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JACC. Case Reports Jul 2024Single coronary artery, giant coronary artery aneurysm, and coronary cameral fistula are rare congenital anomalies, and can cause a range of presentations. To our...
Single coronary artery, giant coronary artery aneurysm, and coronary cameral fistula are rare congenital anomalies, and can cause a range of presentations. To our knowledge, this is the first reported case of all 3 entities occurring simultaneously in 1 patient, with largely unknown implications. Multimodal imaging was essential in prompt diagnosis and management.
PubMed: 38948493
DOI: 10.1016/j.jaccas.2024.102396 -
Proceedings of the International... May 2024High performance RF coils are needed for better SNR so that higher resolution and spectral dispersion can be obtained in small animal MR imaging.
MOTIVATION
High performance RF coils are needed for better SNR so that higher resolution and spectral dispersion can be obtained in small animal MR imaging.
GOALS
To develop a surface coil with improved SNR over the conventional surface coil for small animal imaging at 7T.
APPROACH
A small animal surface coil is designed based on multimodal surface coil technique. The coil is investigated and compared with conventional surface coil using full-wave electromagnetic simulations.
RESULTS
The multimodal surface coil shows superior B1 field efficiency and lower E field over standard coils, indicating a potential to gain SNR and resolution.
IMPACT
The proposed multimodal surface coil can operate at high frequency and provides improved SNR over conventional surface coils at 7T, opening avenues for highly efficient coil design in small animal imaging, ultimately enabling the detection of previously indiscernible physiological details.
PubMed: 38948448
DOI: No ID Found -
Proceedings of the International... May 2024
PubMed: 38948447
DOI: No ID Found -
Contemporary Clinical Trials... Jun 2024Post-stroke spasticity (PSS) is among the prevalent complications of stroke, greatly affecting motor function recovery and reducing patients' quality of life without...
INTRODUCTION
Post-stroke spasticity (PSS) is among the prevalent complications of stroke, greatly affecting motor function recovery and reducing patients' quality of life without timely treatment. Sangdantongluo granule, a modern traditional Chinese patent medicine, has significant clinical efficacy in treating PSS. However, the mechanism of Sangdantongluo granule in treating PSS is still unknown. We designed this study to explore the mechanism of Sangdantongluo granule in treating PSS through multimodal functional magnetic resonance imaging (fMRI) combined with transcranial magnetic stimulation (TMS).
METHODS AND ANALYSIS
In a single-center, randomized, double-blind, parallel placebo-controlled study, 60 PSS patients will be recruited in China and randomly assigned to either the experimental or control groups at a ratio of 1:1. For eight weeks, Sangdantongluo granule or placebo will be utilized for intervention. The main outcome is the Modified Ashworth Scale (MAS), the secondary outcome includes the Fugl-Meyer Assessment Scale-upper Extremity (FMA-UE), National Institute of Health Stroke Scale (NIHSS), and Modified Rankin Scale (mRS), the mechanism measure is the changes in cortical excitability and multimodal fMRI at baseline and after eight weeks.
ETHICS AND DISSEMINATION
This study was approved by the Ethics Committee of the Affiliated Hospital of Hunan Academy of Traditional Chinese Medicine (approval number: [202364]).
CLINICAL TRIAL REGISTRATION
Chinese Clinical Trial Registry, identifier: ChiCTR2300074793. Registered on 16 August 2023.
PubMed: 38948333
DOI: 10.1016/j.conctc.2024.101317 -
Theranostics 2024Immune checkpoint inhibitors (ICI) are routinely used in advanced clear cell renal cell carcinoma (ccRCC). However, a substantial group of patients does not respond to...
