To address the urgent need for effective sonodynamic therapy (SDT), a facile and efficient strategy is developed for fabricating a new class of sonotheranostics. Herein, a Fe(III)-porphyrin nano-sonosensitizer is prepared by a one-step coordination synthesis, and then anchored with Bis(DPA-Zn)-RGD and manganese superoxide dismutase (SOD2) siRNA. Benefiting from the delicate interactions of the Fe(III)-coordinated nanoformulations, a highly potent sono/gene combinational therapy guided by fluorescence/magnetic resonance imaging is achieved. Importantly, this sonotheranostics significantly enhances the SDT effect of porphyrin through the cancer-targeted delivery capability and enhanced reactive oxygen species production via triple-regulated approaches, including down-regulation of SOD2, depletion of glutathione, and generation of Fenton reaction. This work opens up new perspectives for developing versatile sonotheranostics to overcome the clinical challenges of SDT.
Background and Purpose:
Aneurysmal wall enhancement (AWE) on vessel wall magnetic resonance imaging (VW-MRI) has been described as a new imaging biomarker of unstable unruptured intracranial aneurysms (UIAs). Previous studies of symptomatic UIAs are limited due to small sample sizes and lack of AWE quantification. Our study aims to investigate whether qualitative and quantitative assessment of AWE can differentiate symptomatic and asymptomatic UIAs.
Methods:
Consecutive patients with UIAs were prospectively recruited for vessel wall magnetic resonance imaging at 3T from October 2014 to October 2019. UIAs were categorized as symptomatic if presenting with sentinel headache or oculomotor nerve palsy directly related to the aneurysm. Evaluation of wall enhancement included enhancement pattern (0=none, 1=focal, and 2=circumferential) and quantitative wall enhancement index (WEI). Univariate and multivariate analyses were used to identify the parameters associated with symptoms.
Results:
Two hundred sixty-seven patients with 341 UIAs (93 symptomatic and 248 asymptomatic) were included in this study. Symptomatic UIAs more frequently showed circumferential AWE than asymptomatic UIAs (66.7% versus 17.3%,
P
<0.001), as well as higher WEI (median [interquartile range], 1.3 [1.0–1.9] versus 0.3 [0.1–0.9],
P
<0.001). In multivariate analysis, both AWE pattern and WEI were independent factors associated with symptoms (odds ratio=2.03 across AWE patterns [95% CI, 1.21–3.39],
P
=0.01; odds ratio=3.32 for WEI [95% CI, 1.51–7.26],
P
=0.003). The combination of AWE pattern and WEI had an area under the curve of 0.91 to identify symptomatic UIAs, with a sensitivity of 95.7% and a specificity of 73.4%.
Conclusions:
In a large cohort of UIAs with vessel wall magnetic resonance imaging, both AWE pattern and WEI were independently associated with aneurysm-related symptoms. The qualitative and quantitative features of AWE can potentially be used to identify unstable intracranial aneurysms.
The 2016 WHO classification of central nervous system tumors has included four molecular subgroups under medulloblastoma (MB) as sonic hedgehog (SHH), wingless (WNT), Grade 3, and Group 4. We aimed to develop machine learning models for predicting MB molecular subgroups based on multi-parameter magnetic resonance imaging (MRI) radiomics, tumor locations, and clinical factors. A total of 122 MB patients were enrolled retrospectively. After selecting robust, non-redundant, and relevant features from 5,529 extracted radiomics features, a random forest model was constructed based on a training cohort (
n
= 92) and evaluated on a testing cohort (
n
= 30). By combining radiographic features and clinical parameters, two combined prediction models were also built. The subgroup can be classified using an 11-feature radiomics model with a high area under the curve (AUC) of 0.8264 for WNT and modest AUCs of 0.6683, 0.6004, and 0.6979 for SHH, Group 3, and Group 4 in the testing cohort, respectively. Incorporating location and hydrocephalus into the radiomics model resulted in improved AUCs of 0.8403 and 0.8317 for WNT and SHH, respectively. After adding gender and age, the AUCs for WNT and SHH were further improved to 0.9097 and 0.8654, while the accuracies were 70 and 86.67% for Group 3 and Group 4, respectively. Prediction performance was excellent for WNT and SHH, while that for Group 3 and Group 4 needs further improvements. Machine learning algorithms offer potentials to non-invasively predict the molecular subgroups of MB.
The Chinese Imaging Genetics (CHIMGEN) study establishes the largest Chinese neuroimaging genetics cohort and aims to identify genetic and environmental factors and their interactions that are associated with neuroimaging and behavioral phenotypes. This study prospectively collected genomic, neuroimaging, environmental, and behavioral data from more than 7000 healthy Chinese Han participants aged 18-30 years. As a pioneer of large-sample neuroimaging genetics cohorts of non-Caucasian populations, this cohort can provide new insights into ethnic differences in genetic-neuroimaging associations by being compared with Caucasian cohorts. In addition to micro-environmental measurements, this study also collects hundreds of quantitative macro-environmental measurements from remote sensing and national survey databases based on the locations of each participant from birth to present, which will facilitate discoveries of new environmental factors associated with neuroimaging phenotypes. With lifespan environmental measurements, this study can also provide insights on the macro-environmental exposures that affect the human brain as well as their timing and mechanisms of action.
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