Water phase transition and signal nulling in 3D dual-echo adiabatic inversion-recovery UTE (IR-UTE) imaging of myelin

PURPOSE

The semisolid myelin sheath has very fast transverse relaxation and is invisible to conventional MRI sequences. UTE sequences can detect signal from myelin. The major challenge is the concurrent detection of various water components.

METHODS

The inversion recovery (IR)-based UTE (IR-UTE) sequence employs an adiabatic inversion pulse to invert and suppress water magnetizations. TI plays a key role in water suppression, with negative water magnetizations (negative phase) before the null point and positive water magnetizations (positive phase) after the null point. A series of dual-echo IR-UTE images were acquired with different TIs to detect water phase transition. The effects of TR in phase transition and water suppression were also investigated using a relatively long TR of 500 ms and a short TR of 106 ms. The water phase transition in dual-echo IR-UTE imaging of myelin was investigated in five ex vivo and five in vivo human brains.

RESULTS

An apparent phase transition was observed in the second echo at the water signal null point, where the myelin signal was selectively detected by the UTE data acquisition at the optimal TI. The water phase transition point varied significantly across the brain when the long TR of 500 ms was used, whereas the convergence of TIs was observed when the short TR of 106 ms was used.

CONCLUSION

The results suggest that the IR-UTE sequence with a short TR allows uniform inversion and nulling of water magnetizations, thereby providing volumetric imaging of myelin.

The effects of CypA on apoptosis: potential target for the treatment of diseases

Cyclophilin A (CypA), the first member of cyclophilins, is distributed extensively in eukaryotic and prokaryotic cells, primarily localized in the cytoplasm. In addition to acting as an intracellular receptor for cyclosporin A (CSA), CypA plays a crucial role in diseases such as aging and tumorigenesis. Apoptosis, a form of programmed cell death, is able to balance the rate of cell viability and death. In this review, we focus on the effects of CypA on apoptosis and the relationship between specific mechanisms of CypA promoting or inhibiting apoptosis and diseases, including tumorigenesis, cardiovascular diseases, organ injury, and microbial infections. Notably, the process of CypA promoting or inhibiting apoptosis is closely related to disease development. Finally, future prospects for the association of CypA and apoptosis are discussed, and a comprehensive understanding of the effects of CypA on apoptosis in relation to diseases is expected to provide new insights into the design of CypA as a therapeutic target for diseases. KEY POINTS: • Understand the effect of CypA on apoptosis. • CypA affects apoptosis through specific pathways. • The effect of CypA on apoptosis is associated with a variety of disease processes.

Brain Iron in signature regions relating to cognitive aging in older adults: the Taizhou Imaging Study

BACKGROUND

Recent magnetic resonance imaging (MRI) studies have established that brain iron accumulation might accelerate cognitive decline in Alzheimer's disease (AD) patients. Both normal aging and AD are associated with cerebral atrophy in specific regions. However, no studies have investigated aging- and AD-selective iron deposition-related cognitive changes during normal aging. Here, we applied quantitative susceptibility mapping (QSM) to detect iron levels in cortical signature regions and assessed the relationships among iron, atrophy, and cognitive changes in older adults.

METHODS

In this Taizhou Imaging Study, 770 older adults (mean age 62.0 ± 4.93 years, 57.5% women) underwent brain MRI to measure brain iron and atrophy, of whom 219 underwent neuropsychological tests nearly every 12 months for up to a mean follow-up of 2.68 years. Global cognition was assessed using the Mini-Mental State Examination (MMSE) and Montreal Cognitive Assessment (MoCA). Domain-specific cognitive scores were obtained from MoCA subscore components. Regional analyses were performed for cortical regions and 2 signature regions where atrophy affected by aging and AD only: Aging (AG) -specific and AD signature meta-ROIs. The QSM and cortical morphometry means of the above ROIs were also computed.

RESULTS

Significant associations were found between QSM levels and cognitive scores. In particular, after adjusting for cortical thickness of regions of interest (ROIs), participants in the upper tertile of the cortical and AG-specific signature QSM exhibited worse ZMMSE than did those in the lower tertile [β = -0.104, p = 0.026;β = -0.118, p = 0.021, respectively]. Longitudinal analysis suggested that QSM values in all ROIs might predict decline in ZMoCA and key domains such as attention and visuospatial function (all p < 0.05). Furthermore, iron levels were negatively correlated with classic MRI markers of cortical atrophy (cortical thickness, gray matter volume, and local gyrification index) in total, AG-specific signature and AD signature regions (all p < 0.05).

CONCLUSION

AG- and AD-selective iron deposition was associated with atrophy and cognitive decline in elderly people, highlighting its potential as a neuroimaging marker for cognitive aging.

Mitochondrial Sulfenated-Protein-Targeted Covalent Immobilization Boosting Efficient Copper(II) Depletion for Enhanced Cancer Treatment

Copper plays a vital role in cellular metabolism and oxidative stress regulation. Visualizing and controlling the copper level in mitochondrion have been proven to be promising and efficient strategies for the diagnosis and treatment of triple-negative breast cancer (TNBC). However, developing an advanced probe for simultaneous visualization and depletion of mitochondrial copper remains a huge challenge. Herein, we for the first time report a mitochondria-anchorable, copper-responsive, and depleting probe d-IR-DPA and evaluate its potential for quantitative visualization of intratumoral copper(II) and anti-TNBC in vivo. Taking advantage of the mitochondrion-targeting and sulfenated-protein-mediated covalent immobilization characteristics, this probe not only enables the quantitative detection of Cu2+ levels in various types of tumors through ratiometric photoacoustic (PA680 nm/PA800 nm) imaging but also scavenges the mitochondrial Cu2+, simultaneously igniting increased oxidative stress and mitochondrial membrane damage and eventually leading to severe TNBC cell apoptosis. More notably, the depletion of Cu2+ by d-IR-DPA can alter the cellular metabolic pathway from oxidative phosphorylation to glycolysis, inducing energy deprivation and significant suppression of TNBC tumor in living mice. Our probe may provide a valuable and powerful means for the effective treatment of TNBC as well as other copper-associated diseases.

The role of iron transporters and regulators in Alzheimer's disease and Parkinson's disease: Pathophysiological insights and therapeutic prospects

Brain iron homeostasis plays a vital role in maintaining brain development and controlling neuronal function under physiological conditions. Many studies have shown that the imbalance of brain iron homeostasis is closely related to the pathogenesis of neurodegenerative diseases (NDs), such as Alzheimer's disease (AD) and Parkinson's disease (PD). Recent advances have revealed the importance of iron transporters and regulatory molecules in the pathogenesis and treatment of NDs. This review summarizes the research progress on brain iron overload and the aberrant expression of several key iron transporters and regulators in AD and PD, emphasizes the pathological roles of these molecules in the pathogenesis of AD and PD, and highlights the therapeutic prospects of targeting these iron transporters and regulators to restore brain iron homeostasis in the treatment of AD and PD. A comprehensive understanding of the pathophysiological roles of iron, iron transporters and regulators, and their regulations in NDs may provide new therapeutic avenues for more targeted neurotherapeutic strategies for treating these diseases.

Axillary lesion in a young adult ends up to a peculiar diagnosis: primitive neuro-ectodermal tumor (PNET) of ulnar nerve: a rare case report

Introduction and importance

Primitive neuro-ectodermal tumor (PNET) is a highly aggressive tumor composed of small round blue cells, mostly developing in children and young adults. Being a member of Ewing's Sarcoma Family of Tumors (ESFT); it has been discussed in two subcategories of central and peripheral PNET. PNETs of peripheral nerves are very uncommon pathologic findings, as to the best of our knowledge only 12 well-documented cases have been yet reported.

Case presentation

A 30-year-old male presented with progressive paresthesia of his right hand's little finger and painless swelling of the right axilla. Magnetic resonance (MR) neurography demonstrated a heterogeneous, high-signal, round mass within the right axilla fossa in proximity to the medial aspect of brachial plexus branches. The clinical and radiological study failed to an accurate diagnosis, thus surgical resection of the tumor was done for tissue evaluation. Histopathologic study of the lesion revealed a neoplasm comprising sheets of small, round, blue cells (Hematoxylin and Eosin stain), which immunohistochemically consisted with the diagnosis of PNET.

Clinical discussion

The differential diagnosis of axillary fossa masses, focusses on peripheral nerve tumors like Schwannoma and PNET. MR neurography aids in evaluation, but tissue diagnosis remains crucial. Treatment involves surgical resection, chemotherapy, and radiotherapy tailored to individual patients.

Conclusion

Although pPNET is not apparently the first differential diagnosis coming to mind when encountering a rapidly growing mass in the axillary fossa with peripheral nerve origin, its highly malignant behavior, makes it crucial to be considered in the differential diagnoses.

Investigating the association between symptoms and functional activity in brain regions in schizophrenia: A cross-sectional fmri-based neuroimaging study

Schizophrenia is a persistent neurological disorder profoundly affecting cognitive, emotional, and behavioral functions, prominently characterized by delusions, hallucinations, disordered speech, and abnormal motor activity. These symptoms often present diagnostic challenges due to their overlap with other forms of psychosis. Therefore, the implementation of automated diagnostic methodologies is imperative. This research leverages Functional Magnetic Resonance Imaging (fMRI), a neuroimaging modality capable of delineating functional activations across diverse brain regions. Furthermore, the utilization of evolving machine learning techniques for fMRI data analysis has significantly progressive. Here, our study stands as a novel attempt, focusing on the comprehensive assessment of both classical and atypical symptoms of schizophrenia. We aim to uncover associated changes in brain functional activity. Our study encompasses two distinct fMRI datasets (1.5T and 3T), each comprising 34 schizophrenia patients for the 1.5T dataset and 25 schizophrenia patients for the 3T dataset, along with an equal number of healthy controls. Machine learning algorithms are applied to assess data subsets, enabling an in-depth evaluation of the current functional condition concerning symptom impact. The identified voxels contribute to determining the brain regions most influenced by each symptom, as quantified by symptom intensity. This rigorous approach has yielded various new findings while maintaining an impressive classification accuracy rate of 97 %. By elucidating variations in activation patterns across multiple brain regions in individuals with schizophrenia, this study contributes to the understanding of functional brain changes associated with the disorder. The insights gained may inform differential clinical interventions and provide a means of assessing symptom severity accurately, offering new avenues for the management of schizophrenia.