Immune checkpoint inhibitors (ICI) are routinely used in advanced clear cell renal cell carcinoma (ccRCC). However, a substantial group of patients does not respond to ICI therapy. Radiation is a promising approach to increase ICI response rates since it can generate anti-tumor immunity. Targeted radionuclide therapy (TRT) is a systemic radiation treatment, ideally suited for precision irradiation of metastasized cancer. Therefore, the aim of this study is to explore the potential of combined TRT, targeting carbonic anhydrase IX (CAIX) which is overexpressed in ccRCC, using [Lu]Lu-DOTA-hG250, and ICI for the treatment of ccRCC. In this study, we evaluated the therapeutic and immunological action of [Lu]Lu-DOTA-hG250 combined with aPD-1/a-CTLA-4 ICI. First, the biodistribution of [Lu]Lu-DOTA-hG250 was investigated in BALB/cAnNRj mice bearing Renca-CAIX or CT26-CAIX tumors. Renca-CAIX and CT26-CAIX tumors are characterized by poor versus extensive T-cell infiltration and homogeneous versus heterogeneous PD-L1 expression, respectively. Tumor-absorbed radiation doses were estimated through dosimetry. Subsequently, [Lu]Lu-DOTA-hG250 TRT efficacy with and without ICI was evaluated by monitoring tumor growth and survival. Therapy-induced changes in the tumor microenvironment were studied by collection of tumor tissue before and 5 or 8 days after treatment and analyzed by immunohistochemistry, flow cytometry, and RNA profiling. Biodistribution studies showed high tumor uptake of [Lu]Lu-DOTA-hG250 in both tumor models. Dose escalation therapy studies in Renca-CAIX tumor-bearing mice demonstrated dose-dependent anti-tumor efficacy of [Lu]Lu-DOTA-hG250 and remarkable therapeutic synergy including complete remissions when a presumed subtherapeutic TRT dose (4 MBq, which had no significant efficacy as monotherapy) was combined with aPD-1+aCTLA-4. Similar results were obtained in the CT26-CAIX model for 4 MBq [Lu]Lu-DOTA-hG250 + a-PD1. analyses of treated tumors revealed DNA damage, T-cell infiltration, and modulated immune signaling pathways in the TME after combination treatment. Subtherapeutic [Lu]Lu-DOTA-hG250 combined with ICI showed superior therapeutic outcome and significantly altered the TME. Our results underline the importance of investigating this combination treatment for patients with advanced ccRCC in a clinical setting. Further investigations should focus on how the combination therapy should be optimally applied in the future.
PubMed: 38948062
DOI: 10.7150/thno.96944 -
Imaging Neuroscience (Cambridge, Mass.) Feb 2024Cortical atrophy and aggregates of misfolded tau proteins are key hallmarks of Alzheimer's disease. Computational models that simulate the propagation of pathogens...
Cortical atrophy and aggregates of misfolded tau proteins are key hallmarks of Alzheimer's disease. Computational models that simulate the propagation of pathogens between connected brain regions have been used to elucidate mechanistic information about the spread of these disease biomarkers, such as disease epicentres and spreading rates. However, the connectomes that are used as substrates for these models are known to contain modality-specific false positive and false negative connections, influenced by the biases inherent to the different methods for estimating connections in the brain. In this work, we compare five types of connectomes for modelling both tau and atrophy patterns with the network diffusion model, which are validated against tau PET and structural MRI data from individuals with either mild cognitive impairment or dementia. We then test the hypothesis that a joint connectome, with combined information from different modalities, provides an improved substrate for the model. We find that a combination of multimodal information helps the model to capture observed patterns of tau deposition and atrophy better than any single modality. This is validated with data from independent datasets. Overall, our findings suggest that combining connectivity measures into a single connectome can mitigate some of the biases inherent to each modality and facilitate more accurate models of pathology spread, thus aiding our ability to understand disease mechanisms, and providing insight into the complementary information contained in different measures of brain connectivity.
PubMed: 38947941
DOI: 10.1162/imag_a_00089 -
Endoscopic Ultrasound 2024Endobronchial ultrasound (EBUS) imaging is a valuable tool for predicting lymph node (LN) metastasis in lung cancer patients. This study aimed to develop a risk-scoring...
BACKGROUND AND OBJECTIVES
Endobronchial ultrasound (EBUS) imaging is a valuable tool for predicting lymph node (LN) metastasis in lung cancer patients. This study aimed to develop a risk-scoring model based on EBUS multimodal imaging (grayscale, Doppler mode, elastography) to predict LN metastasis in lung cancer patients.
PATIENTS AND METHODS
This retrospective study analyzed 350 metastatic LNs in 314 patients with lung cancer and 124 reactive LNs in 96 patients with nonspecific inflammation. The sonographic findings were compared with the final pathology results and clinical follow-up. Univariate and multivariate logistic regression analyses were performed to evaluate the independent risk factors of metastatic LNs. According to the coefficients of corresponding indicators in logistic regression analysis, a risk-scoring model was established. Receiver operating characteristic curve was applied to evaluate the predictive capability of model.
