The significant roles of extracellular vesicles (EVs) as intracellular mediators, disease biomarkers, and therapeutic agents, make them a scientific hotspot. In particular, EVs secreted by human stem cells show significance in treating neurological disorders, such as Alzheimer’s disease and ischemic stroke. However, the clinical applications of EVs are limited due to their poor targeting capabilities and low therapeutic efficacies after intravenous administration. Superparamagnetic iron oxide (SPIO) nanoparticles are biocompatible and have been shown to improve the targeting ability of EVs. In particular, ultrasmall SPIO (USPIO, <50 nm) are more suitable for labeling nanoscale EVs due to their small size. In this study, induced forebrain neural progenitor cortical organoids (iNPCo) were differentiated from human induced pluripotent stem cells (iPSCs), and the iNPCo expressed FOXG1, Nkx2.1, α-catenin, as well as β-tubulin III. EVs were isolated from iNPCo media, then loaded with USPIOs by sonication. Size and concentration of EV particles were measured by nanoparticle tracking analysis, and no significant changes were observed in size distribution before and after sonication, but the concentration decreased after labeling. miR-21 and miR-133b decreased after sonication. Magnetic resonance imaging (MRI) demonstrated contrast visualized for the USPIO labeled EVs embedded in agarose gel phantoms. Upon calculation, USPIO labeled EVs exhibited considerably shorter relaxation times, quantified as T₂ and T₂^* values, reducing the signal intensity and generating higher MRI contrast compared to unlabeled EVs and gel only. Our study demonstrated that USPIO labeling was a feasible approach for in vitro tracking of brain organoid-derived EVs, which paves the way for further in vivo examination.

The controversially discussed taxonomy of the Cryptococcus neoformans/Cryptococcus gattii species complex encompasses at least eight major molecular types. Cerebral cryptococcomas are a common manifestation of cryptococcal neurological disease. In this study, we compared neurotypical symptoms and differential neurovirulence induced by one representative isolate for each of the eight molecular types studied. We compared single focal lesions caused by the different isolates and evaluated the potential relationships between the fungal burden and properties obtained with quantitative magnetic resonance imaging (qMRI) techniques such as diffusion MRI, T₂ relaxometry and magnetic resonance spectroscopy (MRS). We observed an inverse correlation between parametric data and lesion density, and we were able to monitor longitudinally biophysical properties of cryptococcomas induced by different molecular types. Because the MRI/MRS techniques are also clinically available, the same approach could be used to assess image-based biophysical properties that correlate with fungal cell density in lesions in patients to determine personalized treatments.

Compromised adult human mesenchymal stem cells (hMSC) can impair cell therapy efficacy and further reverse ischemic recovery. However, in vitro assays require extended passage to characterize cells, limiting rapid assessment for therapeutic potency. Multinuclear magnetic resonance imaging and spectroscopy (MRI/S) provides near real-time feedback on disease progression and tissue recovery. Applied to ischemic stroke, ²³ Na MRI evaluates treatment efficacy within 24 h after middle cerebral artery occlusion, showing recovery of sodium homeostasis and lesion reduction in specimens treated with hMSC while ¹ H MRS identifies reduction in lactate levels. This combined metric was confirmed by evaluating treatment groups receiving healthy or compromised hMSC versus control (sham saline injection) over 21 d. Behavioral tests to assess functional recovery and cell analysis for immunomodulatory and macrophage activity to detect hMSC potency confirm MR findings. Clinically, these MR metrics may prove critical to early evaluations of therapeutic efficacy and overall stroke recovery.

To explore pre-treatment risk factors for cognitive decline in patients with breast cancer using a machine learning approach applied to a comprehensive multimodal clinical, biological, and neuroimaging dataset. Sixty-seven women with early breast cancer were assessed at diagnosis (T0), 8 months (T1), and 17 months after diagnosis (T2). Cognitive decline was defined using the Reliable Change Index. Patients were classified as showing no decline or decline at either follow-up. Five feature sets were evaluated: (1) patient characteristics, treatment, and psychosocial measures; (2) inflammatory and neural health markers, (3) structural brain volumes, (4) resting-state functional MRI connectivity, and (5) diffusion MRI measures. Random forest models were first trained on feature set 1, then sequentially combined with sets 2-5 to explore their additive predictive value. Each model underwent standardized preprocessing, recursive feature selection of the top 6 predictors, and tuning before random forest classification. A final composite model was constructed by pooling the six top predictors from each feature set to assess potential complementary multimodal information. Feature contributions were examined using SHAP values. Of 67 patients, 33 (49%) experienced cognitive decline following treatment. Models achieved prediction accuracies of 76%, improving up to 81% when MRI measures and/or serum markers were included. Key baseline predictors of cognitive decline included more aggressive subtypes, planned systemic therapy, perceived stress, and limited cognitive and brain reserve. Machine learning explored potential pre-treatment risk factors for cancer-related cognitive decline in patients with breast cancer. These findings highlight potential risk factors that could support risk-stratification.