Glymphatic System Impairment Contributes to the Formation of Brain Edema After Ischemic Stroke.

BACKGROUND: Blood-brain barrier damage has traditionally been considered to determine the occurrence and development of poststroke brain edema, a devastating and life-threatening complication. However, no treatment strategy targeting blood-brain barrier damage has been proven clinically effective in ameliorating brain edema. METHODS: In mice with stroke models induced by transient middle cerebral artery occlusion (MCAO), the changes in glymphatic system (GS) function impairment were detected by ex vivo fluorescence imaging, 2-photon in vivo imaging, and magnetic resonance imaging within 1 week after MCAO, and the effects of GS impairment and recovery on the formation and resolution of brain edema were evaluated. In addition, in patients with ischemic stroke within 1 week after onset, changes in GS function and brain edema were also observed by magnetic resonance imaging. RESULTS: We found that the extravasation of protein-rich fluids into the brain was not temporally correlated with edema formation after MCAO in mice, as brain edema reabsorption preceded blood-brain barrier closure. Strikingly, the time course of edema progression matched well with the GS dysfunction after MCAO. Pharmacological enhancement of the GS function significantly alleviated brain edema developed on day 2 after MCAO, accompanied by less deposition of Aß (amyloid-ß) and better cognitive function. Conversely, functional suppression of the GS delayed the absorption of brain edema on day 7 after MCAO. Moreover, patients with ischemic stroke revealed a consistent trend of GS dysfunction after reperfusion as MCAO mice, which was correlated with the severity of brain edema and functional outcomes. CONCLUSIONS: GS is a key contributor to the formation of brain edema after ischemic stroke, and targeting the GS may be a promising strategy for treating brain edema in ischemic stroke. REGISTRATION: URL: https://www.chictr.org.cn/showproj.html?proj=162857; Unique identifier: NFEC-2019-189.

Kilogram-Scale Synthesis of Extremely Small Gadolinium Oxide Nanoparticles as a T1-Weighted Contrast Agent for Magnetic Resonance Imaging.

Magnetic resonance imaging contrast agents are frequently used in clinics to enhance the contrast between diseased and normal tissues. The previously reported poly(acrylic acid) stabilized exceedingly small gadolinium oxide nanoparticles (ES-GdON-PAA) overcame the problems of commercial Gd chelates, but limitations still exist, i.e., high r2/r1 ratio, long blood circulation half-life, and no data for large scale synthesis and formulation optimization. In this study, polymaleic acid (PMA) is found to be an ideal stabilizer to synthesize ES-GdONs. Compared with ES-GdON-PAA, the PMA-stabilized ES-GdON (ES-GdON-PMA) has a lower r2/r1 ratio (2.05, 7.0 T) and a lower blood circulation half-life (37.51 min). The optimized ES-GdON-PMA-9 has an exceedingly small particle size (2.1 nm), excellent water dispersibility, and stability. A facile, efficient, and environmental friendly synthetic method is developed for large-scale synthesis of the ES-GdONs-PMA. The weight of the optimized freeze-dried ES-GdON-PMA-26 synthesized in a 20 L of reactor reaches the kilogram level. The formulation optimization is also finished, and the concentrated ES-GdON-PMA-26 formulation (CGd = 100 mm) after high-pressure steam sterilization possesses eligible physicochemical properties (i.e., pH value, osmolality, viscosity, and density) for investigational new drug application.

MRI-Guided Tumor Therapy Based on Synergy of Ferroptosis, Immunosuppression Reversal and Disulfidptosis.

Triple negative breast cancer (TNBC) cells have a high demand for oxygen and glucose to fuel their growth and spread, shaping the tumor microenvironment (TME) that can lead to a weakened immune system by hypoxia and increased risk of metastasis. To disrupt this vicious circle and improve cancer therapeutic efficacy, a strategy is proposed with the synergy of ferroptosis, immunosuppression reversal and disulfidptosis. An intelligent nanomedicine GOx-IA@HMON@IO is successfully developed to realize this strategy. The Fe release behaviors indicate the glutathione (GSH)-responsive degradation of HMON. The results of titanium sulfate assay, electron spin resonance (ESR) spectra, 5,5'-Dithiobis-(2-nitrobenzoic acid (DTNB) assay and T1-weighted magnetic resonance imaging (MRI) demonstrate the mechanism of the intelligent iron atom (IA)-based cascade reactions for GOx-IA@HMON@IO, generating robust reactive oxygen species (ROS). The results on cells and mice reinforce the synergistic mechanisms of ferroptosis, immunosuppression reversal and disulfidptosis triggered by the GOx-IA@HMON@IO with the following steps: 1) GSH peroxidase 4 (GPX4) depletion by disulfidptosis; 2) IA-based cascade reactions; 3) tumor hypoxia reversal; 4) immunosuppression reversal; 5) GPX4 depletion by immunotherapy. Based on the synergistic mechanisms of ferroptosis, immunosuppression reversal and disulfidptosis, the intelligent nanomedicine GOx-IA@HMON@IO can be used for MRI-guided tumor therapy with excellent biocompatibility and safety.

