Unraveling sources of emission heterogeneity in Silicon Vacancy color centers with cryo-cathodoluminescence microscopy.

Diamond color centers have proven to be versatile quantum emitters and exquisite sensors of stress, temperature, electric and magnetic fields, and biochemical processes. Among color centers, the silicon-vacancy (SiV[Formula: see text]) defect exhibits high brightness, minimal phonon coupling, narrow optical linewidths, and high degrees of photon indistinguishability. Yet the creation of reliable and scalable SiV[Formula: see text]-based color centers has been hampered by heterogeneous emission, theorized to originate from surface imperfections, crystal lattice strain, defect symmetry, or other lattice impurities. Here, we advance high-resolution cryo-electron microscopy combined with cathodoluminescence spectroscopy and 4D scanning transmission electron microscopy (STEM) to elucidate the structural sources of heterogeneity in SiV[Formula: see text] emission from nanodiamond with sub-nanometer-scale resolution. Our diamond nanoparticles are grown directly on TEM membranes from molecular-level seedings, representing the natural formation conditions of color centers in diamond. We show that individual subcrystallites within a single nanodiamond exhibit distinct zero-phonon line (ZPL) energies and differences in brightness that can vary by 0.1 meV in energy and over 70% in brightness. These changes are correlated with the atomic-scale lattice structure. We find that ZPL blue-shifts result from tensile strain, while ZPL red shifts are due to compressive strain. We also find that distinct crystallites host distinct densities of SiV[Formula: see text] emitters and that grain boundaries impact SiV[Formula: see text] emission significantly. Finally, we interrogate nanodiamonds as small as 40 nm in diameter and show that these diamonds exhibit no spatial change to their ZPL energy. Our work provides a foundation for atomic-scale structure-emission correlation, e.g., of single atomic defects in a range of quantum and two-dimensional materials.

Investigating pulse-echo sound speed estimation in breast ultrasound with deep learning.

Ultrasound is an adjunct tool to mammography that can quickly and safely aid physicians in diagnosing breast abnormalities. Clinical ultrasound often assumes a constant sound speed to form diagnostic B-mode images. However, the components of breast tissue, such as glandular tissue, fat, and lesions, differ in sound speed. Given a constant sound speed assumption, these differences can degrade the quality of reconstructed images via phase aberration. Sound speed images can be a powerful tool for improving image quality and identifying diseases if properly estimated. To this end, we propose a supervised deep-learning approach for sound speed estimation from analytic ultrasound signals. We develop a large-scale simulated ultrasound dataset that generates representative breast tissue samples by modeling breast gland, skin, and lesions with varying echogenicity and sound speed. We adopt a fully convolutional neural network architecture trained on a simulated dataset to produce an estimated sound speed map. The simulated tissue is interrogated with a plane wave transmit sequence, and the complex-value reconstructed images are used as input for the convolutional network. The network is trained on the sound speed distribution map of the simulated data, and the trained model can estimate sound speed given reconstructed pulse-echo signals. We further incorporate thermal noise augmentation during training to enhance model robustness to artifacts found in real ultrasound data. To highlight the ability of our model to provide accurate sound speed estimations, we evaluate it on simulated, phantom, and in-vivo breast ultrasound data.

Sound Speed Estimation for Distributed Aberration Correction in Laterally Varying Media.

Spatial variation in sound speed causes aberration in medical ultrasound imaging. Although our previous work has examined aberration correction in the presence of a spatially varying sound speed, practical implementations were limited to layered media due to the sound speed estimation process involved. Unfortunately, most models of layered media do not capture the lateral variations in sound speed that have the greatest aberrative effect on the image. Building upon a Fourier split-step migration technique from geophysics, this work introduces an iterative sound speed estimation and distributed aberration correction technique that can model and correct for aberrations resulting from laterally varying media. We first characterize our approach in simulations where the scattering in the media is known a-priori. Phantom and in-vivo experiments further demonstrate the capabilities of the iterative correction technique. As a result of the iterative correction scheme, point target resolution improves by up to a factor of 4 and lesion contrast improves by up to 10.0 dB in the phantom experiments presented.

Synthesis and Evaluation of Clinically Translatable Targeted Microbubbles Using a Microfluidic Device for In Vivo Ultrasound Molecular Imaging.