Alzheimer's disease diagnosis from multi-modal data via feature inductive learning and dual multilevel graph neural network

Multi-modal data can provide complementary information of Alzheimer's disease (AD) and its development from different perspectives. Such information is closely related to the diagnosis, prevention, and treatment of AD, and hence it is necessary and critical to study AD through multi-modal data. Existing learning methods, however, usually ignore the influence of feature heterogeneity and directly fuse features in the last stages. Furthermore, most of these methods only focus on local fusion features or global fusion features, neglecting the complementariness of features at different levels and thus not sufficiently leveraging information embedded in multi-modal data. To overcome these shortcomings, we propose a novel framework for AD diagnosis that fuses gene, imaging, protein, and clinical data. Our framework learns feature representations under the same feature space for different modalities through a feature induction learning (FIL) module, thereby alleviating the impact of feature heterogeneity. Furthermore, in our framework, local and global salient multi-modal feature interaction information at different levels is extracted through a novel dual multilevel graph neural network (DMGNN). We extensively validate the proposed method on the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset and experimental results demonstrate our method consistently outperforms other state-of-the-art multi-modal fusion methods. The code is publicly available on the GitHub website. (https://github.com/xiankantingqianxue/MIA-code.git).

The Azalea Hypothesis of Alzheimer Disease: A Functional Iron Deficiency Promotes Neurodegeneration

Chlorosis in azaleas is characterized by an interveinal yellowing of leaves that is typically caused by a deficiency of iron. This condition is usually due to the inability of cells to properly acquire iron as a consequence of unfavorable conditions, such as an elevated pH, rather than insufficient iron levels. The causes and effects of chlorosis were found to have similarities with those pertaining to a recently presented hypothesis that describes a pathogenic process in Alzheimer disease. This hypothesis states that iron becomes sequestered (e.g., by amyloid β and tau), causing a functional deficiency of iron that disrupts biochemical processes leading to neurodegeneration. Additional mechanisms that contribute to iron becoming unavailable include iron-containing structures not undergoing proper recycling (e.g., disrupted mitophagy and altered ferritinophagy) and failure to successfully translocate iron from one compartment to another (e.g., due to impaired lysosomal acidification). Other contributors to a functional deficiency of iron in patients with Alzheimer disease include altered metabolism of heme or altered production of iron-containing proteins and their partners (e.g., subunits, upstream proteins). A review of the evidence supporting this hypothesis is presented. Also, parallels between the mechanisms underlying a functional iron-deficient state in Alzheimer disease and those occurring for chlorosis in plants are discussed. Finally, a model describing the generation of a functional iron deficiency in Alzheimer disease is put forward.

High Luminescent Carbon Dots Derived from Fermented Beverages for Sensitive Sensing of Tartrazine in Foods

Highly luminescent carbon dots (CDs) derived from fermented beverages-kvass (K-CDs) were synthesized through a one-step hydrothermal method with ethylenediamine (EDA) as a surface passivation reagent. Purified K-CDs with a fluorescent quantum yield of 35.1% were obtained after a dialysis process. The K-CDs were characterized by TEM, FT-IR, XPS, fluorescence and UV-vis spectroscopy. The results indicated that K-CDs possess typical excitation wavelength-dependent blue fluorescence emission, and the strongest excitation and emission wavelengths are 350 nm and 440 nm, respectively. The great spectral overlap between the emission peak (440 nm) of K-CDs and the absorption peak (430 nm) of tartrazine (TAR) leads to an effective fluorescence quenching phenomenon by TAR through inner filter effect (IFE) and the calculated (lg(I0/I)) showed a linear response to TAR concentration in the range of 0.1-70 µM. The detection limit of the developed method is 23 nM for TAR, and the relative standard deviation (RSD) is 3.9% (c = 10 µM, n = 7). The fluorescent sensor for TAR based on K-CDs through the IFE mechanism possesses the characteristics of rapid, sensitive, and high selectivity. It has been successfully applied to detect of trace TAR in foods.

Explainable breast cancer molecular expression prediction using multi-task deep-learning based on 3D whole breast ultrasound

OBJECTIVES

To noninvasively estimate three breast cancer biomarkers, estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER2) and enhance performance and interpretability via multi-task deep learning.

METHODS

The study included 388 breast cancer patients who received the 3D whole breast ultrasound system (3DWBUS) examinations at Xijing Hospital between October 2020 and September 2021. Two predictive models, a single-task and a multi-task, were developed; the former predicts biomarker expression, while the latter combines tumor segmentation with biomarker prediction to enhance interpretability. Performance evaluation included individual and overall prediction metrics, and Delong's test was used for performance comparison. The models' attention regions were visualized using Grad-CAM + + technology.

RESULTS

All patients were randomly split into a training set (n = 240, 62%), a validation set (n = 60, 15%), and a test set (n = 88, 23%). In the individual evaluation of ER, PR, and HER2 expression prediction, the single-task and multi-task models achieved respective AUCs of 0.809 and 0.735 for ER, 0.688 and 0.767 for PR, and 0.626 and 0.697 for HER2, as observed in the test set. In the overall evaluation, the multi-task model demonstrated superior performance in the test set, achieving a higher macro AUC of 0.733, in contrast to 0.708 for the single-task model. The Grad-CAM + + method revealed that the multi-task model exhibited a stronger focus on diseased tissue areas, improving the interpretability of how the model worked.

CONCLUSION

Both models demonstrated impressive performance, with the multi-task model excelling in accuracy and offering improved interpretability on noninvasive 3DWBUS images using Grad-CAM + + technology.

CRITICAL RELEVANCE STATEMENT

The multi-task deep learning model exhibits effective prediction for breast cancer biomarkers, offering direct biomarker identification and improved clinical interpretability, potentially boosting the efficiency of targeted drug screening.

KEY POINTS

Tumoral biomarkers are paramount for determining breast cancer treatment. The multi-task model can improve prediction performance, and improve interpretability in clinical practice. The 3D whole breast ultrasound system-based deep learning models excelled in predicting breast cancer biomarkers.

Magnetic resonance imaging-based machine learning classification of schizophrenia spectrum disorders: a meta-analysis

BACKGROUND

Recent advances in multivariate pattern recognition have fostered the search for reliable neuroimaging-based biomarkers in psychiatric conditions, including schizophrenia. These approaches consider the complex pattern of alterations in brain function and structure, overcoming the limitations of traditional univariate methods. To assess the reliability of neuroimaging-based biomarkers and the contribution of study characteristics in distinguishing individuals with schizophrenia spectrum disorder (SSD) from healthy controls (HCs), we conducted a systematic review of the studies that used multivariate pattern recognition for this objective.

METHODS

We systematically searched PubMed, Scopus, and Web of Science for studies on SSD classification using multivariate pattern analysis on magnetic resonance imaging data. We employed a bivariate random-effects meta-analytic model to explore the classification of sensitivity (SE) and specificity (SP) across studies while also evaluating the moderator effects of clinical and non-clinical variables.

RESULTS

A total of 119 studies (with 12,723 patients with SSD and 13,196 HCs) were identified. The meta-analysis estimated a SE of 79.1% (95% confidence interval [CI], 77.1%-81.0%) and a SP of 80.0% (95% CI, 77.8%-82.0%). In particular, the Positive and Negative Syndrome Scale and the Global Assessment of Functioning scores, age, age of onset, duration of untreated psychosis, deep learning, algorithm type, features selection, and validation methods had significant effects on classification performance.

CONCLUSIONS

Multivariate pattern analysis reliably identifies neuroimaging-based biomarkers of SSD, achieving ∼80% SE and SP. Despite clinical heterogeneity, discernible brain modifications effectively differentiate SSD from HCs. Classification performance depends on patient-related and methodological factors crucial for the development, validation, and application of prospective models in clinical settings.

Cine-MRI and T1TSE Sequence for Mediastinal Mass

Background/Objectives: Contrast-enhanced computed tomography (CT) is the standard radiologic examination for evaluating the extent of mediastinal tumors. If tumor infiltration into the large central thoracic vessels, the pericardium, or the myocardium is suspected, cine magnetic resonance imaging (cine-MRI) can provide additional valuable information. Methods: We conducted a retrospective study of patients with mediastinal tumors who were staged with CT, cine-MRI, and a T1-weighted turbo spin echo (T1TSE) prior to surgical resection. Imaging was re-evaluated regarding tumor infiltration into the pericardium, myocardium, superior vena cava, aorta, pulmonary arteries, and atria and compared with intraoperative findings and postoperative histopathological reports (gold standard). Unclear CT findings were further investigated. Results: Forty-seven patients (29 female and 18 male patients; median age: 58 years) met the inclusion criteria. Cine-MRI was able to predict infiltration of the aorta in 86%, pulmonary arteries in 85%, and atria in 80% of unclear CT cases. Aortic tumor infiltration in unclear CT cases was significantly more often correctly diagnosed with cine-MRI than with T1TSE sequence. Conclusions: Additional cine-MRI is of crucial benefit in unclear CT cases. We recommend performing cine-MRI if infiltration into the large central vessels and atria is suspected. T1TSE sequence is of very limited additional value.