Glymphatic abnormality in systemic lupus erythematosus detected by diffusion tensor image analysis along the perivascular space.

OBJECTIVES: This study aimed to evaluate the activity of the glymphatic system in systemic lupus erythematosus (SLE) by a diffusion-based method termed "Diffusion Tensor Image Analysis aLong the Perivascular Space (DTI-ALPS)", and examined its correlations with morphological changes in the brain. METHODS: In this cross-sectional study, forty-five female patients with SLE and thirty healthy controls (HCs) were included. Voxel-based and surface-based morphometric analyses were performed to examine T1 weighted images, and diffusion tensor images were acquired to determine diffusivity along the x-, y-, and z-axes in the plane of the lateral ventricle body. The ALPS-index was calculated. The differences in values between SLE patients and HC group were compared using the independent samples t test or Mann-Whitney U test. For the correlations between the ALPS-index and brain morphological parameters, partial correlation analysis and Pearson's correlation analysis were conducted. RESULTS: SLE patients showed lower values for the ALPS-index in left (1.543 ± 0.141 vs 1.713 ± 0.175, p < 0.001), right (1.428 ± 0.142 vs 1.556 ± 0.139, p < 0.001) and whole (1.486 ± 0.121 vs 1.635 ± 0.139, p < 0.001) brain compared with the HC group. The reduced ALPS-index showed significant positive correlations with gray matter loss. CONCLUSION: The non-invasive ALPS-index could serve as a sensitive and effective neuroimaging biomarker for individually quantifying glymphatic activity in patients with SLE. Glymphatic system abnormality may be involved in the pathophysiologic mechanism underlying central nervous system damage in SLE patients.

SERS as a Probe of Surface Chemistry Enabled by Surface-Accessible Plasmonic Nanomaterials.

ConspectusWhen the size of materials is reduced, their volume decreases much faster than their surface area, which in the most extreme case leads to 2D nanomaterials which are "all surface". Since atoms at the surface have free energies, electronic states, and mobility which are very different from bulk atoms, nanomaterials that have large surface-to-volume ratios can display remarkable new properties compared to their bulk counterparts. More generally, the surface is where nanomaterials interact with their environment, which in turn places surface chemistry at the heart of catalysis, nanotechnology, and sensing applications. Understanding and utilizing nanosurfaces are not possible without appropriate spectroscopic and microscopic characterization techniques. An emerging technique in this area is surface-enhanced Raman spectroscopy (SERS), which utilizes the interaction between plasmonic nanoparticles and light to enhance the Raman signals of molecules near the nanoparticles' surfaces. SERS has the great advantage that it can provide detailed in situ information on surface orientation and binding between molecules and the nanosurface. A long-standing dilemma that has limited the applications of SERS in surface chemistry studies is the choice between surface-accessibility and plasmonic activity. More specifically, the synthesis of metal nanomaterials with strong plasmonic and SERS-enhancing properties typically involves the use of strongly adsorbing modifier molecules, but these modifiers also passivate the surface of the product material, which prevents the general application of SERS in the analysis of weaker molecule-metal interactions.In this Account, we discuss our efforts in the development of modifier-free synthetic approaches to synthesize surface-accessible, plasmonic nanomaterials for SERS. We start by discussing the definition of "modifiers" and "surface-accessibility", especially in the context of surface chemistry studies in SERS. As a general rule of thumb, the chemical ligands on surface-accessible nanomaterials should be easily displaceable by a wide range of target molecules relevant to potential applications. We then introduce modifier-free approaches for the bottom-up synthesis of colloidal nanoparticles, which are the basic building blocks for nanotechnology. Following this, we introduce modifier-free interfacial self-assembly approaches developed by our group that allow the creation of multidimensional plasmonic nanoparticle arrays from different types of nanoparticle-building blocks. These multidimensional arrays can be further combined with different types of functional materials to form surface-accessible multifunctional hybrid plasmonic materials. Finally, we demonstrate applications for surface-accessible nanomaterials as plasmonic substrates for SERS studies of surface chemistry. Importantly, our studies revealed that the removal of modifiers led to not only significantly enhanced properties but also the observation of new surface chemistry phenomena that had been previously overlooked or misunderstood in the literature. Realizing the current limitations of modifier-based approaches provides new perspectives in manipulating molecule-metal interactions in nanotechnology and can have significant implications in the design and synthesis of the next generation of nanomaterials.