The main aim of this study is to synthesize contrast microbubbles (MB) functionalized with engineered protein ligands using a microfluidic device to target breast cancer specific vascular B7-H3 receptor in vivo for diagnostic ultrasound imaging. We used a high-affinity affibody (ABY) selected against human/mouse B7-H3 receptor for engineering targeted MBs (TMBs). We introduced a C-terminal cysteine residue to this ABY ligand for facilitating site-specific conjugation to DSPE-PEG-2K-maleimide (M. Wt = 2.9416 kDa) phospholipid for MB formulation. We optimized the reaction conditions of bioconjugations and applied it for microfluidic based synthesis of TMBs using DSPE-PEG-ABY and DPPC liposomes (5:95 mole %). The binding affinity of TMBs to B7-H3 (MBB7-H3) was tested in vitro in MS1 endothelial cells expressing human B7-H3 (MS1B7-H3) by flow chamber assay, and by ex vivo in the mammary tumors of a transgenic mouse model (FVB/N-Tg (MMTV-PyMT)634Mul/J), expressing murine B7-H3 in the vascular endothelial cells by immunostaining analyses. We successfully optimized the conditions needed for generating TMBs using a microfluidic system. The synthesized MBs showed higher affinity to MS1 cells engineered to express higher level of hB7-H3, and in the endothelial cells of mouse tumor tissue upon injecting TMBs in a live animal. The average number (mean ± SD) of MBB7-H3 binding to MS1B7-H3 cells was estimated to be 354.4 ± 52.3 per field of view (FOV) compared to wild-type control cells (MS1WT; 36.2 ± 7.5/FOV). The non-targeted MBs did not show any selective binding affinity to both the cells (37.7 ± 7.8/FOV for MS1B7-H3 and 28.3 ± 6.7/FOV for MS1WT cells). The fluorescently labeled MBB7-H3 upon systemic injection in vivo co-localized to tumor vessels, expressing B7-H3 receptor, as validated by ex vivo immunofluorescence analyses. We have successfully synthesized a novel MBB7-H3 via microfluidic device, which allows us to produce on demand TMBs for clinical applications. This clinically translatable MBB7-H3 showed significant binding affinity to vascular endothelial cells expressing B7-H3 both in vitro and in vivo, which shows its potential for clinical translation as a molecular ultrasound contrast agent for human applications.

Fast volumetric ultrasound facilitates high-resolution 3D mapping of tissue compartments.

Volumetric ultrasound imaging has the potential for operator-independent acquisition and enhanced field of view. Panoramic acquisition has many applications across ultrasound; spanning musculoskeletal, liver, breast, and pediatric imaging; and image-guided therapy. Challenges in high-resolution human imaging, such as subtle motion and the presence of bone or gas, have limited such acquisition. These issues can be addressed with a large transducer aperture and fast acquisition and processing. Programmable, ultrafast ultrasound scanners with a high channel count provide an unprecedented opportunity to optimize volumetric acquisition. In this work, we implement nonlinear processing and develop distributed beamformation to achieve fast acquisition over a 47-centimeter aperture. As a result, we achieve a 50-micrometer -6-decibel point spread function at 5 megahertz and resolve in-plane targets. A large volume scan of a human limb is completed in a few seconds, and in a 2-millimeter dorsal vein, the image intensity difference between the vessel center and surrounding tissue was ~50 decibels, facilitating three-dimensional reconstruction of the vasculature.

Optimal transmit apodization for the maximization of lag-one coherence with applications to aberration delay estimation.

Phase aberration is one of the major sources of image degradation in medical ultrasound imaging. One of the earliest and simplest techniques to correct for phase aberration involves nearest-neighbor cross correlation to estimate delays between neighboring receive channels and the compensation of aberration delays in a delay-and-sum beamformer. The main challenge is that neighboring receive channels may not have sufficient signal correlation to accurately estimate the aberration delays. Although algorithms such as the translating transmit aperture or the common midpoint gather are designed to perfectly maximize signal correlations between received signals, these algorithms require the use of different transmit apertures for each received signal. Instead, this work proposes the use of a single globally-applicable transmit apodization function that optimizes the lag-one coherence based on the van Cittert-Zernike theorem. For the application to phase aberration correction, it is shown across 20 different zero-mean Gaussian-random aberrators that the proposed optimal apodization function reduces the estimation error in the aberration delay profile from 22.85% to 15.72%.