Optimizing graph neural network architectures for schizophrenia spectrum disorder prediction using evolutionary algorithms

BACKGROUND AND OBJECTIVE

The accurate diagnosis of schizophrenia spectrum disorder plays an important role in improving patient outcomes, enabling timely interventions, and optimizing treatment plans. Functional connectivity analysis, utilizing functional magnetic resonance imaging data, has been demonstrated to offer invaluable biomarkers conducive to clinical diagnosis. However, previous studies mainly focus on traditional machine learning methods or hand-crafted neural networks, which may not fully capture the spatial topological relationship between brain regions.

METHODS

This paper proposes an evolutionary algorithm (EA) based graph neural architecture search (GNAS) method. EA-GNAS has the ability to search for high-performance graph neural networks for schizophrenia spectrum disorder prediction. Moreover, we adopt GNNExplainer to investigate the explainability of the acquired architectures, ensuring that the model's predictions are both accurate and comprehensible.

RESULTS

The results suggest that the graph neural network model, derived using genetic algorithm search, outperforms under five-fold cross-validation, achieving a fitness of 0.1850. Relative to conventional machine learning and other deep learning approaches, the proposed method yields superior accuracy, F1 score, and AUC values of 0.8246, 0.8438, and 0.8258, respectively.

CONCLUSION

Based on a multi-site dataset from schizophrenia spectrum disorder patients, the findings reveal an enhancement over prior methods, advancing our comprehension of brain function and potentially offering a biomarker for diagnosing schizophrenia spectrum disorder.

Efficacy of Repetitive Transcranial Magnetic Stimulation (rTMS) in the Treatment of Bulimia Nervosa (BN): A Review and Insight into Potential Mechanisms of Action

INTRODUCTION

Bulimia nervosa (BN) is a disorder primarily affecting adolescent females, characterized by episodes of binge eating followed by inappropriate compensatory behaviors aimed at preventing weight gain, including self-induced vomiting and the misuse of diuretics, laxatives, and insulin. The precise etiology of BN remains unknown, with factors such as genetics, biological influences, emotional disturbances, societal pressures, and other challenges contributing to its prevalence. First-line treatment typically includes pharmacotherapy, which has shown moderate effectiveness. Neuroimaging evidence suggests that altered brain activity may contribute to the development of BN, making interventions that directly target the brain extremely valuable. One such intervention is repetitive transcranial magnetic stimulation (rTMS), a non-invasive stimulation technique that has been garnering interest in the medical community for many years.

METHODS

This review explores the use of rTMS in the treatment of BN. Searches were conducted in the PubMed/Medline, ResearchGate, and Cochrane databases.

RESULTS

Twelve relevant studies were identified. Analysis of the results from these studies reveals promising findings, particularly regarding key parameters in the pathophysiology of BN. Several studies assessed the impact of rTMS on binge episodes. While some studies did not find significant reductions, most reported decreases in binge eating and purging behaviors, with some cases showing complete remission. Reductions in symptoms of depression and food cravings were also demonstrated. However, results regarding cognitive improvement were mixed. The discussion focused heavily on potential mechanisms of action, including neuromodulation of brain networks, induction of neuroplasticity, impact on serotonergic dysfunction, anti-inflammatory action, and HPA axis modulation. rTMS was found to be a safe intervention with no serious side effects.

CONCLUSIONS

rTMS in the treatment of BN appears to be a promising intervention that alleviates some symptoms characteristic of the pathophysiology of this disorder. An additional effect is a significant reduction in depressive symptoms. However, despite these findings, further research is required to confirm its effectiveness and elucidate the mechanisms of action. It is also recommended to further investigate the potential mechanisms of action described in this review.

Uncertainty estimation using a 3D probabilistic U-Net for segmentation with small radiotherapy clinical trial datasets

BACKGROUND AND OBJECTIVES

Bio-medical image segmentation models typically attempt to predict one segmentation that resembles a ground-truth structure as closely as possible. However, as medical images are not perfect representations of anatomy, obtaining this ground truth is not possible. A surrogate commonly used is to have multiple expert observers define the same structure for a dataset. When multiple observers define the same structure on the same image there can be significant differences depending on the structure, image quality/modality and the region being defined. It is often desirable to estimate this type of aleatoric uncertainty in a segmentation model to help understand the region in which the true structure is likely to be positioned. Furthermore, obtaining these datasets is resource intensive so training such models using limited data may be required. With a small dataset size, differing patient anatomy is likely not well represented causing epistemic uncertainty which should also be estimated so it can be determined for which cases the model is effective or not.

METHODS

We use a 3D probabilistic U-Net to train a model from which several segmentations can be sampled to estimate the range of uncertainty seen between multiple observers. To ensure that regions where observers disagree most are emphasised in model training, we expand the Generalised Evidence Lower Bound (ELBO) with a Constrained Optimisation (GECO) loss function with an additional contour loss term to give attention to this region. Ensemble and Monte-Carlo dropout (MCDO) uncertainty quantification methods are used during inference to estimate model confidence on an unseen case. We apply our methodology to two radiotherapy clinical trial datasets, a gastric cancer trial (TOPGEAR, TROG 08.08) and a post-prostatectomy prostate cancer trial (RAVES, TROG 08.03). Each dataset contains only 10 cases each for model development to segment the clinical target volume (CTV) which was defined by multiple observers on each case. An additional 50 cases are available as a hold-out dataset for each trial which had only one observer define the CTV structure on each case. Up to 50 samples were generated using the probabilistic model for each case in the hold-out dataset. To assess performance, each manually defined structure was matched to the closest matching sampled segmentation based on commonly used metrics.

RESULTS

The TOPGEAR CTV model achieved a Dice Similarity Coefficient (DSC) and Surface DSC (sDSC) of 0.7 and 0.43 respectively with the RAVES model achieving 0.75 and 0.71 respectively. Segmentation quality across cases in the hold-out datasets was variable however both the ensemble and MCDO uncertainty estimation approaches were able to accurately estimate model confidence with a p-value < 0.001 for both TOPGEAR and RAVES when comparing the DSC using the Pearson correlation coefficient.

CONCLUSIONS

We demonstrated that training auto-segmentation models which can estimate aleatoric and epistemic uncertainty using limited datasets is possible. Having the model estimate prediction confidence is important to understand for which unseen cases a model is likely to be useful.

Radiogenomics: bridging the gap between imaging and genomics for precision oncology

Genomics allows the tracing of origin and evolution of cancer at molecular scale and underpin modern cancer diagnosis and treatment systems. Yet, molecular biomarker-guided clinical decision-making encounters major challenges in the realm of individualized medicine, consisting of the invasiveness of procedures and the sampling errors due to high tumor heterogeneity. By contrast, medical imaging enables noninvasive and global characterization of tumors at a low cost. In recent years, radiomics has overcomes the limitations of human visual evaluation by high-throughput quantitative analysis, enabling the comprehensive utilization of the vast amount of information underlying radiological images. The cross-scale integration of radiomics and genomics (hereafter radiogenomics) has the enormous potential to enhance cancer decoding and act as a catalyst for digital precision medicine. Herein, we provide a comprehensive overview of the current framework and potential clinical applications of radiogenomics in patient care. We also highlight recent research advances to illustrate how radiogenomics can address common clinical problems in solid tumors such as breast cancer, lung cancer, and glioma. Finally, we analyze existing literature to outline challenges and propose solutions, while also identifying future research pathways. We believe that the perspectives shared in this survey will provide a valuable guide for researchers in the realm of radiogenomics aiming to advance precision oncology.

Brain network dynamics in patients with single- and multiple-domain amnestic mild cognitive impairment

INTRODUCTION

Brain network dynamics have been extensively explored in patients with amnestic mild cognitive impairment (aMCI); however, differences in single- and multiple-domain aMCI (SD-aMCI and MD-aMCI) remain unclear.

METHODS

Using multicenter datasets, coactivation patterns (CAPs) were constructed and compared among normal control (NC), SD-aMCI, MD-aMCI, and Alzheimer's disease (AD) patients based on individual high-order cognitive network (HOCN) and primary sensory network (PSN) parcellations. Correlations between spatiotemporal characteristics and neuropsychological scores were analyzed.

RESULTS

Compared to NC, SD-aMCI showed temporal alterations in HOCN-dominant CAPs, while MD-aMCI showed alterations in PSN-dominant CAPs. In addition, transitions from SD-aMCI to AD may involve PSN, while MD-aMCI to AD involves both PSN and HOCN. Results were generally consistent across datasets from Chinese and White populations.

DISCUSSION

The HOCN and PSN are distinctively involved in aMCI subtypes and in the transformation between aMCI subtypes and AD, highlighting the necessity of aMCI subtype classification in AD studies.

HIGHLIGHTS

Individual functional network parcellations and coactivation pattern (CAP) analysis were performed to characterize spatiotemporal differences between single- and multiple-domain amnestic mild cognitive impairment (SD-aMCI and MD-aMCI), and between distinct aMCI subtypes and Alzheimer's disease (AD). The analysis of multicenter datasets converged on four pairs of recurrent CAPs, including primary sensory networks (PSN)-dominant CAPs, high-order cognitive networks (HOCN)-dominant CAPs, and PSN-HOCN-interacting CAPs. The HOCN and PSN are distinctively involved in aMCI subtypes and in the transformation between distinct aMCI subtypes and AD.

