have improved visualization of these molecular structures, enhancing our understanding of collagen organization and cross-linking. Proteoglycans such as decorin and lumican regulate collagen spacing and hydration, directly influencing corneal clarity. Biochemical processes within the cornea involve signaling molecules, growth factors, and cytokines, which are essential for wound healing, inflammation, and injury response. Despite progress, questions remain regarding corneal wound healing mechanisms, the impact of oxidative stress, and the roles of microRNAs. This review synthesizes recent discoveries to advance our understanding of corneal physiology and biochemical functions.

RADT-18. DEDICATED 3D-FSE SEQUENCE-BASED RADIOSURGERY REDUCES DISTANT INTRACRANIAL RELAPSE RATES IN A HISTOLOGY-AGNOSTIC PATIENT COHORT

Abstract INTRODUCTION Stereotactic radiosurgery (SRS) is a cornerstone in the treatment of brain metastasis (BM), despite a known risk of distant intracranial failure (DIF). Recent imaging advances, specifically, sensitive 3D-FSE sequences, have started to be incorporated into SRS practice; however, comparisons of outcomes to historical imaging approaches are limited. We evaluated the impact of adding 3D-FSE to MPRAGE in BM detection and prolongation of DIF in a histology-agnostic patient group. METHODS Outcomes of patients treated with SRS for BM between 01/2019-01/2024 were evaluated. MPRAGE sequences were obtained for all; 3D-FSE sequences were implemented in 02/2020. Median times to DIF were estimated using the Kaplan-Meier method. RESULTS 215 patients underwent 332 SRS courses for 1456 BM imaged with MPRAGE and 3D-FSE (primary cohort), compared to the control cohort (92 patients, 135 SRS courses, 462 BM). MPRAGE sequences revealed 1636 lesions in 307 patients (462 in 92 control cohort, 1174 in 215 primary cohort). In the primary cohort, an additional 282 lesions were identified with 3D-FSE (24% increase) by neuroradiologists and multidisciplinary review. There was a statistically significant prolongation in the median time to DIF between the cohorts (11.4 vs. 6.8 months, p=0.029), most pronounced in the 1-4 metastases subgroup (14.7 vs. 8.1 months, p=0.008) and in those with a single BM (36.4 vs. 10.9 months, p=0.001). While patients relapsing on immunotherapy or targeted therapy did not significantly benefit from 3D-FSE (7.2 vs. 5.7 months, p=0.280), those who relapsed on chemotherapy or were off systemic therapy at least four weeks prior to SRS (including synchronous metastases) exhibited a trend towards longer time to DIF with 3D-FSE (14.7 vs. 7.9 months, p=0.057). CONCLUSIONS 3D-FSE added to MPRAGE lengthens time to DIF via improving BM detection, especially for limited BM, and those relapsing on systemic therapy with limited intracranial penetration or no systemic therapy.

What is new in imaging to assist in the diagnosis of giant cell arteritis and Takayasu's arteritis since the EULAR and ACR/VF recommendations?

Over the past decades, fundamental insights have been gained to establish the pivotal role of imaging in the diagnosis of large-vessel vasculitis, including giant cell arteritis (GCA) and Takayasu's arteritis (TAK). A deeper comprehension of imaging modalities has prompted earlier diagnosis leading to expedited treatment for better prognosis. The European Alliance of Associations in Rheumatology (EULAR) recommended in 2023 that ultrasound should be the initial imaging test in suspected GCA, and Magnetic Resonance Imaging (MRI) remains the first-line imaging modality in suspected TAK. We summarize the recent advances in diagnostic imaging in large vessel vasculitis, highlighting use of combination imaging modalities, and discuss progress in newer imaging techniques such as contrast-enhanced ultrasound, shear wave elastography, ocular ultrasound, ultrasound biomicroscopy, integration of Positron Emission Tomography (PET) with MRI, novel tracer in PET, black blood MRI, orbital MRI, and implementation of artificial intelligence (AI) to existing imaging modalities. Our aim is to offer a perspective on ongoing advancements in imaging for the diagnosis of GCA and TAK, particularly innovative technology, which could potentially boost diagnostic precision.