Aberration correction in diagnostic ultrasound: A review of the prior field and current directions.

Medical ultrasound images are reconstructed with simplifying assumptions on wave propagation, with one of the most prominent assumptions being that the imaging medium is composed of a constant sound speed. When the assumption of a constant sound speed are violated, which is true in most in vivoor clinical imaging scenarios, distortion of the transmitted and received ultrasound wavefronts appear and degrade the image quality. This distortion is known as aberration, and the techniques used to correct for the distortion are known as aberration correction techniques. Several models have been proposed to understand and correct for aberration. In this review paper, aberration and aberration correction are explored from the early models and correction techniques, including the near-field phase screen model and its associated correction techniques such as nearest-neighbor cross-correlation, to more recent models and correction techniques that incorporate spatially varying aberration and diffractive effects, such as models and techniques that rely on the estimation of the sound speed distribution in the imaging medium. In addition to historical models, future directions of ultrasound aberration correction are proposed.

Separation of mainlobe and sidelobe contributions to B-mode ultrasound images based on the aperture spectrum.

Purpose: Isolating the mainlobe and sidelobe contribution to the ultrasound image can improve imaging contrast by removing off-axis clutter. Previous work achieves this separation of mainlobe and sidelobe contributions based on the covariance of received signals. However, the formation of a covariance matrix at each imaging point can be computationally burdensome and memory intensive for real-time applications. Our work demonstrates that the mainlobe and sidelobe contributions to the ultrasound image can be isolated based on the receive aperture spectrum, greatly reducing computational and memory requirements. Approach: The separation of mainlobe and sidelobe contributions to the ultrasound image is shown in simulation, in vitro, and in vivo using the aperture spectrum method and multicovariate imaging of subresolution targets (MIST). Contrast, contrast-to-noise-ratio (CNR), and speckle signal-to-noise-ratio are used to compare the aperture spectrum approach with MIST and conventional delay-and-sum (DAS) beamforming. Results: The aperture spectrum approach improves contrast by 1.9 to 6.4 dB beyond MIST and 8.9 to 13.5 dB beyond conventional DAS B-mode imaging. However, the aperture spectrum approach yields speckle texture similar to DAS. As a result, the aperture spectrum-based approach has less CNR than MIST but greater CNR than conventional DAS. The CPU implementation of the aperture spectrum-based approach is shown to reduce computation time by a factor of 9 and memory consumption by a factor of 128 for a 128-element transducer. Conclusions: The mainlobe contribution to the ultrasound image can be isolated based on the receive aperture spectrum, which greatly reduces the computational cost and memory requirement of this approach as compared with MIST.

Immunotheranostic microbubbles (iMBs) - a modular platform for dendritic cell vaccine delivery applied to breast cancer immunotherapy.

BACKGROUND: Therapeutic strategies engaging the immune system against malignant cells have revolutionized the field of oncology. Proficiency of dendritic cells (DCs) for antigen presentation and immune response has spurred interest on DC-based vaccines for anti-cancer therapy. However, despite favorable safety profiles in patients, current DC-vaccines have not yet presented significant outcome due to technical barriers in active DC delivery, tumor progression, and immune dysfunction. To maximize the therapeutic response, we present here a unique cell-free DC-based vaccine capable of lymphoid organ targeting and eliciting T-cell-mediated anti-tumor effect. METHODS: We developed this novel immunotheranostic platform using plasma membranes derived from activated DCs incorporated into ultrasound contrast microbubbles (MBs), thereby offering real-time visualization of MBs' trafficking and homing in vivo. Human PBMC-derived DCs were cultured ex vivo for controlled maturation and activation using cell membrane antigens from breast cancer cells. Following DC membrane isolation, immunotheranostic microbubbles, called DC-iMBs, were formed for triple negative breast cancer treatment in a mouse model harboring a human reconstituted immune system. RESULTS: Our results demonstrated that DC-iMBs can accumulate in lymphoid organs and induce anti-tumor immune response, which significantly reduced tumor growth via apoptosis while increasing survival length of the treated animals. The phenotypic changes in immune cell populations upon DC-iMBs delivery further confirmed the T-cell-mediated anti-tumor effect. CONCLUSION: These early findings strongly support the potential of DC-iMBs as a novel immunotherapeutic cell-free vaccine for anti-cancer therapy.

