Current targeting strategies and advanced nanoplatforms for atherosclerosis therapy

Atherosclerosis is one of the major causes of death worldwide, and it is closely related to many cardiovascular diseases, such as stroke, myocardial infraction and angina. Although traditional surgical and pharmacological interventions can effectively retard or slow down the progression of atherosclerosis, it is very difficult to prevent or even reverse this disease. In recent years, with the rapid development of nanotechnology, various nanoagents have been designed and applied to different diseases including atherosclerosis. The unique atherosclerotic microenvironment with signature biological components allows nanoplatforms to distinguish atherosclerotic lesions from normal tissue and to approach plaques specifically. Based on the process of atherosclerotic plaque formation, this review summarises the nanodrug delivery strategies for atherosclerotic therapy, trying to provide help for researchers to understand the existing atherosclerosis management approaches as well as challenges and to reasonably design anti-atherosclerotic nanoplatforms.

Unleashing the biomimetic targeting potential of platelet-derived nanocarriers on atherosclerosis

Atherosclerosis, the primary mechanism underlying the development of many cardiovascular illnesses, continues to be one of the leading causes of mortality worldwide. Platelet (PLT), which are essential for maintaining body homeostasis, have been strongly linked to the onset of atherosclerosis at various stages due to their inherent tendency to bind to atherosclerotic lesions and show an affinity for plaques. Therefore, mimicking PLT's innate adhesive features may be necessary to effectively target plaques. PLT-derived nanocarriers have emerged as a promising biomimetic targeting strategy for treating atherosclerosis due to their numerous advantages. These advantages include excellent biocompatibility, minimal macrophage phagocytosis, prolonged circulation time, targeting capability for impaired vascular sites, and suitability as carriers for anti-atherosclerotic drugs. Herein, we discuss the role of PLT in atherogenesis and propose the design of nanocarriers based on PLT-membrane coating and PLT-derived vesicles. These nanocarriers can target multiple biological elements relevant to plaque development. The review also emphasizes the current challenges and future research directions for the effective utilization of PLT-derived nanocarriers in treating atherosclerosis.

Nanotechnology-empowered combination therapy for rheumatoid arthritis: principles, strategies, and challenges

Rheumatoid arthritis (RA) is an autoimmune disease with multifactorial etiology and intricate pathogenesis. In RA, repeated monotherapy is frequently associated with inadequate efficacy, drug resistance, and severe side effects. Therefore, a shift has occurred in clinical practice toward combination therapy. However, conventional combination therapy encounters several hindrances, including low selectivity to arthritic joints, short half-lives, and varying pharmacokinetics among coupled drugs. Emerging nanotechnology offers an incomparable opportunity for developing advanced combination therapy against RA. First, it allows for co-delivering multiple drugs with augmented physicochemical properties, targeted delivery capabilities, and controlled release profiles. Second, it enables therapeutic nanomaterials development, thereby expanding combination regimens to include multifunctional nanomedicines. Lastly, it facilitates the construction of all-in-one nanoplatforms assembled with multiple modalities, such as phototherapy, sonodynamic therapy, and imaging. Thus, nanotechnology offers a promising solution to the current bottleneck in both RA treatment and diagnosis. This review summarizes the rationale, advantages, and recent advances in nano-empowered combination therapy for RA. It also discusses safety considerations, drug-drug interactions, and the potential for clinical translation. Additionally, it provides design tips and an outlook on future developments in nano-empowered combination therapy. The objective of this review is to achieve a comprehensive understanding of the mechanisms underlying combination therapy for RA and unlock the maximum potential of nanotechnology, thereby facilitating the smooth transition of research findings from the laboratory to clinical practice.

Predictive value of MRI-based deep learning model for lymphovascular invasion status in node-negative invasive breast cancer

To retrospectively assess the effectiveness of deep learning (DL) model, based on breast magnetic resonance imaging (MRI), in predicting preoperative lymphovascular invasion (LVI) status in patients diagnosed with invasive breast cancer who have negative axillary lymph nodes (LNs). Data was gathered from 280 patients, including 148 with LVI-positive and 141 with LVI-negative lesions. These patients had undergone preoperative breast MRI and were histopathologically confirmed to have invasive breast cancer without axillary LN metastasis. The cohort was randomly split into training and validation groups in a 7:3 ratio. Radiomics features for each lesion were extracted from the first post-contrast dynamic contrast-enhanced (DCE)-MRI. The Least Absolute Shrinkage and Selection Operator (LASSO) regression method and logistic regression analyses were employed to identify significant radiomic features and clinicoradiological variables. These models were established using four machine learning (ML) algorithms and one DL algorithm. The predictive performance of the models (radiomics, clinicoradiological, and combination) was assessed through discrimination and compared using the DeLong test. Four clinicoradiological parameters and 10 radiomic features were selected by LASSO for model development. The Multilayer Perceptron (MLP) model, constructed using both radiomic and clinicoradiological features, demonstrated excellent performance in predicting LVI, achieving a high area under the curve (AUC) of 0.835 for validation. The DL model (MLP-radiomic) achieved the highest accuracy (AUC = 0.896), followed by DL model (MLP-combination) with an AUC of 0.835. Both DL models were significantly superior to the ML model (RF-clinical) with an AUC of 0.720. The DL model (MLP), which integrates radiomic features with clinicoradiological information, effectively aids in the preoperative determination of LVI status in patients with invasive breast cancer and negative axillary LNs. This is beneficial for making informed clinical decisions.

The value of deep learning-based X-ray techniques in detecting and classifying K-L grades of knee osteoarthritis: a systematic review and meta-analysis

OBJECTIVES

Knee osteoarthritis (KOA), a prevalent degenerative joint disease, is primarily diagnosed through X-ray imaging. The Kellgren-Lawrence grading system (K-L) is the gold standard for evaluating KOA severity through X-ray analysis. However, this method is highly subjective and non-quantifiable, limiting its effectiveness in detecting subtle joint changes on X-rays. Recent researchers have been directed towards developing deep-learning (DL) techniques for a more accurate diagnosis of KOA using X-ray images. Despite advancements in these intelligent methods, the debate over their diagnostic sensitivity continues. Hence, we conducted the current meta-analysis.

METHODS

A comprehensive search was conducted in PubMed, Cochrane, Embase, Web of Science, and IEEE up to July 11, 2023. The QUADAS-2 tool was employed to assess the risk of bias in the included studies. Given the multi-classification nature of DL tasks, the sensitivity of DL across different K-L grades was meta-analyzed.

RESULTS

A total of 19 studies were included, encompassing 62,158 images. These images consisted of 22,388 for K-L0, 13,415 for K-L1, 15,597 for K-L2, 7768 for K-L3, and 2990 for K-L4. The meta-analysis demonstrated that the sensitivity of DL was 86.74% for K-L0 (95% CI: 80.01%-92.28%), 64.00% for K-L1 (95% CI: 51.81%-75.35%), 75.03% for K-L2 (95% CI: 66.00%-83.09%), 84.76% for K-L3 (95% CI: 78.34%-90.25%), and 90.32% for K-L4 (95% CI: 85.39%-94.40%).

CONCLUSIONS

The DL multi-classification methods based on X-ray imaging generally demonstrate a favorable sensitivity rate (over 50%) in distinguishing between K-L0-K-L4. Specifically, for K-L4, the sensitivity is highly satisfactory at 90.32%. In contrast, the sensitivity rates for K-L1-2 still need improvement.

CLINICAL RELEVANCE STATEMENT

Deep-learning methods have been useful to some extent in assessing the effectiveness of X-rays for osteoarthritis of the knee. However, this requires further research and reliable data to provide specific recommendations for clinical practice.

KEY POINTS

X-ray deep-learning (DL) methods are debatable for evaluating knee osteoarthritis (KOA) under The Kellgren-Lawrence system (K-L). Multi-classification deep-learning methods are more clinically relevant for assessing K-L grading than dichotomous results. For K-L3 and K-L4, X-ray-based DL has high diagnostic performance; early KOA needs to be further improved.

CT-derived Radiomics Predicts the Efficacy of Tyrosine Kinase Inhibitors in Osteosarcoma Patients with Pulmonary Metastasis

BACKGROUND

To construct and validate the CT-based radiomics model for predicting the tyrosine kinase inhibitors (TKIs) effects in osteosarcoma (OS) patients with pulmonary metastasis.

METHODS

OS patients with pulmonary metastasis treated with TKIs were randomly separated into training and testing cohorts (2:1 ratio). Radiomic features were extracted from the baseline unenhanced chest CT images. The random survival forest (RSF) and Kaplan-Meier survival analyses were performed to construct and evaluate radiomics signatures (R-model-derived). The univariant and multivariant Cox regression analyses were conducted to establish clinical (C-model) and combined models (RC-model). The discrimination abilities, goodness of fit and clinical benefits of the three models were assessed and validated in both training and testing cohorts.

