Early Detection of Aortic Degeneration in a Mouse Model of Sporadic Aortic Aneurysm and Dissection Using Nanoparticle Contrast-Enhanced Computed Tomography.

[Figure: see text].

Hybrid, dual visible and near-infrared fluorescence emission of (6,5) single-walled carbon nanotubes modified with fluorescein through aryl diazonium salt chemistry.

The aryl diazonium salt chemistry offers enhancement of near-infrared (NIR) emission of single-walled carbon nanotubes (SWCNTs), although, the attachment of functional molecules which could bring hybrid properties through the process is underdeveloped. In this work, we utilize aryl diazonium salt of fluorescein to createsp3defects on (6,5) SWCNTs. We study the influence of pH on the grafting process identifying that pH 5-6 is necessary for a successful reaction. The fluorescein-modified (6,5) SWCNTs (F-(6,5) SWCNTs) exhibit red-shiftedE11* emission in the NIR region attributed to luminescentsp3defects, but also visible (Vis) fluorescence at 515 nm from surface-attached fluorescein molecules. The fluorescence in both Vis and NIR regions of F-(6,5) SWCNTs exhibit strong pH-dependency associated with the dissociation of fluorescein molecules with an indication of photoinduced-electron transfer quenching the Vis emission of fluorescein dianion. The F-(6,5) SWCNTs could potentially be used for dual-channel medical imaging as indicated by our preliminary experiments. We hope that our research will encourage new, bold modifications of SWCNTs with functional molecules introducing new, unique hybrid properties.

Critical Role of Cytosolic DNA and Its Sensing Adaptor STING in Aortic Degeneration, Dissection, and Rupture.

BACKGROUND: Sporadic aortic aneurysm and dissection (AAD), caused by progressive aortic smooth muscle cell (SMC) loss and extracellular matrix degradation, is a highly lethal condition. Identifying mechanisms that drive aortic degeneration is a crucial step in developing an effective pharmacologic treatment to prevent disease progression. Recent evidence has indicated that cytosolic DNA and abnormal activation of the cytosolic DNA sensing adaptor STING (stimulator of interferon genes) play a critical role in vascular inflammation and destruction. Here, we examined the involvement of this mechanism in aortic degeneration and sporadic AAD formation. METHODS: The presence of cytosolic DNA in aortic cells and activation of the STING pathway were examined in aortic tissues from patients with sporadic ascending thoracic AAD. The role of STING in AAD development was evaluated in Sting-deficient (Stinggt/gt) mice in a sporadic AAD model induced by challenging mice with a combination of a high-fat diet and angiotensin II. We also examined the direct effects of STING on SMC death and macrophage activation in vitro. RESULTS: In human sporadic AAD tissues, we observed the presence of cytosolic DNA in SMCs and macrophages and significant activation of the STING pathway. In the sporadic AAD model, Stinggt/gt mice showed significant reductions in challenge-induced aortic enlargement, dissection, and rupture in both the thoracic and abdominal aortic regions. Single-cell transcriptome analysis revealed that aortic challenge in wild-type mice induced the DNA damage response, the inflammatory response, dedifferentiation and cell death in SMCs, and matrix metalloproteinase expression in macrophages. These changes were attenuated in challenged Stinggt/gt mice. Mechanistically, nuclear and mitochondrial DNA damage in SMCs and the subsequent leak of DNA to the cytosol activated STING signaling, which induced cell death through apoptosis and necroptosis. In addition, DNA from damaged SMCs was engulfed by macrophages in which it activated STING and its target interferon regulatory factor 3, which directly induced matrix metalloproteinase-9 expression. We also found that pharmacologically inhibiting STING activation partially prevented AAD development. CONCLUSIONS: Our findings indicate that the presence of cytosolic DNA and subsequent activation of cytosolic DNA sensing adaptor STING signaling represent a key mechanism in aortic degeneration and that targeting STING may prevent sporadic AAD development.

Application of 3-D printing (rapid prototyping) for creating physical models of pediatric orthopedic disorders.