Adaptation of Range-Doppler Algorithm for Efficient Beamforming of Monostatic and Multistatic Ultrasound Signals.

Algorithmic changes that increase beamforming speed have become increasingly relevant to processing synthetic aperture (SA) ultrasound data. In particular, beamforming SA data in a spatio-temporal frequency domain using the F-k (Stolt) migration have been shown to reduce the beamforming time by up to two orders of magnitude compared with the conventional delay-and-sum (DAS) beamforming, and it has been used in applications where large amounts of raw data make real-time frame rates difficult to attain, such as multistatic SA imaging and plane-wave Doppler imaging with large ensemble lengths. However, beamforming signals in a spatio-temporal Fourier space can require loading large blocks of data at once, making it memory-intensive and less suited for parallel (i.e., multithreaded) processing. As an alternative, we propose beamforming in a range-Doppler (RD) frequency domain using the range-Doppler algorithm (RDA) that has originally been developed for SA radar (SAR) imaging. Through simulation and phantom experiments, we show that RDA achieves similar lateral resolution and contrast compared with DAS and F-k migration. At the same time, higher axial sidelobes in RDA images can be reduced via (temporal) frequency binning. Like the F-k migration, RDA significantly reduces the overall number of computations relative to DAS, and it achieves ten times lower processing time on a single CPU. Because RDA uses only a spatial Fourier transform (FT), it requires two times less memory than the F-k migration to process the simulated multistatic data and can be executed on as many as a thousand parallel threads (compared with eight parallel threads for the F-k migration), making it more suitable for implementation on modern graphics processing units (GPUs). While RDA is not as parallelizable as DAS, it is expected to hold a significant speed advantage on devices with moderate parallel processing capabilities (up to several thousand cores), such as point-of-care and low-cost ultrasound devices.

Comparative Study of Raw Ultrasound Data Representations in Deep Learning to Classify Hepatic Steatosis.

Adiposity accumulation in the liver is an early-stage indicator of non-alcoholic fatty liver disease. Analysis of ultrasound (US) backscatter echoes from liver parenchyma with deep learning (DL) may offer an affordable alternative for hepatic steatosis staging. The aim of this work was to compare DL classification scores for liver steatosis using different data representations constructed from raw US data. Steatosis in N = 31 patients with confirmed or suspected non-alcoholic fatty liver disease was stratified based on fat-fraction cutoff values using magnetic resonance imaging as a reference standard. US radiofrequency (RF) frames (raw data) and clinical B-mode images were acquired. Intermediate image formation stages were modeled from RF data. Power spectrum representations and phase representations were also calculated. Co-registered patches were used to independently train 1-, 2- and 3-D convolutional neural networks (CNNs), and classifications scores were compared with cross-validation. There were 67,800 patches available for 2-D/3-D classification and 1,830,600 patches for 1-D classification. The results were also compared with radiologist B-mode annotations and quantitative ultrasound (QUS) metrics. Patch classification scores (area under the receiver operating characteristic curve [AUROC]) revealed significant reductions along successive stages of the image formation process (p < 0.001). Patient AUROCs were 0.994 for RF data and 0.938 for clinical B-mode images. For all image formation stages, 2-D CNNs revealed higher patch and patient AUROCs than 1-D CNNs. CNNs trained with power spectrum representations converged faster than those trained with RF data. Phase information, which is usually discarded in the image formation process, provided a patient AUROC of 0.988. DL models trained with RF and power spectrum data (AUROC = 0.998) provided higher scores than conventional QUS metrics and multiparametric combinations thereof (AUROC = 0.986). Radiologist annotations indicated lower hepatic steatosis classification accuracies (Acc = 0.914) with respect to magnetic resonance imaging proton density fat fraction that DL models (Acc = 0.989). Access to raw ultrasound data combined with artificial intelligence techniques may offer superior opportunities for quantitative tissue diagnostics than conventional sonographic images.

Improving Transcranial Acoustic Targeting: The Limits of CT-Based Velocity Estimates and the Role of MR.

