Locally Advanced Breast Cancer (LABC) is a serious type of cancer with a poor prognosis despite advances in cancer treatment. As the disease is often inoperable, current guidelines recommend upfront aggressive neoadjuvant chemotherapy (NAC). While conventional ultrasound provides tissue echogenicity data, comparing images remains challenging due to the varied hardware configurations and instrument settings. Quantitative ultrasound (QUS) corrects this by using normalized power spectra calculations to derive quantitative parameters that are independent of instrument settings. In this work, we present an integrated deep-learning pipeline that reduces the possibility of data leakage and facilitates efficient data processing by combining scaling, oversampling, feature selection, and classification into a single framework. The pipeline is used to predict the three-year survival in LABC patients receiving NAC using QUS imaging before treatment initiation. The pipeline was trained on five quantitative ultrasound maps at the pre-treatment stage. The average acoustic concentration was the most predictive feature, achieving a recall and precision of 95% and 91%, respectively, for the survivor class. This work demonstrates that QUS may be used as a non-invasive biomarker for differentiating between LABC survivors and non-survivors at the pre-treatment stage. Prediction of the three-year survival rates of LABC patients before treatment can be used for prognosis, treatment planning, and patient decision-making.

Kidney inflammation is a central driver of acute kidney injury (AKI) and its progression to chronic kidney disease (CKD). While several imaging and biomarker-based approaches are under development, clinically validated non-invasive methods to directly quantify renal inflammation remain limited. This study introduces a novel approach using contrast-enhanced ultrasound (CEUS) with Cy5-labeled nanobubbles (NBs) to address this critical knowledge gap. Using a murine ischemia-reperfusion injury (IRI) model, CEUS imaging enabled real-time visualization of inflammation-induced changes in kidney perfusion and vascular integrity. Parametric analyses of non-linear imaging revealed delayed time-to-peak (TTP) and increased area under the falling curve (AUfC) in IRI kidneys, suggesting impaired microvascular perfusion and NB retention. Decorrelation time (DT) mapping further identified prolonged NB retention in the IRI group, indicating increased capillary permeability and NB extravasation. These findings correlated with histological and immunofluorescent analyses, which confirmed the presence of tubular injury, extravascular Cy5 signal localization, and increased neutrophil infiltration in inflamed kidney tissues. This study is the first to establish CEUS with NBs as a non-invasive, quantitative method for measuring kidney inflammation. With strong correlations between imaging metrics and histologic injury scores, this technology provides an accessible and non-invasive tool for monitoring renal inflammation and reducing reliance on invasive renal biopsies.

Tissue-engineering scaffolds require interconnected porous networks to support cell infiltration, nutrient diffusion, and waste removal. Conventional methods to introduce porosity-such as particulate leaching, gas foaming, and freeze-drying-can leave cytotoxic residues. We propose a scalable, cytocompatible approach to tune hydrogel porosity using lipid-shelled gas microbubbles as a transient porogen. In this study, we demonstrate that lipid-shelled microbubbles can be incorporated into alginate, poly(ethylene glycol) diacrylate (PEGDA), or gelatin methacrylate (GelMA) precursors, and subsequently expanded post-gelation with mild heat or vacuum to yield controlled porosity. In alginate fibers, the vacuum expansion of embedded microbubbles increased the swelling capacity by approximately 74% relative to nonporous control, without reducing compressive strength. Porous PEGDA hydrogels showed faster degradation (approximately 40% reduction in degradation time) and a lower compressive modulus compared to the dense PEGDA control, reflecting a tunable trade-off between porosity and stiffness. Unlike traditional porogen-based or 3D-printing techniques, this microbubble method requires no toxic additives or specialized equipment and is compatible with both ionic (alginate) and photo-crosslinked (PEGDA, GelMA) systems. We further demonstrate integration of this approach with a microfluidic fiber production platform. We validate that porosity modulation via microbubbles does not adversely affect the viability of mesenchymal stem cells on GelMA hydrogels. Overall, this work establishes a broadly applicable and easily scaled strategy in which porosity can be tuned post-gelation with simple triggers (heat or vacuum), enabling application-specific control of nutrient transport, degradation, and mechanics across multiple biomaterials.