Three-dimensional printing called rapid prototyping, a technology that is used to create physical models based on a 3-D computer representation, is now commercially available and can be created from CT or MRI datasets. This technical innovation paper reviews the specific requirements and steps necessary to apply biomedical 3-D printing of pediatric musculoskeletal disorders. We discuss its role for the radiologist, orthopedist and patient.

Stratification of Pediatric COVID-19 Cases Using Inflammatory Biomarker Profiling and Machine Learning.

While pediatric COVID-19 is rarely severe, a small fraction of children infected with SARS-CoV-2 go on to develop multisystem inflammatory syndrome (MIS-C), with substantial morbidity. An objective method with high specificity and high sensitivity to identify current or imminent MIS-C in children infected with SARS-CoV-2 is highly desirable. The aim was to learn about an interpretable novel cytokine/chemokine assay panel providing such an objective classification. This retrospective study was conducted on four groups of pediatric patients seen at multiple sites of Texas Children's Hospital, Houston, TX who consented to provide blood samples to our COVID-19 Biorepository. Standard laboratory markers of inflammation and a novel cytokine/chemokine array were measured in blood samples of all patients. Group 1 consisted of 72 COVID-19, 70 MIS-C and 63 uninfected control patients seen between May 2020 and January 2021 and predominantly infected with pre-alpha variants. Group 2 consisted of 29 COVID-19 and 43 MIS-C patients seen between January and May 2021 infected predominantly with the alpha variant. Group 3 consisted of 30 COVID-19 and 32 MIS-C patients seen between August and October 2021 infected with alpha and/or delta variants. Group 4 consisted of 20 COVID-19 and 46 MIS-C patients seen between October 2021 andJanuary 2022 infected with delta and/or omicron variants. Group 1 was used to train an L1-regularized logistic regression model which was tested using five-fold cross validation, and then separately validated against the remaining naïve groups. The area under receiver operating curve (AUROC) and F1-score were used to quantify the performance of the cytokine/chemokine assay-based classifier. Standard laboratory markers predict MIS-C with a five-fold cross-validated AUROC of 0.86 ± 0.05 and an F1 score of 0.78 ± 0.07, while the cytokine/chemokine panel predicted MIS-C with a five-fold cross-validated AUROC of 0.95 ± 0.02 and an F1 score of 0.91 ± 0.04, with only sixteen of the forty-five cytokines/chemokines sufficient to achieve this performance. Tested on Group 2 the cytokine/chemokine panel yielded AUROC = 0.98 and F1 = 0.93, on Group 3 it yielded AUROC = 0.89 and F1 = 0.89, and on Group 4 AUROC = 0.99 and F1 = 0.97. Adding standard laboratory markers to the cytokine/chemokine panel did not improve performance. A top-10 subset of these 16 cytokines achieves equivalent performance on the validation data sets. Our findings demonstrate that a sixteen-cytokine/chemokine panel as well as the top ten subset provides a highly sensitive, and specific method to identify MIS-C in patients infected with SARS-CoV-2 of all the major variants identified to date.

Stratification of Pediatric COVID-19 cases by inflammatory biomarker profiling and machine learning.