RESULTS
Multivariate analysis showed that short axis >10 mm, distinct margin, absence of central hilar structure, presence of necrosis, nonhilar vascularity, and elastography score 4 to 5 were independent predictors of metastatic LNs. Both short axis and margin were scored 1 point, and the rest of independent predictors were scored 2 points. The combination of 3 EBUS modes had the highest area under the receiver operating characteristic and accuracy of 0.884 (95% confidence interval, 0.846-0.922) and 87.55%, respectively. The risk stratification was as follows: 0 to 2 points, malignancy rate of 11.11%, low suspicion; 3 to 10 points, malignancy rate of 86.77%, high suspicion.
CONCLUSIONS
The risk-scoring model based on EBUS multimodal imaging can effectively evaluate metastatic LNs in lung cancer patients to support clinical decision making.
PubMed: 38947743
DOI: 10.1097/eus.0000000000000051 -
Journal of Cancer 2024It's a major public health problem of global concern that malignant gliomas tend to grow rapidly and infiltrate surrounding tissues. Accurate grading of the tumor can...
It's a major public health problem of global concern that malignant gliomas tend to grow rapidly and infiltrate surrounding tissues. Accurate grading of the tumor can determine the degree of malignancy to formulate the best treatment plan, which can eliminate the tumor or limit widespread metastasis of the tumor, saving the patient's life and improving their prognosis. To more accurately predict the grading of gliomas, we proposed a novel method of combining the advantages of 2D and 3D Convolutional Neural Networks for tumor grading by multimodality on Magnetic Resonance Imaging. The core of the innovation lies in our combination of tumor 3D information extracted from multimodal data with those obtained from a 2D ResNet50 architecture. It solves both the lack of temporal-spatial information provided by 3D imaging in 2D convolutional neural networks and avoids more noise from too much information in 3D convolutional neural networks, which causes serious overfitting problems. Incorporating explicit tumor 3D information, such as tumor volume and surface area, enhances the grading model's performance and addresses the limitations of both approaches. By fusing information from multiple modalities, the model achieves a more precise and accurate characterization of tumors. The model I s trained and evaluated using two publicly available brain glioma datasets, achieving an AUC of 0.9684 on the validation set. The model's interpretability is enhanced through heatmaps, which highlight the tumor region. The proposed method holds promise for clinical application in tumor grading and contributes to the field of medical diagnostics for prediction.
PubMed: 38947386
DOI: 10.7150/jca.95987 -
Research Square Jun 2024Objective The aim of this study was to develop a predictive model for uncorrected/actual fluid intelligence scores in 9-10 year old children using magnetic resonance...
Objective The aim of this study was to develop a predictive model for uncorrected/actual fluid intelligence scores in 9-10 year old children using magnetic resonance T1-weighted imaging. Explore the predictive performance of an autoencoder model based on reconstruction regularization for fluid intelligence in adolescents. Methods We collected actual fluid intelligence scores and T1-weighted MRIs of 11,534 adolescents who completed baseline tasks from ABCD Data Release 3.0. A total of 148 ROIs were selected and 604 features were proposed by FreeSurfer segmentation. The training and testing sets were divided in a ratio of 7:3. To predict fluid intelligence scores, we used AE, MLP and classic machine learning models, and compared their performance on the test set. In addition, we explored their performance across gender subpopulations. Moreover, we evaluated the importance of features using the SHapley Additive Explain method. Results: The proposed model achieves optimal performance on the test set for predicting actual fluid intelligence scores (PCC = 0.209 ± 0.02, MSE = 105.212 ± 2.53). Results show that autoencoders with refactoring regularization are significantly more effective than MLPs and classical machine learning models. In addition, all models performed better on female adolescents than on male adolescents. Further analysis of relevant characteristics in different populations revealed that this may be related to gender differences in underlying fluid intelligence mechanisms. Conclusions We construct a weak but stable correlation between brain structural features and raw fluid intelligence using autoencoders. Future research may need to explore ensemble regression strategies utilizing multiple machine learning algorithms on multimodal data in order to improve the predictive performance of fluid intelligence based on neuroimaging features.
PubMed: 38946976
DOI: 10.21203/rs.3.rs-4482953/v1 -
The American Journal of Psychiatry Jul 2024
Topics: Humans; Brain; Multimodal Imaging; Neuroimaging; Magnetic Resonance Imaging; Brain Mapping; Depressive Disorder, Major; Stress, Psychological
PubMed: 38946274
DOI: 10.1176/appi.ajp.20240400