Longitudinal Assessment of Tumor-Infiltrating Lymphocytes in Primary Breast Cancer Following Neoadjuvant Radiation Therapy.

PURPOSE: Tumor-infiltrating lymphocytes (TILs) have prognostic significance in several cancers, including breast cancer. Despite interest in combining radiation therapy with immunotherapy, little is known about the effect of radiation therapy itself on the tumor-immune microenvironment, including TILs. Here, we interrogated longitudinal dynamics of TILs and systemic lymphocytes in patient samples taken before, during, and after neoadjuvant radiation therapy (NART) from PRADA and Neo-RT breast clinical trials. METHODS AND MATERIALS: We manually scored stromal TILs (sTILs) from longitudinal tumor samples using standardized guidelines as well as deep learning-based scores at cell-level (cTIL) and cell- and tissue-level combination analyses (SuperTIL). In parallel, we interrogated absolute lymphocyte counts from routine blood tests at corresponding time points during treatment. Exploratory analyses studied the relationship between TILs and pathologic complete response (pCR) and long-term outcomes. RESULTS: Patients receiving NART experienced a significant and uniform decrease in sTILs that did not recover at the time of surgery (P < .0001). This lymphodepletive effect was also mirrored in peripheral blood. Our SuperTIL deep learning score showed good concordance with manual sTILs and importantly performed comparably to manual scores in predicting pCR from diagnostic biopsies. The analysis suggested an association between baseline sTILs and pCR, as well as sTILs at surgery and relapse, in patients receiving NART. CONCLUSIONS: This study provides novel insights into TIL dynamics in the context of NART in breast cancer and demonstrates the potential for artificial intelligence to assist routine pathology. We have identified trends that warrant further interrogation and have a bearing on future radioimmunotherapy trials.

Auto-segmentation of pelvic organs at risk on 0.35T MRI using 2D and 3D Generative Adversarial Network models.

PURPOSE: Manual recontouring of targets and Organs At Risk (OARs) is a time-consuming and operator-dependent task. We explored the potential of Generative Adversarial Networks (GAN) to auto-segment the rectum, bladder and femoral heads on 0.35T MRIs to accelerate the online MRI-guided-Radiotherapy (MRIgRT) workflow. METHODS: 3D planning MRIs from 60 prostate cancer patients treated with 0.35T MR-Linac were collected. A 3D GAN architecture and its equivalent 2D version were trained, validated and tested on 40, 10 and 10 patients respectively. The volumetric Dice Similarity Coefficient (DSC) and 95th percentile Hausdorff Distance (HD95th) were computed against expert drawn ground-truth delineations. The networks were also validated on an independent external dataset of 16 patients. RESULTS: In the internal test set, the 3D and 2D GANs showed DSC/HD95th of 0.83/9.72 mm and 0.81/10.65 mm for the rectum, 0.92/5.91 mm and 0.85/15.72 mm for the bladder, and 0.94/3.62 mm and 0.90/9.49 mm for the femoral heads. In the external test set, the performance was 0.74/31.13 mm and 0.72/25.07 mm for the rectum, 0.92/9.46 mm and 0.88/11.28 mm for the bladder, and 0.89/7.00 mm and 0.88/10.06 mm for the femoral heads. The 3D and 2D GANs required on average 1.44 s and 6.59 s respectively to generate the OARs' volumetric segmentation for a single patient. CONCLUSIONS: The proposed 3D GAN auto-segments pelvic OARs with high accuracy on 0.35T, in both the internal and the external test sets, outperforming its 2D equivalent in both segmentation robustness and volume generation time.

Infrared-reflective ultrathin-metal-film-based transparent electrode with ultralow optical loss for high efficiency in solar cells.

In this work we study in-depth the antireflection and filtering properties of ultrathin-metal-film-based transparent electrodes (MTEs) integrated in thin-film solar cells. Based on numerical optimization of the MTE design and the experimental characterization of thin-film perovskite solar cell (PSC) samples, we show that reflection in the visible spectrum can be strongly suppressed, in contrast to common belief (due to the compact metal layer). The optical loss of the optimized electrode (~ 2.9%), composed of a low-resistivity metal and an insulator, is significantly lower than that of a conventional transparent conductive oxide (TCO ~ 6.3%), thanks to the very high transmission of visible light within the cell (> 91%) and low thickness (< 70 nm), whereas the reflection of infrared light (~ 70%) improves by > 370%. To assess the application potentials, integrated current density > 25 mA/cm2, power conversion efficiency > 20%, combined with vastly reduced device heat load by 177.1 W/m2 was achieved in state-of-the-art PSCs. Our study aims to set the basis for a novel interpretation of composite electrodes/structures, such as TCO-metal-TCO, dielectric-metal-dielectric or insulator-metal-insulator, and hyperbolic metamaterials, in high-efficiency optoelectronic devices, such as solar cells, semi-transparent, and concentrated systems, and other electro-optical components including smart windows, light-emitting diodes, and displays.

