Inhibition of Notch1 signal promotes brain recovery by modulating glial activity after stroke.

BACKGROUND: Notch1 signaling inhibiton with N-[N-(3,5-difluorophenacetyl)-1-alanyl]-S-phenylglycine t-butylester] (DAPT) treatment could promote brain recovery and the intervention effect is different between striatum (STR) and cortex (CTX), which might be accounted for different changes of glial activities, but the in-depth mechanism is still unknown. The purpose of this study was to identify whether DAPT could modulate microglial subtype shifts and astroglial-endfeet aquaporin-4 (AQP4) mediated waste solute drainage. METHODS: Sprague-Dawley rats (n=10) were subjected to 90min of middle cerebral artery occlusion (MCAO) and were treated with DAPT (n=5) or act as control with no treatment (n=5). Two groups of rats underwent MRI scans at 24h and 4 week, and sacrificed at 4 week after stroke for immunofluorescence (IF). RESULTS: Compared with control rats, MRI data showed structural recovery in ipsilateral STR but not CTX. And IF showed decreased pro-inflammatory M1 microglia and increased anti-inflammatory M2 microglia in striatal lesion core and peri-lesions of STR, CTX. Meanwhile, IF showed decreased AQP4 polarity in ischemic brain tissue, however, AQP4 polarity in striatal peri-lesions of DAPT treated rats was higher than that in control rats but shows no difference in cortical peri-lesions between control and treated rats. CONCLUSIONS: The present study indicated that DAPT could promote protective microglia subtype shift and striatal astrocyte mediated waste solute drainage, that the later might be the major contributor of waste solute metabolism and one of the accounts for discrepant recovery of STR and CTX.

Magnetic resonance imaging with upconversion nanoprobes capable of crossing the blood-cerebrospinal fluid barrier.

The central nervous system (CNS) maintains homeostasis with its surrounding environment by restricting the ingress of large hydrophilic molecules, immune cells, pathogens, and other external harmful substances to the brain. This function relies heavily on the blood-cerebrospinal fluid (B-CSF) and blood-brain barrier (BBB). Although considerable research has examined the structure and function of the BBB, the B-CSF barrier has received little attention. Therapies for disorders associated with the central nervous system have the potential to benefit from targeting the B-CSF barrier to enhance medication penetration into the brain. In this study, we synthesized a nanoprobe ANG-PEG-UCNP capable of crossing the B-CSF barrier with high targeting specificity using a hydrocephalus model for noninvasive magnetic resonance ventriculography to understand the mechanism by which the CSF barrier may be crossed and identify therapeutic targets of CNS diseases. This magnetic resonance nanoprobe ANG-PEG-UCNP holds promising potential as a safe and effective means for accurately defining the ventricular anatomy and correctly locating sites of CSF obstruction.

A Convolutional Neural Network Model for Distinguishing Hemangioblastomas From Other Cerebellar-and-Brainstem Tumors Using Contrast-Enhanced MRI.