RESULTS

A total of 90 patients, 57 men and 33 women, with a mean age of 18 years and median progression-free survival (PFS) of 7.2 months, were enrolled. The R-model was developed with nine radiomic features and demonstrated significant predictive and prognostic values. In both training and testing cohorts, the time-dependent area under the receiver operating characteristic curves (AUC) of the R-model and RC-model exhibited obvious superiority over C-model. The calibration and decision curve analysis (DCA) curves indicated that the accuracy of the R-model was comparable to RC-model, which exhibited significantly better performance than C-model.

CONCLUSIONS

The R-model showed promising potential as a predictor for TKI responses in OS patients with pulmonary metastasis. It can potentially identify pulmonary metastatic OS patients most likely to benefit from TKIs treatment and help guide optimized clinical decisions.

Artificial intelligence in musculoskeletal imaging: realistic clinical applications in the next decade

This article will provide a perspective review of the most extensively investigated deep learning (DL) applications for musculoskeletal disease detection that have the best potential to translate into routine clinical practice over the next decade. Deep learning methods for detecting fractures, estimating pediatric bone age, calculating bone measurements such as lower extremity alignment and Cobb angle, and grading osteoarthritis on radiographs have been shown to have high diagnostic performance with many of these applications now commercially available for use in clinical practice. Many studies have also documented the feasibility of using DL methods for detecting joint pathology and characterizing bone tumors on magnetic resonance imaging (MRI). However, musculoskeletal disease detection on MRI is difficult as it requires multi-task, multi-class detection of complex abnormalities on multiple image slices with different tissue contrasts. The generalizability of DL methods for musculoskeletal disease detection on MRI is also challenging due to fluctuations in image quality caused by the wide variety of scanners and pulse sequences used in routine MRI protocols. The diagnostic performance of current DL methods for musculoskeletal disease detection must be further evaluated in well-designed prospective studies using large image datasets acquired at different institutions with different imaging parameters and imaging hardware before they can be fully implemented in clinical practice. Future studies must also investigate the true clinical benefits of current DL methods and determine whether they could enhance quality, reduce error rates, improve workflow, and decrease radiologist fatigue and burnout with all of this weighed against the costs.

Exploring habitats-based spatial distributions: improving predictions of lymphovascular invasion in invasive breast cancer

RATIONALE AND OBJECTIVES

Accurate assessment of lymphovascular invasion (LVI) in invasive breast cancer (IBC) plays a pivotal role in tailoring personalized treatment plans. This study aimed to investigate habitats-based spatial distributions to quantitatively measure tumor heterogeneity on multiparametric magnetic resonance imaging (MRI) scans and assess their predictive capability for LVI in patients with IBC.

MATERIALS AND METHODS

In this retrospective cohort study, we consecutively enrolled 241 women diagnosed with IBC between July 2020 and July 2023 and who had 1.5 T/T1-weighted images, fat-suppressed T2-weighted images, and dynamic contrast-enhanced MRI. Habitats-based spatial distributions were derived from the gross tumor volume (GTV) and gross tumor volume plus peritumoral volume (GPTV). GTV_habitats and GPTV_habitats were generated through sub-region segmentation, and their performances were compared. Subsequently, a combined nomogram was developed by integrating relevant spatial distributions with the identified MR morphological characteristics. Diagnostic performance was compared using receiver operating characteristic curve analysis and decision curve analysis. Statistical significance was set at p < 0.05.

RESULTS

GPTV_habitats exhibited superior performance compared to GTV_habitats. Consequently, the GPTV_habitats, diffusion-weighted imaging rim signs, and peritumoral edema were integrated to formulate the combined nomogram. This combined nomogram outperformed individual MR morphological characteristics and the GPTV_habitats index, achieving area under the curve values of 0.903 (0.847 -0.959), 0.770 (0.689 -0.852), and 0.843 (0.776 -0.910) in the training set and 0.931 (0.863 -0.999), 0.747 (0.613 -0.880), and 0.849 (0.759 -0.938) in the validation set.

CONCLUSION

The combined nomogram incorporating the GPTV_habitats and identified MR morphological characteristics can effectively predict LVI in patients with IBC.

Transformer-based Multi-label Deep Learning Model is Efficient for Detecting Ankle Lateral and Medial Ligament Injuries on MRI and Improving Clinicians' Diagnostic Accuracy for Rotational Chronic Ankle Instability

PURPOSE

To develop a deep learning (DL) model that can simultaneously detect lateral and medial collateral ligament injuries of the ankle, aiding in the diagnosis of chronic ankle instability (CAI), and assess its impact on clinicians' diagnostic performance.

METHODS

DL models were developed and external validated on retrospectively collected ankle MRIs between April 2016 and March 2022 respectively at three centers. Included patients were confirmed diagnoses of CAI through arthroscopy, as well as individuals who had undergone MRI and physical examinations that ruled out ligament injuries. DL models were constructed based on a multi-label paradigm. A transformer-based multi-label DL model (AnkleNet) was developed and compared with four convolution neural network (CNN) models. Subsequently, a reader study was conducted to evaluate the impact of model assistance on clinicians when diagnosing challenging cases: identifying rotational CAI (RCAI). Diagnostic performance was assessed using area under the receiver operating characteristic curve (AUC).

RESULTS

Our transformer-based model achieved AUC of 0.910 and 0.892 for detecting lateral and medial collateral ligament injury, respectively, both of which was significantly higher than that of CNN-based models (all P < 0.001). In terms of further CAI diagnosis, it exhibited a macro-average AUC of 0.870 and a balanced accuracy of 0.805. The reader study indicated that incorporation with our model significantly enhanced the diagnostic accuracy of clinicians (P = 0.042), particularly junior clinicians, and led to a reduction in diagnostic variability. The code of the model can be accessed at https://github.com/ChiariRay/AnkleNet.

CONCLUSION

Our transformer-based model was able to detect lateral and medial collateral ligament injuries based on MRI and outperformed CNN-based models, demonstrating a promising performance in diagnosing CAI, especially RCAI patients.

Prevalence of asymptomatic TFCC tears on MRI: A systematic review

BACKGROUND

Recent studies show a high prevalence of triangular fibrocartilage complex (TFCC) tears in asymptomatic wrists. While a TFCC tear may be identified when evaluating ulnar sided wrist pain, this could be incidental and not the true cause of pain. The purpose of this review was to (1) examine the frequency of which TFCC tears are diagnosed on MRI in asymptomatic versus symptomatic wrists and (2) determine whether rates of asymptomatic TFCC tears are higher in two important subgroups commonly at risk for this pathology: elderly patients and high-impact athletes.

METHODS

Articles of level IV or higher evidence were selected from PubMed, Ovid MEDLINE, and Cochrane Central Register of Controlled Trials Database to compare patient demographics, study parameters, and clinical outcomes.

RESULTS

Seven studies met inclusion criteria with a total of 501 wrists (205 symptomatic and 296 asymptomatic). All studies included asymptomatic patients with wrist MR imaging and included information on the structural integrity of the TFCC. Variability in outcome measures reported across studies prevented the conduction of a meta-analysis.

CONCLUSIONS

TFCC abnormalities are present in patients of all ages, symptomatology, and levels of involvement in high-impact sports. Although, there are differences in tear and abnormality prevalence when comparing these three factors, the difference was not significant. Given these findings, using MRI to assess ulnar-sided wrist pain should be fortified with clinical suspicion, physical exam, and physician judgment.

Advances in the treatment of atherosclerosis with ligand-modified nanocarriers

Atherosclerosis, a chronic disease associated with metabolism, poses a significant risk to human well-being. Currently, existing treatments for atherosclerosis lack sufficient efficiency, while the utilization of surface-modified nanoparticles holds the potential to deliver highly effective therapeutic outcomes. These nanoparticles can target and bind to specific receptors that are abnormally over-expressed in atherosclerotic conditions. This paper reviews recent research (2018-present) advances in various ligand-modified nanoparticle systems targeting atherosclerosis by specifically targeting signature molecules in the hope of precise treatment at the molecular level and concludes with a discussion of the challenges and prospects in this field. The intention of this review is to inspire novel concepts for the design and advancement of targeted nanomedicines tailored specifically for the treatment of atherosclerosis.

Detection of atherosclerotic plaques with HDL-like porphyrin nanoparticles using an intravascular dual-modality optical coherence tomography and fluorescence system

Atherosclerosis is the build-up of fatty plaques within blood vessel walls, which can occlude the vessels and cause strokes or heart attacks. It gives rise to both structural and biomolecular changes in the vessel walls. Current single-modality imaging techniques each measure one of these two aspects but fail to provide insight into the combined changes. To address this, our team has developed a dual-modality imaging system which combines optical coherence tomography (OCT) and fluorescence imaging that is optimized for a porphyrin lipid nanoparticle that emits fluorescence and targets atherosclerotic plaques. Atherosclerosis-prone apolipoprotein (Apo)e-/- mice were fed a high cholesterol diet to promote plaque development in descending thoracic aortas. Following infusion of porphyrin lipid nanoparticles in atherosclerotic mice, the fiber-optic probe was inserted into the aorta for imaging, and we were able to robustly detect a porphyrin lipid-specific fluorescence signal that was not present in saline-infused control mice. We observed that the nanoparticle fluorescence colocalized in areas of CD68+ macrophages. These results demonstrate that our system can detect the fluorescence from nanoparticles, providing complementary biological information to the structural information obtained from simultaneously acquired OCT.