Cardiovascular Imaging: Recent Advances in Atherosclerosis Diagnosis

Recent advancements in cardiovascular imaging have significantly enhanced the diagnosis and management of atherosclerosis, a leading cause of cardiovascular disease. Techniques such as coronary computed tomography angiography (CCTA), magnetic resonance imaging (MRI), and positron emission tomography (PET) have improved our ability to detect and quantify atherosclerotic plaques, assess their stability, and predict adverse cardiovascular events. This review explores the latest developments in imaging modalities, their clinical applications, and the implications for personalized patient care.

Advanced quantitative magnetic resonance imaging of lower extremity muscle microtrauma after marathon: a mini review.

This article reviews the existing literature and outlines recent advances in quantitative Magnetic Resonance Imaging (MRI) techniques for the assessment of lower extremity muscle microtrauma following a marathon. Single-modality quantitative MRI techniques include T2 mapping to assess the dynamics of muscle inflammatory edema and variability at the site of injury, Diffusion Tensor Imaging (DTI) to detect subclinical changes in muscle injury, Intravoxel Incoherent Motion (IVIM) imaging to provide simultaneous information on perfusion and diffusion in muscle tissue without the need for intravenous contrast, and Magnetic Resonance Spectroscopy (MRS) to noninvasively detect intramyocellular lipid (IMCL)content in muscle before and after marathon exercise to explain the use of fatty acids as an energy source in skeletal muscle during long-distance running. As well as Chemical Exchange Saturation Transfer (CEST) is particularly suitable for detecting changes in free creatine, pH values and lactate concentrations in muscles before and after exercise, providing a more detailed picture of muscle physiology and chemistry. These metabolic MRI methods enhance the understanding of biochemical alterations occurring in muscles pre- and post-exercise. Multimodal techniques combine different modalities to provide a comprehensive evaluation of muscle structural and functional changes. These advanced techniques aim to better assess microtrauma and guide clinical treatment, though further validation with larger studies is needed to establish their potential over traditional qualitative methods.

Incidence and clinical characteristics of cardiac amyloidosis in Tuscany and Umbria

Abstract Background Recent advances in imaging and increased disease awareness have led to a significant rise in newly diagnosed cases of cardiac amyloidosis (CA) over the past decade. However, there remains a lack of epidemiological data regarding the true incidence of the disease. Purpose To determine the incidence and clinical characteristics of newly diagnosed cases of CA in a large Italian population. Methods Subjects with suspected CA underwent diagnostic work-up in 20 Centers in Tuscany and Umbria regions within the Cardiac Amyloidosis RegistRY (CARRY), between January 1st 2022 and December 31st 2022. Results A total of 551 subjects were enrolled in the registry, with complete data on the final diagnosis available for 448 individuals. CA was diagnosed in 179 patients (40%), with 28 (16%) classified as light chain CA (AL-CA), 114 (64%) wild-type transthyretin CA (ATTR-CA), 12 (7%) variant ATTR-CA, and 25 (13%) undetermined CA. Considering a reference population in Tuscany and Umbria in 2022 (4,518,388, according to the Italian National Institute of Statistics), this yields an estimated cumulative incidence of 39.6 cases per million inhabitants per year. Alternative diagnoses in the remaining patients mostly included hypertensive heart disease (n=157, 58%), hypertrophic cardiomyopathy (n=25, 9%), moderate-to-severe valvular heart disease (n=11, 4%), or a mixed phenotype (n=72, 27%). The primary referral pathway for the suspicion of CA was cardiological (n=345, 77%), followed by incidental finding during screening visits (n=58, 13%), and haematology referral (n=33, 7%). Patients with confirmed CA were older (81 [75-85] vs. 77 [72-83] years, p<0.001), and more frequently reported musculoskeletal red flags of CA, such as carpal tunnel syndrome (85 [48%] vs. 53 [20%] years, p<0.001), and spontaneous tendon rupture (39 [22%] vs. 18 [7%] years, p<0.001). Median time to diagnosis from clinical suspicion was 73 (37-140) days for CA and 84 (36-156) days for alternative diagnoses (p=0.564). Conclusions The diagnosis of CA is on the rise globally, with an estimated incidence of 39.6 new cases per million inhabitants per year in two large regions of Italy. These findings should inform policymakers in redefining the status of CA as a rare disease, potentially influencing patient care and treatment costs.