Individual large-scale functional network mapping for major depressive disorder with electroconvulsive therapy

Personalized functional connectivity mapping has been demonstrated to be promising in identifying underlying neurophysiological basis for brain disorders and treatment effects. Electroconvulsive therapy (ECT) has been proved to be an effective treatment for major depressive disorder (MDD) while its active mechanisms remain unclear. Here, 46 MDD patients before and after ECT as well as 46 demographically matched healthy controls (HC) underwent resting-state functional magnetic resonance imaging (rs-fMRI) scans. A spatially regularized form of non-negative matrix factorization (NMF) was used to accurately identify functional networks (FNs) in individuals to map individual-level static and dynamic functional network connectivity (FNC) to reveal the underlying neurophysiological basis of therepetical effects of ECT for MDD. Moreover, these static and dynamic FNCs were used as features to predict the clinical treatment outcomes for MDD patients. We found that ECT could modulate both static and dynamic large-scale FNCs at individual level in MDD patients, and dynamic FNCs were closely associated with depression and anxiety symptoms. Importantly, we found that individual FNCs, particularly the individual dynamic FNCs could better predict the treatment outcomes of ECT suggesting that dynamic functional connectivity analysis may be better to link brain functional characteristics with clinical symptoms and treatment outcomes. Taken together, our findings provide new evidence for the active mechanisms and biomarkers for ECT to improve diagnostic accuracy and to guide individual treatment selection for MDD patients.

Associations between sex, systemic iron and inflammatory status and subcortical brain iron

Brain iron increases in several neurodegenerative diseases are associated with disease progression. However, the causes of increased brain iron remain unclear. This study investigates relationships between subcortical iron, systemic iron and inflammatory status. Brain magnetic resonance imaging (MRI) scans and blood plasma samples were collected from cognitively healthy females (n = 176, mean age = 61.4 ± 4.5 years, age range = 28-72 years) and males (n = 152, mean age = 62.0 ± 5.1 years, age range = 32-74 years). Regional brain iron was quantified using quantitative susceptibility mapping. To assess systemic iron, haematocrit, ferritin and soluble transferrin receptor were measured, and total body iron index was calculated. To assess systemic inflammation, C-reactive protein (CRP), neutrophil:lymphocyte ratio (NLR), macrophage colony-stimulating factor 1 (MCSF), interleukin 6 (IL6) and interleukin 1β (IL1β) were measured. We demonstrated that iron levels in the right hippocampus were higher in males compared with females, while iron in the right caudate was higher in females compared with males. There were no significant associations observed between subcortical iron levels and blood markers of iron and inflammatory status indicating that such blood measures are not markers of brain iron. These results suggest that brain iron may be regulated independently of blood iron and so directly targeting global iron change in the treatment of neurodegenerative disease may have differential impacts on blood and brain iron.

Optimizing Flexible Microelectrode Designs for Enhanced Efficacy in Electrical Stimulation Therapy

To investigate the impact of electrode structure on Electrical Stimulation Therapy (EST) for chronic wound healing, this study designed three variants of flexible microelectrodes (FMs) with Ag-Cu coverings (ACCs), each exhibiting distinct geometrical configurations: hexagonal, cross-shaped, and serpentine. These were integrated with PPY/PDA/PANI (3/6) (full name: polypyrrole/polydopamine/polyaniline 3/6). Hydrogel dressing comprehensive animal studies, coupled with detailed electrical and mechanical modeling and simulations, were conducted to assess their performance. Results indicated that the serpentine-shaped FM outperformed its counterparts in terms of flexibility and safety, exhibiting minimal thermal effects and a reduced risk of burns. Notably, FMs with metal coverings under 3% demonstrated promising potential for optoelectronic self-powering capabilities. Additionally, simulation data highlighted the significant influence of hydrogel non-uniformity on the distribution of electrical properties across the skin surface, providing critical insights for optimizing EST protocols when employing hydrogel dressings.

Schizophrenia and Neurodevelopment: Insights From Connectome Perspective

BACKGROUND

Schizophrenia is conceptualized as a brain connectome disorder that can emerge as early as late childhood and adolescence. However, the underlying neurodevelopmental basis remains unclear. Recent interest has grown in children and adolescent patients who experience symptom onset during critical brain development periods. Inspired by advanced methodological theories and large patient cohorts, Chinese researchers have made significant original contributions to understanding altered brain connectome development in early-onset schizophrenia (EOS).

STUDY DESIGN

We conducted a search of PubMed and Web of Science for studies on brain connectomes in schizophrenia and neurodevelopment. In this selective review, we first address the latest theories of brain structural and functional development. Subsequently, we synthesize Chinese findings regarding mechanisms of brain structural and functional abnormalities in EOS. Finally, we highlight several pivotal challenges and issues in this field.

STUDY RESULTS

Typical neurodevelopment follows a trajectory characterized by gray matter volume pruning, enhanced structural and functional connectivity, improved structural connectome efficiency, and differentiated modules in the functional connectome during late childhood and adolescence. Conversely, EOS deviates with excessive gray matter volume decline, cortical thinning, reduced information processing efficiency in the structural brain network, and dysregulated maturation of the functional brain network. Additionally, common functional connectome disruptions of default mode regions were found in early- and adult-onset patients.

CONCLUSIONS

Chinese research on brain connectomes of EOS provides crucial evidence for understanding pathological mechanisms. Further studies, utilizing standardized analyses based on large-sample multicenter datasets, have the potential to offer objective markers for early intervention and disease treatment.

An end-to-end deep learning pipeline to derive blood input with partial volume corrections for automated parametric brain PET mapping

Dynamic 2-[18F] fluoro-2-deoxy-D-glucose positron emission tomography (dFDG-PET) for human brain imaging has considerable clinical potential, yet its utilization remains limited. A key challenge in the quantitative analysis of dFDG-PET is characterizing a patient-specific blood input function, traditionally reliant on invasive arterial blood sampling. This research introduces a novel approach employing non-invasive deep learning model-based computations from the internal carotid arteries (ICA) with partial volume (PV) corrections, thereby eliminating the need for invasive arterial sampling. We present an end-to-end pipeline incorporating a 3D U-Net based ICA-net for ICA segmentation, alongside a Recurrent Neural Network (RNN) based MCIF-net for the derivation of a model-corrected blood input function (MCIF) with PV corrections. The developed 3D U-Net and RNN was trained and validated using a 5-fold cross-validation approach on 50 human brain FDG PET scans. The ICA-net achieved an average Dice score of 82.18% and an Intersection over Union of 68.54% across all tested scans. Furthermore, the MCIF-net exhibited a minimal root mean squared error of 0.0052. The application of this pipeline to ground truth data for dFDG-PET brain scans resulted in the precise localization of seizure onset regions, which contributed to a successful clinical outcome, with the patient achieving a seizure-free state after treatment. These results underscore the efficacy of the ICA-net and MCIF-net deep learning pipeline in learning the ICA structure's distribution and automating MCIF computation with PV corrections. This advancement marks a significant leap in non-invasive neuroimaging.

Multiple Transcriptomic Analyses Explore Potential Synaptic Biomarker Rabphilin-3A for Alzheimer's Disease

Alzheimer's disease (AD) is a prevalent neurodegenerative disorder afflicting the elderly population worldwide. The identification of potential gene candidates for AD holds promises for diagnostic biomarkers and therapeutic targets. Employing a comprehensive strategy, this study integrated transcriptomic data from diverse data sources, including microarray and single-cell datasets from blood and tissue samples, enabling a detailed exploration of gene expression dynamics. Through this thorough investigation, 19 notable candidate genes were found with consistent expression changes across both blood and tissue datasets, suggesting their potential as biomarkers for AD. In addition, single cell sequencing analysis further highlighted their specific expression in excitatory and inhibitory neurons, the primary functional units in the brain, underscoring their relevance to AD pathology. Moreover, the functional enrichment analysis revealed that three of the candidate genes were downregulated in synaptic signaling pathway. Further validation experiments significantly showed reduced levels of rabphilin-3A (RPH3A) in 3xTg-AD model mice, implying its role in disease pathogenesis. Given its role in neurotransmitter exocytosis and synaptic function, further investigation into RPH3A and its interactions with neurotrophic proteins may provide valuable insights into the complex molecular mechanisms underlying synaptic dysfunction in AD.

Pilot Study of Personalized Transcranial Magnetic Stimulation with Spectral Electroencephalogram Analyses for Assessing and Treating Persons with Autism

Autism spectrum condition (ASC) is a neurodevelopmental condition that is only partly responsive to prevailing interventions. ASC manifests core challenges in social skills, communication, and sensory function and by repetitive stereotyped behaviors, along with imbalances in the brain's excitatory (E) and inhibitory (I) signaling. Repetitive transcranial magnetic stimulation (rTMS) has shown promise in ASC and may be a useful addition to applied behavioral analysis (ABA), a gold-standard psychotherapeutic intervention. We report an open-label clinical pilot (initial) study in which ABA-treated ASC persons (n = 123) received our personalized rTMS protocol (PrTMS). PrTMS uses low TMS pulse intensities and continuously updates multiple cortical stimulation locales and stimulation frequencies based on the spectral EEG and psychometrics. No adverse effects developed, and 44% of subjects had ASC scale scores reduced to below diagnostic cutoffs. Importantly, in PrTMS responders, the spectral EEG regression flattened, implying a more balanced E/I ratio. Moreover, with older participants, alpha peak frequency increased, a positive correlate of non-verbal cognition. PrTMS may be an effective ASC intervention, offering improved cognitive function and overall symptomatology. This warrants further research into PrTMS mechanisms and specific types of subjects who may benefit, along with validation of the present results and exploration of broader clinical applicability.

Enhancing the ophthalmic AI assessment with a fundus image quality classifier using local and global attention mechanisms

Background

The assessment of image quality (IQA) plays a pivotal role in the realm of image-based computer-aided diagnosis techniques, with fundus imaging standing as the primary method for the screening and diagnosis of ophthalmic diseases. Conventional studies on fundus IQA tend to rely on simplistic datasets for evaluation, predominantly focusing on either local or global information, rather than a synthesis of both. Moreover, the interpretability of these studies often lacks compelling evidence. In order to address these issues, this study introduces the Local and Global Attention Aggregated Deep Neural Network (LGAANet), an innovative approach that integrates both local and global information for enhanced analysis.