Quantitative radiomics analysis of imaging features in adults and children Mycoplasma pneumonia

Purpose

This study aims to explore the value of clinical features, CT imaging signs, and radiomics features in differentiating between adults and children with Mycoplasma pneumonia and seeking quantitative radiomic representations of CT imaging signs.

Materials and methods

In a retrospective analysis of 981 cases of mycoplasmal pneumonia patients from November 2021 to December 2023, 590 internal data (adults:450, children: 140) randomly divided into a training set and a validation set with an 8:2 ratio and 391 external test data (adults:121; children:270) were included. Using univariate analysis, CT imaging signs and clinical features with significant differences (p < 0.05) were selected. After segmenting the lesion area on the CT image as the region of interest, 1,904 radiomic features were extracted. Then, Pearson correlation analysis (PCC) and the least absolute shrinkage and selection operator (LASSO) were used to select the radiomic features. Based on the selected features, multivariable logistic regression analysis was used to establish the clinical model, CT image model, radiomic model, and combined model. The predictive performance of each model was evaluated using ROC curves, AUC, sensitivity, specificity, accuracy, and precision. The AUC between each model was compared using the Delong test. Importantly, the radiomics features and quantitative and qualitative CT image features were analyzed using Pearson correlation analysis and analysis of variance, respectively.

Results

For the individual model, the radiomics model, which was built using 45 selected features, achieved the highest AUCs in the training set, validation set, and external test set, which were 0.995 (0.992, 0.998), 0.952 (0.921, 0.978), and 0.969 (0.953, 0.982), respectively. In all models, the combined model achieved the highest AUCs, which were 0.996 (0.993, 0.998), 0.972 (0.942, 0.995), and 0.986 (0.976, 0.993) in the training set, validation set, and test set, respectively. In addition, we selected 11 radiomics features and CT image features with a correlation coefficient r greater than 0.35.

Conclusion

The combined model has good diagnostic performance for differentiating between adults and children with mycoplasmal pneumonia, and different CT imaging signs are quantitatively represented by radiomics.

Comparative transcriptomics revealed neurodevelopmental impairments and ferroptosis induced by extremely small iron oxide nanoparticles

Iron oxide nanoparticles are a type of nanomaterial composed of iron oxide (Fe3O4 or Fe2O3) and have a wide range of applications in magnetic resonance imaging. Compared to iron oxide nanoparticles, extremely small iron oxide nanoparticles (ESIONPs) (∼3 nm in diameter) can improve the imaging performance due to a smaller size. However, there are currently no reports on the potential toxic effects of ESIONPs on the human body. In this study, we applied ESIONPs to a zebrafish model and performed weighted gene co-expression network analysis (WGCNA) on differentially expressed genes (DEGs) in zebrafish embryos of 48 hpf, 72 hpf, 96 hpf, and 120 hpf using RNA-seq technology. The key hub genes related to neurotoxicity and ferroptosis were identified, and further experiments also demonstrated that ESIONPs impaired the neuronal and muscle development of zebrafish, and induced ferroptosis, leading to oxidative stress, cell apoptosis, and inflammatory response. Here, for the first time, we analyzed the potential toxic effects of ESIONPs through WGCNA. Our studies indicate that ESIONPs might have neurotoxicity and could induce ferroptosis, while abnormal accumulation of iron ions might increase the risk of early degenerative neurological diseases.

DOVE: Doodled vessel enhancement for photoacoustic angiography super resolution

Deep-learning-based super-resolution photoacoustic angiography (PAA) has emerged as a valuable tool for enhancing the resolution of blood vessel images and aiding in disease diagnosis. However, due to the scarcity of training samples, PAA super-resolution models do not generalize well, especially in the challenging in-vivo imaging of organs with deep tissue penetration. Furthermore, prolonged exposure to high laser intensity during the image acquisition process can lead to tissue damage and secondary infections. To address these challenges, we propose an approach doodled vessel enhancement (DOVE) that utilizes hand-drawn doodles to train a PAA super-resolution model. With a training dataset consisting of only 32 real PAA images, we construct a diffusion model that interprets hand-drawn doodles as low-resolution images. DOVE enables us to generate a large number of realistic PAA images, achieving a 49.375% fool rate, even among experts in photoacoustic imaging. Subsequently, we employ these generated images to train a self-similarity-based model for super-resolution. During cross-domain tests, our method, trained solely on generated images, achieves a structural similarity value of 0.8591, surpassing the scores of all other models trained with real high-resolution images. DOVE successfully overcomes the limitation of insufficient training samples and unlocks the clinic application potential of super-resolution-based biomedical imaging.

Role and uptake of metal-based nanoconstructs as targeted therapeutic carriers for rheumatoid arthritis

Rheumatoid Arthritis (RA) is a chronic autoimmune systemic inflammatory disease that affects the joints and other vital organs and diminishes the quality of life. The current developments and innovative treatment options have significantly slowed disease progression and improved their quality of life. Medicaments can be delivered to the inflamed synovium via nanoparticle systems, minimizing systemic and undesirable side effects. Numerous nanoparticles such as polymeric, liposomal, and metallic nanoparticles reported are impending as a good carrier with therapeutic properties. Other issues to be considered along are nontoxicity, nanosize, charge, optical property, and ease of high surface functionalization that make them suitable carriers for drug delivery. Metallic nanoparticles (MNPs) (such as silver, gold, zinc, iron, titanium oxide, and selenium) not only act as good carrier with desired optical property, and high surface modification ability but also have their own therapeutical potential such as anti-oxidant, anti-inflammatory, and anti-arthritic properties, making them one of the most promising options for RA treatment. Regardless, cellular uptake of MNPs is one of the most significant criterions for targeting the medication. This paper discusses the numerous interactions of nanoparticles with cells, as well as cellular uptake of NPs. This review provides the mechanistic overview on MNPs involved in RA therapies and regulation anti-arthritis response such as ability to reduce oxidative stress, suppressing the release of proinflammatory cytokines and expression of LPS induced COX-2, and modulation of MAPK and PI3K pathways in Kuppfer cells and hepatic stellate cells. Despite of that MNPs have also ability to regulates enzymes like glutathione peroxidases (GPxs), thioredoxin reductases (TrxRs) and act as an anti-inflammatory agent.

Advances in Atherosclerosis Theranostics Harnessing Iron Oxide-Based Nanoparticles

Atherosclerosis, a multifaceted chronic inflammatory disease, has a profound impact on cardiovascular health. However, the critical limitations of atherosclerosis management include the delayed detection of advanced stages, the intricate assessment of plaque stability, and the absence of efficacious therapeutic strategies. Nanotheranostic based on nanotechnology offers a novel paradigm for addressing these challenges by amalgamating advanced imaging capabilities with targeted therapeutic interventions. Meanwhile, iron oxide nanoparticles have emerged as compelling candidates for theranostic applications in atherosclerosis due to their magnetic resonance imaging capability and biosafety. This review delineates the current state and prospects of iron oxide nanoparticle-based nanotheranostics in the realm of atherosclerosis, including pivotal aspects of atherosclerosis development, the pertinent targeting strategies involved in disease pathogenesis, and the diagnostic and therapeutic roles of iron oxide nanoparticles. Furthermore, this review provides a comprehensive overview of theranostic nanomedicine approaches employing iron oxide nanoparticles, encompassing chemical therapy, physical stimulation therapy, and biological therapy. Finally, this review proposes and discusses the challenges and prospects associated with translating these innovative strategies into clinically viable anti-atherosclerosis interventions. In conclusion, this review offers new insights into the future of atherosclerosis theranostic, showcasing the remarkable potential of iron oxide-based nanoparticles as versatile tools in the battle against atherosclerosis.

Advancements in Image-Based Models for High-Grade Gliomas Might Be Accelerated

The first half of 2022 saw the publication of several major research advances in image-based models and artificial intelligence applications to optimize treatment strategies for high-grade gliomas, the deadliest brain tumors. We review them and discuss the barriers that delay their entry into clinical practice; particularly, the small sample size and the heterogeneity of the study designs and methodologies used. We will also write about the poor and late palliation that patients suffering from high-grade glioma can count on at the end of life, as well as the current legislative instruments, with particular reference to Italy. We suggest measures to accelerate the gradual progress in image-based models and end of life care for patients with high-grade glioma.