BACKGROUND: Hemangioblastoma (HB) is a highly vascularized tumor most commonly occurring in the posterior cranial fossa, requiring accurate preoperative diagnosis to avoid accidental intraoperative hemorrhage and even death. PURPOSE: To accurately distinguish HBs from other cerebellar-and-brainstem tumors using a convolutional neural network model based on a contrast-enhanced brain MRI dataset. STUDY TYPE: Retrospective. POPULATION: Four hundred five patients (182 = HBs; 223 = other cerebellar-and brainstem tumors): 305 cases for model training, and 100 for evaluation. FIELD STRENGTH/SEQUENCE: 3 T/contrast-enhanced T1-weighted imaging (T1WI + C). ASSESSMENT: A CNN-based 2D classification network was trained by using sliced data along the z-axis. To improve the performance of the network, we introduced demographic information, various data-augmentation methods and an auxiliary task to segment tumor region. Then, this method was compared with the evaluations performed by experienced and intermediate-level neuroradiologists, and the heatmap of deep feature, which indicates the contribution of each pixel to model prediction, was visualized by Grad-CAM for analyzing the misclassified cases. STATISTICAL TESTS: The Pearson chi-square test and an independent t-test were used to test for distribution difference in age and sex. And the independent t-test was exploited to evaluate the performance between experts and our proposed method. P value <0.05 was considered significant. RESULTS: The trained network showed a higher accuracy for identifying HBs (accuracy = 0.902 ± 0.031, F1 = 0.891 ± 0.035, AUC = 0.926 ± 0.040) than experienced (accuracy = 0.887 ± 0.013, F1 = 0.868 ± 0.011, AUC = 0.881 ± 0.008) and intermediate-level (accuracy = 0.827 ± 0.037, F1 = 0.768 ± 0.068, AUC = 0.810 ± 0.047) neuroradiologists. The recall values were 0.910 ± 0.050, 0.659 ± 0.084, and 0.828 ± 0.019 for the trained network, intermediate and experienced neuroradiologists, respectively. Additional ablation experiments verified the utility of the introduced demographic information, data augmentation, and the auxiliary-segmentation task. DATA CONCLUSION: Our proposed method can successfully distinguish HBs from other cerebellar-and-brainstem tumors and showed diagnostic efficiency comparable to that of experienced neuroradiologists. EVIDENCE LEVEL: 3 TECHNICAL EFFICACY: Stage 2.

CT-based deep learning model for predicting hospital discharge outcome in spontaneous intracerebral hemorrhage.

OBJECTIVES: To predict the functional outcome of patients with intracerebral hemorrhage (ICH) using deep learning models based on computed tomography (CT) images. METHODS: A retrospective, bi-center study of ICH patients was conducted. Firstly, a custom 3D convolutional model was built for predicting the functional outcome of ICH patients based on CT scans from randomly selected ICH patients in H training dataset collected from H hospital. Secondly, clinical data and radiological features were collected at admission and the Extreme Gradient Boosting (XGBoost) algorithm was used to establish a second model, named the XGBoost model. Finally, the Convolution model and XGBoost model were fused to build the third "Fusion model." Favorable outcome was defined as modified Rankin Scale score of 0-3 at discharge. The prognostic predictive accuracy of the three models was evaluated using an H test dataset and an external Y dataset, and compared with the performance of ICH score and ICH grading scale (ICH-GS). RESULTS: A total of 604 patients with ICH were included in this study, of which 450 patients were in the H training dataset, 50 patients in the H test dataset, and 104 patients in the Y dataset. In the Y dataset, the areas under the curve (AUCs) of the Convolution model, XGBoost model, and Fusion model were 0.829, 0.871, and 0.905, respectively. The Fusion model prognostic performance exceeded that of ICH score and ICH-GS (p = 0.043 and p = 0.045, respectively). CONCLUSIONS: Deep learning models have good accuracy for predicting functional outcome of patients with spontaneous intracerebral hemorrhage. CLINICAL RELEVANCE STATEMENT: The proposed deep learning Fusion model may assist clinicians in predicting functional outcome and developing treatment strategies, thereby improving the survival and quality of life of patients with spontaneous intracerebral hemorrhage. KEY POINTS: • Integrating clinical presentations, CT images, and radiological features to establish deep learning model for functional outcome prediction of patients with intracerebral hemorrhage. • Deep learning applied to CT images provides great help in prognosing functional outcome of intracerebral hemorrhage patients. • The developed deep learning model performs better than clinical prognostic scores in predicting functional outcome of patients with intracerebral hemorrhage.

DKI can distinguish high-grade gliomas from IDH1-mutant low-grade gliomas and correlate with their different nuclear-to-cytoplasm ratio: a localized biopsy-based study.