In this work, we highlight recent advances in computational modeling that have significantly enhanced prospects of personalized cancer therapies by enabling insightful integration of patient-specific data, including medical images. Computational models, encompassing multi-physics and multi-scale approaches, can simulate drug transport and interactions within tissues and environments, including the tumor microenvironment, and facilitate the development of targeted diagnostic and therapeutic strategies. The incorporation of machine learning algorithms has further refined modeling, improving predictive accuracy and enabling real-time adaptive treatment planning. Although challenges remain in model validation and clinical translation, ongoing advancements are steadily bridging these gaps, bringing computational models and technologies closer to routine clinical application for the improvement of patient outcomes.

We developed a non-invasive imaging method to track CAR-T cells using internalized nanobubble ultrasound contrast agents.

Nanobubbles (NBs), consisting of a lipid shell surrounding a gas core, have gained significant interest as contrast agents for ultrasound molecular imaging. Their acoustic response is strongly influenced by size and shell properties, yet most prior work has focused on microbubble characterization. Building on insights from microbubble studies, this work investigates the viscoelastic properties of in-house synthesized phospholipid-coated submicron NBs (average diameters of 650-720 nm) using ultrasound bulk attenuation measurements. Three NB formulations with distinct shell compositions were examined. The results highlight the critical role of shell properties in determining NB resonance frequencies. Furthermore, pressure-dependent shifts in resonance revealed strong nonlinear behavior at higher acoustic driving pressures (up to 280 kPa). Comparison with microbubbles of identical shell types showed that shell stiffness and friction are size-dependent, likely due to shell properties and the shear-thinning behavior of phospholipids. These findings provide new insights into NB dynamics with potential implications for both diagnostic and therapeutic ultrasound applications.

CAR-T cell therapy has led to remarkable advances in the outcomes of patients with acute lymphoblastic leukemia (ALL), B cell lymphomas, and multiple myeloma. Given these successes in hematologic malignancies, extensive efforts are now focused on developing CAR-T cell therapies to treat solid tumors. The treatment of solid tumors poses significant hurdles with cell trafficking necessary to achieve efficacy and minimize off-tumor side effects. The development of simple, safe and inexpensive modalities for tracking CAR-T cell distribution in clinical use in vivo could provide critical insights to facilitate the development of improved CAR-T products for solid tumors. Here, we demonstrate a strategy to monitor CAR-T cells in vivo using ultrasound imaging of nanobubble (NB) labeled cells. NBs are ultrasound contrast agents composed of a lipid shell and a C4F10 gas core that can be efficiently internalized into cells. This approach enables us to image the CAR-T cells using nonlinear contrast-enhanced ultrasound (CEUS). Utilizing this method, we found that CAR-T cells can be visualized after injection into both tumor-bearing and non-tumor bearing mice. In summary, our ultrasound-based tracking approach can effectively monitor the trafficking of CAR-T cells in vivo, offering a valuable new strategy that can further enable the development of new CAR-T products and strategies to modulate cell trafficking.

Pancreatic ductal adenocarcinoma (PDAC) remains one of the most lethal malignancies due to its dense stroma, which limits drug delivery and therapeutic efficacy. Ultrasound (US) mediated strategies using nanobubbles (NBs) offer a promising approach to enhance treatment, yet the biological effects of NB exposure and the timing of US application remain unclear. Here, we investigated how NB exposure with immediate (0h) or delayed (1h) US affects viability, proliferation, metabolism, and stress signaling in PANC-1 and BxPC-3 cells. Immediate US exposure in the presence of extracellular nanobubbles resulted in a greater reduction in cell viability at 24 h compared to delayed US application. Proliferation analysis showed that Ki67 positivity decreased following USNB treatments in both cell lines. Metabolically, NB treatment alone increased cellular activity, whereas combined USNB treatment reduced metabolic activity over time. Seahorse analysis revealed higher basal respiration in PANC-1 cells compared to BxPC-3 cells, consistent with a more glycolytic phenotype, while USNB treatment enhanced glycolytic responses, particularly in PANC-1. Moreover, stress responses were also more pronounced in PANC-1 cells, with HSP70 expression increasing up to 2-fold in NB incubated group and decreasing in USNB groups compared to untreated, whereas BxPC-3 cells exhibited only modest and opposite changes to PANC-1 in HSP70 expression decreasing with NB incubation. Treatment timing critically influenced outcomes, with immediate US producing stronger antiproliferative and cytotoxic effects, highlighting the importance of sequencing in USNB therapeutic strategies. Moreover, NBs alone stimulated metabolic and stress responses that may promote proliferation, whereas NBs combined with US induced stronger stress responses associated with metabolic reprogramming and reduced proliferation.