A Deep Learning Model Enhances Clinicians' Diagnostic Accuracy to More Than 96% for Anterior Cruciate Ligament Ruptures on Magnetic Resonance Imaging

PURPOSE

To develop a deep learning model to accurately detect anterior cruciate ligament (ACL) ruptures on magnetic resonance imaging (MRI) and to evaluate its effect on the diagnostic accuracy and efficiency of clinicians.

METHODS

A training dataset was built from MRIs acquired from January 2017 to June 2021, including patients with knee symptoms, irrespective of ACL ruptures. An external validation dataset was built from MRIs acquired from January 2021 to June 2022, including patients who underwent knee arthroscopy or arthroplasty. Patients with fractures or prior knee surgeries were excluded in both datasets. Subsequently, a deep learning model was developed and validated using these datasets. Clinicians of varying expertise levels in sports medicine and radiology were recruited, and their capacities in diagnosing ACL injuries in terms of accuracy and diagnosing time were evaluated both with and without artificial intelligence (AI) assistance.

RESULTS

A deep learning model was developed based on the training dataset of 22,767 MRIs from 5 centers and verified with external validation dataset of 4,086 MRIs from 6 centers. The model achieved an area under the receiver operating characteristic curve of 0.987 and a sensitivity and specificity of 95.1%. Thirty-eight clinicians from 25 centers were recruited to diagnose 3,800 MRIs. The AI assistance significantly improved the accuracy of all clinicians, exceeding 96%. Additionally, a notable reduction in diagnostic time was observed. The most significant improvements in accuracy and time efficiency were observed in the trainee groups, suggesting that AI support is particularly beneficial for clinicians with moderately limited diagnostic expertise.

CONCLUSIONS

This deep learning model demonstrated expert-level diagnostic performance for ACL ruptures, serving as a valuable tool to assist clinicians of various specialties and experience levels in making accurate and efficient diagnoses.

LEVEL OF EVIDENCE

Level III, retrospective comparative case series.

VCAM-1-targeted nanoparticles to diagnose, monitor and treat atherosclerosis

Vascular cell adhesion molecule-1 (VCAM-1) was identified over 2 decades ago as an endothelial adhesion receptor involved in leukocyte recruitment and cell-based immune responses. In atherosclerosis, a chronic inflammatory disease of the blood vessels that is the leading cause of death in the USA, endothelial VCAM-1 is robustly expressed beginning in the early stages of the disease. The interactions of circulating immune cells with VCAM-1 on the activated endothelial cell surface promote the uptake of monocytes and the progression of atherosclerotic lesions in susceptible vessels. Herein, we review the role of VCAM-1 in atherosclerosis and the use of VCAM-1 binding peptides, antibodies and aptamers as targeting agents for nanoplatforms for early detection and treatment of atherosclerotic disease.

Intravoxel incoherent motion as a surrogate marker of perfused vascular density in rat brain

Intravoxel incoherent motion (IVIM) MRI has emerged as a valuable technique for the assessment of tissue characteristics and perfusion. However, there is limited knowledge about the relationship between IVIM-derived measures and changes at the level of the vascular network. In this study, we investigated the potential use of IVIM MRI as a noninvasive tool for measuring changes in cerebral vascular density. Variations in quantitative immunohistochemical measurements of the vascular density across different regions in the rat brain (cortex, corpus callosum, hippocampus, thalamus, and hypothalamus) were related to the pseudo-diffusion coefficient D* and the flowing blood fraction f in healthy Wistar rats. We assessed whether region-wise differences in the vascular density are reflected by variations in the IVIM measurements and found a significant positive relationship with the pseudo-diffusion coefficient (p < 0.05, β = 0.24). The effect of cerebrovascular alterations, such as blood-brain barrier (BBB) disruption on the perfusion-related IVIM parameters, is not well understood. Therefore, we investigated the effect of BBB disruption on the IVIM measures in a rat model of metabolic and vascular comorbidities (ZSF1 obese rat) and assessed whether this affects the relationship between the cerebral vascular density and the noninvasive IVIM measurements. We observed increased vascular permeability without detecting any differences in diffusivity, suggesting that BBB leakage is present before changes in the tissue integrity. We observed no significant difference in the relationship between cerebral vascular density and the IVIM measurements in our model of comorbidities compared with healthy normotensive rats.

SARS-CoV-2 Spike protein triggers gut impairment since mucosal barrier to innermost layers: From basic science to clinical relevance

Studies have reported the occurrence of gastrointestinal (GI) symptoms, primarily diarrhea, in COVID-19. However, the pathobiology regarding COVID-19 in the GI tract remains limited. This work aimed to evaluate SARS-CoV-2 Spike protein interaction with gut lumen in different experimental approaches. Here, we present a novel experimental model with the inoculation of viral protein in the murine jejunal lumen, in vitro approach with human enterocytes, and molecular docking analysis. Spike protein led to increased intestinal fluid accompanied by Cl- secretion, followed by intestinal edema, leukocyte infiltration, reduced glutathione levels, and increased cytokine levels [interleukin (IL)-6, tumor necrosis factor-α, IL-1β, IL-10], indicating inflammation. Additionally, the viral epitope caused disruption in the mucosal histoarchitecture with impairment in Paneth and goblet cells, including decreased lysozyme and mucin, respectively. Upregulation of toll-like receptor 2 and toll-like receptor 4 gene expression suggested potential activation of local innate immunity. Moreover, this experimental model exhibited reduced contractile responses in jejunal smooth muscle. In barrier function, there was a decrease in transepithelial electrical resistance and alterations in the expression of tight junction proteins in the murine jejunal epithelium. Additionally, paracellular intestinal permeability increased in human enterocytes. Finally, in silico data revealed that the Spike protein interacts with cystic fibrosis transmembrane conductance regulator (CFTR) and calcium-activated chloride conductance (CaCC), inferring its role in the secretory effect. Taken together, all the events observed point to gut impairment, affecting the mucosal barrier to the innermost layers, establishing a successful experimental model for studying COVID-19 in the GI context.

Systematic review of artificial intelligence development and evaluation for MRI diagnosis of knee ligament or meniscus tears

OBJECTIVE

The purpose of this systematic review was to summarize the results of original research studies evaluating the characteristics and performance of deep learning models for detection of knee ligament and meniscus tears on MRI.

MATERIALS AND METHODS

We searched PubMed for studies published as of February 2, 2022 for original studies evaluating development and evaluation of deep learning models for MRI diagnosis of knee ligament or meniscus tears. We summarized study details according to multiple criteria including baseline article details, model creation, deep learning details, and model evaluation.

RESULTS

19 studies were included with radiology departments leading the publications in deep learning development and implementation for detecting knee injuries via MRI. Among the studies, there was a lack of standard reporting and inconsistently described development details. However, all included studies reported consistently high model performance that significantly supplemented human reader performance.

CONCLUSION

From our review, we found radiology departments have been leading deep learning development for injury detection on knee MRIs. Although studies inconsistently described DL model development details, all reported high model performance, indicating great promise for DL in knee MRI analysis.

AI applications in musculoskeletal imaging: a narrative review

This narrative review focuses on clinical applications of artificial intelligence (AI) in musculoskeletal imaging. A range of musculoskeletal disorders are discussed using a clinical-based approach, including trauma, bone age estimation, osteoarthritis, bone and soft-tissue tumors, and orthopedic implant-related pathology. Several AI algorithms have been applied to fracture detection and classification, which are potentially helpful tools for radiologists and clinicians. In bone age assessment, AI methods have been applied to assist radiologists by automatizing workflow, thus reducing workload and inter-observer variability. AI may potentially aid radiologists in identifying and grading abnormal findings of osteoarthritis as well as predicting the onset or progression of this disease. Either alone or combined with radiomics, AI algorithms may potentially improve diagnosis and outcome prediction of bone and soft-tissue tumors. Finally, information regarding appropriate positioning of orthopedic implants and related complications may be obtained using AI algorithms. In conclusion, rather than replacing radiologists, the use of AI should instead help them to optimize workflow, augment diagnostic performance, and keep up with ever-increasing workload.Relevance statement This narrative review provides an overview of AI applications in musculoskeletal imaging. As the number of AI technologies continues to increase, it will be crucial for radiologists to play a role in their selection and application as well as to fully understand their potential value in clinical practice. Key points • AI may potentially assist musculoskeletal radiologists in several interpretative tasks.• AI applications to trauma, age estimation, osteoarthritis, tumors, and orthopedic implants are discussed.• AI should help radiologists to optimize workflow and augment diagnostic performance.

Medical Imaging Applications Developed Using Artificial Intelligence Demonstrate High Internal Validity Yet Are Limited in Scope and Lack External Validation

PURPOSE

To (1) review definitions and concepts necessary to interpret applications of deep learning (DL; a domain of artificial intelligence that leverages neural networks to make predictions on media inputs such as images) and (2) identify knowledge and translational gaps in the literature to provide insight into specific areas for improvement as adoption of this technology continues.