Methods

The LGAANet was developed and validated using a Multi-Source Heterogeneous Fundus (MSHF) database, encompassing a diverse collection of images. This dataset includes 802 color fundus photography (CFP) images (302 from portable cameras), and 500 ultrawide-field (UWF) images from 904 patients with diabetic retinopathy (DR) and glaucoma, as well as healthy individuals. The assessment of image quality was meticulously carried out by a trio of ophthalmologists, leveraging the human visual system as a benchmark. Furthermore, the model employs attention mechanisms and saliency maps to bolster its interpretability.

Results

In testing with the CFP dataset, LGAANet demonstrated remarkable accuracy in three critical dimensions of image quality (illumination, clarity and contrast based on the characteristics of human visual system, and indicates the potential aspects to improve the image quality), recording scores of 0.947, 0.924, and 0.947, respectively. Similarly, when applied to the UWF dataset, the model achieved accuracies of 0.889, 0.913, and 0.923, respectively. These results underscore the efficacy of LGAANet in distinguishing between varying degrees of image quality with high precision.

Conclusion

To our knowledge, LGAANet represents the inaugural algorithm trained on an MSHF dataset specifically for fundus IQA, marking a significant milestone in the advancement of computer-aided diagnosis in ophthalmology. This research significantly contributes to the field, offering a novel methodology for the assessment and interpretation of fundus images in the detection and diagnosis of ocular diseases.

Feasibility and long-term survival of proximal gastrectomy after neoadjuvant therapy for locally advanced proximal gastric cancer: A propensity-score-matched analysis

BACKGROUND

Neoadjuvant therapy enhances the possibility of achieving radical resection and improves the prognosis for locally advanced gastric cancer (GC). However, there is a lack of evidence regarding the optimal extent of resection for locally advanced proximal GC after neoadjuvant therapy.

METHODS

In this study, 330 patients underwent resection in Peking University Cancer Hospital, with curative intent after neoadjuvant therapy for histologically confirmed proximal GC from January 2009 to December 2022. Among them, 45 patients underwent proximal gastrectomy (PG), while 285 underwent total gastrectomy (TG).

RESULTS

In this study, 45 patients underwent proximal gastrectomy (PG), while 285 underwent total gastrectomy (TG). After propensity-score matching, 110 patients (71 TG and 39 PG) were included in the analysis. No significant differences between PG and TG regarding short-term outcomes and long-term prognosis were found. Specifically, PG demonstrated comparable overall survival to TG (P = 0.47). Subgroup analysis revealed that although not statistically significant, PG showed a potential advantage over TG in overall survival for patients with tumor-long diameters less than 4 cm (P = 0.31). However, for those with a long diameter larger than 4 cm, TG had a better survival probability (P = 0.81). No substantial differences were observed in baseline characteristics, surgical safety, postoperative recovery, and postoperative complications.

CONCLUSION

For locally advanced proximal GC with objective response to neoadjuvant therapy (long diameter <4 cm), PG is an alternative surgical procedure. Further research and prospective studies are warranted to validate these findings and guide clinical decision-making.

Reduced oxygen extraction fraction in deep cerebral veins associated with cognitive impairment in multiple sclerosis

Studying the relationship between cerebral oxygen utilization and cognitive impairment is essential to understanding neuronal functional changes in the disease progression of multiple sclerosis (MS). This study explores the potential of using venous susceptibility in internal cerebral veins (ICVs) as an imaging biomarker for cognitive impairment in relapsing-remitting MS (RRMS) patients. Quantitative susceptibility mapping derived from fully flow-compensated MRI phase data was employed to directly measure venous blood oxygen saturation levels (SvO2) in the ICVs. Results revealed a significant reduction in the susceptibility of ICVs (212.4 ± 30.8 ppb vs 239.4 ± 25.9 ppb) and a significant increase of SvO2 (74.5 ± 1.89% vs 72.4 ± 2.23%) in patients with RRMS compared with age- and sex-matched healthy controls. Both the susceptibility of ICVs (r = 0.508, p = 0.031) and the SvO2 (r = -0.498, p = 0.036) exhibited a moderate correlation with cognitive decline in these patients assessed by the Paced Auditory Serial Addition Test, while no significant correlation was observed with clinical disability measured by the Expanded Disability Status Scale. The findings suggest that venous susceptibility in ICVs has the potential to serve as a specific indicator of oxygen metabolism and cognitive function in RRMS. .

Differentiating low-risk thymomas from high-risk thymomas: preoperative radiomics nomogram based on contrast enhanced CT to minimize unnecessary invasive thoracotomy

BACKGROUND

This study was designed to develop a combined radiomics nomogram to preoperatively predict the risk categorization of thymomas based on contrast-enhanced computed tomography (CE-CT) images.

MATERIALS

The clinical and CT data of 178 patients with thymoma (100 patients with low-risk thymomas and 78 patients with high-risk thymomas) collected in our hospital from March 2018 to July 2023 were retrospectively analyzed. The patients were randomly divided into a training set (n = 125) and a validation set (n = 53) in a 7:3 ratio. Qualitative radiological features were recorded, including (a) tumor diameter, (b) location, (c) shape, (d) capsule integrity, (e) calcification, (f) necrosis, (g) fatty infiltration, (h) lymphadenopathy, and (i) enhanced CT value. Radiomics features were extracted from each CE-CT volume of interest (VOI), and the least absolute shrinkage and selection operator (LASSO) algorithm was performed to select the optimal discriminative ones. A combined radiomics nomogram was further established based on the clinical factors and radiomics scores. The differentiating efficacy was determined using receiver operating characteristic (ROC) analysis.

RESULTS

Only one clinical factor (incomplete capsule) and seven radiomics features were found to be independent predictors and were used to establish the radiomics nomogram. In differentiating low-risk thymomas (types A, AB, and B1) from high-risk ones (types B2 and B3), the nomogram demonstrated better diagnostic efficacy than any single model, with the respective area under the curve (AUC), accuracy, sensitivity, and specificity of 0.974, 0.921, 0.962 and 0.900 in the training cohort, 0.960, 0.892, 0923 and 0.897 in the validation cohort, respectively. The calibration curve showed good agreement between the prediction probability and actual clinical findings.

CONCLUSIONS

The nomogram incorporating clinical factors and radiomics features provides additional value in differentiating the risk categorization of thymomas, which could potentially be useful in clinical practice for planning personalized treatment strategies.

Screening of gastric cancer diagnostic biomarkers in the homologous recombination signaling pathway and assessment of their clinical and radiomic correlations

BACKGROUND

Homologous recombination plays a vital role in the occurrence and drug resistance of gastric cancer. This study aimed to screen new gastric cancer diagnostic biomarkers in the homologous recombination pathway and then used radiomic features to construct a prediction model of biomarker expression to guide the selection of chemotherapy regimens.

METHODS

Gastric cancer transcriptome data were downloaded from The Cancer Genome Atlas database. Machine learning methods were used to screen for diagnostic biomarkers of gastric cancer and validate them experimentally. Computed Tomography image data of gastric cancer patients and corresponding clinical data were downloaded from The Cancer Imaging Archive and our imaging centre, and then the Computed Tomography images were subjected to feature extraction, and biomarker expression prediction models were constructed to analyze the correlation between the biomarker radiomics scores and clinicopathological features.

RESULTS

We screened RAD51D and XRCC2 in the homologous recombination pathway as biomarkers for gastric cancer diagnosis by machine learning, and the expression of RAD51D and XRCC2 was significantly positively correlated with pathological T stage, N stage, and TNM stage. Homologous recombination pathway blockade inhibits gastric cancer cell proliferation, promotes apoptosis, and reduces the sensitivity of gastric cancer cells to chemotherapeutic drugs. Our predictive RAD51D and XRCC2 expression models were constructed using radiomics features, and all the models had high accuracy. In the external validation cohort, the predictive models still had decent accuracy. Moreover, the radiomics scores of RAD51D and XRCC2 were also significantly positively correlated with the pathologic T, N, and TNM stages.

CONCLUSIONS

The gastric cancer diagnostic biomarkers RAD51D and XRCC2 that we screened can, to a certain extent, reflect the expression status of genes through radiomic characteristics, which is of certain significance in guiding the selection of chemotherapy regimens for gastric cancer patients.

Unraveling multi-scale neuroimaging biomarkers and molecular foundations for schizophrenia: A combined multivariate pattern analysis and transcriptome-neuroimaging association study

AIMS

Schizophrenia is characterized by alterations in resting-state spontaneous brain activity; however, it remains uncertain whether variations at diverse spatial scales are capable of effectively distinguishing patients from healthy controls. Additionally, the genetic underpinnings of these alterations remain poorly elucidated. We aimed to address these questions in this study to gain better understanding of brain alterations and their underlying genetic factors in schizophrenia.

METHODS

A cohort of 103 individuals with diagnosed schizophrenia and 110 healthy controls underwent resting-state functional MRI scans. Spontaneous brain activity was assessed using the regional homogeneity (ReHo) metric at four spatial scales: voxel-level (Scale 1) and regional-level (Scales 2-4: 272, 53, 17 regions, respectively). For each spatial scale, multivariate pattern analysis was performed to classify schizophrenia patients from healthy controls, and a transcriptome-neuroimaging association analysis was performed to establish connections between gene expression data and ReHo alterations in schizophrenia.

RESULTS

The ReHo metrics at all spatial scales effectively discriminated schizophrenia from healthy controls. Scale 2 showed the highest classification accuracy at 84.6%, followed by Scale 1 (83.1%) and Scale 3 (78.5%), while Scale 4 exhibited the lowest accuracy (74.2%). Furthermore, the transcriptome-neuroimaging association analysis showed that there were not only shared but also unique enriched biological processes across the four spatial scales. These related biological processes were mainly linked to immune responses, inflammation, synaptic signaling, ion channels, cellular development, myelination, and transporter activity.

