Conventional analyses of urinary stones like estimation of urinary markers provide ambiguous predictions on the type of stone while XRD, and FT-IR can only be performed on surgically removed samples. Hence, current clinical practice lacks non-invasive methods for early and accurate classification of stones based on their composition. This study presents an ultrasound-based non-invasive system integrated with a machine-learning algorithm for analyzing acoustic signals to classify in vitro fabricated urinary crystals. Calcium oxalate, calcium phosphate, and uric acid crystals were synthesized, characterized, and embedded in gelatin-based gel phantoms. Following morphological characterization of the crystal-infused phantoms, ultrasonic echo signals were acquired and processed using machine learning-based analytical models. The results demonstrated that the ultrasound-based system effectively enables non-invasive analysis of urinary stones and can provide information on their location and structural characteristics. In parallel, the machine-learning component enhances diagnostic accuracy by classifying the stone composition from the analyzed acoustic signals, facilitating early and precise diagnosis. This approach highlights the potential of ultrasound and AI integration as a reliable diagnostic tool for personalized stone-management strategies.

Hydrogels, possessing biocompatibility and flexibility, are widely used across biomedical and industrial domains, with their concentration serving as a critical determinant of their physicochemical properties. However, conventional methods for concentration assessment exhibit significant limitations; invasive techniques damage the original state of the sample, while existing non-invasive approaches often lack precision at extreme concentration levels. To address these challenges, this study introduces a novel, highly accurate, non-invasive ultrasound-based methodology for hydrogel concentration analysis. A single-element ultrasound transducer was used to collect concentration data while preserving sample integrity. This approach mitigates the accuracy variation observed in existing technologies, enabling precise classification across all concentration levels. In particular, complex ultrasound signal pattern analysis was conducted using a convolutional neural network-based machine learning framework, achieving concentration classification with an accuracy exceeding 99%. Through highly accurate and non-destructive concentration classification, the proposed method holds substantial potential as a core technology for improving the quality control of hydrogel-based constructs.

Mechanophores embedded in synthetic polymers enable force-mediated chemical transformations but often require extended sonication and complicated analysis. Here, we employ DNA as a programmable scaffold for mechanochemical force transmission to achieve efficient ultrasound-induced molecular cleavage. By designing DNA-mechanophore-DNA architectures with tunable lengths (100–1,000 bp) and different mechanophores, we achieve nearly complete scission (99.9% for constructs >250 bp) within 15 min. Scission occurs at mechanophore sites or off-site, depending on mechanophore reactivity. DNA sequencing and mass spectrometry confirm selective bond cleavage and provide molecular-level mechanistic insight. This DNA-based platform offers an efficient and precise tool for mechanophore characterization and demonstrates potential for biomedical applications under mild ultrasound conditions, including operation at clinical frequencies (1 MHz) in tissue-mimicking skin phantoms.

In this study, we report the high-intensity focused ultrasound-triggered burst release of gallic acid from a gelatin-polyvinyl pyrrolidone (PVP) based hydrogel crosslinked with magnesium gallate (Mg-Gal) microparticles. Hydrogel was fabricated with 5% gelatin and 5% PVP as polymeric components crosslinked with 0.5% of Mg-Gal microparticles as crosslinker. Following physico-chemical characterization of the hydrogel, gallic acid release pattern was studied at different pH and temperature. In vitro biocompatibility & anti-cancer potential of microparticles and hydrogels were established. Further, high-intensity focused ultrasound (HIFU) induced drug release and enhanced bioactivity were also demonstrated. Fabricated hydrogels were characterized to show strong crosslinking network reinforced by ionic interactions. Mechanical and hydration properties correlated with release kinetics of gallic acid signifying the pH & temperature responsiveness of the hydrogel. PG-Mg-Gal showed excellent biocompatibility with dermal fibroblast (<10%) and inhibited the proliferation & migration of 4T1 cells. HIFU triggered the rapid release of gallic acid from PG-Mg-Gal, where the release rate was consistent for 30 min (30%) and up to 60% release was achieved at 90 mins. The drug release process showed consistently significant release rate compared to the untreated control. Enhanced bioactivity (20%; > 5 times of untreated hydrogel) of PG-Mg-Gal followed by burst release of gallic acid through HIFU treatment was demonstrated after 4 hrs using Live/Dead cells staining. In conclusion, HIFU-triggered delivery of gallic acid from PG-Mg-Gal showed great potential as a biocompatible and rapid delivery system which enhances the cytotoxic potential of Mg-Gal formulation through delivery at the targeted site.