METHODS

A comprehensive search of the literature was performed in December 2023 for articles regarding the use of DL in sports medicine. For each study, information regarding the joint of focus, specific anatomic structure/pathology to which DL was applied, imaging modality utilized, source of images used for model training and testing, data set size, model performance, and whether the DL model was externally validated was recorded. A numerical scale was used to rate each DL model's clinical impact, with 1 corresponding to proof-of-concept studies with little to no direct clinical impact and 5 corresponding to practice-changing clinical impact and ready for clinical deployment.

RESULTS

Fifty-five studies were identified, all of which were published within the past 5 years, while 82% were published within the past 3 years. Of the DL models identified, 84% were developed for classification tasks, 9% for automated measurements, and 7% for segmentation. A total of 62% of studies utilized magnetic resonance imaging as the imaging modality, 25% radiographs, and 7% ultrasound, while 1 study each used computed tomography, arthroscopic images, or arthroscopic video. Sixty-five percent of studies focused on the detection of tears (anterior cruciate ligament [ACL], rotator cuff [RC], and meniscus). The diagnostic performance of ACL tears, as determined by the area under the receiver operator curve (AUROC), ranged from 0.81 to 0.99 for ACL tears (excellent to near perfect), 0.83 to 0.94 for RC tears (excellent), and from 0.75 to 0.96 for meniscus tears (acceptable to excellent). In addition, 3 studies focused on detection of cartilage lesions had AUROC ranging from 0.90 to 0.92 (excellent performance). However, only 4 (7%) studies externally validated their models, suggesting that they may not be generalizable or may not perform well when applied to populations other than that used to develop the model. Finally, the mean clinical impact score was 2 (range, 1-3) on scale of 1 to 5, corresponding to limited clinical applicability.

CONCLUSIONS

DL models in orthopaedic sports medicine show generally excellent performance (high internal validity) but require external validation to facilitate clinical deployment. In addition, current models have low clinical applicability and fail to advance the field due to a focus on routine tasks and a narrow conceptual framework.

LEVEL OF EVIDENCE

Level IV, scoping review of Level I to IV studies.

Radiomics in Musculoskeletal Tumors

Sarcomas are heterogeneous rare tumors predominantly affecting the musculoskeletal (MSK) system. Due to significant variations in their natural history and variable response to conventional treatments, the discovery of novel diagnostic and prognostic biomarkers to guide therapeutic decision-making is an active and ongoing field of research. As new cellular, molecular, and metabolic biomarkers continue to be discovered, quantitative radiologic imaging is becoming increasingly important in sarcoma management. Radiomics offers the potential for discovering novel imaging diagnostic and predictive biomarkers using standard-of-care medical imaging. In this review, we detail the core concepts of radiomics and the application of radiomics to date in MSK sarcoma research. Also described are specific challenges related to radiomic studies, as well as viewpoints on clinical adoption and future perspectives in the field.

Navigating the landscape: Prospects and hurdles in targeting vascular smooth muscle cells for atherosclerosis diagnosis and therapy

Vascular smooth muscle cells (VSMCs) have emerged as pivotal contributors throughout all phases of atherosclerotic plaque development, effectively dispelling prior underestimations of their prevalence and significance. Recent lineage tracing studies have unveiled the clonal nature and remarkable adaptability inherent to VSMCs, thereby illuminating their intricate and multifaceted roles in the context of atherosclerosis. This comprehensive review provides an in-depth exploration of the intricate mechanisms and distinctive characteristics that define VSMCs across various physiological processes, firmly underscoring their paramount importance in shaping the course of atherosclerosis. Furthermore, this review offers a thorough examination of the significant strides made over the past two decades in advancing imaging techniques and therapeutic strategies with a precise focus on targeting VSMCs within atherosclerotic plaques, notably spotlighting meticulously engineered nanoparticles as a promising avenue. We envision the potential of VSMC-targeted nanoparticles, thoughtfully loaded with medications or combination therapies, to effectively mitigate pro-atherogenic VSMC processes. These advancements are poised to contribute significantly to the pivotal objective of modulating VSMC phenotypes and enhancing plaque stability. Moreover, our paper also delves into recent breakthroughs in VSMC-targeted imaging technologies, showcasing their remarkable precision in locating microcalcifications, dynamically monitoring plaque fibrous cap integrity, and assessing the therapeutic efficacy of medical interventions. Lastly, we conscientiously explore the opportunities and challenges inherent in this innovative approach, providing a holistic perspective on the potential of VSMC-targeted strategies in the evolving landscape of atherosclerosis research and treatment.

Application of machine learning-based multi-sequence MRI radiomics in diagnosing anterior cruciate ligament tears

OBJECTIVE

To compare the diagnostic power among various machine learning algorithms utilizing multi-sequence magnetic resonance imaging (MRI) radiomics in detecting anterior cruciate ligament (ACL) tears. Additionally, this research aimed to create and validate the optimal diagnostic model.

METHODS

In this retrospective analysis, 526 patients were included, comprising 178 individuals with ACL tears and 348 with a normal ACL. Radiomics features were derived from multi-sequence MRI scans, encompassing T1-weighted imaging and proton density (PD)-weighted imaging. The process of selecting the most reliable radiomics features involved using interclass correlation coefficient (ICC) testing, t tests, and the least absolute shrinkage and selection operator (LASSO) technique. After the feature selection process, five machine learning classifiers were created. These classifiers comprised logistic regression (LR), support vector machine (SVM), K-nearest neighbors (KNN), light gradient boosting machine (LightGBM), and multilayer perceptron (MLP). A thorough performance evaluation was carried out, utilizing diverse metrics like the area under the receiver operating characteristic curve (ROC), specificity, accuracy, sensitivity positive predictive value, and negative predictive value. The classifier exhibiting the best performance was chosen. Subsequently, three models were developed: the PD model, the T1 model, and the combined model, all based on the optimal classifier. The diagnostic performance of these models was assessed by employing AUC values, calibration curves, and decision curve analysis.

RESULTS

Out of 2032 features, 48 features were selected. The SVM-based multi-sequence radiomics outperformed all others, achieving AUC values of 0.973 and 0.927, sensitivities of 0.933 and 0.857, and specificities of 0.930 and 0.829, in the training and validation cohorts, respectively.

CONCLUSION

The multi-sequence MRI radiomics model, which is based on machine learning, exhibits exceptional performance in diagnosing ACL tears. It provides valuable insights crucial for the diagnosis and treatment of knee joint injuries, serving as an accurate and objective supplementary diagnostic tool for clinical practitioners.

Dual-modality probe nanodrug delivery systems with ROS-sensitivity for atherosclerosis diagnosis and therapy

Most acute cardiovascular and cerebrovascular diseases are caused by atherosclerotic plaque rupture leading to blocked arteries. Targeted nanodelivery systems deliver imaging agents or drugs to target sites for diagnostic imaging or the treatment of various diseases, providing new insights for the detection and treatment of atherosclerosis. Based on the pathological characteristics of atherosclerosis, a hydrogen peroxide-sensitive bimodal probe PPIS@FC with integrated diagnosis and treatment function was designed. Bimodal probes Fe3O4@SiO2-CDs (FC) were prepared by coupling superparamagnetic iron oxide and carbon quantum dots synthesized with citric acid, and self-assembled with hydrogen peroxide stimulus-responsive amphiphilic block polymer PGMA-PEG modified with simvastatin (Sim) and target molecule ISO-1 to obtain drug-loaded micelles PGMA-PEG-ISO-1-Sim@FC (PPIS@FC). PPIS@FC could release Sim and FC in an H2O2-triggered manner, achieving the goal of releasing drugs using the special microenvironment at the plaque. At the same time, in vivo magnetic resonance and fluorescence imaging results proved that PPIS@FC possessed targeting ability, magnetic resonance imaging and fluorescence imaging effects. The results of the FeCl3 and ApoE-/- model showed that PPIS@FC had an excellent therapeutic effect and in vivo safety. Therefore, dual-modality imaging drug delivery systems with ROS response will become a promising strategy for the diagnosis and treatment of atherosclerosis.