CONCLUSIONS

This study highlights the potential of multi-scale ReHo as a valuable neuroimaging biomarker in the diagnosis of schizophrenia. By elucidating the complex molecular basis underlying the ReHo alterations of this disorder, this study not only enhances our understanding of its pathophysiology, but also pave the way for future advancements in genetic diagnosis and treatment of schizophrenia.

A survey of brain functional network extraction methods using fMRI data

Functional network (FN) analyses play a pivotal role in uncovering insights into brain function and understanding the pathophysiology of various brain disorders. This paper focuses on classical and advanced methods for deriving brain FNs from functional magnetic resonance imaging (fMRI) data. We systematically review their foundational principles, advantages, shortcomings, and interrelations, encompassing both static and dynamic FN extraction approaches. In the context of static FN extraction, we present hypothesis-driven methods such as region of interest (ROI)-based approaches as well as data-driven methods including matrix decomposition, clustering, and deep learning. For dynamic FN extraction, both window-based and windowless methods are surveyed with respect to the estimation of time-varying FN and the subsequent computation of FN states. We also discuss the scope of application of the various methods and avenues for future improvements.

3D balanced SSFP UTE MRI for multiple contrasts whole brain imaging

PURPOSE

This study aimed to develop a new high-resolution MRI sequence for the imaging of the ultra-short transverse relaxation time (uT2) components in the brain, while simultaneously providing proton density (PD) contrast for reference and quantification.

THEORY

The sequence combines low flip angle balanced SSFP (bSSFP) and UTE techniques, together with a 3D dual-echo rosette k-space trajectory for readout.

METHODS

The expected image contrast was evaluated by simulations. A study cohort of six healthy volunteers and eight multiple sclerosis (MS) patients was recruited to test the proposed sequence. Subtraction between two TEs was performed to extract uT2 signals. In addition, conventional longitudinal relaxation time (T1) weighted, T2-weighted, and PD-weighted MRI sequences were also acquired for comparison.

RESULTS

Typical PD-contrast was found in the second TE images, while uT2 signals were selectively captured in the first TE images. The subtraction images presented signals primarily originating from uT2 components, but only if the first TE is short enough. Lesions in the MS subjects showed hyperintense signals in the second TE images but were hypointense signals in the subtraction images. The lesions had significantly lower signal intensity in subtraction images than normal white matter (WM), which indicated a reduction of uT2 components likely associated with myelin.

CONCLUSION

3D isotropic sub-millimeter (0.94 mm) spatial resolution images were acquired with the novel bSSFP UTE sequence within 3 min. It provided easy extraction of uT2 signals and PD-contrast for reference within a single acquisition.

Spatial-Temporal Co-Attention Learning for Diagnosis of Mental Disorders From Resting-State fMRI Data

Neuroimaging techniques have been widely adopted to detect the neurological brain structures and functions of the nervous system. As an effective noninvasive neuroimaging technique, functional magnetic resonance imaging (fMRI) has been extensively used in computer-aided diagnosis (CAD) of mental disorders, e.g., autism spectrum disorder (ASD) and attention deficit/hyperactivity disorder (ADHD). In this study, we propose a spatial-temporal co-attention learning (STCAL) model for diagnosing ASD and ADHD from fMRI data. In particular, a guided co-attention (GCA) module is developed to model the intermodal interactions of spatial and temporal signal patterns. A novel sliding cluster attention module is designed to address global feature dependency of self-attention mechanism in fMRI time series. Comprehensive experimental results demonstrate that our STCAL model can achieve competitive accuracies of 73.0 ± 4.5%, 72.0 ± 3.8%, and 72.5 ± 4.2% on the ABIDE I, ABIDE II, and ADHD-200 datasets, respectively. Moreover, the potential for feature pruning based on the co-attention scores is validated by the simulation experiment. The clinical interpretation analysis of STCAL can allow medical professionals to concentrate on the discriminative regions of interest and key time frames from fMRI data.

Yet more evidence that non-aqueous myelin lipids can be directly imaged with ultrashort echo time (UTE) MRI on a clinical 3T scanner: a lyophilized red blood cell membrane lipid study

Direct imaging of semi-solid lipids, such as myelin, is of great interest as a noninvasive biomarker of neurodegenerative diseases. Yet, the short T2 relaxation times of semi-solid lipid protons hamper direct detection through conventional magnetic resonance imaging (MRI) pulse sequences. In this study, we examined whether a three-dimensional ultrashort echo time (3D UTE) sequence can directly acquire signals from membrane lipids. Membrane lipids from red blood cells (RBC) were collected from commercially available blood as a general model of the myelin lipid bilayer and subjected to D2O exchange and freeze-drying for complete water removal. Sufficiently high MR signals were detected with the 3D UTE sequence, which showed an ultrashort T2* of ∼77-271 µs and a short T1 of ∼189 ms for semi-solid RBC membrane lipids. These measurements can guide designing UTE-based sequences for direct in vivo imaging of membrane lipids.

Exploiting Geometric Features via Hierarchical Graph Pyramid Transformer for Cancer Diagnosis Using Histopathological Images

Cancer is widely recognized as the primary cause of mortality worldwide, and pathology analysis plays a pivotal role in achieving accurate cancer diagnosis. The intricate representation of features in histopathological images encompasses abundant information crucial for disease diagnosis, regarding cell appearance, tumor microenvironment, and geometric characteristics. However, recent deep learning methods have not adequately exploited geometric features for pathological image classification due to the absence of effective descriptors that can capture both cell distribution and gathering patterns, which often serve as potent indicators. In this paper, inspired by clinical practice, a Hierarchical Graph Pyramid Transformer (HGPT) is proposed to guide pathological image classification by effectively exploiting a geometric representation of tissue distribution which was ignored by existing state-of-the-art methods. First, a graph representation is constructed according to morphological feature of input pathological image and learn geometric representation through the proposed multi-head graph aggregator. Then, the image and its graph representation are feed into the transformer encoder layer to model long-range dependency. Finally, a locality feature enhancement block is designed to enhance the 2D local representation of feature embedding, which is not well explored in the existing vision transformers. An extensive experimental study is conducted on Kather-5K, MHIST, NCT-CRC-HE, and GasHisSDB for binary or multi-category classification of multiple cancer types. Results demonstrated that our method is capable of consistently reaching superior classification outcomes for histopathological images, which provide an effective diagnostic tool for malignant tumors in clinical practice.

Effects of Non-invasive Brain Stimulation on Hereditary Ataxia: a Systematic Review and Meta-analysis

Numerous studies have demonstrated the potential of non-invasive brain stimulation (NIBS) techniques as a viable treatment option for cerebellar ataxia. However, there is a notable dearth of research investigating the efficacy of NIBS specifically for hereditary ataxia (HA), a distinct subgroup within the broader category of cerebellar ataxia. This study aims to conduct a comprehensive systematic review and meta-analysis in order to assess the efficacy of various NIBS methods for the treatment of HA. A thorough review of the literature was conducted, encompassing both English and Chinese articles, across eight electrical databases. The focus was on original articles investigating the therapeutic effectiveness of non-invasive brain stimulation for hereditary ataxia, with a publication date prior to March 2023. Subsequently, a meta-analysis was performed specifically on randomized controlled trials (RCTs) that fulfilled the eligibility criteria, taking into account the various modalities of non-invasive brain stimulation. A meta-analysis was conducted, comprising five RCTs, which utilized the Scale for the Assessment and Rating of Ataxia (SARA) as the outcome measure to evaluate the effects of transcranial magnetic stimulation (TMS). The findings revealed a statistically significant mean decrease of 1.77 in the total SARA score following repetitive TMS (rTMS) (p=0.006). Subgroup analysis based on frequency demonstrated a mean decrease of 1.61 in the total SARA score after high-frequency rTMS (p=0.05), while no improvement effects were observed after low-frequency rTMS (p=0.48). Another meta-analysis was performed on three studies, utilizing ICARS scores, to assess the impact of rTMS. The results indicated that there were no statistically significant differences in pooled ICARS scores between the rTMS group and the sham group (MD=0.51, 95%CI: -5.38 to 6.39; p=0.87). These findings align with the pooled results of two studies that evaluated alterations in post-intervention BBS scores (MD=0.74, 95%CI: -5.48 to 6.95; p=0.82). Despite the limited number of studies available, this systematic review and meta-analysis have revealed promising potential benefits of rTMS for hereditary ataxia. However, it is strongly recommended that further high-quality investigations be conducted in this area. Furthermore, the significance of standardized protocols for NIBS in future studies was also emphasized.

Local Delivery of Dual Stem Cell-Derived Exosomes Using an Electrospun Nanofibrous Platform for the Treatment of Traumatic Brain Injury

Traumatic brain injury poses serious physical, psychosocial, and economic threats. Although systemic administration of stem cell-derived exosomes has recently been proven to be a promising modality for traumatic brain injury treatment, they come with distinct drawbacks. Luckily, various biomaterials have been developed to assist local delivery of exosomes to improve the targeting of organs, minimize nonspecific accumulation in vital organs, and ensure the protection and release of exosomes. In this study, we developed an electrospun nanofibrous scaffold to provide sustained delivery of dual exosomes derived from mesenchymal stem cells and neural stem cells for traumatic brain injury treatment. The electrospun nanofibrous scaffold employed a functionalized layer of polydopamine on electrospun poly(ε-caprolactone) nanofibers, thereby enhancing the efficient incorporation of exosomes through a synergistic interplay of adhesive forces, hydrogen bonding, and electrostatic interactions. First, the mesenchymal stem cell-derived exosomes and the neural stem cell-derived exosomes were found to modulate microglial polarization toward M2 phenotype, play an important role in the modulation of inflammatory responses, and augment axonal outgrowth and neural repair in PC12 cells. Second, the nanofibrous scaffold loaded with dual stem cell-derived exosomes (Duo-Exo@NF) accelerated functional recovery in a murine traumatic brain injury model, as it mitigated the presence of reactive astrocytes and microglia while elevating the levels of growth associated protein-43 and doublecortin. Additionally, multiomics analysis provided mechanistic insights into how dual stem cell-derived exosomes exerted its therapeutic effects. These findings collectively suggest that our novel Duo-Exo@NF system could function as an effective treatment modality for traumatic brain injury using sustained local delivery of dual exosomes from stem cells.