Radiomics systematic review in cervical cancer: gynecological oncologists' perspective.

OBJECTIVE: Radiomics is the process of extracting quantitative features from radiological images, and represents a relatively new field in gynecological cancers. Cervical cancer has been the most studied gynecological tumor for what concerns radiomics analysis. The aim of this study was to report on the clinical applications of radiomics combined and/or compared with clinical-pathological variables in patients with cervical cancer. METHODS: A systematic review of the literature from inception to February 2023 was performed, including studies on cervical cancer analysing a predictive/prognostic radiomics model, which was combined and/or compared with a radiological or a clinical-pathological model. RESULTS: A total of 57 of 334 (17.1%) screened studies met inclusion criteria. The majority of studies used magnetic resonance imaging (MRI), but positron emission tomography (PET)/computed tomography (CT) scan, CT scan, and ultrasound scan also underwent radiomics analysis. In apparent early-stage disease, the majority of studies (16/27, 59.3%) analysed the role of radiomics signature in predicting lymph node metastasis; six (22.2%) investigated the prediction of radiomics to detect lymphovascular space involvement, one (3.7%) investigated depth of stromal infiltration, and one investigated (3.7%) parametrial infiltration. Survival prediction was evaluated both in early-stage and locally advanced settings. No study focused on the application of radiomics in metastatic or recurrent disease. CONCLUSION: Radiomics signatures were predictive of pathological and oncological outcomes, particularly if combined with clinical variables. These may be integrated in a model using different clinical-pathological and translational characteristics, with the aim to tailor and personalize the treatment of each patient with cervical cancer.

A Stochastically Optimized Two-Echelon Supply Chain Model: An Entropy Approach for Operational Risk Assessment.

Minimizing a company's operational risk by optimizing the performance of the manufacturing and distribution supply chain is a complex task that involves multiple elements, each with their own supply line constraints. Traditional approaches to optimization often assume determinism as the underlying principle. However, this paper, adopting an entropy approach, emphasizes the significance of subjective and objective uncertainty in achieving optimized decisions by incorporating stochastic fluctuations into the supply chain structure. Stochasticity, representing randomness, quantifies the level of uncertainty or risk involved. In this study, we focus on a processing production plant as a model for a chain of operations and supply chain actions. We consider the stochastically varying production and transportation costs from the site to the plant, as well as from the plant to the customer base. Through stochastic optimization, we demonstrate that the plant producer can benefit from improved financial outcomes by setting higher sale prices while simultaneously lowering optimized production costs. This can be accomplished by selectively choosing producers whose production cost probability density function follows a Pareto distribution. Notably, a lower Pareto exponent yields better supply chain cost optimization predictions. Alternatively, a Gaussian stochastic fluctuation may be proposed as a more suitable choice when trading off optimization and simplicity. Although this may result in slightly less optimal performance, it offers advantages in terms of ease of implementation and computational efficiency.

Radiomics and Radiogenomics of Ovarian Cancer: Implications for Treatment Monitoring and Clinical Management.

Ovarian cancer, one of the deadliest gynecologic malignancies, is characterized by high intra- and inter-site genomic and phenotypic heterogeneity. The traditional information provided by the conventional interpretation of diagnostic imaging studies cannot adequately represent this heterogeneity. Radiomics analyses can capture the complex patterns related to the microstructure of the tissues and provide quantitative information about them. This review outlines how radiomics and its integration with other quantitative biological information, like genomics and proteomics, can impact the clinical management of ovarian cancer.

Investigating the contribution of hyaluronan to the breast tumour microenvironment using multiparametric MRI and MR elastography.