Evaluating facial dermis aging in healthy Caucasian females with LC-OCT and deep learning

Recent advancements in high-resolution imaging have significantly improved our understanding of microstructural changes in the skin and their relationship to the aging process. Line Field Confocal Optical Coherence Tomography (LC-OCT) provides detailed 3D insights into various skin layers, including the papillary dermis and its fibrous network. In this study, a deep learning model utilizing a 3D ResNet-18 network was trained to predict chronological age from LC-OCT images of 100 healthy Caucasian female volunteers, aged 20 to 70 years. The AI-based protocol focused on regions of interest delineated between the segmented dermal-epidermal junction and the superficial dermis, exploiting complex patterns within the collagen network for age prediction. The model achieved a mean absolute error of 4.2 years and exhibited a Pearson correlation coefficient of 0.937 with actual ages. Furthermore, there was a notable correlation (r = 0.87) between quantified clinical scoring, encompassing parameters such as firmness, elasticity, density, and wrinkle appearance, and the ages predicted by deep learning model. This strong correlation underscores how integrating emerging imaging technologies with deep learning can accelerate aging research and deepen our understanding of how alterations in skin microstructure are related to visible signs of aging.

Advancements in Hyperspectral Imaging and Computer-Aided Diagnostic Methods for the Enhanced Detection and Diagnosis of Head and Neck Cancer.

Background/Objectives: Head and neck cancer (HNC), predominantly squamous cell carcinoma (SCC), presents a significant global health burden. Conventional diagnostic approaches often face challenges in terms of achieving early detection and accurate diagnosis. This review examines recent advancements in hyperspectral imaging (HSI), integrated with computer-aided diagnostic (CAD) techniques, to enhance HNC detection and diagnosis. Methods: A systematic review of seven rigorously selected studies was performed. We focused on CAD algorithms, such as convolutional neural networks (CNNs), support vector machines (SVMs), and linear discriminant analysis (LDA). These are applicable to the hyperspectral imaging of HNC tissues. Results: The meta-analysis findings indicate that LDA surpasses other algorithms, achieving an accuracy of 92%, sensitivity of 91%, and specificity of 93%. CNNs exhibit moderate performance, with an accuracy of 82%, sensitivity of 77%, and specificity of 86%. SVMs demonstrate the lowest performance, with an accuracy of 76% and sensitivity of 48%, but maintain a high specificity level at 89%. Additionally, in vivo studies demonstrate superior performance when compared to ex vivo studies, reporting higher accuracy (81%), sensitivity (83%), and specificity (79%). Conclusion: Despite these promising findings, challenges persist, such as HSI's sensitivity to external conditions, the need for high-resolution and high-speed imaging, and the lack of comprehensive spectral databases. Future research should emphasize dimensionality reduction techniques, the integration of multiple machine learning models, and the development of extensive spectral libraries to enhance HSI's clinical utility in HNC diagnostics. This review underscores the transformative potential of HSI and CAD techniques in revolutionizing HNC diagnostics, facilitating more accurate and earlier detection, and improving patient outcomes.