An objective method to identify imminent or current Multi-Inflammatory Syndrome in Children (MIS-C) infected with SARS-CoV-2 is highly desirable. The aims was to define an algorithmically interpreted novel cytokine/chemokine assay panel providing such an objective classification. This study was conducted on 4 groups of patients seen at multiple sites of Texas Children's Hospital, Houston, TX who consented to provide blood samples to our COVID-19 Biorepository. Standard laboratory markers of inflammation and a novel cytokine/chemokine array were measured in blood samples of all patients. Group 1 consisted of 72 COVID-19, 66 MIS-C and 63 uninfected control patients seen between May 2020 and January 2021 and predominantly infected with pre-alpha variants. Group 2 consisted of 29 COVID-19 and 43 MIS-C patients seen between January-May 2021 infected predominantly with the alpha variant. Group 3 consisted of 30 COVID-19 and 32 MIS-C patients seen between August-October 2021 infected with alpha and/or delta variants. Group 4 consisted of 20 COVID-19 and 46 MIS-C patients seen between October 2021-January 2022 infected with delta and/or omicron variants. Group 1 was used to train a L1-regularized logistic regression model which was validated using 5-fold cross validation, and then separately validated against the remaining naïve groups. The area under receiver operating curve (AUROC) and F1-score were used to quantify the performance of the algorithmically interpreted cytokine/chemokine assay panel. Standard laboratory markers predict MIS-C with a 5-fold cross-validated AUROC of 0.86 ± 0.05 and an F1 score of 0.78 ± 0.07, while the cytokine/chemokine panel predicted MIS-C with a 5-fold cross-validated AUROC of 0.95 ± 0.02 and an F1 score of 0.91 ± 0.04, with only sixteen of the forty-five cytokines/chemokines sufficient to achieve this performance. Tested on Group 2 the cytokine/chemokine panel yielded AUROC =0.98, F1=0.93, on Group 3 it yielded AUROC=0.89, F1 = 0.89, and on Group 4 AUROC= 0.99, F1= 0.97). Adding standard laboratory markers to the cytokine/chemokine panel did not improve performance. A top-10 subset of these 16 cytokines achieves equivalent performance on the validation data sets. Our findings demonstrate that a sixteen-cytokine/chemokine panel as well as the top ten subset provides a sensitive, specific method to identify MIS-C in patients infected with SARS-CoV-2 of all the major variants identified to date.

Rational Design of a Self-Assembling High Performance Organic Nanofluorophore for Intraoperative NIR-II Image-Guided Tumor Resection of Oral Cancer.

The first line of treatment for most solid tumors is surgical resection of the primary tumor with adequate negative margins. Incomplete tumor resections with positive margins account for over 75% of local recurrences and the development of distant metastases. In cases of oral cavity squamous cell carcinoma (OSCC), the rate of successful tumor removal with adequate margins is just 50-75%. Advanced real-time imaging methods that improve the detection of tumor margins can help improve success rates,overall safety, and reduce the cost. Fluorescence imaging in the second near-infrared (NIR-II) window has the potential to revolutionize the field due to its high spatial resolution, low background signal, and deep tissue penetration properties, but NIR-II dyes with adequate in vivo performance and safety profiles are scarce. A novel NIR-II fluorophore, XW-03-66, with a fluorescence quantum yield (QY) of 6.0% in aqueous media is reported. XW-03-66 self-assembles into nanoparticles (≈80 nm) and has a systemic circulation half-life (t1/2 ) of 11.3 h. In mouse models of human papillomavirus (HPV)+ and HPV- OSCC, XW-03-66 outperformed indocyanine green (ICG), a clinically available NIR dye, and enabled intraoperative NIR-II image-guided resection of the tumor and adjacent draining lymph node with negative margins. In vitro and in vivo toxicity assessments revealed minimal safety concerns for in vivo applications.

Augmenting Mitochondrial Respiration in Immature Smooth Muscle Cells with an ACTA2 Pathogenic Variant Mitigates Moyamoya-like Cerebrovascular Disease.

ACTA2 pathogenic variants altering arginine 179 cause childhood-onset strokes due to moyamoya disease (MMD)-like occlusion of the distal internal carotid arteries. A smooth muscle cell (SMC)-specific knock-in mouse model (Acta2SMC-R179C/+) inserted the mutation into 67% of aortic SMCs, whereas explanted SMCs were uniformly heterozygous. Acta2R179C/+ SMCs fail to fully differentiate and maintain stem cell-like features, including high glycolytic flux, and increasing oxidative respiration (OXPHOS) with nicotinamide riboside (NR) drives the mutant SMCs to differentiate and decreases migration. Acta2SMC-R179C/+ mice have intraluminal MMD-like occlusive lesions and strokes after carotid artery injury, whereas the similarly treated WT mice have no strokes and patent lumens. Treatment with NR prior to the carotid artery injury attenuates the strokes, MMD-like lumen occlusions, and aberrant vascular remodeling in the Acta2SMC-R179C/+ mice. These data highlight the role of immature SMCs in MMD-associated occlusive disease and demonstrate that altering SMC metabolism to drive quiescence of Acta2R179C/+ SMCs attenuates strokes and aberrant vascular remodeling in the Acta2SMC-R179C/+ mice.