Quantitative assessment of hyperperfusion using arterial spin labeling to predict hemorrhagic transformation in acute ischemic stroke patients with mechanical endovascular therapy.

OBJECTIVES: This study was aimed to quantitatively assess hyperperfusion using arterial spin labeling (ASL) to predict hemorrhagic transformation (HT) in acute ischemic stroke (AIS) patients. METHODS: This study enrolled 98 AIS patients with anterior circulation large vessel occlusion within 24 h of symptom onset. ASL was performed before mechanical endovascular therapy. On pre-treatment ASL maps, a region with relative cerebral blood flow (CBF) ≥ 1.4 was defined as an area of hyperperfusion. The maximum CBF (CBFmax) of hyperperfusion was calculated for each patient. A non-contrast CT scan was performed during the subacute phase for the evaluation of HT. Good clinical outcome was defined as a 90-day modified Rankin scale score of 0-2. RESULTS: The CBFmax of hyperperfusion (odds ratio, 1.023; 95% confidence interval [CI], 1.005-1.042; p = 0.012) was an independent risk factor for the status of HT. The CBFmax of hyperperfusion for HT showed an area under the curve of 0.735 (95% CI, 0.588-0.882) with optimal cutoff value, sensitivity, and specificity being 146.5 mL/100 g/min, 76.9%, and 69.6%, respectively. There was a statistically significant relationship between HT grades (from no HT to PH2) and CBFmax of hyperperfusion with a Spearman rank correlation of 0.446 (p = 0.001). In addition, low CBFmax of hyperperfusion were associated with good functional outcome (95% CI, 17.130-73.910; p = 0.002). CONCLUSIONS: High CBFmax of hyperperfusion was independently associated with subsequent HT and low CBFmax of hyperperfusion linked to good functional outcome. There was a positive correlation between HT grade and CBFmax. CLINICAL RELEVANCE STATEMENT: Arterial spin labeling is a noninvasive and contrast agent-independent technique, which is sensitive in detecting hyperperfusion. This study shows that the cerebral blood flow of hyperperfusion is associated with clinical prognosis, which will benefit more patients. KEY POINTS: • Quantitative assessment of hyperperfusion using pre-treatment arterial spin labeling to predict hemorrhagic transformation and prognosis in acute ischemic stroke patients. • The maximum cerebral blood flow of hyperperfusion was associated with hemorrhagic transformation and clinical prognosis and higher maximum cerebral blood flow of hyperperfusion was associated with higher grade hemorrhagic transformation. • The maximum cerebral blood flow of hyperperfusion can predict hemorrhagic transformation which enables timely intervention to prevent parenchymal hematoma.

Impact on Image Quality and Diagnostic Performance of Dual-Layer Detector Spectral CT for Pulmonary Subsolid Nodules: Comparison With Hybrid and Model-Based Iterative Reconstruction.

OBJECTIVE: To evaluate the image quality and diagnostic performance of pulmonary subsolid nodules on conventional iterative algorithms, virtual monoenergetic images (VMIs), and electron density mapping (EDM) using a dual-layer detector spectral CT (DLSCT). METHODS: This retrospective study recruited 270 patients who underwent DLSCT scan for lung nodule screening or follow-up. All CT examinations with subsolid nodules (pure ground-glass nodules [GGNs] or part-solid nodules) were reconstructed with hybrid and model-based iterative reconstruction, VMI at 40, 70, 100, and 130 keV levels, and EDM. The CT number, objective image noise, signal-to-noise ratio, contrast-to-noise ratio, diameter, and volume of subsolid nodules were measured for quantitative analysis. The overall image quality, image noise, visualization of nodules, artifact, and sharpness were subjectively rated by 2 thoracic radiologists on a 5-point scale (1 = unacceptable, 5 = excellent) in consensus. The objective image quality measurements, diameter, and volume were compared among the 7 groups with a repeated 1-way analysis of variance. The subjective scores were compared with Kruskal-Wallis test. RESULTS: A total of 198 subsolid nodules, including 179 pure GGNs, and 19 part-solid nodules were identified. Based on the objective analysis, EDM had the highest signal-to-noise ratio (164.71 ± 133.60; P < 0.001) and contrast-to-noise ratio (227.97 ± 161.96; P < 0.001) among all image sets. Furthermore, EDM had a superior mean subjective rating score (4.80 ± 0.42) for visualization of GGNs compared to other reconstructed images (all P < 0.001), although the model-based iterative reconstruction had superior subjective scores of overall image quality. For pure GGNs, the measured diameter and volume did not significantly differ among different reconstructions (both P > 0.05). CONCLUSIONS: EDM derived from DLSCT enabled improved image quality and lesion conspicuity for the evaluation of lung subsolid nodules compared to conventional iterative reconstruction algorithms and VMIs.