Nasal Vaccine: A Special Prominence on SARS-CoV-2

Abstract: Nasal administration of mucosal medications and vaccines is an alternate and promising method of drug and vaccine delivery. Mucosal pathways can be used selectively for various disorders due to many benefits. Many initiatives are now being made to yield effective medications and cutting-edge nasal delivery systems. The structure of the nasal cavity and key features were discussed in this review. With a focus on vaccine distribution, the benefits, successes, and difficulties of using the nasal route for medical purposes were examined. The strong result supports the benefits and security, of the safety of nasal medication and vaccination administration. This alternate path may help many unmet medical needs and may also make big vaccination drives or expensive, protracted chronic therapies possible. Today, despite some lingering criticism, the area of nasal medicine and vaccine delivery is expanding quickly, supported by recent advancements in nanotechnology, imaging, and administration devices. The variety of drugs that have been licensed for nasal delivery is expected to increase significantly.

Impactful Cardiac CT and MRI Articles from 2023.

Cardiac imaging is important in diagnosing, treating, and predicting prognosis in patients with cardiovascular disease. Imaging protocols and analysis are consistently evolving, and the implementation of artificial intelligence-based applications is of increasing interest. This review presents recent advancements in noninvasive cardiac imaging, specifically focusing on cardiac CT and MRI, from notable publications across multidisciplinary journals in 2023 of interest to both radiologists and referring clinicians in the field. The discussion encompasses the latest trials of CT fractional flow reserve and the performance of the newest generation of photon-counting detector CT, particularly in coronary stenosis quantification. Additionally, it addresses coronary plaque quantification using artificial intelligence applications and their implications from large patient cohorts, alongside prognostic outcomes, and the value of coronary artery calcification scores. Various aspects of CT trials, such as anatomic planning before revascularization, high-risk plaque features, outcomes, and pericoronary fat index, are evaluated. New insights from cardiac MRI trials for cardiomyopathies, including cardiac amyloidosis, dilated cardiomyopathy, hypertrophic cardiomyopathy, myocarditis, and valvular disease, are also outlined. The review concludes by highlighting impactful societal statements and guidelines. Keywords: CT Angiography, MR Imaging, Transcatheter Aortic Valve Implantation/Replacement (TAVI/TAVR), Cardiac, Coronary Arteries, Heart, Left Ventricle © RSNA, 2024.

Application of multimodal deep learning and multi-instance learning fusion techniques in predicting STN-DBS outcomes for Parkinson's disease patients

Parkinson's Disease (PD) is a progressive neurodegenerative disorder with substantial impact on patients' quality of life. Subthalamic nucleus deep brain stimulation (STN-DBS) is an effective treatment for advanced PD, but patient responses vary, necessitating predictive models for personalized care. Recent advancements in medical imaging and machine learning offer opportunities to enhance predictive accuracy, particularly through deep learning and multi-instance learning (MIL) techniques. This retrospective study included 127 PD patients undergoing STN-DBS. Medical records and imaging data were collected, and patients were categorized based on treatment outcomes. Advanced segmentation models were trained for automated region of interest (ROI) delineation. A novel 2.5D deep learning approach incorporating multi-slice representation was developed to extract detailed ROI features. Multi-instance learning fusion techniques integrated predictions across multiple slices, combining radiomics and deep learning features to enhance model performance. Various machine learning algorithms were evaluated, and model robustness was assessed using cross-validation and hyperparameter optimization. The MIL model achieved an area under the curve (AUC) of 0.846 for predicting STN-DBS outcomes, surpassing the radiomics model's AUC of 0.825. Integration of MIL and radiomics features in the DLRad model further improved discriminative ability to an AUC of 0.871. Calibration tests showed good model reliability, and decision curve analysis demonstrated clinical utility, affirming the model's predictive advantage. This study demonstrates the efficacy of integrating MIL, radiomics, and deep learning techniques to predict STN-DBS outcomes in PD patients. The multimodal fusion approach enhances predictive accuracy, supporting personalized treatment planning and advancing patient care.