Automated tracing of filaments in 3D electron tomography reconstructions using Sculptor and Situs.

The molecular graphics program Sculptor and the command-line suite Situs are software packages for the integration of biophysical data across spatial resolution scales. Herein, we provide an overview of recently developed tools relevant to cryo-electron tomography (cryo-ET), with an emphasis on functionality supported by Situs 2.7.1 and Sculptor 2.1.1. We describe a work flow for automatically segmenting filaments in cryo-ET maps including denoising, local normalization, feature detection, and tracing. Tomograms of cellular actin networks exhibit both cross-linked and bundled filament densities. Such filamentous regions in cryo-ET data sets can then be segmented using a stochastic template-based search, VolTrac. The approach combines a genetic algorithm and a bidirectional expansion with a tabu search strategy to localize and characterize filamentous regions. The automated filament segmentation by VolTrac compares well to a manual one performed by expert users, and it allows an efficient and reproducible analysis of large data sets. The software is free, open source, and can be used on Linux, Macintosh or Windows computers.

1-Indanone and 1,3-indandione Derivatives as Ligands for Misfolded α-Synuclein Aggregates.

The development of imaging agents for in vivo detection of alpha-synuclein (α-syn) pathologies faces several challenges. A major gap in the field is the lack of diverse molecular scaffolds with high affinity and selectivity to α-syn fibrils for in vitro screening assays. Better in vitro scaffolds can instruct the discovery of better in vivo agents. We report the rational design, synthesis, and in vitro evaluation of a series of novel 1-indanone and 1,3-indandione derivatives from a Structure-Activity Relationship (SAR) study centered on some existing α-syn fibril binding ligands. Our results from fibril saturation binding experiments show that two of the lead candidates compounds 8 and 32 bind α-syn fibrils with binding constants (Kd ) of 9.0 and 18.8 nM, respectively, and selectivity of greater than 10× for α-syn fibrils compared with amyloid-ß (Aß) and tau fibrils. Our results demonstrate that the lead ligands avidly label all forms of α-syn on PD brain tissue sections, but only the dense core of senile plaques in AD brain tissue, respectively. These results are corroborated by ligand-antibody colocalization data from Syn211, which shows immunoreactivity toward all forms of α-syn aggregates, and Syn303, which displays preferential reactivity toward mature Lewy pathology. Our results reveal that 1-indanone derivatives have desirable properties for the biological evaluation of α-synucleinopathies.

A Novel Method for Improving the Accuracy of MR-derived Patient-specific Vascular Models using X-ray Angiography.

MR imaging, a noninvasive radiation-free imaging modality commonly used during clinical follow up, has been widely utilized to reconstruct realistic 3D vascular models for patient-specific analysis. In recent work, we used patient-specific hemodynamic analysis of the circle of Willis to noninvasively assess stroke risk in pediatric Moyamoya disease (MMD)-a progressive steno-occlusive cerebrovascular disorder that leads to recurrent stroke. The objective was to identify vascular regions with critically high wall shear rate (WSR) that signifies elevated stroke risk. However, sources of error such as insufficient resolution of MR images can negatively impact vascular model accuracy, especially in areas of severe pathological narrowing, and thus diminish clinical relevance of simulation results, as local hemodynamics are sensitive to vessel geometry. To improve the accuracy of MR-derived vascular models, we have developed a novel method for adjusting model vessel geometry utilizing 2D X-ray angiography (XA), which is considered the gold standard for clinically assessing vessel caliber. In this workflow, "virtual angiographies" (VAs) of 3D MR-derived vascular models are conducted, producing 2D projections that are compared with corresponding XA images to guide the local adjustment of modeled vessels. This VA-comparison-adjustment loop is iterated until the two agree, as confirmed by an expert neuroradiologist. Using this method, we generated models of the circle of Willis of two patients with a history of unilateral stroke. Blood flow simulations were performed using a Navier-Stokes solver within an isogeometric analysis framework, and WSR distributions were quantified. Results for one patient show as much as 45% underestimation of local WSR in the stenotic left anterior cerebral artery (LACA), and up to a 56% underestimation in the right anterior cerebral artery when using the initial MR-derived model compared to the XA-adjusted model. To evaluate whether XA-based adjustment improves model accuracy, vessel cross-sectional areas of the pre- and post-adjustment models were compared to those seen in 3D CTA images of the same patient. CTA has superior resolution and signal-to-noise ratio compared to MR imaging but is not commonly used in the clinic due to radiation exposure concerns, especially in pediatric patients. While the vessels in the initial model had normalized root mean squared deviations (NRMSDs) ranging from 26% to 182% and 31% to 69% in two patients with respect to CTA, the adjusted vessel NRMSDs were comparatively smaller (32% to 53% and 11% to 42%). In the mildly stenotic LACA of patient 1, the NRMSDs for the pre- and post-adjusted models were 49% and 32%, respectively. These findings suggest that our XA-based adjustment method can considerably improve the accuracy of vascular models, and thus, stroke-risk prediction. An accurate, individualized assessment of stroke risk would be of substantial help in guiding the timing of preventive surgical interventions in pediatric MMD patients.