Radiotherapy-sensitized cancer immunotherapy via cGAS-STING immune pathway by activatable nanocascade reaction.

Radiotherapy-induced immune activation holds great promise for optimizing cancer treatment efficacy. Here, we describe a clinically used radiosensitizer hafnium oxide (HfO2) that was core coated with a MnO2 shell followed by a glucose oxidase (GOx) doping nanoplatform (HfO2@MnO2@GOx, HMG) to trigger ferroptosis adjuvant effects by glutathione depletion and reactive oxygen species production. This ferroptosis cascade potentiation further sensitized radiotherapy by enhancing DNA damage in 4T1 breast cancer tumor cells. The combination of HMG nanoparticles and radiotherapy effectively activated the damaged DNA and Mn2+-mediated cGAS-STING immune pathway in vitro and in vivo. This process had significant inhibitory effects on cancer progression and initiating an anticancer systemic immune response to prevent distant tumor recurrence and achieve long-lasting tumor suppression of both primary and distant tumors. Furthermore, the as-prepared HMG nanoparticles "turned on" spectral computed tomography (CT)/magnetic resonance dual-modality imaging signals, and demonstrated favorable contrast enhancement capabilities activated by under the GSH tumor microenvironment. This result highlighted the potential of nanoparticles as a theranostic nanoplatform for achieving molecular imaging guided tumor radiotherapy sensitization induced by synergistic immunotherapy.

A mesoporous superparamagnetic iron oxide nanoparticle as a generic drug delivery system for tumor ferroptosis therapy.

As a famous drug delivery system (DDS), mesoporous organosilica nanoparticles (MON) are degraded slowly in vivo and the degraded components are not useful for cell nutrition or cancer theranostics, and superparamagnetic iron oxide nanoparticles (SPION) are not mesoporous with low drug loading content (DLC). To overcome the problems of MON and SPION, we developed mesoporous SPIONs (MSPIONs) with an average diameter of 70 nm and pore size of 3.9 nm. Sorafenib (SFN) and/or brequinar (BQR) were loaded into the mesopores of MSPION, generating SFN@MSPION, BQR@MSPION and SFN/BQR@MSPION with high DLC of 11.5% (SFN), 10.1% (BQR) and 10.0% (SNF + BQR), demonstrating that our MSPION is a generic DDS. SFN/BQR@MSPION can be used for high performance ferroptosis therapy of tumors because: (1) the released Fe2+/3+ in tumor microenvironment (TME) can produce •OH via Fenton reaction; (2) the released SFN in TME can inhibit the cystine/glutamate reverse transporter, decrease the intracellular glutathione (GSH) and GSH peroxidase 4 levels, and thus enhance reactive oxygen species and lipid peroxide levels; (3) the released BQR in TME can further enhance the intracellular oxidative stress via dihydroorotate dehydrogenase inhibition. The ferroptosis therapeutic mechanism, efficacy and biosafety of MSPION-based DDS were verified on tumor cells and tumor-bearing mice.

Exceedingly Small Magnetic Iron Oxide Nanoparticles for T1 -Weighted Magnetic Resonance Imaging and Imaging-Guided Therapy of Tumors.

Magnetic iron oxide nanoparticles (MIONs) based T2 -weighted magnetic resonance imaging (MRI) contrast agents (CAs) are liver-specific with good biocompatibility, but have been withdrawn from the market and replaced with Eovist (Gd-EOB-DTPA) due to their inherent limitations (e.g., susceptibility to artifacts, high magnetic moment, dark signals, long processing time of T2 imaging, and long waiting time for patients after administration). Without the disadvantages of Gd-chelates and MIONs, the recently emerging exceedingly small MIONs (ES-MIONs) (<5 nm) are promising T1 CAs for MRI. However, there are rare review articles focusing on ES-MIONs for T1 -weighted MRI. Herein, the recent progress of ES-MIONs, including synthesis methods (the current basic synthesis methods and improved methods), surface modifications (artificial polymers, natural polymers, zwitterions, and functional protein), T1 -MRI visual strategies (structural remodeling, reversible self-assemblies, metal ions doped, T1 /T2 dual imaging modes, and PET/MRI strategy), and imaging-guided cancer therapy (chemotherapy, gene therapy, ferroptosis therapy, photothermal therapy, photodymatic therapy, radiotherapy, immuotherapy, sonodynamic therapy, and multimode therapy), is summarized. The detailed description of synthesis methods and applications of ES-MIONs in this review is anticipated to attract extensive interest from researchers in different fields and promote their participation in the establishment of ES-MIONs based nanoplatforms for tumor theranostics.