T1-weighted MRI of targeting atherosclerotic plaque based on CD40 expression on engulfed USPIO's cell surface

Atherosclerosis is a chronic inflammatory disease characterized by the accumulation of cholesterol within the arterial wall. Its progression can be monitored via magnetic resonance imaging (MRI). Ultrasmall Superparamagnetic Particles of Iron Oxide (USPIO) (<5 nm) have been employed as T1 contrast agents for MRI applications. In this study, we synthesized USPIO with an average surface carboxylation of approximately 5.28 nm and a zeta potential of -47.8 mV. These particles were phagocytosed by mouse aortic endothelial cells (USPIO-MAECs) and endothelial progenitor cells (USPIO-EPCs), suggesting that they can be utilized as potential contrast agent and delivery vehicle for the early detection of atherosclerosis. However, the mechanism by which this contrast agent is delivered to the plaque remains undetermined. Our results demonstrated that with increasing USPIO concentration during 10-100 μg ml-1, consistent change appeared in signal enhancement on T1-weighted MRI. Similarly, T1-weighted MRI of MAECs and EPCs treated with these concentrations exhibited a regular change in signal enhancement. Prussian blue staining of USPIO revealed substantial absorption into MAECs and EPCs after treatment with 50 μg ml-1USPIO for 24 h. The iron content in USPIO-EPCs was much higher (5 pg Fe/cell) than in USPIO-MAECs (0.8 pg Fe/cell). In order to substantiate our hypothesis that CD40 protein on the cell surface facilitates migration towards inflammatory cells, we utilized AuNPs-PEI (gold nanoparticles-polyethylenimine) carrying siRNACD40to knockout CD40 expression in MAECs. It has been documented that gold nanoparticle-oligonucleotide complexes could be employed as intracellular gene regulation agents for the control of protein level in cells. Our results confirmed that macrophages are more likely to bind to MAECs treated with AuNPs-PEI-siRNANC(control) for 72 h than to MAECs treated with AuNPs-PEI-siRNACD40(reduced CD40 expression), thus confirming CD40 targeting at the cellular level. When USPIO-MAECs and MAECs (control) were delivered to mice (high-fat-fed) via tail vein injection respectively, we observed a higher iron accumulation in plaques on blood vessels in high-fat-fed mice treated with USPIO-MAECs. We also demonstrated that USPIO-EPCs, when delivered to high-fat-fed mice via tail vein injection, could indeed label plaques by generating higher T1-weighted MRI signals 72 h post injection compared to controls (PBS, USPIO and EPCs alone). In conclusion, we synthesized a USPIO suitable for T1-weighted MRI. Our results have confirmed separately at the cellular and tissue andin vivolevel, that USPIO-MAECs or USPIO-EPCs are more accessible to atherosclerotic plaques in a mouse model. Furthermore, the high expression of CD40 on the cell surface is a key factor for targeting and USPIO-EPCs may have potential therapeutic effects.

The Role of Artificial Intelligence in Anterior Cruciate Ligament Injuries: Current Concepts and Future Perspectives

The remarkable progress in data aggregation and deep learning algorithms has positioned artificial intelligence (AI) and machine learning (ML) to revolutionize the field of medicine. AI is becoming more and more prevalent in the healthcare sector, and its impact on orthopedic surgery is already evident in several fields. This review aims to examine the literature that explores the comprehensive clinical relevance of AI-based tools utilized before, during, and after anterior cruciate ligament (ACL) reconstruction. The review focuses on current clinical applications and future prospects in preoperative management, encompassing risk prediction and diagnostics; intraoperative tools, specifically navigation, identifying complex anatomic landmarks during surgery; and postoperative applications in terms of postoperative care and rehabilitation. Additionally, AI tools in educational and training settings are presented. Orthopedic surgeons are showing a growing interest in AI, as evidenced by the applications discussed in this review, particularly those related to ACL injury. The exponential increase in studies on AI tools applicable to the management of ACL tears promises a significant future impact in its clinical application, with growing attention from orthopedic surgeons.

The State-of-the-Art and Perspectives of Laser Ablation for Tumor Treatment

Tumors significantly impact individuals' physical well-being and quality of life. With the ongoing advancements in optical technology, information technology, robotic technology, etc., laser technology is being increasingly utilized in the field of tumor treatment, and laser ablation (LA) of tumors remains a prominent area of research interest. This paper presents an overview of the recent progress in tumor LA therapy, with a focus on the mechanisms and biological effects of LA, commonly used ablation lasers, image-guided LA, and robotic-assisted LA. Further insights and future prospects are discussed in relation to these aspects, and the paper proposed potential future directions for the development of tumor LA techniques.

Total Hip Arthroplasty in Ankylosing Spondylitis: A Case Report of Ankylosed Hip

Ankylosing spondylitis (AS) is a chronic inflammatory arthritic disease that primarily affects the axial skeleton, and its association with the secondary development of osteoarthritis (OA) in peripheral joints, particularly the hips, is increasingly recognized. This case report elucidates the diagnostic and therapeutic challenges encountered in a patient with bilateral hip osteoarthritis secondary to AS. The patient's medical history included AS and a failed attempt at core decompression of the left hip joint. The patient was managed with total hip arthroplasty (THA) on the left side due to persistent symptoms. Total hip arthroplasty on the left side involved a meticulous surgical approach, addressing the unique challenges posed by underlying ankylosis. The procedure was conducted uneventfully, with the implantation of a modular femoral head, uncemented femoral stem, and modular shell. Postoperatively, the patient experienced significant pain relief and improved functionality. Successful rehabilitation and management were integral to the overall positive outcome. This case report highlights the complex interplay between AS and hip osteoarthritis, emphasizing the importance of tailored diagnostic and therapeutic strategies. Successful total hip arthroplasty in the setting of AS-related hip osteoarthritis suggests that joint replacement can be effective, but ongoing research is necessary to optimize surgical planning and long-term outcomes in this patient population.

Advances of nanoparticle-mediated diagnostic and theranostic strategies for atherosclerosis

Atherosclerotic plaque remains the primary cause of morbidity and mortality worldwide. Accurate assessment of the degree of atherosclerotic plaque is critical for predicting the risk of atherosclerotic plaque and monitoring the results after intervention. Compared with traditional technology, the imaging technologies of nanoparticles have distinct advantages and great development prospects in the identification and characterization of vulnerable atherosclerotic plaque. Here, we systematically summarize the latest advances of targeted nanoparticle approaches in the diagnosis of atherosclerotic plaque, including multimodal imaging, fluorescence imaging, photoacoustic imaging, exosome diagnosis, and highlighted the theranostic progress as a new therapeutic strategy. Finally, we discuss the major challenges that need to be addressed for future development and clinical transformation.

MRI radiomics for the preoperative evaluation of lymphovascular invasion in breast cancer: A meta-analysis

PURPOSE

To evaluate the ability of preoperative MRI-based radiomic features in predicting lymphovascular invasion (LVI) in patients with breast cancer.

METHODS

PubMed, Embase, Web of Science, Cochrane Library databases, and four Chinese databases were searched to identify relevant studies published up until June 15, 2023. Two reviewers screened all papers independently for eligibility. We included diagnostic accuracy studies that used radiomics-MRI for LVI in patients with breast cancer, using histopathology as the reference standard. Quality was assessed using the Quality Assessment of Diagnostic Accuracy Studies 2 and Radiomics Quality Score. Overall diagnostic odds ratio (DOR), sensitivity, specificity and area under the curve (AUC) were calculated to assess the prediction efficacy of MRI-based radiomic features in patients with breast cancer. Spearman's correlation coefficient was calculated and subgroup analysis performed to investigate causes of heterogeneity.

RESULTS

Eight studies comprising 1685 female patients were included. The pooled DOR, sensitivity, specificity, and AUC of radiomics in detecting LVI were 23 [confidence interval (CI) 16,32], 0.89(0.86,0.92), 0.82 (0.78,0.86), and 0.83(0.78,0.87), respectively. The meta-analysis showed significant heterogeneity among the included studies. No threshold effect was detected. Subgroup analysis showed that more than 200 participants, radiomics with clinical factors, semiautomatic segmentation method and peritumoral or intra- and peritumoral model [DOR: 28(18,42), 26(19,37), 34(16,70), 40(10,156), respectively] could improve diagnostic performance compared with less than 200 participants, only radiomics, manual segmentation method, and tumor model [DOR: 16(7,37), 21(6,73), 20(12,32), 21(13,32), respectively], but 3.0 T MR and multiple sequences approach [DOR: 27(15,49),17(8,35)] couldn't improve diagnostic performance compared with 1.5 T and DCE radiomic features [DOR:27(7,99),25(17,37)].

CONCLUSION

Our meta-analysis showed that preoperative MRI-based radiomic features performs well in predicting LVI in patients with breast cancer. This noninvasive and convenient tool may be used to facilitate preoperative identification of LVI in breast cancer.

Nanotechnology in coronary heart disease

Coronary heart disease (CHD) is one of the major causes of death and disability worldwide, especially in low- and middle-income countries and among older populations. Conventional diagnostic and therapeutic approaches have limitations such as low sensitivity, high cost and side effects. Nanotechnology offers promising alternative strategies for the diagnosis and treatment of CHD by exploiting the unique properties of nanomaterials. In this review, we use bibliometric analysis to identify research hotspots in the application of nanotechnology in CHD and provide a comprehensive overview of the current state of the art. Nanomaterials with enhanced imaging and biosensing capabilities can improve the early detection of CHD through advanced contrast agents and high-resolution imaging techniques. Moreover, nanomaterials can facilitate targeted drug delivery, tissue engineering and modulation of inflammation and oxidative stress, thus addressing multiple aspects of CHD pathophysiology. We discuss the application of nanotechnology in CHD diagnosis (imaging and sensors) and treatment (regulation of macrophages, cardiac repair, anti-oxidative stress), and provide insights into future research directions and clinical translation. This review serves as a valuable resource for researchers and clinicians seeking to harness the potential of nanotechnology in the management of CHD. STATEMENT OF SIGNIFICANCE: Coronary heart disease (CHD) is the one of leading cause of death and disability worldwide. Nanotechnology offers new strategies for diagnosing and treating CHD by exploiting the unique properties of nanomaterials. This review uses bibliometric analysis to uncover research trends in the use of nanotechnology for CHD. We discuss the potential of nanomaterials for early CHD detection through advanced imaging and biosensing, targeted drug delivery, tissue engineering, and modulation of inflammation and oxidative stress. We also offer insights into future research directions and potential clinical applications. This work aims to guide researchers and clinicians in leveraging nanotechnology to improve CHD patient outcomes and quality of life.