Hyaluronan (HA) is a key component of the dense extracellular matrix in breast cancer, and its accumulation is associated with poor prognosis and metastasis. Pegvorhyaluronidase alfa (PEGPH20) enzymatically degrades HA and can enhance drug delivery and treatment response in preclinical tumour models. Clinical development of stromal-targeted therapies would be accelerated by imaging biomarkers that inform on therapeutic efficacy in vivo. Here, PEGPH20 response was assessed by multiparametric magnetic resonance imaging (MRI) in three orthotopic breast tumour models. Treatment of 4T1/HAS3 tumours, the model with the highest HA accumulation, reduced T1 and T2 relaxation times and the apparent diffusion coefficient (ADC), and increased the magnetisation transfer ratio, consistent with lower tissue water content and collapse of the extracellular space. The transverse relaxation rate R2 * increased, consistent with greater erythrocyte accessibility following vascular decompression. Treatment of MDA-MB-231 LM2-4 tumours reduced ADC and dramatically increased tumour viscoelasticity measured by MR elastography. Correlation matrix analyses of data from all models identified ADC as having the strongest correlation with HA accumulation, suggesting that ADC is the most sensitive imaging biomarker of tumour response to PEGPH20.

Deep-learned estimation of uncertainty in measurements of apparent diffusion coefficient from whole-body diffusion-weighted MRI.

PURPOSE: To use deep learning to calculate the uncertainty in apparent diffusion coefficient (σADC) voxel-wise measurements to clinically impact the monitoring of treatment response and improve the quality of ADC maps. MATERIALS AND METHODS: We use a uniquely designed diffusion-weighted imaging (DWI) acquisition protocol that provides gold-standard measurements of σADC to train a deep learning model on two separate cohorts: 16 patients with prostate cancer and 28 patients with mesothelioma. Our network was trained with a novel cost function, which incorporates a perception metric and a b-value regularisation term, on ADC maps calculated by combinations of 2 or 3 b-values (e.g. 50/600/900, 50/900, 50/600, 600/900 s/mm2). We compare the accuracy of the deep-learning based approach for estimation of σADC with gold-standard measurements. RESULTS: The model accurately predicted the σADC for every b-value combination in both cohorts. Mean values of σADC within areas of active disease deviated from those measured by the gold-standard by 4.3% (range, 2.87-6.13%) for the prostate and 3.7% (range, 3.06-4.54%) for the mesothelioma cohort. We also showed that the model can easily be adapted for a different DWI protocol and field-of-view with only a few images (as little as a single patient) using transfer learning. CONCLUSION: Deep learning produces maps of σADC from standard clinical diffusion-weighted images (DWI) when 2 or more b-values are available.

Accelerating Whole-Body Diffusion-weighted MRI with Deep Learning-based Denoising Image Filters.

PURPOSE: To use deep learning to improve the image quality of subsampled images (number of acquisitions = 1 [NOA1]) to reduce whole-body diffusion-weighted MRI (WBDWI) acquisition times. MATERIALS AND METHODS: Both retrospective and prospective patient groups were used to develop a deep learning-based denoising image filter (DNIF) model. For initial model training and validation, 17 patients with metastatic prostate cancer with acquired WBDWI NOA1 and NOA9 images (acquisition period, 2015-2017) were retrospectively included. An additional 22 prospective patients with advanced prostate cancer, myeloma, and advanced breast cancer were used for model testing (2019), and the radiologic quality of DNIF-processed NOA1 (NOA1-DNIF) images were compared with NOA1 images and clinical NOA16 images by using a three-point Likert scale (good, average, or poor; statistical significance was calculated by using a Wilcoxon signed ranked test). The model was also retrained and tested in 28 patients with malignant pleural mesothelioma (MPM) who underwent lung MRI (2015-2017) to demonstrate feasibility in other body regions. RESULTS: The model visually improved the quality of NOA1 images in all test patients, with the majority of NOA1-DNIF and NOA16 images being graded as either "average" or "good" across all image-quality criteria. From validation data, the mean apparent diffusion coefficient (ADC) values within NOA1-DNIF images of bone disease deviated from those within NOA9 images by an average of 1.9% (range, 1.1%-2.6%). The model was also successfully applied in the context of MPM; the mean ADCs from NOA1-DNIF images of MPM deviated from those measured by using clinical-standard images (NOA12) by 3.7% (range, 0.2%-10.6%). CONCLUSION: Clinical-standard images were generated from subsampled images by using a DNIF.Keywords: Image Postprocessing, MR-Diffusion-weighted Imaging, Neural Networks, Oncology, Whole-Body Imaging, Supervised Learning, MR-Functional Imaging, Metastases, Prostate, Lung Supplemental material is available for this article. Published under a CC BY 4.0 license.