Disrupted resting-state brain functional network properties in non-neuropsychiatric systemic lupus erythematosus patients.

INTRODUCTION: Previous fMRI studies revealed that the abnormal functional connectivity (FC) was related to cognitive impairment in patients with SLE. However, it remains unclear how the disease severity affects the functional topological organization of the whole-brain network in SLE patients without neuropsychiatric symptoms (non-NPSLE). OBJECTIVE: We aim to examine the impairment of the whole-brain functional network in SLE patients without neuropsychiatric symptoms (non-NPSLE), which may improve the understanding of neural mechanism in SLE. METHODS: We acquired resting-state fMRI data from 32 non-NPSLE patients and 32 healthy controls (HC), constructed their whole-brain functional network, and then estimated the topological properties including global and nodal parameters by using graph theory. Meanwhile, we also investigated the differences in intra- and inter-network FC between the non-NPSLE patients and the HC. RESULTS: The non-NPSLE patients showed significantly lower clustering coefficient, global and local efficiency, but higher characteristic path length than the HC. The non-NPSLE patients had significantly lower nodal strength in two regions, ventromedial prefrontal cortex (vmPFC) and anterior PFC (aPFC) than the HC. We found the non-NPSLE patients had significantly lower intra-network FC within frontal-parietal network (FPN) and within default mode network (DMN), and significantly lower inter-network FC between DMN and FPN than the HC. The intra-network FC within DMN was negatively correlated with systemic lupus erythematosus disease activity index (SLEDAI). CONCLUSION: Abnormal whole-brain functional network properties and abnormal intra- and inter-network FC may be related to cognitive impairment and disease degree in the non-NPSLE patients. Our findings provide a network perspective to understand the neural mechanisms of SLE.

Machine learning-based multiparametric magnetic resonance imaging radiomics model for distinguishing central neurocytoma from glioma of lateral ventricle.

OBJECTIVES: To develop a machine learning-based radiomics model based on multiparametric magnetic resonance imaging (MRI) for preoperative discrimination between central neurocytomas (CNs) and gliomas of lateral ventricles. METHODS: A total of 132 patients from two medical centers were enrolled in this retrospective study. Patients from the first medical center were divided into a training cohort (n = 74) and an internal validation cohort (n = 30). Patients from the second medical center were used as the external validation cohort (n = 28). Features were extracted from contrast-enhanced T1-weighted and T2-weighted images. A support vector machine was used for radiomics model investigation. Performance was evaluated using the sensitivity, specificity, and the area under the receiver operating characteristic curve (AUC). The model's performance was also compared with those of three radiologists. RESULTS: The radiomics model achieved an AUC of 0.986 in the training cohort, 0.933 in the internal validation cohort, and 0.903 in the external validation cohort. In the three cohorts, the AUC values were 0.657, 0.786, and 0.708 for radiologist 1; 0.838, 0.799, and 0.790 for radiologist 2; and 0.827, 0.871, and 0.862 for radiologist 3. When assisted by the radiomics model, two radiologists improved their performance in the training cohort (p < 0.05) but not in the internal or external validation cohorts. CONCLUSIONS: The machine learning radiomics model based on multiparametric MRI showed better performance for distinguishing CNs from lateral ventricular gliomas than did experienced radiologists, and it showed the potential to improve radiologist performance. KEY POINTS: • The machine learning radiomics model shows excellent performance in distinguishing CNs from gliomas. • The radiomics model outweighs two experienced radiologists (area under the receiver operating characteristic curve, 0.90 vs 0.79 and 0.86, respectively). • The radiomics model has the potential to enhance radiologist performance.

Image-based deep learning identifies glioblastoma risk groups with genomic and transcriptomic heterogeneity: a multi-center study.