OBJECTIVES: To explore whether differences in diffusional kurtosis imaging (DKI) between therapy-naïve high-grade gliomas (HGGs) and low-grade gliomas (LGGs) are related to the cellularity and/or the nuclear-to-cytoplasmic (N/C) ratio. METHODS: We analyzed 44 and 40 diffuse glioma samples that were pathologically confirmed as HGGs and IDH1-mutant LGGs, respectively. The DKI parameters included kurtosis metrics (mean kurtosis [MK], axial kurtosis [K//], and radial kurtosis [K⊥]), and the diffusional metrics (fractional anisotropy [FA], mean diffusion [MD], axial diffusion [λ//], and radial diffusion [λ⊥]). The cellularity and the N/C ratio were compared within LGGs and HGGs using the Mann-Whitney U test (significant level, p < 0.007 [0.05/7]); Bonferroni correction). Spearman's correlation analysis was used to calculate the correlation coefficients among DKI metrics, cellularity, and the N/C ratio at a significant level of p = 0.05. RESULTS: Excluding FA, all DKI metrics showed significant differences between HGGs and LGGs (all p ≤ 0.001). The N/C ratio of HGGs was significantly higher than that of LGGs; however, differences in cellularity were not significant between the two glioma groups (p = 0.525). Similarly, excluding FA, all DKI metrics were significantly correlated with the N/C ratio in LGGs, with correlation coefficients of - 0.365 (MD), - 0.313 (λ//), - 0.376 (λ⊥), 0.859 (MK), 0.772 (K//), and 0.842 (K//). There was a non-significant correlation between any DKI parameters and the cellularity in LGGs. Additionally, the cellularity and N/C ratios in HGGs did not correlate with any DKI metrics. CONCLUSIONS: DKI differentiate LGGs from HGGs associated with their different N/C ratios. CLINICAL RELEVANCE STATEMENT: This study shows that DKI differentiates LGGs from HGGs may correlated with their different N/C ratios, this could provide a possible histopathological mechanism about why DKI can DKI differentiate LGGs from HGGs. KEY POINTS: • Excluding FA, all DKI metrics showed a significant difference between high-grade gliomas and IDH1-mutant low-grade gliomas. • The nuclear-to-cytoplasm ratios in high-grade gliomas were significantly more extensive than that in IDH1-mutant low-grade gliomas, but not the cellularity. • Significant associations were seen between DKI measures and the N/C ratio; a non-significant correlation was noted between any DKI metric and cellularity in glioma specimens.

Fusing Positive and Negative CT Contrast Nanoagent for the Sensitive Detection of Hepatoma.

Positive computed tomography (CT) contrast nanoagent has significant applications in diagnosing tumors. However, the sensitive differentiation between hepatoma and normal liver tissue remains challenging. This challenge arises primarily because both normal liver and hepatoma tissues capture the nanoagent, resulting in similar positive CT contrasts. Here, a strategy for fusing positive and negative CT contrast nanoagent is proposed to detect hepatoma. A nanoagent Hf-MOF@AB@PVP initially generates a positive CT contrast signal of 120.3 HU in the liver. Subsequently, it can specifically respond to the acidic microenvironment of hepatoma to generate H2 , further achieving a negative contrast of -96.0 HU. More importantly, the relative position between the negative and positive signals area is helpful to determine the location of hepatoma and normal liver tissues. The distinct contrast difference of 216.3 HU and relative orientation between normal liver and tumor tissues are meaningful to sensitively distinguish hepatoma from normal liver tissue utilizing CT imaging.

Genome-wide association study of hippocampal blood-oxygen-level-dependent-cerebral blood flow correlation in Chinese Han population.