Metal Halide Perovskites for High-Energy Radiation Detection.

Metal halide perovskites (MHPs) have emerged as a frontrunner semiconductor technology for application in third generation photovoltaics while simultaneously making significant strides in other areas of optoelectronics. Photodetectors are one of the latest additions in an expanding list of applications of this fascinating family of materials. The extensive range of possible inorganic and hybrid perovskites coupled with their processing versatility and ability to convert external stimuli into easily measurable optical/electrical signals makes them an auspicious sensing element even for the high-energy domain of the electromagnetic spectrum. Key to this is the ability of MHPs to accommodate heavy elements while being able to form large, high-quality crystals and polycrystalline layers, making them one of the most promising emerging X-ray and γ-ray detector technologies. Here, the fundamental principles of high-energy radiation detection are reviewed with emphasis on recent progress in the emerging and fascinating field of metal halide perovskite-based X-ray and γ-ray detectors. The review starts with a discussion of the basic principles of high-energy radiation detection with focus on key performance metrics followed by a comprehensive summary of the recent progress in the field of perovskite-based detectors. The article concludes with a discussion of the remaining challenges and future perspectives.

An extensive case study on the dispersion parameters of HI-assisted reduced graphene oxide and its graphene oxide precursor.

The formation of highly concentrated and stable graphene derivatives dispersions remains a challenge towards their exploitation in various applications, including flexible optoelectronics, photovoltaics, 3D-printing, and biomedicine. Here, we demonstrate our extensive investigation on the dispersibility of graphene oxide (GO) and reduced graphene oxide (RGO) in 25 different solvents, without the use of any surfactant or stabilizer. Although there is a significant amount of work covering the general field, this is the first report on the dispersibility of: a) RGO prepared by a HI/AcOH assisted reduction process, the method which yields RGO of higher graphitization degree than the other well-known reductants met in the literature, b) both GO and RGO, explored in such a great range of solvents, with some of them not previously reported. In addition, through calculation of their Hansen Solubility Parameters (HSP), we confirmed their dispersibility behavior in each solvent, while we indirectly validated the most advanced graphitization degree of the studied RGO compared to other reported RGOs, since its HSPs exhibit the highest similarity with the respective ones of pure graphene. Finally, high concentrations of up to 189 µg mL-1 for GO and ~ 87.5 µg mL-1 for RGO were achieved, in deionized water and o-Dichlorobenzene respectively, followed by flakes size distribution and polydispersity indices estimation, through dynamic light scattering as a quality control of the effect of a solvent's nature on the dispersion behavior of these graphene-based materials.

Noninvasive MRI Native T1 Mapping Detects Response to MYCN-targeted Therapies in the Th-MYCN Model of Neuroblastoma.

Noninvasive early indicators of treatment response are crucial to the successful delivery of precision medicine in children with cancer. Neuroblastoma is a common solid tumor of young children that arises from anomalies in neural crest development. Therapeutic approaches aiming to destabilize MYCN protein, such as small-molecule inhibitors of Aurora A and mTOR, are currently being evaluated in early phase clinical trials in children with high-risk MYCN-driven disease, with limited ability to evaluate conventional pharmacodynamic biomarkers of response. T1 mapping is an MRI scan that measures the proton spin-lattice relaxation time T1. Using a multiparametric MRI-pathologic cross-correlative approach and computational pathology methodologies including a machine learning-based algorithm for the automatic detection and classification of neuroblasts, we show here that T1 mapping is sensitive to the rich histopathologic heterogeneity of neuroblastoma in the Th-MYCN transgenic model. Regions with high native T1 corresponded to regions dense in proliferative undifferentiated neuroblasts, whereas regions characterized by low T1 were rich in apoptotic or differentiating neuroblasts. Reductions in tumor-native T1 represented a sensitive biomarker of response to treatment-induced apoptosis with two MYCN-targeted small-molecule inhibitors, Aurora A kinase inhibitor alisertib (MLN8237) and mTOR inhibitor vistusertib (AZD2014). Overall, we demonstrate the potential of T1 mapping, a scan readily available on most clinical MRI scanners, to assess response to therapy and guide clinical trials for children with neuroblastoma. The study reinforces the potential role of MRI-based functional imaging in delivering precision medicine to children with neuroblastoma. SIGNIFICANCE: This study shows that MRI-based functional imaging can detect apoptotic responses to MYCN-targeted small-molecule inhibitors in a genetically engineered murine model of MYCN-driven neuroblastoma.