Transcranial magnetic resonance-guided focused ultrasound (tcMRgFUS) enables the noninvasive treatment of the deep brain. This capacity relies on the ability to focus acoustic energy through the in-tact skull, a feat that requires accurate estimates of the acoustic velocity in individual patient skulls. In current practice, these estimates are generated using a pretreatment computed tomography (CT) scan and then registered to a magnetic resonance (MR) dataset on the day of the treatment. Treatment safety and efficacy can be improved by eliminating the need to register the CT data to the MR images and by improving the accuracy of acoustic velocity measurements. In this study, we examine the capacity of MR to supplement or replace CT as a means of estimating velocity in the skull. We find that MR can predict velocity with less but comparable accuracy to CT. We then use micro-CT imaging to better understand the limitations of Hounsfield unit (HU)-based estimates of velocity, demonstrating that the macrostructure of pores in the skull contributes to the acoustic velocity of the bone. We find evidence that detailed T2 measurements provide information about pore macrostructure similar to the information obtained with micro-CT, offering a potential clinical mechanism for improving patient-specific estimates of acoustic velocity in the human skull.

Biomimetic nanobubbles for triple-negative breast cancer targeted ultrasound molecular imaging.

Triple-negative breast cancer (TNBC) is a highly heterogeneous breast cancer subtype with poor prognosis. Although anatomical imaging figures prominently for breast lesion screening, TNBC is often misdiagnosed, thus hindering early medical care. Ultrasound (US) molecular imaging using nanobubbles (NBs) capable of targeting tumor cells holds great promise for improved diagnosis and therapy. However, the lack of conventional biomarkers in TNBC impairs the development of current targeted agents. Here, we exploited the homotypic recognition of cancer cells to synthesize the first NBs based on TNBC cancer cell membrane (i.e., NBCCM) as a targeted diagnostic agent. We developed a microfluidic technology to synthesize NBCCM based on the self-assembly property of cell membranes in aqueous solutions. In vitro, optimal NBCCM had a hydrodynamic diameter of 683 ± 162 nm, showed long-lasting US contrast enhancements and homotypic affinity. In vivo, we demonstrated that NBCCM showed increased extravasation and retention in a TNBC mouse model compared to non-targeted NBs by US molecular imaging. Peak intensities and areas under the curves from time-intensity plots showed a significantly enhanced signal from NBCCM compared to non-targeted NBs (2.1-fold, P = 0.004, and, 3.6-fold, P = 0.0009, respectively). Immunofluorescence analysis further validated the presence of NBCCM in the tumor microenvironment. Circumventing the challenge for universal cancer biomarker identification, our approach could enable TNBC targeting regardless of tumor tissue heterogeneity, thus improving diagnosis and potentially gene/drug targeted delivery. Ultimately, our approach could be used to image many cancer types using biomimetic NBs prepared from their respective cancer cell membranes.

Angular spectrum method for curvilinear arrays: Theory and application to Fourier beamforming.

Fourier beamforming techniques for medical ultrasound imaging have largely been limited to linear transducer arrays. This work extends the angular spectrum method to curvilinear arrays and demonstrates a migration-based Fourier beamforming technique that has implications for sound speed estimation and distributed aberration correction for abdominal imaging applications. When compared to Field II simulations, the proposed angular spectrum method simulates the pressure field from a focused transmission to within 3.7% normalized root mean square error. The resulting Fourier beamforming technique is then compared to virtual source synthetic aperture using in vivo abdominal imaging examples where resolution and imaging quality improvements are observed.

Synthetic Doping of Diamondoids through Skeletal Editing.

We present a strategy for the skeletal editing of diamondoid structures to selectively displace methylene for heteroatom moieties in the carbon framework. This constitutes a synthetic approach to doping diamond-like structures with electron donor dopants (O, N, and S). The key steps involve two subsequent retro-Barbier fragmentations followed by cage reconstruction in the presence of a dopant. Remarkably, the incorporation of n-dopants reduces the strain of the diamondoid cage as shown through homodesmotic equations.

Distributed Aberration Correction Techniques Based on Tomographic Sound Speed Estimates.

Phase aberration is widely considered a major source of image degradation in medical pulse-echo ultrasound. Traditionally, near-field phase aberration correction techniques are unable to account for distributed aberrations due to a spatially varying speed of sound in the medium, while most distributed aberration correction techniques require the use of point-like sources and are impractical for clinical applications where diffuse scattering is dominant. Here, we present two distributed aberration correction techniques that utilize sound speed estimates from a tomographic sound speed estimator that builds on our previous work with diffuse scattering in layered media. We first characterize the performance of our sound speed estimator and distributed aberration correction techniques in simulations where the scattering in the media is known a priori. Phantom and in vivo experiments further demonstrate the capabilities of the sound speed estimator and the aberration correction techniques. In phantom experiments, point target resolution improves from 0.58 to 0.26 and 0.27 mm, and lesion contrast improves from 17.7 to 23.5 and 25.9 dB, as a result of distributed aberration correction using the eikonal and wavefield correlation techniques, respectively.