Patient-Specific Modeling Could Predict Occurrence of Pediatric Stroke.

Moyamoya disease (MMD) is a progressive steno-occlusive cerebrovascular disease leading to recurrent stroke. There is a lack of reliable biomarkers to identify unilateral stroke MMD patients who are likely to progress to bilateral disease and experience subsequent contralateral stroke(s). We hypothesized that local hemodynamics are predictive of future stroke and set out to noninvasively assess this stroke risk in pediatric MMD patients. MR and X-ray angiography imaging were utilized to reconstruct patient-specific models of the circle of Willis of six pediatric MMD patients who had previous strokes, along with a control subject. Blood flow simulations were performed by using a Navier-Stokes solver within an isogeometric analysis framework. Vascular regions with a wall shear rate (WSR) above the coagulation limit (>5,000 s-1) were identified to have a higher probability of thrombus formation, potentially leading to ischemic stroke(s). Two metrics, namely, "critical WSR coverage" and "WSR score," were derived to assess contralateral stroke risk and compared with clinical follow-up data. In two patients that suffered a contralateral stroke within 2 months of the primary stroke, critical WSR coverages exceeding 50% of vessel surface and WSR scores greater than 6× the control were present in multiple contralateral vessels. These metrics were not as clearly indicative of stroke in two additional patients with 3-5 year gaps between primary and contralateral strokes. However, a longitudinal study of one of these two cases, where a subsequent timepoint was analyzed, suggested disease stabilization on the primary stroke side and an elevated contralateral stroke risk, which was confirmed by patient outcome data. This indicates that post-stroke follow-up at regular intervals might be warranted for secondary stroke prevention. The findings of this study suggest that WSR-based metrics could be predictive of future stroke risk after an initial stroke in pediatric MMD patients. In addition, better predictions may be possible by performing patient-specific hemodynamic analysis at multiple timepoints during patient follow-up to monitor changes in the WSR-based metrics.

HepT1-derived murine models of high-risk hepatoblastoma display vascular invasion, metastasis, and circulating tumor cells.

Hepatoblastoma (HB) is the most common pediatric primary liver malignancy, and survival for high-risk disease approaches 50%. Mouse models of HB fail to recapitulate hallmarks of high-risk disease. The aim of this work was to generate murine models that show high-risk features including multifocal tumors, vascular invasion, metastasis, and circulating tumor cells (CTCs). HepT1 cells were injected into the livers or tail veins of mice, and tumor growth was monitored with magnetic resonance and bioluminescent imaging. Blood was analyzed with fluorescence-activated cell sorting to identify CTCs. Intra- and extra-hepatic tumor samples were harvested for immunohistochemistry and RNA and DNA sequencing. Cell lines were grown from tumor samples and profiled with RNA sequencing. With intrahepatic injection of HepT1 cells, 100% of animals grew liver tumors and showed vascular invasion, metastasis, and CTCs. Mutation profiling revealed genetic alterations in seven cancer-related genes, while transcriptomic analyses showed changes in gene expression with cells that invade vessels. Tail vein injection of HepT1 cells resulted in multifocal, metastatic disease. These unique models will facilitate further meaningful studies of high-risk HB. This article has an associated First Person interview with the first author of the paper.