Emphasizing the Operational Role of a Novel Graphene-Based Ink into High Performance Ternary Organic Solar Cells.

A novel solution-processed, graphene-based material was synthesized by treating graphene oxide (GO) with 2,5,7-trinitro-9-oxo-fluorenone-4-carboxylic acid (TNF-COOH) moieties, via simple synthetic routes. The yielded molecule N-[(carbamoyl-GO)ethyl]-N'-[(carbamoyl)-(2,5,7-trinitro-9-oxo-fluorene)] (GO-TNF) was thoroughly characterized and it was shown that it presents favorable highest occupied molecular orbital (HOMO) and lowest unoccupied molecular orbital (LUMO) energy levels to function as a bridge component between the polymeric donor poly({4,8-bis[(2-ethylhexyl)oxy]benzo[1,2-b:4,5-b']dithiophene-2,6-diyl}{3-fluoro-2-[(2-ethylhexyl)carbonyl] thieno[3,4-b]thiophenediyl}) (PTB7) and the fullerene derivative acceptor [6,6]-phenyl-C71-butyric-acid-methylester (PC71BM). In this context, a GO-TNF based ink was prepared and directly incorporated within the binary photoactive layer, in different volume ratios (1%-3% ratio to the blend) for the effective realization of inverted ternary organic solar cells (OSCs) of the structure ITO/PFN/PTB7:GO-TNF:PC71BM/MoO3/Al. The addition of 2% v/v GO-TNF ink led to a champion power conversion efficiency (PCE) of 8.71% that was enhanced by ~13% as compared to the reference cell.

SuperHistopath: A Deep Learning Pipeline for Mapping Tumor Heterogeneity on Low-Resolution Whole-Slide Digital Histopathology Images.

High computational cost associated with digital pathology image analysis approaches is a challenge towards their translation in routine pathology clinic. Here, we propose a computationally efficient framework (SuperHistopath), designed to map global context features reflecting the rich tumor morphological heterogeneity. SuperHistopath efficiently combines i) a segmentation approach using the linear iterative clustering (SLIC) superpixels algorithm applied directly on the whole-slide images at low resolution (5x magnification) to adhere to region boundaries and form homogeneous spatial units at tissue-level, followed by ii) classification of superpixels using a convolution neural network (CNN). To demonstrate how versatile SuperHistopath was in accomplishing histopathology tasks, we classified tumor tissue, stroma, necrosis, lymphocytes clusters, differentiating regions, fat, hemorrhage and normal tissue, in 127 melanomas, 23 triple-negative breast cancers, and 73 samples from transgenic mouse models of high-risk childhood neuroblastoma with high accuracy (98.8%, 93.1% and 98.3% respectively). Furthermore, SuperHistopath enabled discovery of significant differences in tumor phenotype of neuroblastoma mouse models emulating genomic variants of high-risk disease, and stratification of melanoma patients (high ratio of lymphocyte-to-tumor superpixels (p = 0.015) and low stroma-to-tumor ratio (p = 0.028) were associated with a favorable prognosis). Finally, SuperHistopath is efficient for annotation of ground-truth datasets (as there is no need of boundary delineation), training and application (~5 min for classifying a whole-slide image and as low as ~30 min for network training). These attributes make SuperHistopath particularly attractive for research in rich datasets and could also facilitate its adoption in the clinic to accelerate pathologist workflow with the quantification of phenotypes, predictive/prognosis markers.

Superpixel-Based Conditional Random Fields (SuperCRF): Incorporating Global and Local Context for Enhanced Deep Learning in Melanoma Histopathology.