Correlation between blood-oxygen-level-dependent (BOLD) and cerebral blood flow (CBF) has been used as an index of neurovascular coupling. Hippocampal BOLD-CBF correlation is associated with neurocognition, and the reduced correlation is associated with neuropsychiatric disorders. We conducted the first genome-wide association study of the hippocampal BOLD-CBF correlation in 4,832 Chinese Han subjects. The hippocampal BOLD-CBF correlation had an estimated heritability of 16.2-23.9% and showed reliable genome-wide significant association with a locus at 3q28, in which many variants have been linked to neuroimaging and cerebrospinal fluid markers of Alzheimer's disease. Gene-based association analyses showed four significant genes (GMNC, CRTC2, DENND4B, and GATAD2B) and revealed enrichment for mast cell calcium mobilization, microglial cell proliferation, and ubiquitin-related proteolysis pathways that regulate different cellular components of the neurovascular unit. This is the first unbiased identification of the association of hippocampal BOLD-CBF correlation, providing fresh insights into the genetic architecture of hippocampal neurovascular coupling.

Conformal order and Poincaré-Klein mapping underlying electrostatics-driven inhomogeneity in tethered membranes.

Understanding the organization of matter under the long-range electrostatic force is a fundamental problem in multiple fields. In this work, based on the electrically charged tethered membrane model, we reveal regular structures underlying the lowest-energy states of inhomogeneously stretched planar lattices by a combination of numerical simulation and analytical geometric analysis. Specifically we show the conformal order characterized by the preserved bond angle in the lattice deformation and reveal the Poincaré-Klein mapping underlying the electrostatics-driven inhomogeneity. The discovery of the Poincaré-Klein mapping, which connects the Poincaré disk and the Klein disk for the hyperbolic plane, implies the connection of long-range electrostatic force and hyperbolic geometry. We also discuss lattices with patterned charges of opposite signs for modulating in-plane inhomogeneity and even creating 3D shapes, which may have a connection to metamaterials design. This work suggests the geometric analysis as a promising approach for elucidating the organization of matter under the long-range force.

A 2-step prediction model for diagnosis of germinomas in the pineal region.

Background: Germinomas are sensitive to radiation and chemotherapy, and their management distinctly differs from other kinds of pineal region tumors. The aim of this study was to construct a prediction model based on clinical features and preoperative magnetic resonance (MR) manifestations to achieve noninvasive diagnosis of germinomas in pineal region. Methods: A total of 126 patients with pineal region tumors were enrolled, including 36 germinomas, 53 nongerminomatous germ cell tumors (NGGCTs), and 37 pineal parenchymal tumors (PPTs). They were divided into a training cohort (n = 90) and a validation cohort (n = 36). Features were extracted from clinical records and conventional MR images. Multivariate analysis was performed to screen for independent predictors to differentiate germ cell tumors (GCTs) and PPTs, germinomas, and NGGCTs, respectively. From this, a 2-step nomogram model was established, with model 1 for discriminating GCTs from PPTs and model 2 for identifying germinomas in GCTs. The model was tested in a validation cohort. Results: Both model 1 and model 2 yielded good predictive efficacy, with c-indexes of 0.967 and 0.896 for the diagnosis of GCT and germinoma, respectively. Calibration curve, decision curve, and clinical impact curve analysis further confirmed their predictive accuracy and clinical usefulness. The validation cohort achieved areas under the receiver operating curves of 0.885 and 0.926, respectively. Conclusions: The 2-step model in this study can noninvasively differentiate GCTs from PPTs and further identify germinomas, thus holding potential to facilitate treatment decision-making for pineal region tumors.

Endometriosis-targeted MRI imaging using bevacizumab-modified nanoparticles aimed at vascular endothelial growth factor.