A natural language processing and deep learning approach to identify child abuse from pediatric electronic medical records.

Child physical abuse is a leading cause of traumatic injury and death in children. In 2017, child abuse was responsible for 1688 fatalities in the United States, of 3.5 million children referred to Child Protection Services and 674,000 substantiated victims. While large referral hospitals maintain teams trained in Child Abuse Pediatrics, smaller community hospitals often do not have such dedicated resources to evaluate patients for potential abuse. Moreover, identification of abuse has a low margin of error, as false positive identifications lead to unwarranted separations, while false negatives allow dangerous situations to continue. This context makes the consistent detection of and response to abuse difficult, particularly given subtle signs in young, non-verbal patients. Here, we describe the development of artificial intelligence algorithms that use unstructured free-text in the electronic medical record-including notes from physicians, nurses, and social workers-to identify children who are suspected victims of physical abuse. Importantly, only the notes from time of first encounter (e.g.: birth, routine visit, sickness) to the last record before child protection team involvement were used. This allowed us to develop an algorithm using only information available prior to referral to the specialized child protection team. The study was performed in a multi-center referral pediatric hospital on patients screened for abuse within five different locations between 2015 and 2019. Of 1123 patients, 867 records were available after data cleaning and processing, and 55% were abuse-positive as determined by a multi-disciplinary team of clinical professionals. These electronic medical records were encoded with three natural language processing (NLP) algorithms-Bag of Words (BOW), Word Embeddings (WE), and Rules-Based (RB)-and used to train multiple neural network architectures. The BOW and WE encodings utilize the full free-text, while RB selects crucial phrases as identified by physicians. The best architecture was selected by average classification accuracy for the best performing model from each train-test split of a cross-validation experiment. Natural language processing coupled with neural networks detected cases of likely child abuse using only information available to clinicians prior to child protection team referral with average accuracy of 0.90±0.02 and average area under the receiver operator characteristic curve (ROC-AUC) 0.93±0.02 for the best performing Bag of Words models. The best performing rules-based models achieved average accuracy of 0.77±0.04 and average ROC-AUC 0.81±0.05, while a Word Embeddings strategy was severely limited by lack of representative embeddings. Importantly, the best performing model had a false positive rate of 8%, as compared to rates of 20% or higher in previously reported studies. This artificial intelligence approach can help screen patients for whom an abuse concern exists and streamline the identification of patients who may benefit from referral to a child protection team. Furthermore, this approach could be applied to develop computer-aided-diagnosis platforms for the challenging and often intractable problem of reliably identifying pediatric patients suffering from physical abuse.

A Nanoradiomics Approach for Differentiation of Tumors Based on Tumor-Associated Macrophage Burden.

Objective: Tumor-associated macrophages (TAMs) within the tumor immune microenvironment (TiME) of solid tumors play an important role in treatment resistance and disease recurrence. The purpose of this study was to investigate if nanoradiomics (radiomic analysis of nanoparticle contrast-enhanced images) can differentiate tumors based on TAM burden. Materials and Methods: In vivo studies were performed in transgenic mouse models of neuroblastoma with low (N = 11) and high (N = 10) tumor-associated macrophage (TAM) burden. Animals underwent delayed nanoparticle contrast-enhanced CT (n-CECT) imaging at 4 days after intravenous administration of liposomal-iodine agent (1.1 g/kg). CT imaging-derived conventional tumor metrics (tumor volume and CT attenuation) were computed for segmented tumor CT datasets. Nanoradiomic analysis was performed using a PyRadiomics workflow implemented in the quantitative image feature pipeline (QIFP) server containing 900 radiomic features (RFs). RF selection was performed under supervised machine learning using a nonparametric neighborhood component method. A 5-fold validation was performed using a set of linear and nonlinear classifiers for group separation. Statistical analysis was performed using the Kruskal-Wallis test. Results: N-CECT imaging demonstrated heterogeneous patterns of signal enhancement in low and high TAM tumors. CT imaging-derived conventional tumor metrics showed no significant differences (p > 0.05) in tumor volume between low and high TAM tumors. Tumor CT attenuation was not significantly different (p > 0.05) between low and high TAM tumors. Machine learning-augmented nanoradiomic analysis revealed two RFs that differentiated (p < 0.002) low TAM and high TAM tumors. The RFs were used to build a linear classifier that demonstrated very high accuracy and further confirmed by 5-fold cross-validation. Conclusions: Imaging-derived conventional tumor metrics were unable to differentiate tumors with varying TAM burden; however, nanoradiomic analysis revealed texture differences and enabled differentiation of low and high TAM tumors.