Computational pathology-based cell classification algorithms are revolutionizing the study of the tumor microenvironment and can provide novel predictive/prognosis biomarkers crucial for the delivery of precision oncology. Current algorithms used on hematoxylin and eosin slides are based on individual cell nuclei morphology with limited local context features. Here, we propose a novel multi-resolution hierarchical framework (SuperCRF) inspired by the way pathologists perceive regional tissue architecture to improve cell classification and demonstrate its clinical applications. We develop SuperCRF by training a state-of-art deep learning spatially constrained- convolution neural network (SC-CNN) to detect and classify cells from 105 high-resolution (20×) H&E-stained slides of The Cancer Genome Atlas melanoma dataset and subsequently, a conditional random field (CRF) by combining cellular neighborhood with tumor regional classification from lower resolution images (5, 1.25×) given by a superpixel-based machine learning framework. SuperCRF led to an 11.85% overall improvement in the accuracy of the state-of-art deep learning SC-CNN cell classifier. Consistent with a stroma-mediated immune suppressive microenvironment, SuperCRF demonstrated that (i) a high ratio of lymphocytes to all lymphocytes within the stromal compartment (p = 0.026) and (ii) a high ratio of stromal cells to all cells (p < 0.0001 compared to p = 0.039 for SC-CNN only) are associated with poor survival in patients with melanoma. SuperCRF improves cell classification by introducing global and local context-based information and can be implemented in combination with any single-cell classifier. SuperCRF provides valuable tools to study the tumor microenvironment and identify predictors of survival and response to therapy.

Investigating the Contribution of Collagen to the Tumor Biomechanical Phenotype with Noninvasive Magnetic Resonance Elastography.

Increased stiffness in the extracellular matrix (ECM) contributes to tumor progression and metastasis. Therefore, stromal modulating therapies and accompanying biomarkers are being developed to target ECM stiffness. Magnetic resonance (MR) elastography can noninvasively and quantitatively map the viscoelastic properties of tumors in vivo and thus has clear clinical applications. Herein, we used MR elastography, coupled with computational histopathology, to interrogate the contribution of collagen to the tumor biomechanical phenotype and to evaluate its sensitivity to collagenase-induced stromal modulation. Elasticity (G d) and viscosity (G l) were significantly greater for orthotopic BT-474 (G d = 5.9 ± 0.2 kPa, G l = 4.7 ± 0.2 kPa, n = 7) and luc-MDA-MB-231-LM2-4 (G d = 7.9 ± 0.4 kPa, G l = 6.0 ± 0.2 kPa, n = 6) breast cancer xenografts, and luc-PANC1 (G d = 6.9 ± 0.3 kPa, G l = 6.2 ± 0.2 kPa, n = 7) pancreatic cancer xenografts, compared with tumors associated with the nervous system, including GTML/Trp53KI/KI medulloblastoma (G d = 3.5 ± 0.2 kPa, G l = 2.3 ± 0.2 kPa, n = 7), orthotopic luc-D-212-MG (G d = 3.5 ± 0.2 kPa, G l = 2.3 ± 0.2 kPa, n = 7), luc-RG2 (G d = 3.5 ± 0.2 kPa, G l = 2.3 ± 0.2 kPa, n = 5), and luc-U-87-MG (G d = 3.5 ± 0.2 kPa, G l = 2.3 ± 0.2 kPa, n = 8) glioblastoma xenografts, intracranially propagated luc-MDA-MB-231-LM2-4 (G d = 3.7 ± 0.2 kPa, G l = 2.2 ± 0.1 kPa, n = 7) breast cancer xenografts, and Th-MYCN neuroblastomas (G d = 3.5 ± 0.2 kPa, G l = 2.3 ± 0.2 kPa, n = 5). Positive correlations between both elasticity (r = 0.72, P < 0.0001) and viscosity (r = 0.78, P < 0.0001) were determined with collagen fraction, but not with cellular or vascular density. Treatment with collagenase significantly reduced G d (P = 0.002) and G l (P = 0.0006) in orthotopic breast tumors. Texture analysis of extracted images of picrosirius red staining revealed significant negative correlations of entropy with G d (r = -0.69, P < 0.0001) and G l (r = -0.76, P < 0.0001), and positive correlations of fractal dimension with G d (r = 0.75, P < 0.0001) and G l (r = 0.78, P < 0.0001). MR elastography can thus provide sensitive imaging biomarkers of tumor collagen deposition and its therapeutic modulation. SIGNIFICANCE: MR elastography enables noninvasive detection of tumor stiffness and will aid in the development of ECM-targeting therapies.

Inorganic and Hybrid Perovskite Based Laser Devices: A Review.