Imaging of Cervical Spine Trauma: Update of Techniques and Clinical Relevance.

Imaging of cervical spine trauma most commonly begins with computed tomography (CT) for initial osseous and basic soft tissue evaluation, followed by magnetic resonance imaging (MRI) for complementary evaluation of the neural structures (i.e., spinal cord, nerves) and soft tissues (i.e., ligaments). Although CT and conventional MRI sequences have been the mainstay of trauma imaging for decades, there have been significant advances in CT processing, imaging sequences and techniques made possible by hardware and software development, and artificial intelligence. These advancements may provide advantages in increasing sensitivity for detection of pathology as well as in decreasing imaging and interpretation time. Unquestionably, the most important role of imaging is to provide information to help direct patient care, including diagnosis, next steps in treatment plan, and prognosis. As such, there has been a growing body of research investigating the clinical relevance of imaging findings to clinical outcomes in the setting of spinal cord injury. This article will focus on these recent advances in imaging of cervical spinal trauma.

Review of In Situ Hybridization (ISH) Stain Images Using Computational Techniques.

Recent advancements in medical imaging have greatly enhanced the application of computational techniques in digital pathology, particularly for the classification of breast cancer using in situ hybridization (ISH) imaging. HER2 amplification, a key prognostic marker in 20-25% of breast cancers, can be assessed through alterations in gene copy number or protein expression. However, challenges persist due to the heterogeneity of nuclear regions and complexities in cancer biomarker detection. This review examines semi-automated and fully automated computational methods for analyzing ISH images with a focus on HER2 gene amplification. Literature from 1997 to 2023 is analyzed, emphasizing silver-enhanced in situ hybridization (SISH) and its integration with image processing and machine learning techniques. Both conventional machine learning approaches and recent advances in deep learning are compared. The review reveals that automated ISH analysis in combination with bright-field microscopy provides a cost-effective and scalable solution for routine pathology. The integration of deep learning techniques shows promise in improving accuracy over conventional methods, although there are limitations related to data variability and computational demands. Automated ISH analysis can reduce manual labor and increase diagnostic accuracy. Future research should focus on refining these computational methods, particularly in handling the complex nature of HER2 status evaluation, and integrate best practices to further enhance clinical adoption of these techniques.

Ultrasound evaluation of chronic liver disease.

Chronic liver disease is a world-wide epidemic. Any etiology that causes inflammation in the liver will lead to chronic liver disease. Presently, the most common inciting factor worldwide is steatotic liver disease. Recent advances in ultrasound imaging provide a multiparametric ultrasound methodology of diagnosing, staging, and monitoring treatment of chronic liver disease. Elastography has become a standard of care technique for the evaluation of liver fibrosis. Quantitative ultrasound allows for determination of the degree of fatty infiltration of the liver. Portal hypertension is the most important factor in determination of liver decompensation. B-mode findings combined with Doppler, and elastography techniques provide qualitative and quantitative methods of determining clinically significant portal hypertension. A newer method using contrast enhanced ultrasound may allow for a non-invasive quantitative estimation of the portal pressures. This paper reviews the use of multiparametric ultrasound in the evaluation of chronic liver disease including conventional B-mode ultrasound, Doppler, elastography and quantitative ultrasound for estimation of liver fat. The recent guidelines are presented and advised protocols reviewed.

The Current and Novel Imaging Modalities for Ocular Vasculitis in Behcet's Disease: A Review.