IR-820@NBs Combined with MG-132 Enhances the Anti-Hepatocellular Carcinoma Effect of Sonodynamic Therapy

Purpose

Sonodynamic therapy (SDT) is a promising and significant measure for the treatment of tumors. However, the internal situation of hepatocellular carcinoma (HCC) is complex, separate SDT treatment is difficult to play a good therapeutic effect. Here, we used SDT combined with MG-132 to mediate apoptosis and autophagy of HCC cells to achieve the purpose of treatment of cancer.

Methods

To determine the generated reactive oxygen species (ROS) and the change of mitochondrial membrane potential (ΔΨm), HepG2 cells were stained by 2,7-dichlorodihydrofluorescein diacetate (DCFH-DA) and 5,5',6,6'-Tetrachloro-1,1',3,3'-tetraethyl-imidacarbocyanine iodide (JC-1) staining to determine the IR-820@NBs-mediated SDT to achieve HCC therapy through the mitochondrial pathway. Cell counting kit 8 (CCK-8) assay and flow cytometry were used to detect cell viability and apoptosis rate of HepG2 cells. Autophagy was detected by mCherry-GFP-LC3B fluorescence labeling. Chloroquine (Cq) pretreatment was used to explore the relationship between autophagy and apoptosis. To detect the ability of HepG2 cells migration and invasion, cell scratch assay and transwell assay were used.

Results

The successfully prepared IR-820@NBs could effectively overcome the shortcomings of IR-820 and induce lethal levels of ROS by ultrasound irradiation. As a dual agonist of apoptosis and autophagy, MG-132 could effectively enhance the efficacy of SDT in the process of treating HCC. After pre-treatment with Cq, the cell activity increased and the level of apoptosis decreased, which proved that apoptosis and autophagy were induced by combined therapy, autophagy, and apoptosis have the synergistic anti-tumor effect, and part of apoptosis was autophagy-dependent. After combined therapy, the activity and invasive ability of HCC cells decreased significantly.

Conclusion

SDT combined with MG-132 in the process of treating liver cancer could effectively induce apoptosis and autophagy anti-tumor therapy, which is helpful to the research of new methods to treat liver cancer.

Functional changes of default mode network and structural alterations of gray matter in patients with irritable bowel syndrome: a meta-analysis of whole-brain studies

Background

Irritable bowel syndrome (IBS) is a brain-gut disorder with high global prevalence, resulting from abnormalities in brain connectivity of the default mode network and aberrant changes in gray matter (GM). However, the findings of previous studies about IBS were divergent. Therefore, we conducted a meta-analysis to identify common functional and structural alterations in IBS patients.

Methods

Altogether, we identified 12 studies involving 194 IBS patients and 230 healthy controls (HCs) from six databases using whole-brain resting state functional connectivity (rs-FC) and voxel-based morphometry. Anisotropic effect-size signed differential mapping (AES-SDM) was used to identify abnormal functional and structural changes as well as the overlap brain regions between dysconnectivity and GM alterations.

Results

Findings indicated that, compared with HCs, IBS patients showed abnormal rs-FC in left inferior parietal gyrus, left lingual gyrus, right angular gyrus, right precuneus, right amygdala, right median cingulate cortex, and left hippocampus. Altered GM was detected in the fusiform gyrus, left triangular inferior frontal gyrus (IFG), right superior marginal gyrus, left anterior cingulate gyrus, left rectus, left orbital IFG, right triangular IFG, right putamen, left superior parietal gyrus and right precuneus. Besides, multimodal meta-analysis identified left middle frontal gyrus, left orbital IFG, and right putamen as the overlapped regions.

Conclusion

Our results confirm that IBS patients have abnormal alterations in rs-FC and GM, and reveal brain regions with both functional and structural alterations. These results may contribute to understanding the underlying pathophysiology of IBS.

Systematic review registration

https://www.crd.york.ac.uk/prospero, identifier CRD42022351342.

The value of radiomics-based CT combined with machine learning in the diagnosis of occult vertebral fractures

PURPOSE

To develop and evaluate the performance of radiomics-based computed tomography (CT) combined with machine learning algorithms in detecting occult vertebral fractures (OVFs).

MATERIALS AND METHODS

128 vertebrae including 64 with OVF confirmed by magnetic resonance imaging and 64 corresponding control vertebrae from 57 patients who underwent chest/abdominal CT scans, were included. The CT radiomics features on mid-axial and mid-sagittal plane of each vertebra were extracted. The fractured and normal vertebrae were randomly divided into training set and validation set at a ratio of 8:2. Pearson correlation analyses and least absolute shrinkage and selection operator were used for selecting sagittal and axial features, respectively. Three machine-learning algorithms were used to construct the radiomics models based on the residual features. Receiver operating characteristic (ROC) analysis was used to verify the performance of model.

RESULTS

For mid-axial CT imaging, 6 radiomics parameters were obtained and used for building the models. The logistic regression (LR) algorithm showed the best performance with area under the ROC curves (AUC) of training and validation sets of 0.682 and 0.775. For mid-sagittal CT imaging, 5 parameters were selected, and LR algorithms showed the best performance with AUC of training and validation sets of 0.832 and 0.882. The LR model based on sagittal CT yielded the best performance, with an accuracy of 0.846, sensitivity of 0.846, and specificity of 0.846.

CONCLUSION

Machine learning based on CT radiomics features allows for the detection of OVFs, especially the LR model based on the radiomics of sagittal imaging, which indicates it is promising to further combine with deep learning to achieve automatic recognition of OVFs to reduce the associated secondary injury.

Artificial intelligence-based radiomics in bone tumors: Technical advances and clinical application

Radiomics is the extraction of predefined mathematic features from medical images for predicting variables of clinical interest. Recent research has demonstrated that radiomics can be processed by artificial intelligence algorithms to reveal complex patterns and trends for diagnosis, and prediction of prognosis and response to treatment modalities in various types of cancer. Artificial intelligence tools can utilize radiological images to solve next-generation issues in clinical decision making. Bone tumors can be classified as primary and secondary (metastatic) tumors. Osteosarcoma, Ewing sarcoma, and chondrosarcoma are the dominating primary tumors of bone. The development of bone tumor model systems and relevant research, and the assessment of novel treatment methods are ongoing to improve clinical outcomes, notably for patients with metastases. Artificial intelligence and radiomics have been utilized in almost full spectrum of clinical care of bone tumors. Radiomics models have achieved excellent performance in the diagnosis and grading of bone tumors. Furthermore, the models enable to predict overall survival, metastases, and recurrence. Radiomics features have exhibited promise in assisting therapeutic planning and evaluation, especially neoadjuvant chemotherapy. This review provides an overview of the evolution and opportunities for artificial intelligence in imaging, with a focus on hand-crafted features and deep learning-based radiomics approaches. We summarize the current application of artificial intelligence-based radiomics both in primary and metastatic bone tumors, and discuss the limitations and future opportunities of artificial intelligence-based radiomics in this field. In the era of personalized medicine, our in-depth understanding of emerging artificial intelligence-based radiomics approaches will bring innovative solutions to bone tumors and achieve clinical application.

Risk factors for distant metastasis of chondrosarcoma: A population-based study

Chondrosarcoma is the second largest bone malignancy after osteosarcoma and mainly affects middle-aged adults, where patients with distant metastasis (DM) often have a poor prognosis. Although nomograms have been widely used to predict distant tumor metastases, there is a lack of large-scale data studies for the diagnostic evaluation of DM in chondrosarcoma. Data on patients diagnosed with chondrosarcoma from 2004 to 2015 were obtained from the Surveillance, Epidemiology, and End Results database. Independent risk factors for having DM from chondrosarcoma were screened using univariate and multivariate logistics regression analysis. A nomogram was created to predict the probability of DM from the screened independent risk factors. The nomogram was then validated using receiver operating characteristic curves and calibration curves. A total of 1870 chondrosarcoma patients were included in the study after data screening, of which 157 patients (8.40%) had DM at the time of diagnosis. Univariate and multivariate logistic regression analysis screened four independent risk factors, including grade, tumor number, T stage, and N stage. receiver operating characteristic curves and calibration curves showed good accuracy of the nomogram in both training and validation sets. The current study screened for independent risk factors for DM from chondrosarcoma, which will help clinicians evaluate patients.