Image-based patient-specific flow simulations are consistent with stroke in pediatric cerebrovascular disease.

Moyamoya disease (MMD) is characterized by narrowing of the distal internal carotid artery and the circle of Willis (CoW) and leads to recurring ischemic and hemorrhagic stroke. A retrospective review of data from 50 pediatric MMD patients revealed that among the 24 who had a unilateral stroke and were surgically treated, 11 (45.8%) had a subsequent, contralateral stroke. There is no reliable way to predict these events. After a pilot study in Acta-/- mice that have features of MMD, we hypothesized that local hemodynamics are predictive of contralateral strokes and sought to develop a patient-specific analysis framework to noninvasively assess this stroke risk. A pediatric MMD patient with an occlusion in the right middle cerebral artery and a right-sided stroke, who was surgically treated and then had a contralateral stroke, was selected for analysis. By using an unsteady Navier-Stokes solver within an isogeometric analysis framework, blood flow was simulated in the CoW model reconstructed from the patient's postoperative imaging data, and the results were compared with those from an age- and sex-matched control subject. A wall shear rate (WSR) > 60,000 s-1 (about 12 × higher than the coagulation threshold of 5000 s-1 and 9 × higher than control) was measured in the terminal left supraclinoid artery; its location coincided with that of the subsequent postsurgical left-sided stroke. A parametric study of disease progression revealed a strong correlation between the degree of vascular morphology altered by MMD and local hemodynamic environment. The results suggest that an occlusion in the CoW could lead to excessive contralateral WSRs, resulting in thromboembolic ischemic events, and that WSR could be a predictor of future stroke.

MDM4 inhibition: a novel therapeutic strategy to reactivate p53 in hepatoblastoma.

Hepatoblastoma (HB) is the most common pediatric liver malignancy. High-risk patients have poor survival, and current chemotherapies are associated with significant toxicities. Targeted therapies are needed to improve outcomes and patient quality of life. Most HB cases are TP53 wild-type; therefore, we hypothesized that targeting the p53 regulator Murine double minute 4 (MDM4) to reactivate p53 signaling may show efficacy. MDM4 expression was elevated in HB patient samples, and increased expression was strongly correlated with decreased expression of p53 target genes. Treatment with NSC207895 (XI-006), which inhibits MDM4 expression, or ATSP-7041, a stapled peptide dual inhibitor of MDM2 and MDM4, showed significant cytotoxic and antiproliferative effects in HB cells. Similar phenotypes were seen with short hairpin RNA (shRNA)-mediated inhibition of MDM4. Both NSC207895 and ATSP-7041 caused significant upregulation of p53 targets in HB cells. Knocking-down TP53 with shRNA or overexpressing MDM4 led to resistance to NSC207895-mediated cytotoxicity, suggesting that this phenotype is dependent on the MDM4-p53 axis. MDM4 inhibition also showed efficacy in a murine model of HB with significantly decreased tumor weight and increased apoptosis observed in the treatment group. This study demonstrates that inhibition of MDM4 is efficacious in HB by upregulating p53 tumor suppressor signaling.

Detection of response to tumor microenvironment-targeted cellular immunotherapy using nano-radiomics.