Behcet's syndrome is a multisystem inflammatory disorder characterized by a chronic relapsing course and diverse clinical manifestations, prominently affecting young adults along the ancient Silk Road and beyond. The disease pathogenesis involves complex interactions between genetic predisposition, environmental triggers, and dysregulated immune responses, leading to systemic vasculitis and tissue damage. Ocular involvement, a hallmark of Behcet's Disease (BD), significantly impacts morbidity, with uveitis as a common initial presentation that can progress to severe vision-threatening complications like retinal vasculitis and occlusive disease. This review consolidates current knowledge on ocular manifestations in BD, emphasizing the pivotal role of multimodal imaging in diagnostic evaluation and management. Fundus photography serves as a baseline tool for documenting intraocular lesions and monitoring treatment responses. Fluorescein angiography remains the gold standard for detecting acute inflammatory changes and vascular leakage patterns essential for disease staging and prognostication. Recent advancements in imaging, such as ultra-wide field imaging (UWF), indocyanine green angiography (ICGA), Doppler ultrasonography, Optical Coherence Tomography (OCT), OCT angiography (OCTA), adaptive optics (AO), and retinal function imaging (RFI), provide unprecedented insights into microvascular dynamics, structural changes, and functional impairments associated with ocular BD. Integration of these advanced imaging modalities enhances early detection of subclinical disease, facilitates precise localization of inflammatory lesions, guides therapeutic interventions, and monitors treatment efficacy. OCT and OCTA, in particular, offer non-invasive, high-resolution assessments of macular edema, vascular perfusion abnormalities, and choroidal thickness alterations critical for optimizing patient care. In conclusion, multimodal imaging represents the cornerstone in the comprehensive management of ocular manifestations in Behcet's Disease, offering clinicians invaluable tools for accurate diagnosis, treatment planning, and long-term monitoring of disease progression and treatment outcomes.

The Role of Local Prostate and Metastasis-Directed Radiotherapy in the Treatment of Oligometastatic Prostate Cancer.

Background/Objectives: Oligometastatic prostate cancer (OMPC) represents an early stage of metastatic disease characterized by a limited number of lesions. Recent advancements in imaging and treatment have revived interest in personalized therapies, including metastasis-directed radiotherapy (OMDRT) and primary prostate radiotherapy (PPR). This study evaluates the impact of OMDRT timing and the role of PPR on survival outcomes in OMPC patients; Methods: In this retrospective cohort study, 82 patients with OMPC who underwent OMDRT between 2010 and 2019 were analyzed. Patients were classified based on OMDRT timing (early vs. late) and disease type (synchronous vs. metachronous). Progression-free survival (PFS) and overall survival (OS) were the primary endpoints, assessed via Kaplan-Meier analysis and Cox proportional hazards models; Results: Among the patients, 36 (43.9%) had synchronous and 46 (56.1%) had metachronous OMD. With a median follow-up of 32 months, the 5-year PFS and OS rates were 77.5% and 88.5%, respectively. Early OMDRT significantly improved PFS (HR 0.461, 95% CI: 0.257-0.826, p = 0.009) and OS (HR 0.219, 95% CI: 0.080-0.603, p = 0.003). Subgroup analysis showed the most favorable outcomes for synchronous OMD patients receiving early OMDRT, with a median PFS of 22.2 months and a 5-year survival rate of 42.1%. The treatment of the primary prostate provided a survival benefit in the OS of synchronous OMD patients (5-year 83.1% vs. 50%, p = 0.025), and there was a further improvement in OS after PPR (5-year 87.7% vs. 50%, p = 0.015). Conclusions: Early OMDRT significantly enhances survival outcomes in OMPC, in both synchronous and metachronous cases. The integration of PPR can further improve results, emphasizing the importance of early intervention and personalized treatment strategies. To more definitively clarify our findings across various clinical situations, further studies with larger cohorts or prospective designs are necessary.

Imaging in vasculitis.