New Challenges for Anatomists in the Era of Omics

Anatomic studies have traditionally relied on macroscopic, microscopic, and histological techniques to investigate the structure of tissues and organs. Anatomic studies are essential in many fields, including medicine, biology, and veterinary science. Advances in technology, such as imaging techniques and molecular biology, continue to provide new insights into the anatomy of living organisms. Therefore, anatomy remains an active and important area in the scientific field. The consolidation in recent years of some omics technologies such as genomics, transcriptomics, proteomics, and metabolomics allows for a more complete and detailed understanding of the structure and function of cells, tissues, and organs. These have been joined more recently by "omics" such as radiomics, pathomics, and connectomics, supported by computer-assisted technologies such as neural networks, 3D bioprinting, and artificial intelligence. All these new tools, although some are still in the early stages of development, have the potential to strongly contribute to the macroscopic and microscopic characterization in medicine. For anatomists, it is time to hitch a ride and get on board omics technologies to sail to new frontiers and to explore novel scenarios in anatomy.

Characteristics and prognostic factors of adult patients with osteosarcoma from the SEER database

Osteosarcoma is the most common bone malignancy. There are many studies on the prognostic factors of children and adolescents, but the characteristics and prognostic factors of adult osteosarcoma are rarely studied. The aim of this study was to construct a nomogram for predicting the prognosis of adult osteosarcoma. Information on all osteosarcoma patients aged ≥ 18 years from 2004 to 2015 was downloaded from the surveillance, epidemiology and end results database. A total of 70% of the patients were included in the training set and 30% of the patients were included in the validation set. Univariate log-rank analysis and multivariate cox regression analysis were used to screen independent risk factors affecting the prognosis of adult osteosarcoma. These risk factors were used to construct a nomogram to predict 3-year and 5-year prognosis in adult osteosarcoma. Multivariate cox regression analysis yielded 6 clinicopathological features (age, primary site, tumor size, grade, American Joint Committee on Cancer stage, and surgery) for the prognosis of adult osteosarcoma patients in the training cohort. A nomogram was constructed based on these predictors to assess the prognosis of adult patients with osteosarcoma. Concordance index, receiver operating characteristic and calibration curves analyses also showed satisfactory performance of the nomogram in predicting prognosis. The constructed nomogram is a helpful tool for exactly predicting the prognosis of adult patients with osteosarcoma, which could enable patients to be more accurately managed in clinical practice.

A Comprehensive Review on the Diagnosis of Knee Injury by Deep Learning-Based Magnetic Resonance Imaging

The continual improvement in the field of medical diagnosis has led to the monopoly of using deep learning (DL)-based magnetic resonance imaging (MRI) for the diagnosis of knee injury related to meniscal injury, ligament injury including the cruciate ligaments, collateral ligaments and medial patella-femoral ligament, and cartilage injury. The present systematic review was done by PubMed and Directory of Open Access Journals (DOAJ), wherein we finalised 24 studies conducted on the accuracy of DL MRI studies for knee injury identification. The studies showed an accuracy of 72.5% to 100% indicating that DL MRI holds an equivalent performance as humans in decision-making and management of knee injuries. This further opens up future exploration for improving MRI-based diagnosis keeping in mind the limitations of verification bias and data imbalance in ground truth subjectivity.

Identification of Radiomic Signatures in Brain MRI Sequences T1 and T2 That Differentiate Tumor Regions of Midline Gliomas with H3.3K27M Mutation

BACKGROUND

Radiomics refers to the acquisition of traces of quantitative features that are usually non-perceptible to human vision and are obtained from different imaging techniques and subsequently transformed into high-dimensional data. Diffuse midline gliomas (DMG) represent approximately 20% of pediatric CNS tumors, with a median survival of less than one year after diagnosis. We aimed to identify which radiomics can discriminate DMG tumor regions (viable tumor and peritumoral edema) from equivalent midline normal tissue (EMNT) in patients with the positive H3.F3K27M mutation, which is associated with a worse prognosis.

PATIENTS AND METHODS

This was a retrospective study. From a database of 126 DMG patients (children, adolescents, and young adults), only 12 had H3.3K27M mutation and available brain magnetic resonance DICOM file. The MRI T1 post-gadolinium and T2 sequences were uploaded to LIFEx software to post-process and extract radiomic features. Statistical analysis included normal distribution tests and the Mann-Whitney U test performed using IBM SPSS® (Version 27.0.0.1, International Business Machines Corp., Armonk, NY, USA), considering a significant statistical p-value ≤ 0.05.

RESULTS

EMNT vs. Tumor: From the T1 sequence 10 radiomics were identified, and 14 radiomics from the T2 sequence, but only one radiomic identified viable tumors in both sequences (p < 0.05) (DISCRETIZED_Q1). Peritumoral edema vs. EMNT: From the T1 sequence, five radiomics were identified, and four radiomics from the T2 sequence. However, four radiomics could discriminate peritumoral edema in both sequences (p < 0.05) (CONVENTIONAL_Kurtosis, CONVENTIONAL_ExcessKurtosis, DISCRETIZED_Kurtosis, and DISCRETIZED_ExcessKurtosis). There were no radiomics useful for distinguishing tumor tissue from peritumoral edema in both sequences.

CONCLUSIONS

Less than 5% of the radiomic characteristics identified tumor regions of medical-clinical interest in T1 and T2 sequences of conventional magnetic resonance imaging. The first-order and second-order radiomic features suggest support to investigators and clinicians for careful evaluation for diagnosis, patient classification, and multimodality cancer treatment planning.

Radiomics and Machine Learning in Brain Tumors and Their Habitat: A Systematic Review

Radiomics is a rapidly evolving field that involves extracting and analysing quantitative features from medical images, such as computed tomography or magnetic resonance images. Radiomics has shown promise in brain tumor diagnosis and patient-prognosis prediction by providing more detailed and objective information about tumors' features than can be obtained from the visual inspection of the images alone. Radiomics data can be analyzed to determine their correlation with a tumor's genetic status and grade, as well as in the assessment of its recurrence vs. therapeutic response, among other features. In consideration of the multi-parametric and high-dimensional space of features extracted by radiomics, machine learning can further improve tumor diagnosis, treatment response, and patients' prognoses. There is a growing recognition that tumors and their microenvironments (habitats) mutually influence each other-tumor cells can alter the microenvironment to increase their growth and survival. At the same time, habitats can also influence the behavior of tumor cells. In this systematic review, we investigate the current limitations and future developments in radiomics and machine learning in analysing brain tumors and their habitats.

Atherosclerosis treatment with nanoagent: potential targets, stimulus signals and drug delivery mechanisms

Cardiovascular disease (CVDs) is the first killer of human health, and it caused up at least 31% of global deaths. Atherosclerosis is one of the main reasons caused CVDs. Oral drug therapy with statins and other lipid-regulating drugs is the conventional treatment strategies for atherosclerosis. However, conventional therapeutic strategies are constrained by low drug utilization and non-target organ injury problems. Micro-nano materials, including particles, liposomes, micelles and bubbles, have been developed as the revolutionized tools for CVDs detection and drug delivery, specifically atherosclerotic targeting treatment. Furthermore, the micro-nano materials also could be designed to intelligently and responsive targeting drug delivering, and then become a promising tool to achieve atherosclerosis precision treatment. This work reviewed the advances in atherosclerosis nanotherapy, including the materials carriers, target sites, responsive model and treatment results. These nanoagents precisely delivery the therapeutic agents to the target atherosclerosis sites, and intelligent and precise release of drugs, which could minimize the potential adverse effects and be more effective in atherosclerosis lesion.

Target and Cell Therapy for Atherosclerosis and CVD

Cardiovascular diseases (CVD) and, in particular, atherosclerosis, remain the main cause of death in the world today. Unfortunately, in most cases, CVD therapy begins after the onset of clinical symptoms and is aimed at eliminating them. In this regard, early pathogenetic therapy for CVD remains an urgent problem in modern science and healthcare. Cell therapy, aimed at eliminating tissue damage underlying the pathogenesis of some pathologies, including CVD, by replacing it with various cells, is of the greatest interest. Currently, cell therapy is the most actively developed and potentially the most effective treatment strategy for CVD associated with atherosclerosis. However, this type of therapy has some limitations. In this review, we have tried to summarize the main targets of cell therapy for CVD and atherosclerosis in particular based on the analysis using the PubMed and Scopus databases up to May 2023.

Magnetic Nanoparticles for Therapy and Diagnosis in Nanomedicine

Magnetic nanoparticles (MNPs) have been widely used for their potential applications, mainly for the diagnosis and/or therapy (theranostic) of several diseases in the field of nanomedicine, as passive contrast agents, through the opsonization process, or active contrast agents, after their functionalization and the subsequent capture of the signal using various techniques such as magnetic resonance imaging (MRI), optical imaging, nuclear imaging, and ultrasound [...].