Cylindrical Transducer Array for Intravascular Shear Wave Elasticity Imaging: Preliminary Development.

We present an intravascular ultrasound (IVUS) transducer array designed to enable shear wave elasticity imaging (SWEI) of arteries for the detection and characterization of atherosclerotic soft plaques. Using a custom dicing fixture, we have fabricated single-element and axially-segmented array transducer prototypes from 4.6-Fr to 7.6-Fr piezoceramic tubes, respectively. Focused excitation of the array prototype at 4 MHz yielded a focal gain of 5× in intensity, for an estimated 60 mW/cm2 [Formula: see text] and 1.6-MPa negative peak pressure at 4.5-mm range in water. The single-element transducer generated a peak radial displacement of [Formula: see text] in a uniform elasticity phantom, with axial shear waves detectable by an external linear array probe up to 5 mm away from the excitation plane. In a vessel phantom with a soft inclusion, the array prototype generated peak displacements of 2.2 and [Formula: see text] in the soft inclusion and vessel wall regions, respectively. A SWEI image of the vessel phantom was reconstructed, with measured shear wave speed (SWS) of 1.66 ± 0.91 m/s and 0.97 ± 0.59 m/s for the soft inclusion and vessel wall regions, respectively. The array prototype was also used to obtain a SWEI image of an ex vivo porcine artery, with a mean SWS of 3.97 ± 1.12 m/s. These results suggest that a cylindrical intravascular ultrasound (IVUS) transducer array could be made capable of SWEI for atherosclerotic plaque detection in coronary arteries.

Ultrasound-Guided Microbubble-Mediated Locoregional Delivery of Multiple MicroRNAs Improves Chemotherapy in Hepatocellular Carcinoma.

Rationale: To assess treatment effects of 4 complementary miRNAs (miRNA-100/miRNA-122/antimiRNA-10b/antimiRNA-21) encapsulated in a biodegradable PLGA-PEG nanoparticle, administered by an ultrasound-guided microbubble-mediated targeted delivery (UGMMTD) approach in mouse models of hepatocellular carcinoma (HCC). Methods:In vitro apoptotic index was measured in HepG2 and Hepa1-6 HCC cells treated with various combinations of the 4 miRNAs with doxorubicin. Three promising combinations were further tested in vivo by using UGMMTD. 63 HepG2 xenografts in mice were randomized into: group 1, miRNA-122/antimiRNA-10b/antimiRNA-21/US/doxorubicin; group 2, miRNA-100/miRNA-122/antimiRNA-10b/antimiRNA-21/US/doxorubicin; group 3, miRNA-100/miRNA-122/antimiRNA-10b/US/doxorubicin; group 4, miRNA-122/anitmiRNA-10b/antimiRNA-21/doxorubicin; group 5, miRNA-100/miRNA-122/antimiRNA-10b/antimiRNA-21/doxorubicin; group 6, miRNA-100/miRNA-122/antimiRNA-10b/doxorubicin; group 7, doxorubicin only treatment; and group 8, without any treatment. Tumor volumes were measured through 18 days. H&E staining, TUNEL assay, and qRT-PCR quantification for delivered miRNAs were performed. Results:In vivo results showed that UGMMTD of miRNAs with doxorubicin in groups 1-3 significantly (P<0.05) delayed tumor growth compared to control without any treatment, and doxorubicin only from day 7 to 18. On qRT-PCR, levels of delivered miRNAs were significantly (P<0.05) higher in groups 1-3 upon UGMMTD treatment compared to controls. TUNEL assay showed that upon UGMMTD, significantly higher levels of apoptotic cell populations were observed in groups 1-3 compared to controls. Toxicity was not observed in various organs of different groups. Conclusions: UGMMTD of miRNA-100/miRNA-122/antimiRNA-10b/antimiRNA-21 combination improved therapeutic outcome of doxorubicin chemotherapy in mouse models of HCC by substantial inhibition of tumor growth and significant increase in apoptotic index.