Endometriosis is a tumor-like disease with high recurrence. In this case, the accurate imaging-based diagnosis of endometriosis can help clinicians eradicate it by improving their surgical plan. However, although contrast agents can improve the visibility of the tissue of interest in vivo via magnetic resonance imaging (MRI), the lack of biomarkers in endometriosis hinders the development of agents for its targeted imaging and diagnosis. Herein, aiming at the enriched vascular endothelial growth factor (VEGF) in endometriosis, we developed a targeting MRI contrast agent modified with bevacizumab, i.e., NaGdF4@PEG@bevacizumab-Cy5.5 nanoparticles (NPBCNs), to detect endometriosis. NPBCNs showed negligible cytotoxicity and high affinity towards VEGF in endometrial cells in vitro. Furthermore, NPBCNs generated a strong signal enhancement in vivo in endometriosis lesions in rats in T1-weighted images via MRI at 3 days post-injection, as confirmed by the histopathological staining results and fluorescence imaging on the same day. Our approach can enable NPBCNs to target endometriosis effectively, thus avoiding missed diagnoses.

Deep Learning-Assisted Quantitative Susceptibility Mapping as a Tool for Grading and Molecular Subtyping of Gliomas.

This study aimed to explore the value of deep learning (DL)-assisted quantitative susceptibility mapping (QSM) in glioma grading and molecular subtyping. Forty-two patients with gliomas, who underwent preoperative T2 fluid-attenuated inversion recovery (T2 FLAIR), contrast-enhanced T1-weighted imaging (T1WI + C), and QSM scanning at 3.0T magnetic resonance imaging (MRI) were included in this study. Histopathology and immunohistochemistry staining were used to determine glioma grades, and isocitrate dehydrogenase (IDH) 1 and alpha thalassemia/mental retardation syndrome X-linked gene (ATRX) subtypes. Tumor segmentation was performed manually using Insight Toolkit-SNAP program (www.itksnap.org). An inception convolutional neural network (CNN) with a subsequent linear layer was employed as the training encoder to capture multi-scale features from MRI slices. Fivefold cross-validation was utilized as the training strategy (seven samples for each fold), and the ratio of sample size of the training, validation, and test dataset was 4:1:1. The performance was evaluated by the accuracy and area under the curve (AUC). With the inception CNN, single modal of QSM showed better performance in differentiating glioblastomas (GBM) and other grade gliomas (OGG, grade II-III), and predicting IDH1 mutation and ATRX loss (accuracy: 0.80, 0.77, 0.60) than either T2 FLAIR (0.69, 0.57, 0.54) or T1WI + C (0.74, 0.57, 0.46). When combining three modalities, compared with any single modality, the best AUC/accuracy/F1-scores were reached in grading gliomas (OGG and GBM: 0.91/0.89/0.87, low-grade and high-grade gliomas: 0.83/0.86/0.81), predicting IDH1 mutation (0.88/0.89/0.85), and predicting ATRX loss (0.78/0.71/0.67). As a supplement to conventional MRI, DL-assisted QSM is a promising molecular imaging method to evaluate glioma grades, IDH1 mutation, and ATRX loss. Supplementary Information: The online version contains supplementary material available at 10.1007/s43657-022-00087-6.

Cross-ancestry genome-wide association meta-analyses of hippocampal and subfield volumes.

The hippocampus is critical for memory and cognition and neuropsychiatric disorders, and its subfields differ in architecture and function. Genome-wide association studies on hippocampal and subfield volumes are mainly conducted in European populations; however, other ancestral populations are under-represented. Here we conduct cross-ancestry genome-wide association meta-analyses in 65,791 individuals for hippocampal volume and 38,977 for subfield volumes, including 7,009 individuals of East Asian ancestry. We identify 339 variant-trait associations at P < 1.13 × 10-9 for 44 hippocampal traits, including 23 new associations. Common genetic variants have similar effects on hippocampal traits across ancestries, although ancestry-specific associations exist. Cross-ancestry analysis improves the fine-mapping precision and the prediction performance of polygenic scores in under-represented populations. These genetic variants are enriched for Wnt signaling and neuron differentiation and affect cognition, emotion and neuropsychiatric disorders. These findings may provide insight into the genetic architectures of hippocampal and subfield volumes.

Multi-b-value diffusion stretched-exponential model parameters correlate with MIB-1 and CD34 expression in Glioma patients, an intraoperative MR-navigated, biopsy-based histopathologic study.