Pancreatic ductal adenocarcinoma (PDAC) is among the most treatment-resistant malignancies, characterized by aggressive progression and limited drug penetration reducing chemotherapeutic efficacy. Gemcitabine, a pyrimidine nucleoside analog and standard-of-care therapy for PDAC, remains clinically important but is limited by the emergence of resistant tumor cell populations that underscore the need for strategies that enhance cytotoxic efficacy and overcome adaptive resistance mechanisms. Ultrasound-stimulated microbubble (USMB) therapy has emerged as a noninvasive, mechanically driven approach capable of transiently perturbing cellular membranes and enhancing therapeutic responses. We hypothesized that gemcitabine-induced metabolic and structural alterations may sensitize PDAC cells to subsequent disruption by USMB, resulting in enhanced cell death. To test this hypothesis, we assessed changes in proliferation, morphology, and cell death following gemcitabine and USMB treatments administered individually and in sequence to PANC-1 cells. Gemcitabine treatment alone (2 µM for 48h) significantly reduced cell proliferation by approximately 22% and induced pronounced morphological remodeling, including statistically increased average cell diameter from ∼19 µm to ∼22 µm, consistent with cytoplasmic expansion and structural reorganization. Notably, when gemcitabine-treated cells were subsequently exposed to USMB (1 MHz, 770 kPa negative pressure for 1 min), cell death increased dramatically to >80%, significantly exceeding the effects observed with either gemcitabine or USMB monotherapy indicating that gemcitabine pretreatment induces a mechanically vulnerable cellular state that can be exploited by USMB to achieve synergistic cytotoxicity. Therefore, the proposed combined biochemical-biophysical strategy offers a promising approach to suppress the rapid compensatory growth and therapeutic resistance commonly associated with monotherapy failure in PDAC cells.

Microplastics are widespread in aquatic and terrestrial environments, yet standard identification techniques remain slow, labor-intensive, and unsuitable for large-scale or in situ monitoring. In this work, we investigate high-frequency ultrasound as a fast, non-destructive alternative for microplastic detection, material identification, and size estimation. A peak-based extraction method isolated particle-specific echoes, from which temporal and spectral features were computed. We evaluated several machine learning methods and introduced a one-dimensional convolutional neural network (1D-CNN) to classify material types. The proposed 1D-CNN achieved 97.14% accuracy, outperforming traditional models. Particle size was further estimated using material-specific multilayer perceptrons, which classified microspheres into four size ranges with an average accuracy of 99.93%. These results show that high-frequency ultrasound encodes discriminative scattering patterns that can be learned directly from raw acoustic signals, offering a fast and scalable framework for microplastic characterization with potential for future real-time or in situ applications.

In nonlinear contrast-enhanced ultrasound (CEUS) imaging, nanobubbles (NBs) offer a promising alternative for enhanced visualization of microvascular structures and molecular imaging. This study explores two amplitude-modulated (AM) techniques-cross amplitude modulation (xAM) and compound amplitude modulation (cAM)-to enhance the capabilities of NB-mediated CEUS imaging. Both methods were tested on the Vevo F2 ultrasound imaging system (Fujifilm VisualSonics Inc.) using the Vevo Advanced Data Acquisition (VADA) mode, allowing full customization of pulse sequences. The xAM technique utilized a three-event pulse sequence that transmits cross-propagating plane-wave beams from dual apertures. This method isolated nonlinear scattered waves from NBs, reducing background noise and enhancing image quality. In contrast, cAM achieved a high frame rate of 706 Hz, a valuable feature for tracking the NB vascular flow dynamics. cAM combined plane-wave compounding with amplitude modulation, transmitting two events (half- and full-amplitude), achieving high frame rates for velocity imaging at the expense of image quality. NBs at a concentration of 109 NBs/mL, intended to mimic estimated in vivo post-injection concentrations, were injected into custom-built tissue-mimicking vessel phantoms. Experiments demonstrated that xAM significantly improved the contrast-to-noise ratio (CNR) and contrast-to-tissue ratio (CTR) by over 10 times compared to B-mode imaging, especially at larger steering angles. Conversely, cAM's CNR and CTR were at least 50% lower than that of xAM, but it achieved a frame rate over 100 times faster than xAM. These results suggest xAM can enhance imaging clarity, while cAM offers high frame rates for velocity imaging, providing an imaging framework for preclinical and clinical applications.