Neuroimage Analysis Methods and Artificial Intelligence Techniques for Reliable Biomarkers and Accurate Diagnosis of Schizophrenia: Achievements Made by Chinese Scholars Around the Past Decade

BACKGROUND AND HYPOTHESIS

Schizophrenia (SZ) is characterized by significant cognitive and behavioral disruptions. Neuroimaging techniques, particularly magnetic resonance imaging (MRI), have been widely utilized to investigate biomarkers of SZ, distinguish SZ from healthy conditions or other mental disorders, and explore biotypes within SZ or across SZ and other mental disorders, which aim to promote the accurate diagnosis of SZ. In China, research on SZ using MRI has grown considerably in recent years.

STUDY DESIGN

The article reviews advanced neuroimaging and artificial intelligence (AI) methods using single-modal or multimodal MRI to reveal the mechanism of SZ and promote accurate diagnosis of SZ, with a particular emphasis on the achievements made by Chinese scholars around the past decade.

STUDY RESULTS

Our article focuses on the methods for capturing subtle brain functional and structural properties from the high-dimensional MRI data, the multimodal fusion and feature selection methods for obtaining important and sparse neuroimaging features, the supervised statistical analysis and classification for distinguishing disorders, and the unsupervised clustering and semi-supervised learning methods for identifying neuroimage-based biotypes. Crucially, our article highlights the characteristics of each method and underscores the interconnections among various approaches regarding biomarker extraction and neuroimage-based diagnosis, which is beneficial not only for comprehending SZ but also for exploring other mental disorders.

CONCLUSIONS

We offer a valuable review of advanced neuroimage analysis and AI methods primarily focused on SZ research by Chinese scholars, aiming to promote the diagnosis, treatment, and prevention of SZ, as well as other mental disorders, both within China and internationally.

Different patterns of structural network impairments in two amyotrophic lateral sclerosis subtypes driven by 18F-fluorodeoxyglucose positron emission tomography/magnetic resonance hybrid imaging

The structural network damages in amyotrophic lateral sclerosis patients are evident but contradictory due to the high heterogeneity of the disease. We hypothesized that patterns of structural network impairments would be different in amyotrophic lateral sclerosis subtypes by a data-driven method using 18F-fluorodeoxyglucose positron emission tomography/magnetic resonance hybrid imaging. The data of positron emission tomography, structural MRI and diffusion tensor imaging in fifty patients with amyotrophic lateral sclerosis and 23 healthy controls were collected by a 18F-fluorodeoxyglucose positron emission tomography/magnetic resonance hybrid. Two amyotrophic lateral sclerosis subtypes were identified as the optimal cluster based on grey matter volume and standardized uptake value ratio. Network metrics at the global, local and connection levels were compared to explore the impaired patterns of structural networks in the identified subtypes. Compared with healthy controls, the two amyotrophic lateral sclerosis subtypes displayed a pattern of a locally impaired structural network centralized in the sensorimotor network and a pattern of an extensively impaired structural network in the whole brain. When comparing the two amyotrophic lateral sclerosis subgroups by a support vector machine classifier based on the decreases in nodal efficiency of structural network, the individualized network scores were obtained in every amyotrophic lateral sclerosis patient and demonstrated a positive correlation with disease severity. We clustered two amyotrophic lateral sclerosis subtypes by a data-driven method, which encompassed different patterns of structural network impairments. Our results imply that amyotrophic lateral sclerosis may possess the intrinsic damaged pattern of white matter network and thus provide a latent direction for stratification in clinical research.

Quantitative transport mapping of multi-delay arterial spin labeling MRI detects early blood perfusion alterations in Alzheimer's disease

BACKGROUND

Quantitative transport mapping (QTM) of blood velocity, based on the transport equation has been demonstrated higher accuracy and sensitivity of perfusion quantification than the traditional Kety's method-based cerebral blood flow (CBF). This study aimed to investigate the associations between QTM velocity and cognitive function in Alzheimer's disease (AD) using multiple post-labeling delay arterial spin labeling (ASL) MRI.

METHODS

A total of 128 subjects (21 normal controls (NC), 80 patients with mild cognitive impairment (MCI), and 27 AD) were recruited prospectively. All participants underwent MRI examination and neuropsychological evaluation. QTM velocity and traditional CBF maps were computed from multiple delay ASL. Regional quantitative perfusion measurements were performed and compared to study group differences. We tested the hypothesis that cognition declines with reduced cerebral blood perfusion with consideration of age and gender effects.

RESULTS

In cortical gray matter (GM) and the hippocampus, QTM velocity and CBF showed decreased values in the AD group compared to NC and MCI groups; QTM velocity, but not CBF, showed a significant difference between MCI and NC groups. QTM velocity and CBF showed values decreasing with age; QTM velocity, but not CBF, showed a significant gender difference between male and female. QTM velocity and CBF in the hippocampus were positively correlated with cognition, including global cognition, memory, executive function, and language function.

CONCLUSION

This study demonstrated an increased sensitivity of QTM velocity as compared with the traditional Kety's method-based CBF. Specifically, we observed only in QTM velocity, reduced perfusion velocity in GM and the hippocampus in MCI compared with NC. Both QTM velocity and CBF demonstrated a reduction in AD vs. controls. Decreased QTM velocity and CBF in the hippocampus were correlated with poor cognitive measures. These findings suggest QTM velocity as potential biomarker for early AD blood perfusion alterations and it could provide an avenue for early intervention of AD.

Anxiety and depression are associated with heightened risk of incident deep vein thrombosis: Mediation through stress-related neural mechanisms

Controversy exists as to whether anxiety and depression increase deep vein thrombosis (DVT) risk, and the mechanisms mediating potential links remain unknown. We aimed to evaluate the association between anxiety and depression and DVT risk and determine whether upregulated stress-related neural activity (SNA), which promotes chronic inflammation, contributes to this link. Our retrospective study included adults (N = 118 871) enrolled in Mass General Brigham Biobank. A subset (N = 1520) underwent clinical 18F-FDG-PET/CT imaging. SNA was measured as the ratio of amygdalar to cortical activity (AmygAC). High-sensitivity C-reactive protein (hs-CRP) and heart rate variability (HRV) were also obtained. Median age was 58 [interquartile range (IQR) 42-70] years with 57% female participants. DVT occurred in 1781 participants (1.5%) over median follow-up of 3.6 years [IQR 2.1-5.2]. Both anxiety and depression independently predicted incident DVT risk after robust adjustment (HR [95% CI]: 1.53 [1.38-1.71], p < .001; and 1.48 [1.33-1.65], p < .001, respectively). Additionally, both anxiety and depression associated with increased AmygAC (standardized beta [95% CI]: 0.16 [0.04-0.27], p = .007, and 0.17 [0.05-0.29], p = .006, respectively). Furthermore, AmygAC associated with incident DVT (HR [95% CI]: 1.30 [1.07-1.59], p = .009). Mediation analysis demonstrated that the link between anxiety/depression and DVT was mediated by: (1) higher AmygAC, (2) higher hs-CRP, and (3) lower HRV ( < .05 for each). Anxiety and depression confer an attributable risk of DVT similar to other traditional DVT risk factors. Mechanisms appear to involve increased SNA, autonomic system activity, and inflammation. Future studies are needed to determine whether treatment of anxiety and depression can reduce DVT risk.

In silico exploration of anti-prostate cancer compounds from differential expressed genes

Prostate cancer (PCa) is a complex and biologically diverse disease with no curative treatment options at present. This study aims to utilize computational methods to explore potential anti-PCa compounds based on differentially expressed genes (DEGs), with the goal of identifying novel therapeutic indications or repurposing existing drugs. The methods employed in this study include DEGs-to-drug prediction, pharmacokinetics prediction, target prediction, network analysis, and molecular docking. The findings revealed a total of 79 upregulated DEGs and 110 downregulated DEGs in PCa, which were used to identify drug compounds capable of reversing the dysregulated conditions (dexverapamil, emetine, parthenolide, dobutamine, terfenadine, pimozide, mefloquine, ellipticine, and trifluoperazine) at a threshold probability of 20% on several molecular targets, such as serotonin receptors 2a/2b/2c, HERG protein, adrenergic receptors alpha-1a/2a, dopamine D3 receptor, inducible nitric oxide synthase (iNOS), epidermal growth factor receptor erbB1 (EGFR), tyrosine-protein kinases, and C-C chemokine receptor type 5 (CCR5). Molecular docking analysis revealed that terfenadine binding to inducible nitric oxide synthase (-7.833 kcal.mol-1) and pimozide binding to HERG (-7.636 kcal.mol-1). Overall, binding energy ΔGbind (Total) at 0 ns was lower than that of 100 ns for both the Terfenadine-iNOS complex (-101.707 to -103.302 kcal.mol-1) and Ellipticine-TOPIIα complex (-42.229 to -58.780 kcal.mol-1). In conclusion, this study provides insight on molecular targets that could possibly contribute to the molecular mechanisms underlying PCa. Further preclinical and clinical studies are required to validate the therapeutic effectiveness of these identified drugs in PCa disease.

Quantitative assessment of preoperative brain development in pediatric congenital heart disease patients by synthetic MRI

OBJECTIVES

This study investigated the quantitative assessment and application of Synthetic MRI (SyMRI) for preoperative brain development in children with congenital heart disease (CHD).