OBJECTIVES: To develop and validate a deep learning imaging signature (DLIS) for risk stratification in patients with multiforme (GBM), and to investigate the biological pathways and genetic alterations underlying the DLIS. METHODS: The DLIS was developed from multi-parametric MRI based on a training set (n = 600) and validated on an internal validation set (n = 164), an external test set 1 (n = 100), an external test set 2 (n = 161), and a public TCIA set (n = 88). A co-profiling framework based on a radiogenomics analysis dataset (n = 127) using multiscale high-dimensional data, including imaging, transcriptome, and genome, was established to uncover the biological pathways and genetic alterations underpinning the DLIS. RESULTS: The DLIS was associated with survival (log-rank p < 0.001) and was an independent predictor (p < 0.001). The integrated nomogram incorporating the DLIS achieved improved C indices than the clinicomolecular nomogram (net reclassification improvement 0.39, p < 0.001). DLIS significantly correlated with core pathways of GBM (apoptosis and cell cycle-related P53 and RB pathways, and cell proliferation-related RTK pathway), as well as key genetic alterations (del_CDNK2A). The prognostic value of DLIS-correlated genes was externally confirmed on TCGA/CGGA sets (p < 0.01). CONCLUSIONS: Our study offers a biologically interpretable deep learning predictor of survival outcomes in patients with GBM, which is crucial for better understanding GBM patient's prognosis and guiding individualized treatment. KEY POINTS: • MRI-based deep learning imaging signature (DLIS) stratifies GBM into risk groups with distinct molecular characteristics. • DLIS is associated with P53, RB, and RTK pathways and del_CDNK2A mutation. • The prognostic value of DLIS-correlated pathway genes is externally demonstrated.

Probing the interaction of ex situ biofilms with plasmonic metal nanoparticles using surface-enhanced Raman spectroscopy.

Biofilms are complex environments where matrix effects from components such as extracellular polymeric substances and proteins can strongly affect SERS performance. Here the interactions between SERS-enhancing Ag and Au particles were studied using ex situ biofilms (es-biofilms), which were more homogenous than in situ biofilm samples. This allowed systematic quantitative studies, where samples could be accurately diluted and analysed, to be carried out. Strong signals from intrinsic marker compounds were found for the Pseudomonas aeruginosa and Staphylococcus aureus extracted es-biofilms, which the standard addition method showed were due to 2 × 10-3 mol dm-3 pyocyanin or the equivalent of 1 × 10-4 mol dm-3 adenine, respectively. The es-biofilms hindered aggregation of Ag colloids more than Au but for both Au and Ag nanospheres the presence of es-biofilm reduced SERS signals through a combination of poorer aggregation and blocking of surface sites. For Ag, the effect of lower aggregation was to reduce the signals by a factor of ca. 2×, while site blocking gave a further 10× reduction for adenine. Similar results were found for Au nanospheres with adenine, although these particles gave low enhancement with pyocyanin. Nanostars were found to be unaffected by reduced aggregation and also showed lower site blocking effects, giving more reproducible signals than those from aggregated particles, which compensated for their lower enhancement factor. These results provide a rational basis for selecting enhancing substrates for use in in situ studies, where the further complexity means that it is important to begin with well-understood and controllable enhancing media.

Taming Tris(bipyridine)ruthenium(II) and Its Reactions in Water by Capture/Release with Shape-Switchable Symmetry-Matched Cyclophanes.

Electron/proton transfers in water proceeding from ground/excited states are the elementary reactions of chemistry. These reactions of an iconic class of molecules─polypyridineRu(II)─are now controlled by capturing or releasing three of them with hosts that are shape-switchable. Reversible erection or collapse of the host walls allows such switchability. Some reaction rates are suppressed by factors of up to 120 by inclusive binding of the metal complexes. This puts nanometric coordination chemistry in a box that can be open or shut as necessary. Such second-sphere complexation can allow considerable control to be exerted on photocatalysis, electrocatalysis, and luminescent sensing involving polypyridineRu(II) compounds. The capturing states of hosts are symmetry-matched to guests for selective binding and display submicromolar affinities. A perching complex, which is an intermediate state between capturing and releasing states, is also demonstrated.

Cycloacceleration of Reactive Oxygen Species Generation Based on Exceedingly Small Magnetic Iron Oxide Nanoparticles for Tumor Ferroptosis Therapy.