This study presents an experimental investigation of the influence of MB concentration on the resonance frequency of lipid-coated microbubbles (MBs). Expanding on theoretical models and numerical simulations from previous research, this work experimentally investigates the effect of MB size on the rate of resonance frequency increase with concentration, a phenomenon observed across MBs with two different lipid compositions: propylene glycol (PG) and propylene glycol and glycerol (PGG). Employing a custom-designed ultrasound attenuation measurement setup, we measured the frequency-dependent attenuation of MBs, isolating MBs based on size to generate distinct monodisperse sub-populations for analysis. The resonance frequency of MBs was determined by identifying the attenuation peak in the broadband attenuation ultrasound attenuation measurements. Our experimental findings confirm that larger MBs (≈2.1μm) demonstrate a more significant shift in resonance frequency (≈ 5 MHz, ≈ 40%) as a function of MB concentration. In contrast, smaller MBs (≈1.3μm) show a minor shift in the resonant frequency (≈ 1.8MHz, ≈ 8%), underlining the importance of size in determining acoustic behavior compared to changes in the lipid shell properties. Additionally, we observed that resonance frequency increase with concentration reaching a saturation point at higher concentrations. This plateau occurs at higher concentrations for larger MBs (≈2.1μm), while smaller MBs (≈1.6μm and ≈1.3μm) reach this saturation point at lower concentrations. Furthermore, the study highlights the small effect of bubble-bubble interactions on the resonance frequency of MB populations, particularly at lower MB concentrations and for smaller MBs. This insight is important for applications utilizing MB clusters, such as contrast-enhanced ultrasound imaging and MB-mediated therapies. While both size and lipid shell composition influence resonance frequency, MB size has a more significant effect. In conclusion, our findings affirm the need to consider both MB size and concentration when utilizing MBs for clinical and industrial ultrasonic applications.

Quantitative ultrasound (QUS) detects early tumor microstructural changes during neoadjuvant chemotherapy (NAC), enabling personalized treatment adaptation. This study assessed the accuracy of machine learning models using serial QUS data to predict treatment response and evaluated their feasibility for guiding treatment personalization. This single-center, phase 2 randomized controlled trial (clinicaltrials.gov NCT04050228, Dec/2019) enrolled stage II-III breast cancer patients planned for standard NAC. QUS imaging was performed at baseline and week 4, with the latter used for response prediction. Patients were randomized 1:1 to standard or experimental arms, stratified by hormone receptor status. In the standard arm, oncologists were blinded to QUS results. In the experimental arm, predictions were disclosed to allow treatment modification at week 4. Final response was determined histopathologically (>30% tumor reduction or <5% cellularity). Between June 2018 and September 2023, 146 patients were enrolled, and 120 randomized (standard: 57, experimental: 63). Response rates were 93.0% (standard) and 96.8% (experimental). The model achieved 92% accuracy, 83% sensitivity, 93% specificity, and 99% positive predictive value. In the experimental arm, 8/63 patients were predicted non-responders, with 4 undergoing treatment modification. QUS-based machine learning enables accurate early response prediction and supports adaptive treatment strategies in future trials.

The distinctive physicochemical properties of gold nanoparticles (AuNPs), such as biocompatibility, easy functionalization, and a high surface area-to-volume ratio, make AuNPs one of the most suitable candidates for cancer nanomedicine applications. However, achieving efficient uptake of drug-loaded AuNPs into cancer cells has remained a significant challenge in drug delivery. One promising non-invasive, pleiotropic modality that could enhance the cellular uptake of drug-loaded AuNPs by facilitating the transport through cell membranes is low-intensity pulsed ultrasound (LIPUS). This study employs cell experiments (viability and flow cytometry tests), finite element simulations, and dark-field/hyperspectral cell imaging to demonstrate that LIPUS significantly enhances the cellular uptake of doxorubicin-loaded AuNPs and free drug in cancer cells. The synergistic effects of low-intensity ultrasound and therapeutic agents further reduce cell viability, exceeding the effects of ultrasound or doxorubicin-loaded AuNPs alone. Driven by the thermal and mechanical mechanisms induced by LIPUS, this approach enhances endocytosis and sonoporation, thereby increasing cellular uptake of AuNPs and free drug through active and passive transport mechanisms. This results in a substantial improvement in treatment efficacy, marking a promising advancement in targeted drug delivery for cancer therapy.