Background: To understand the pathological correlations of multi-b-value diffusion-weighted imaging (MDWI) stretched-exponential model (SEM) parameters of α and diffusion distribution index (DDC) in patients with glioma. SEM parameters, as promising biomarkers, played an important role in histologically grading gliomas. Methods: Biopsy specimens were grouped as high-grade glioma (HGG) or low-grade glioma (LGG). MDWI-SEM parametric mapping of DDC1500, α1500 fitted by 15 b-values (0-1,500 sec/mm2)and DDC5000 and α5000 fitted by 22 b-values (0-5,000 sec/mm2) were matched with pathological samples (stained by MIB-1 and CD34) by coregistered localized biopsies, and all SEM parameters were correlated with these pathological indices pMIB-1(percentage of MIB-1 expression positive rate) and CD34-MVD (CD34 expression positive microvascular density for each specimen). The two-tailed Spearman's correlation was calculated for pathological indexes and SEM parameters, as well as WHO grades and SEM parameters. Results: MDWI-derived α1500 negatively correlated with CD34-MVD in both LGG (6 specimens) and HGG (26 specimens) (r=-0.437, P =0.012). MDWI-derived DDC1500 and DDC5000 negatively correlated with MIB-1 expression in all glioma patients (P<0.05). WHO grades negatively correlated with α1500(r=-0.485; P=0.005) and α5000(r=-0.395; P=0.025). Conclusions: SEM-derived DDC and α are significant in histologically grading gliomas, DDC may indicate the proliferative ability, and CD34 stained microvascular perfusion may be an important determinant of water diffusion inhomogeneity α in glioma.

The impact of pre-adulthood urbanicity on hippocampal subfield volumes and neurocognitive abilities in young adults.

BACKGROUND: Urbanicity refers to the conditions that are particular to urban areas and is a growing environmental challenge that may affect hippocampus and neurocognition. This study aimed to investigate the effects of the average pre-adulthood urbanicity on hippocampal subfield volumes and neurocognitive abilities as well as the sensitive age windows of the urbanicity effects. PARTICIPANTS AND METHODS: We included 5,390 CHIMGEN participants (3,538 females; age: 23.69 ± 2.26 years, range: 18-30 years). Pre-adulthood urbanicity of each participant was defined as the average value of annual night-time light (NL) or built-up% from age 0-18, which were extracted from remote-sensing satellite data based on annual residential coordinates of the participants. The hippocampal subfield volumes were calculated based on structural MRI and eight neurocognitive measures were assessed. The linear regression was applied to investigate the associations of pre-adulthood NL with hippocampal subfield volumes and neurocognitive abilities, mediation models were used to find the underlying pathways among urbanicity, hippocampus and neurocognition, and distributed lag models were used to identify sensitive age windows of urbanicity effect. RESULTS: Higher pre-adulthood NL was associated with greater volumes in the left (ß = 0.100, 95%CI: [0.075, 0.125]) and right (0.078, [0.052, 0.103]) fimbria and left subiculum body (0.045, [0.020, 0.070]) and better neurocognitive abilities in information processing speed (-0.212, [-0.240, -0.183]), working memory (0.085, [0.057, 0.114]), episodic memory (0.107, [0.080, 0.135]), and immediate (0.094, [0.065, 0.123]) and delayed (0.087, [0.058, 0.116]) visuospatial recall, and hippocampal subfield volumes and visuospatial memory showed bilateral mediations for the urbanicity effects. Urbanicity effects were greatest on the fimbria in preschool and adolescence, on visuospatial memory and information processing from childhood to adolescence and on working memory after 14 years. CONCLUSION: These findings improve our understanding of the impact of urbanicity on hippocampus and neurocognitive abilities and will benefit for designing more targeted intervention for neurocognitive improvement.