METHODS

Forty-three CHD patients aged 2-24 months were prospectively included in the observation group, and 43 healthy infants were included in the control group. The SyMRI scans were processed by postprocessing software to obtain T1, T2, and PD maps. The values of T1, T2, and PD in different brain regions were compared with the scores of the five ability areas of the Gesell Development Scale by Pearson correlation analysis.

RESULTS

In the observation group, the T1 values of the posterior limb of the internal capsule (PLIC), Optic radiation (PTR), cerebral peduncle, centrum semiovale, occipital white matter, temporal white matter, and dentate nucleus were greater than those in the control group. In the observation group, the T2 values of the PLIC, PTR, frontal white matter, occipital white matter, temporal white matter, and dentate nucleus were greater than those in the control group. Pearson correlation analysis revealed that the observation group had significantly lower Development Scale scores. In the observation group, the T2 value of the splenium of the corpus callosum was significantly positively correlated with the personal social behavior score. The AUCs for diagnosing preoperative brain developmental abnormalities in children with CHD using T1 values of the temporal white matter and dentate nucleus were both greater than 0.60.

CONCLUSIONS

Quantitative assessment using SyMRI can aid in the early detection of preoperative brain development abnormalities in children with CHD.

CRITICAL RELEVANCE STATEMENT

T1 and T2 relaxation values from SyMRI can be considered as a quantitative imaging marker to detect abnormalities, allowing for early clinical evaluation and timely intervention, thereby reducing neurodevelopmental disorders in these children.

KEY POINTS

T1 and T2 relaxation values by SyMRI are related to myelin development. Evaluated development quotient markers were lower in the observation compared to the control group. SyMRI can act as a reference indicator for brain development in CHD children.

A nomogram based on dual-layer detector spectral computed tomography quantitative parameters and morphological quantitative indicator for distinguishing metastatic and nonmetastatic regional lymph nodes in pancreatic ductal adenocarcinoma

Background

There is no unified scope for regional lymph node (LN) dissection in patients with pancreatic ductal adenocarcinoma (PDAC). Incomplete regional LN dissection can lead to postoperative recurrence, while blind expansion of the scope of regional LN dissection significantly increases the perioperative risk without significantly prolonging overall survival. We aimed to establish a noninvasive visualization tool based on dual-layer detector spectral computed tomography (DLCT) to predict the probability of regional LN metastasis in patients with PDAC.

Methods

A total of 163 regional LNs were reviewed and divided into a metastatic cohort (n=58 LNs) and nonmetastatic cohort (n=105 LNs). The DLCT quantitative parameters and the nodal ratio of the longest axis to the shortest axis (L/S) of the regional LNs were compared between the two cohorts. The DLCT quantitative parameters included the iodine concentration in the arterial phase (APIC), normalized iodine concentration in the arterial phase (APNIC), effective atomic number in the arterial phase (APZeff), normalized effective atomic number in the arterial phase (APNZeff), slope of the spectral attenuation curves in the arterial phase (APλHU), iodine concentration in the portal venous phase (PVPIC), normalized iodine concentration in the portal venous phase (PVPNIC), effective atomic number in the portal venous phase (PVPZeff), normalized effective atomic number in the portal venous phase (PVPNZeff), and slope of the spectral attenuation curves in the portal venous phase (PVPλHU). Logistic regression analysis based on area under the curve (AUC) was used to analyze the diagnostic performance of significant DLCT quantitative parameters, L/S, and the models combining significant DLCT quantitative parameters and L/S. A nomogram based on the models with highest diagnostic performance was developed as a predictor. The goodness of fit and clinical applicability of the nomogram were assessed through calibration curve and decision curve analysis (DCA).

Results

The combined model of APNIC + L/S (APNIC + L/S) had the highest diagnostic performance among all models, yielding an AUC, sensitivity, and specificity of 0.878 [95% confidence interval (CI): 0.825-0.931], 0.707, and 0.886, respectively. The calibration curve indicated that the APNIC-L/S nomogram had good agreement between the predicted probability and the actual probability. Meanwhile, the decision curve indicated that the APNIC-L/S nomogram could produce a greater net benefit than could the all- or-no-intervention strategy, with threshold probabilities ranging from 0.0 to 0.75.

Conclusions

As a valid and visual noninvasive prediction tool, the APNIC-L/S nomogram demonstrated favorable predictive efficacy for identifying metastatic LNs in patients with PDAC.

Defining clinically useful biomarkers of immune checkpoint inhibitors in solid tumours

Although more than a decade has passed since the approval of immune checkpoint inhibitors (ICIs) for the treatment of melanoma and non-small-cell lung, breast and gastrointestinal cancers, many patients still show limited response. US Food and Drug Administration (FDA)-approved biomarkers include programmed cell death 1 ligand 1 (PDL1) expression, microsatellite status (that is, microsatellite instability-high (MSI-H)) and tumour mutational burden (TMB), but these have limited utility and/or lack standardized testing approaches for pan-cancer applications. Tissue-based analytes (such as tumour gene signatures, tumour antigen presentation or tumour microenvironment profiles) show a correlation with immune response, but equally, these demonstrate limited efficacy, as they represent a single time point and a single spatial assessment. Patient heterogeneity as well as inter- and intra-tumoural differences across different tissue sites and time points represent substantial challenges for static biomarkers. However, dynamic biomarkers such as longitudinal biopsies or novel, less-invasive markers such as blood-based biomarkers, radiomics and the gut microbiome show increasing potential for the dynamic identification of ICI response, and patient-tailored predictors identified through neoadjuvant trials or novel ex vivo tumour models can help to personalize treatment. In this Perspective, we critically assess the multiple new static, dynamic and patient-specific biomarkers, highlight the newest consortia and trial efforts, and provide recommendations for future clinical trials to make meaningful steps forwards in the field.

Randomizing Human Brain Function Representation for Brain Disease Diagnosis

Resting-state fMRI (rs-fMRI) is an effective tool for quantifying functional connectivity (FC), which plays a crucial role in exploring various brain diseases. Due to the high dimensionality of fMRI data, FC is typically computed based on the region of interest (ROI), whose parcellation relies on a pre-defined atlas. However, utilizing the brain atlas poses several challenges including 1) subjective selection bias in choosing from various brain atlases, 2) parcellation of each subject's brain with the same atlas yet disregarding individual specificity; 3) lack of interaction between brain region parcellation and downstream ROI-based FC analysis. To address these limitations, we propose a novel randomizing strategy for generating brain function representation to facilitate neural disease diagnosis. Specifically, we randomly sample brain patches, thus avoiding ROI parcellations of the brain atlas. Then, we introduce a new brain function representation framework for the sampled patches. Each patch has its function description by referring to anchor patches, as well as the position description. Furthermore, we design an adaptive-selection-assisted Transformer network to optimize and integrate the function representations of all sampled patches within each brain for neural disease diagnosis. To validate our framework, we conduct extensive evaluations on three datasets, and the experimental results establish the effectiveness and generality of our proposed method, offering a promising avenue for advancing neural disease diagnosis beyond the confines of traditional atlas-based methods. Our code is available at https://github.com/mjliu2020/RandomFR.

So You Want to Image Myelin Using MRI: Magnetic Susceptibility Source Separation for Myelin Imaging

In MRI, researchers have long endeavored to effectively visualize myelin distribution in the brain, a pursuit with significant implications for both scientific research and clinical applications. Over time, various methods such as myelin water imaging, magnetization transfer imaging, and relaxometric imaging have been developed, each carrying distinct advantages and limitations. Recently, an innovative technique named as magnetic susceptibility source separation has emerged, introducing a novel surrogate biomarker for myelin in the form of a diamagnetic susceptibility map. This paper comprehensively reviews this cutting-edge method, providing the fundamental concepts of magnetic susceptibility, susceptibility imaging, and the validation of the diamagnetic susceptibility map as a myelin biomarker that indirectly measures myelin content. Additionally, the paper explores essential aspects of data acquisition and processing, offering practical insights for readers. A comparison with established myelin imaging methods is also presented, and both current and prospective clinical and scientific applications are discussed to provide a holistic understanding of the technique. This work aims to serve as a foundational resource for newcomers entering this dynamic and rapidly expanding field.

Theranostic nanoparticles for detection and treatment of pancreatic cancer

Pancreatic ductal adenocarcinoma (PDAC) is one of the most recalcitrant cancers due to its late diagnosis, poor therapeutic response, and highly heterogeneous microenvironment. Nanotechnology has the potential to overcome some of the challenges to improve diagnostics and tumor-specific drug delivery but they have not been plausibly viable in clinical settings. The review focuses on active targeting strategies to enhance pancreatic tumor-specific uptake for nanoparticles. Additionally, this review highlights using actively targeted liposomes, micelles, gold nanoparticles, silica nanoparticles, and iron oxide nanoparticles to improve pancreatic tumor targeting. Active targeting of nanoparticles toward either differentially expressed receptors or PDAC tumor microenvironment (TME) using peptides, antibodies, small molecules, polysaccharides, and hormones has been presented. We focus on microenvironment-based hallmarks of PDAC and the potential for actively targeted nanoparticles to overcome the challenges presented in PDAC. It describes the use of nanoparticles as contrast agents for improved diagnosis and the delivery of chemotherapeutic agents that target various aspects within the TME of PDAC. Additionally, we review emerging nano-contrast agents detected using imaging-based technologies and the role of nanoparticles in energy-based treatments of PDAC. This article is categorized under: Implantable Materials and Surgical Technologies > Nanoscale Tools and Techniques in Surgery Therapeutic Approaches and Drug Discovery > Nanomedicine for Oncologic Disease Diagnostic Tools > In Vivo Nanodiagnostics and Imaging.