Background/Objectives: Patients with breast cancer who do not achieve a complete response to neoadjuvant chemotherapy (NAC) may benefit from intensified adjuvant systemic therapy. However, such treatment escalation is typically delayed until after tumour resection, which occurs several months into the treatment course. Quantitative ultrasound (QUS) can detect early microstructural changes in tumours and may enable timely identification of non-responders during NAC, allowing for earlier treatment intensification. In our previous prospective observational study, 100 breast cancer patients underwent QUS imaging before and four times during NAC. Machine learning algorithms based on QUS texture features acquired in the first week of treatment were developed and achieved 78% accuracy in predicting treatment response. In the current study, we aimed to validate these algorithms in an independent prospective cohort to assess reproducibility and confirm their clinical utility. Methods: We included breast cancer patients eligible for NAC per standard of care, with tumours larger than 1.5 cm. QUS imaging was acquired at baseline and during the first week of treatment. Tumour response was defined as a ≥30% reduction in target lesion size on the resection specimen compared to baseline imaging. Results: A total of 51 patients treated between 2018 and 2021 were included (median age 49 years; median tumour size 3.6 cm). Most were estrogen receptor–positive (65%) or HER2-positive (33%), and the majority received dose-dense AC-T (n = 34, 67%) or FEC-D (n = 15, 29%) chemotherapy, with or without trastuzumab. The support vector machine algorithm achieved an area under the curve of 0.71, with 86% accuracy, 91% specificity, 50% sensitivity, 93% negative predictive value, and 43% positive predictive value for predicting treatment response. Misclassifications were primarily associated with poorly defined tumours and difficulties in accurately identifying the region of interest. Conclusions: Our findings validate QUS-based machine learning models for early prediction of chemotherapy response and support their potential as non-invasive tools for treatment personalization and clinical trial development focused on early treatment intensification.

OBJECTIVE:High-frequency ultrasound elastography (USE) can measure the mechanical properties of biomaterials and engineered tissues in vitro. Previously developed USE systems have been limited by contact acoustic radiation force (ARF) excitations and insufficient spatiotemporal resolution for sub-millimetre sub-surface mechanical property measurements. METHODS:We present a novel high-frequency USE system with a highly focused (f-number 1) 15 MHz ARF excitation transducer and a broadband (f-number 3) 40 MHz ARF tracking transducer. RESULTS:When comparing shear moduli measured via USE with shear rheometry, shear moduli of 1% and 5% agar-silica phantoms estimated by USE, were 8.8 ± 2.2 kPa and 117.0 ± 12.3 kPa (8.0 ± 0.4 kPa by rheometry, p = 0.573 for 1%; 114.4 ± 7.2 kPa, p = 0.777 for 5%) and oil-agar silica phantoms were 105.0 ± 3.4 kPa (0%) and 77.0 ± 22.1 kPa (10%) by USE (101.0 ± 4.8 kPa by rheometry; p = 0.311 for 0%; 75.8 ± 5.3 kPa; p = 0.938 for 10%). The speed of sound, acoustic impedance, and acoustic attenuation of these samples were also determined. We also used in silico analysis to mimic our experimental system and analyze the spectral content of the resulting shear waves in elastic and viscoelastic tissues with parametric changes to the ARF excitation duration, shear modulus, and viscosity. Notably, we observed a nonlinear dependency of shear wave frequency on ARF excitation duration and material properties, where shear wave frequency was most sensitive to tissue elastic modulus at longer ARF durations but more sensitive to tissue viscosity at shorter ARF durations. CONCLUSION:Our system enables noninvasive, nondestructive estimation of the mechanical properties of thin biomaterials via focused axial localization of the ARF, opening new avenues for future USE applications in engineered tissue systems.

We describe an approach to enhancing microfluidic mixing by generating acoustic microstreaming flows around microposts in a microfluidic device. Specifically, we synthesize microposts with various cross-sectional shapes (i.e., circles, triangles, and stars) using photocrosslinkable polymers, allowing for precise control over their geometry. We also ensure unobstructed micropost vibration via carefully designed gaps between the microposts and the channel ceiling. Experimental findings reveal that the shape of microposts is critical in influencing microstreaming patterns and mixing efficiency. Circular microposts generate semi-symmetrical circular vortices, resulting in superior mixing performance (86.7