Correlations between intravoxel incoherent motion-derived fast diffusion and perfusion fraction parameters and VEGF- and MIB-1-positive rates in brain gliomas: an intraoperative MR-navigated, biopsy-based histopathologic study.

OBJECTIVES: To explore the correlations between histopathologic findings and intravoxel incoherent motion (IVIM)-derived perfusion and diffusion parameters in brain gliomas. METHODS: Thirty-two biopsy samples from twenty-one patients with newly diagnosed gliomas from a previous prospective cohort study were retrospectively analyzed. All patients underwent diffusion-weighted MRI with 22 b values (0-5000 s/mm2), followed by intraoperative MR-guided biopsy surgery and surgical resection. All 32 biopsy samples underwent immunohistochemical staining followed by quantitative analysis of cell density (cellularity), percent of MIB-1 (Ki67)-positive expression (pMIB-1), number of CD34-stained vessels (CD34-MVD), and percent of VEGF-positive expressing cells (pVEGF) using a multispectral phenotyping microscope. Based on the co-registered localized biopsy, correlation analysis was performed between the IVIM-derived biexponential model-based parameters (Dfast1500 and Dfast5000, Dslow1500 and Dslow5000, PF1500 and PF5000) and the above four pathological biomarkers and glioma grades. RESULTS: Significant positive correlations were revealed between Dfast5000 and pVEGF (rho (r) = 0.466, p = 0.007), and Dfast1500 and pVEGF (r = 0.371, p = 0.037). A significant negative correlation was revealed between PF5000 with pMIB-1 (r = - 0.456, p = 0.01). Moderate to good positive correlations were shown between Dfast5000 and glioma grades (r = 0.509, p = 0.003) and Dfast1500 and glioma grades (r = 0.476, p = 0.006). CONCLUSIONS: IVIM-DWI-derived Dfast and PF correlate, respectively, with intratumor pVEGF and pMIB-1. When using the wide-high b value scheme, IVIM-derived Dfast and PF tend to demonstrate better efficacy in evaluating malignancy-related characteristics such as angiogenesis and cellular proliferation in gliomas. KEY POINTS: • Intravoxel incoherent motion-diffusion-weighted imaging (IVIM-DWI)-derived fast diffusion (Dfast) and perfusion fraction (PF) can quantitatively reflect intratumor pVEGF and pMIB-1. • IVIM-DWI-derived Dfast and PF tend to demonstrate better efficacy in evaluating glioma malignancy when an optimized scheme is used. • IVIM-DWI-derived Dfast5000 and PF5000 are promising non-invasive parameters correlating with pVEGF and pMIB-1 in gliomas.

Inhibition of Notch 1 signaling in the subacute stage after stroke promotes striatal astrocyte-derived neurogenesis.

Inhibition of Notch1 signaling has been shown to promote astrocyte-derived neurogenesis after stroke. To investigate the regulatory role of Notch1 signaling in this process, in this study, we used a rat model of stroke based on middle cerebral artery occlusion and assessed the behavior of reactive astrocytes post-stroke. We used the γ-secretase inhibitor N-[N-(3,5-diuorophenacetyl)-1-alanyl]-S-phenylglycine t-butylester (DAPT) to block Notch1 signaling at 1, 4, and 7 days after injury. Our results showed that only administration of DAPT at 4 days after stroke promoted astrocyte-derived neurogenesis, as manifested by recovery of white matter fiber bundle integrity on magnetic resonance imaging, which is consistent with recovery of neurologic function. These findings suggest that inhibition of Notch1 signaling at the subacute stage post-stroke mediates neural repair by promoting astrocyte-derived neurogenesis.

Blocking Spatiotemporal Crosstalk between Subcellular Organelles for Enhancing Anticancer Therapy with Nanointercepters.