Inorganic and organic-inorganic (hybrid) perovskite semiconductor materials have attracted worldwide scientific attention and research effort as the new wonder semiconductor material in optoelectronics. Their excellent physical and electronic properties have been exploited to boost the solar cells efficiency beyond 23% and captivate their potential as competitors to the dominant silicon solar cells technology. However, the fundamental principles in Physics, dictate that an excellent direct band gap material for photovoltaic applications must be also an excellent light emitter candidate. This has been realized for the case of perovskite-based light emitting diodes (LEDs) but much less for the case of the respective laser devices. Here, the strides, exclusively in lasing, made since 2014 are presented for the first time. The solution processability, low temperature crystallization, formation of nearly defect free, nanostructures, the long range ambipolar transport, the direct energy band gap, the high spectral emission tunability over the entire visible spectrum and the almost 100% external luminescence efficiency show perovskite semiconductors' potential to transform the nanophotonics sector. The operational principles, the various adopted material and laser configurations along the future challenges are reviewed and presented in this paper.

MRI Imaging of the Hemodynamic Vasculature of Neuroblastoma Predicts Response to Antiangiogenic Treatment.

Childhood neuroblastoma is a hypervascular tumor of neural origin, for which antiangiogenic drugs are currently being evaluated; however, predictive biomarkers of treatment response, crucial for successful delivery of precision therapeutics, are lacking. We describe an MRI-pathologic cross-correlative approach using intrinsic susceptibility (IS) and susceptibility contrast (SC) MRI to noninvasively map the vascular phenotype in neuroblastoma Th-MYCN transgenic mice treated with the vascular endothelial growth factor receptor inhibitor cediranib. We showed that the transverse MRI relaxation rate R 2* (second-1) and fractional blood volume (fBV, %) were sensitive imaging biomarkers of hemorrhage and vascular density, respectively, and were also predictive biomarkers of response to cediranib. Comparison with MRI and pathology from patients with MYCN-amplified neuroblastoma confirmed the high degree to which the Th-MYCN model vascular phenotype recapitulated that of the clinical phenotype, thereby supporting further evaluation of IS- and SC-MRI in the clinic. This study reinforces the potential role of functional MRI in delivering precision medicine to children with neuroblastoma. SIGNIFICANCE: This study shows that functional MRI predicts response to vascular-targeted therapy in a genetically engineered murine model of neuroblastoma.

Updating the Role of Reduced Graphene Oxide Ink on Field Emission Devices in Synergy with Charge Transfer Materials.

Hydroiodic acid (HI)-treated reduced graphene oxide (rGO) ink/conductive polymeric composites are considered as promising cold cathodes in terms of high geometrical aspect ratio and low field emission (FE) threshold devices. In this study, four simple, cost-effective, solution-processed approaches for rGO-based field effect emitters were developed, optimized, and compared; rGO layers were coated on (a) n+ doped Si substrate, (b) n⁺-Si/P3HT:rGO, (c) n⁺-Si/PCDTBT:rGO, and (d) n⁺-Si/PCDTBT:PC71BM:rGO composites, respectively. The fabricated emitters were optimized by tailoring the concentration ratios of their preparation and field emission characteristics. In a critical composite ratio, FE performance was remarkably improved compared to the pristine Si, as well as n⁺-Si/rGO field emitter. In this context, the impact of various materials, such as polymers, fullerene derivatives, as well as different solvents on rGO function reinforcement and consequently on FE performance upon rGO-based composites preparation was investigated. The field emitter consisted of n⁺-Si/PCDTBT:PC71BM(80%):rGO(20%)/rGO displayed a field enhancement factor of ~2850, with remarkable stability over 20 h and low turn-on field in 0.6 V/µm. High-efficiency graphene-based FE devices realization paves the way towards low-cost, large-scale electron sources development. Finally, the contribution of this hierarchical, composite film morphology was evaluated and discussed.

Solution Processed CH3NH3PbI3-xClx Perovskite Based Self-Powered Ozone Sensing Element Operated at Room Temperature.

Hybrid lead halide spin coated perovskite films have been successfully tested as portable, flexible, operated at room temperature, self-powered, and ultrasensitive ozone sensing elements. The electrical resistance of the hybrid lead mixed halide perovskite (CH3NH3PbI3-xClx) sensing element, was immediately decreased when exposed to an ozone (O3) environment and manage to recover its pristine electrical conductivity values within few seconds after the complete removal of ozone gas. The sensing measurements showed different response times at different gas concentrations, good repeatability, ultrahigh sensitivity and fast recovery time. To the best of our knowledge, this is the first time that a lead halide perovskite semiconductor material is demonstrating its sensing properties in an ozone environment. This work shows the potential of hybrid lead halide based perovskites as reliable sensing elements, serving the objectives of environmental control, with important socioeconomic impact.