Immunotherapies, including cell-based therapies, targeting the tumor microenvironment (TME) result in variable and delayed responses. Thus, it has been difficult to gauge the efficacy of TME-directed therapies early after administration. We investigated a nano-radiomics approach (quantitative analysis of nanoparticle contrast-enhanced three-dimensional images) for detection of tumor response to cellular immunotherapy directed against myeloid-derived suppressor cells (MDSCs), a key component of TME. Animals bearing human MDSC-containing solid tumor xenografts received treatment with MDSC-targeting human natural killer (NK) cells and underwent nanoparticle contrast-enhanced computed tomography (CT) imaging. Whereas conventional CT-derived tumor metrics were unable to differentiate NK cell immunotherapy tumors from untreated tumors, nano-radiomics revealed texture-based features capable of differentiating treatment groups. Our study shows that TME-directed cellular immunotherapy causes subtle changes not effectively gauged by conventional imaging metrics but revealed by nano-radiomics. Our work provides a method for noninvasive assessment of TME-directed immunotherapy potentially applicable to numerous solid tumors.

Pre-clinical magnetic resonance imaging of retroplacental clear space throughout gestation.

INTRODUCTION: Visualization of the retroplacental clear space (RPCS) may provide critical insight into the development of abnormally invasive placenta (AIP). In this pre-clinical study, we characterized the appearance of the RPCS on magnetic resonance imaging (MRI) during the second half of gestation using a liposomal gadolinium contrast agent (liposomal-Gd). MATERIALS AND METHODS: Studies were performed in fifteen pregnant C57BL/6 mice at 10, 12, 14, 16, and 18 days of gestation. MRI was performed on a 1T permanent magnet scanner. Pre-contrast and post-contrast images were acquired using T1-weighted gradient-recalled echo (T1w-GRE) and T2-weighted fast spin echo (T2w-FSE) sequences. Animals were euthanized after imaging and feto-placental units harvested for histological examination. Visualization of the RPCS was scored by a maternal-fetal radiologist and quantified by measuring the contrast-to-noise ratio (CNR) on T1w images. Feto-placental features were segmented for analysis of volumetric changes during gestation. RESULTS: Contrast-enhanced T1w images enabled the visualization of structural changes in placental development between days 10-18 of gestation. Although the placental margin on the fetal side was clearly visible at all time points, the RPCS was partially visible at day 10 of gestation, and clearly visible by day 12. Hematoxylin and eosin (H&E) staining of the placental tissue corroborated MRI findings of structural and morphological changes in the placenta. CONCLUSIONS: Contrast-enhanced MR imaging using liposomal-Gd enabled adequate visualization of the retroplacental clear space starting at day 12 of gestation. The agent also enabled characterization of placental structure and morphological changes through gestation.

NIR-II fluorescence imaging using indocyanine green nanoparticles.

Fluorescence imaging in the second near-infrared window (NIR-II) holds promise for real-time deep tissue imaging. In this work, we investigated the NIR-II fluorescence properties of a liposomal formulation of indocyanine green (ICG), a FDA-approved dye that was recently shown to exhibit NIR-II fluorescence. Fluorescence spectra of liposomal-ICG were collected in phosphate-buffered saline (PBS) and plasma. Imaging studies in an Intralipid® phantom were performed to determine penetration depth. In vivo imaging studies were performed to test real-time visualization of vascular structures in the hind limb and intracranial regions. Free ICG, NIR-I imaging, and cross-sectional imaging modalities (MRI and CT) were used as comparators. Fluorescence spectra demonstrated the strong NIR-II fluorescence of liposomal-ICG, similar to free ICG in plasma. In vitro studies demonstrated superior performance of liposomal-ICG over free ICG for NIR-II imaging of deep (≥4 mm) vascular mimicking structures. In vivo, NIR-II fluorescence imaging using liposomal-ICG resulted in significantly (p < 0.05) higher contrast-to-noise ratio compared to free ICG for extended periods of time, allowing visualization of hind limb and intracranial vasculature for up to 4 hours post-injection. In vivo comparisons demonstrated higher vessel conspicuity with liposomal-ICG-enhanced NIR-II imaging compared to NIR-I imaging.