The spatiotemporal characterization of signaling crosstalk between subcellular organelles is crucial for the therapeutic effect of malignant tumors. Blocking interactive crosstalk in this fashion is significant but challenging. Herein, a communication interception strategy is reported, which blocks spatiotemporal crosstalk between subcellular organelles for cancer therapy with underlying molecular mechanisms. Briefly, amorphous-core@crystalline-shell Fe@Fe3 O4 nanoparticles (ACFeNPs) are fabricated to specifically block the crosstalk between lysosomes and endoplasmic reticulum (ER) by hydroxyl radicals generated along with their trajectory through heterogeneous Fenton reaction. ACFeNPs initially enter lysosomes and trigger autophagy, then continuous lysosomal damage blocks the generation of functional autolysosomes, which mediates ER-lysosome crosstalk, thus the autophagy is paralyzed. Thereafter, released ACFeNPs from lysosomes induce ER stress. Without the alleviation by autophagy, the ER-stress-associated apoptotic pathway is fully activated, resulting in a remarkable therapeutic effect. This strategy provides a wide venue for nanomedicine to exert biological advantages and confers new perspective for the design of novel anticancer drugs.

Harnessing Reconstructed Macrophage Modulation of Infiltration-Excluded Immune Microenvironments To Delineate Glioma Infiltrative Region.

High invasiveness of glioma produces residual glioma cells in the brain parenchyma after surgery and ultimately causes recurrence. Precise delineation of glioma infiltrative region is critical for an accurate complete resection, which is challenging. The glioma-infiltrating area constitutes infiltration-excluded immune microenvironments (I-E TIMEs), which recruits endogenous or adoptive macrophages to the invasive edge of glioma. Thus, combined with immune cell tracing technology, we provided a novel strategy for the preoperative precise definition of the glioma infiltration boundary, even satellite-like infiltration stoves. Herein, the biomimetic probe was constructed by internalizing fluorophore labeled PEGylated KMnF3 nanoparticles into bone-marrow-derived macrophages using magnetic resonance imaging (MRI)/fluorescence imaging (FI). The biomimetic probe was able to cross the blood-brain barrier and home to the orthotopic glioma infiltrates including satellite stove under MRI and FI tracing, which was validated using hematoxylin and eosin staining, indicating its excellent performance in distinguishing the margins between the glioma cell and normal tissues. This study guides the precise definition of glioma infiltration boundaries at the cellular level, including the observation of any residual glioma cells after surgery. Thus, it has the potential to guide surgery to maximize resection and predict recurrence in the clinic.

Glioma grade discrimination with dynamic contrast-enhanced MRI: An accurate analysis based on MRI guided stereotactic biopsy.

PURPOSE: To evaluate the diagnostic performance of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) metrics for glioma grading on a point-to-point basis. METHODS: Forty patients with treatment-naïve glioma underwent DCE-MR examination and stereotactic biopsy. DCE-derived parameters including endothelial transfer constant (Ktrans), volume of extravascular-extracellular space (ve), fractional plasma volume (fpv), and reflux transfer rate (kep) were measured within ROIs on DCE maps accurately matched with biopsies used for histologic grades diagnosis. Differences in parameters between grades were evaluated by Kruskal-Wallis tests. Diagnostic accuracy of each parameter and their combination was assessed using receiver operating characteristic curve. RESULTS: Eighty-four independent biopsy samples from 40 patients were analyzed in our study. Significant statistical differences in Ktrans and ve were observed between grades except ve between grade 2 and 3. Ktrans showed good to excellent accuracy in discriminating grade 2 from 3, 3 from 4, and 2 from 4 (area under the curve = 0.802, 0.801 and 0.971, respectively). Ve indicated good accuracy in discriminating grade 3 from 4 and 2 from 4 (AUC = 0.874 and 0.899, respectively). The combined parameter demonstrated fair to excellent accuracy in discriminating grade 2 from 3, 3 from 4, and 2 from 4 (AUC = 0.794, 0.899 and 0.982, respectively). CONCLUSION: Our study had identified Ktrans, ve and the combination of parameters to be an accurate predictor for grading glioma.