Systemic vasculitides are characterized by inflammation of blood vessels. Their classification is based on the size of the blood vessels involved - large, medium, or small. Vasculitis early diagnosis and reliable monitoring are crucial to establish a treatment plan and prevent serious complications. Based on these considerations and depending on the location of the affected vessels, the importance of imaging modalities including ultrasonography (US), magnetic resonance Imaging (MRI), magnetic resonance angiography (MRA), computed tomography (CT), computed tomography angiography (CTA), and [18F]-fluoro-2-deoxy-d-glucose positron emission tomography/computed tomography (FDG-PET/CT) has progressively increased. In addition to physical exam and laboratory data, these imaging tools offer complementary information about vascular changes occurring in vasculitis.This review summarizes the different imaging modalities being utilized to diagnose and monitor vasculitis. The most recent update for the use of imaging in vasculitis is referenced in the 2023 European Alliance of Associations for Rheumatology (EULAR) recommendations and the American College of Rheumatology (ACR) guidelines in 2021. Recent advances in PET imaging in large vessel vasculitis include improved technological imaging acquisition and the use of novel radiotracers for cellular and immune targets. FDG-PET has now been demonstrated to have high sensitivity and specificity to detect temporal arteritis. Imaging plays a significant role in the evaluation of vasculitis and continues to gain importance in the diagnosis and monitoring of disease activity. Differences exist between the ACR guidelines, which advocates for temporal artery biopsy, and the EULAR guidelines, which favors imaging modalities for the initial evaluation and diagnosis of large vessel vasculitis (LVV). Prerequisites for appropriate clinical management utilizing imaging in patients with vasculitis are the availability and access to skilled clinicians to interpret the images and the cost of these techniques not being prohibitive.

Allosteric modulation of the CXCR4:CXCL12 axis by targeting receptor nanoclustering via the TMV-TMVI domain

CXCR4 is a ubiquitously expressed chemokine receptor that regulates leukocyte trafficking and arrest in both homeostatic and pathological states. It also participates in organogenesis, HIV-1 infection, and tumor development. Despite the potential therapeutic benefit of CXCR4 antagonists, only one, plerixafor (AMD3100), which blocks the ligand-binding site, has reached the clinic. Recent advances in imaging and biophysical techniques have provided a richer understanding of the membrane organization and dynamics of this receptor. Activation of CXCR4 by CXCL12 reduces the number of CXCR4 monomers/dimers at the cell membrane and increases the formation of large nanoclusters, which are largely immobile and are required for correct cell orientation to chemoattractant gradients. Mechanistically, CXCR4 activation involves a structural motif defined by residues in TMV and TMVI. Using this structural motif as a template, we performed in silico molecular modeling followed by in vitro screening of a small compound library to identify negative allosteric modulators of CXCR4 that do not affect CXCL12 binding. We identified AGR1.137, a small molecule that abolishes CXCL12-mediated receptor nanoclustering and dynamics and blocks the ability of cells to sense CXCL12 gradients both in vitro and in vivo while preserving ligand binding and receptor internalization.

Allosteric modulation of the CXCR4:CXCL12 axis by targeting receptor nanoclustering via the TMV-TMVI domain

CXCR4 is a ubiquitously expressed chemokine receptor that regulates leukocyte trafficking and arrest in both homeostatic and pathological states. It also participates in organogenesis, HIV-1 infection, and tumor development. Despite the potential therapeutic benefit of CXCR4 antagonists, only one, plerixafor (AMD3100), which blocks the ligand-binding site, has reached the clinic. Recent advances in imaging and biophysical techniques have provided a richer understanding of the membrane organization and dynamics of this receptor. Activation of CXCR4 by CXCL12 reduces the number of CXCR4 monomers/dimers at the cell membrane and increases the formation of large nanoclusters, which are largely immobile and are required for correct cell orientation to chemoattractant gradients. Mechanistically, CXCR4 activation involves a structural motif defined by residues in TMV and TMVI. Using this structural motif as a template, we performed in silico molecular modeling followed by in vitro screening of a small compound library to identify negative allosteric modulators of CXCR4 that do not affect CXCL12 binding. We identified AGR1.137, a small molecule that abolishes CXCL12-mediated receptor nanoclustering and dynamics and blocks the ability of cells to sense CXCL12 gradients both in vitro and in vivo while preserving ligand binding and receptor internalization.