Because of the insufficiency of hydrogen peroxide, the relatively low rate of Fenton reaction, and the active glutathione (GSH) peroxidase 4 (GPX4) in tumor cells, it is difficult to achieve a desirable efficacy of ferroptosis therapy (FT) for tumors based on nanomaterials. Inspired by the concept of "cyclotron" in physics, in this study, a new concept of cycloacceleration of reactive oxygen species (ROS) generation in tumor cells to realize high-performance FT of tumors is proposed. Typically, a magnetic resonance imaging (MRI) contrast agent of dotted core-shell Fe3 O4 /Gd2 O3 hybrid nanoparticles (FGNPs) is prepared based on exceedingly small magnetic iron oxide nanoparticles (ES-MIONs). Sorafenib (SFN) is loaded and poly(ethylene glycol) methyl ether-poly(propylene sulfide)-NH2 (mPEG-PPS-NH2 ) is grafted on the surface of FGNP to generate SA-SFN-FGNP via self-assembly. The results of in vitro and in vivo demonstrate SA-SFN-FGNP can work with the acidic tumor microenvironment and endosomal conditions, Fenton reaction and system XC - , and generate cyclic reactions in tumor cells, resulting in specific cycloacceleration of ROS generation for high-performance FT of tumors. The very high longitudinal relaxivity (r1 , 33.43 mM-1 s-1 , 3.0 T) makes sure that the SA-SFN-FGNP can be used for MRI-guided FT of tumors.

IVIM using convolutional neural networks predicts microvascular invasion in HCC.

OBJECTIVES: The study aimed to investigate the diagnostic performance of intravoxel incoherent motion (IVIM) diffusion-weighted magnetic resonance imaging for prediction of microvascular invasion (MVI) in hepatocellular carcinoma (HCC) using convolutional neural networks (CNNs). METHODS: This retrospective study included 114 patients with pathologically confirmed HCC from December 2014 to August 2021. All patients underwent MRI examination including IVIM sequence with 9 b-values preoperatively. First, 9 b-value images were superimposed in the channel dimension, and a b-value volume with a shape of 32 × 32 × 9 dimension was obtained. Secondly, an image resampling method was performed for data augmentation to generate more samples for training. Finally, deep features to predict MVI in HCC were directly derived from a b-value volume based on the CNN. Moreover, a deep learning model based on parameter maps and a fusion model combined with deep features of IVIM, clinical characteristics, and IVIM parameters were also constructed. Receiver operating characteristic (ROC) curve analysis was performed to assess the diagnostic performance for MVI prediction in HCC. RESULTS: Deep features directly extracted from IVIM-DWI (0.810 (range 0.760, 0.829)) using CNN yielded better performance for prediction of MVI than those from IVIM parameter maps (0.590 (range 0.555, 0.643)). Furthermore, the performance of the fusion model combined with deep features of IVIM-DWI, clinical features (α-fetoprotein (AFP) level and tumor size), and apparent diffusion coefficient (ADC) (0.829 (range 0.776, 0.848)) was slightly improved. CONCLUSIONS: Deep learning with CNN based on IVIM-DWI can be conducive to preoperative prediction of MVI in patients with HCC. KEY POINTS: • Deep learning assessment of IVIM data for prediction of MVI in HCC can overcome the unstable and low performance of IVIM parameters. • Deep learning model based on IVIM performs better than parameter values, clinical features, and deep learning model based on parameter maps. • The fusion model combined with deep features of IVIM, clinical characteristics, and ADC yields better performance for prediction of MVI than the model only based on IVIM.

A fully automatic artificial intelligence-based CT image analysis system for accurate detection, diagnosis, and quantitative severity evaluation of pulmonary tuberculosis.

OBJECTIVES: An accurate and rapid diagnosis is crucial for the appropriate treatment of pulmonary tuberculosis (TB). This study aims to develop an artificial intelligence (AI)-based fully automated CT image analysis system for detection, diagnosis, and burden quantification of pulmonary TB. METHODS: From December 2007 to September 2020, 892 chest CT scans from pathogen-confirmed TB patients were retrospectively included. A deep learning-based cascading framework was connected to create a processing pipeline. For training and validation of the model, 1921 lesions were manually labeled, classified according to six categories of critical imaging features, and visually scored regarding lesion involvement as the ground truth. A "TB score" was calculated based on a network-activation map to quantitively assess the disease burden. Independent testing datasets from two additional hospitals (dataset 2, n = 99; dataset 3, n = 86) and the NIH TB Portals (n = 171) were used to externally validate the performance of the AI model. RESULTS: CT scans of 526 participants (mean age, 48.5 ± 16.5 years; 206 women) were analyzed. The lung lesion detection subsystem yielded a mean average precision of the validation cohort of 0.68. The overall classification accuracy of six pulmonary critical imaging findings indicative of TB of the independent datasets was 81.08-91.05%. A moderate to strong correlation was demonstrated between the AI model-quantified TB score and the radiologist-estimated CT score. CONCLUSIONS: The proposed end-to-end AI system based on chest CT can achieve human-level diagnostic performance for early detection and optimal clinical management of patients with pulmonary TB. KEY POINTS: • Deep learning allows automatic detection, diagnosis, and evaluation of pulmonary tuberculosis. • Artificial intelligence helps clinicians to assess patients with tuberculosis. • Pulmonary tuberculosis disease activity and treatment management can be improved.

Intestinal fibrosis classification in patients with Crohn's disease using CT enterography-based deep learning: comparisons with radiomics and radiologists.

OBJECTIVES: Accurate evaluation of bowel fibrosis in patients with Crohn's disease (CD) remains challenging. Computed tomography enterography (CTE)-based radiomics enables the assessment of bowel fibrosis; however, it has some deficiencies. We aimed to develop and validate a CTE-based deep learning model (DLM) for characterizing bowel fibrosis more efficiently. METHODS: We enrolled 312 bowel segments of 235 CD patients (median age, 33 years old) from three hospitals in this retrospective study. A training cohort and test cohort 1 were recruited from center 1, while test cohort 2 from centers 2 and 3. All patients performed CTE within 3 months before surgery. The histological fibrosis was semi-quantitatively assessed. A DLM was constructed in the training cohort based on a 3D deep convolutional neural network with 10-fold cross-validation, and external independent validation was conducted on the test cohorts. The radiomics model (RM) was developed with 4 selected radiomics features extracted from CTE images by using logistic regression. The evaluation of CTE images was performed by two radiologists. DeLong's test and a non-inferiority test were used to compare the models' performance. RESULTS: DLM distinguished none-mild from moderate-severe bowel fibrosis with an area under the receiver operator characteristic curve (AUC) of 0.828 in the training cohort and 0.811, 0.808, and 0.839 in the total test cohort, test cohorts 1 and 2, respectively. In the total test cohort, DLM achieved better performance than two radiologists (*1 AUC = 0.579, *2 AUC = 0.646; both p < 0.05) and was not inferior to RM (AUC = 0.813, p < 0.05). The total processing time for DLM was much shorter than that of RM (p < 0.001). CONCLUSION: DLM is better than radiologists in diagnosing intestinal fibrosis on CTE in patients with CD and not inferior to RM; furthermore, it is more time-saving compared to RM. KEY POINTS: • Question Could computed tomography enterography (CTE)-based deep learning model (DLM) accurately distinguish intestinal fibrosis severity in patients with Crohn's disease (CD)? • Findings In this cross-sectional study that included 235 patients with CD, DLM achieved better performance than that of two radiologists' interpretation and was not inferior to RM with significant differences and much shorter processing time. • Meaning This DLM may accurately distinguish the degree of intestinal fibrosis in patients with CD and guide gastroenterologists to formulate individualized treatment strategies for those with bowel strictures.

Biodegradable and biocompatible exceedingly small magnetic iron oxide nanoparticles for T1-weighted magnetic resonance imaging of tumors.

Magnetic resonance imaging (MRI) has been widely using in clinical diagnosis, and contrast agents (CAs) can improve the sensitivity MRI. To overcome the problems of commercial Gd chelates-based T1 CAs, commercial magnetic iron oxide nanoparticles (MIONs)-based T2 CAs, and reported exceedingly small MIONs (ES-MIONs)-based T1 CAs, in this study, a facile co-precipitation method was developed to synthesize biodegradable and biocompatible ES-MIONs with excellent water-dispersibility using poly (aspartic acid) (PASP) as a stabilizer for T1-weighted MRI of tumors. After optimization of the synthesis conditions, the final obtained ES-MION9 with 3.7 nm of diameter has a high r1 value (7.0 ± 0.4 mM-1 s-1) and a low r2/r1 ratio (4.9 ± 0.6) at 3.0 T. The ES-MION9 has excellent water dispersibility because of the excessive -COOH from the stabilizer PASP. The pharmacokinetics and biodistribution of ES-MION9 in vivo demonstrate the better tumor targetability and MRI time window of ES-MION9 than commercial Gd chelates. T1-weighted MR images of aqueous solutions, cells and tumor-bearing mice at 3.0 T or 7.0 T demonstrate that our ES-MION9 has a stronger capability of enhancing the MRI contrast comparing with the commercial Gd chelates. The MTT assay, live/dead staining of cells, and H&E-staining indicate the non-toxicity and biosafety of our ES-MION9. Consequently, the biodegradable and biocompatible ES-MION9 with excellent water-dispersibility is an ideal T1-weighted CAs with promising translational possibility to compete with the commercial Gd chelates.