Abstract Acoustically transparent interface materials that maintain both mechanical durability and signal fidelity are essential for stable operation of biomedical ultrasound transducers and related acoustic applications. Although polydimethylsiloxane (PDMS) is widely used for ultrasound encapsulation, PDMS acoustic interfaces are sensitive to thickness nonuniformity on curvilinear surfaces, leading to reduced mechanical properties and unstable signal variability. Poly(vinyl alcohol) (PVA) is a hydrophilic polymer with abundant hydroxyl groups and can contribute to morphological stabilization in physically blended polymer systems. Herein, we introduce a physically blended PDMS–PVA composite acoustic interface that enhances conformal contact and interfacial integrity while preserving acoustic transparency. PDMS–PVA showed a 5.3-fold increase in viscosity and a 58% reduction in pore volume compared with PDMS, enhancing surface conformity and structural integrity. Mechanical characterization revealed that structural densification significantly increased the ultimate tensile strength to 8.3 MPa and improved fracture resistance, demonstrating robust tolerance to physical deformation. Despite these mechanical enhancements, acoustic impedance (∼1.25 MRayl) was preserved, enabling efficient signal transmission suitable for biomedical imaging and precise control. Consequently, the PDMS–PVA composite provides a rigid-particle-free route to mechanically reinforced protective ultrasound interfaces that preserve acoustic impedance and signal-transmission characteristics.

Phytoplankton, as primary producers through photosynthesis, are fundamental to marine ecosystems. However, their rapid proliferation in coastal regions has increasingly led to harmful algal blooms (HABs), commonly known as red tides, which pose significant environmental, economic, and health risks. A deeper understanding of phytoplankton dynamics, particularly their movement patterns such as diel vertical migration (DVM), is essential for effective monitoring and prevention of HABs. Traditional measurement techniques, including acoustic Doppler current profiling (ADCP) and acoustic backscatter, are limited by spatial and temporal resolution constraints, making it challenging to capture fine-scale phytoplankton behavior. In this study, we introduce a novel application of single-beam acoustic tweezer (SBAT), which employs a single concave bulk ultrasonic transducer to generate a focused acoustic beam, to confine and track the swimming behavior of Cochlodinium polykrikoides (C. polykrikoides), a representative HABs-forming species. SBAT provides precise, non-invasive manipulation and clustering of individual cells, offering a powerful alternative to conventional methods. Our findings highlight the versatility of SBAT in studying phytoplankton motility and environmental interactions, paving the way for improved HABs management strategies.

Pharmacological vasodilators are widely used to treat vascular disorders, but their systemic nature often leads to undesirable side effects. Moreover, conventional techniques for evaluating vascular responses-including ultrasound and wire myography-suffer from limited resolution or require invasive procedures. This study aimed to develop an integrated platform that overcomes both challenges simultaneously. A high-intensity focused ultrasound (HIFU) transducer was attached to the photoacoustic microscopy (PAM) system for integrated operation. Baseline images of murine ear and abdominal vessels were acquired prior to HIFU exposure. During the subsequent scan, HIFU was applied, while PAM captured the vascular response. As a result, HIFU application induced consistent and localized vasodilation in both the ear and abdominal vessels. PAM visualized the vascular changes, showing vessel diameter increases of approximately 9 to 21%. The integration of HIFU and PAM enables drug-free, localized modulation of vascular tone together with high-resolution in vivo vascular imaging. This integrated platform is intended as a preclinical research tool for studying localized vascular physiology and ultrasound-induced vascular responses without pharmacological intervention.

Hydrogel-based drug delivery systems have been actively explored for precision and personalized medicine, leveraging their localized, sustained, and biocompatible approach for drug administration. However, conventional platforms generally lack real-time monitoring capabilities, as therapeutic evaluation typically relies on invasive or end-point analyses. While stimuli-responsive hydrogels offer controlled release, non-invasive strategies to track in vivo release kinetics is still challenging. We addressed these limitations using an acoustic system coupled with a deep learning framework to analyze ultrasound (US) signal variations from a gelatin-magnesium gallate (GMG) hydrogel, thereby demonstrating the feasibility of non-invasive, time-dependent drug release monitoring. A machine-learning framework quantitatively decodes subtle acoustic variations, enabling non-invasive monitoring with > 95% accuracy. GMG hydrogel was fabricated using highly porous magnesium gallate MOFs (metal-organic frameworks), and the drug (gallic acid) release-mediated structural changes in the hydrogel were observed through US signal variations. Drug release was validated under diverse experimental conditions to ensure reproducibility of the results. Integration of US with AI-based analysis demonstrates the potential for non-invasive, real-time monitoring of drug release from hydrogel-based systems by analyzing the complex pattern of US signals and maintaining hydrogel integrity that enables insight into structural changes during drug release.

Teeth serve as valuable indicators for assessing biological age or determining the age of unidentified individuals due to the distinct age-related changes they undergo. However, previous studies have highlighted limitations in using dental radiographs for age estimation, such as measurement errors due to evaluator expertise, radiation exposure, and the requirement for bulky equipment. In this study, we propose a novel method for dental age estimation that is highly accurate, non-invasive, and utilizes compact equipment. We acquired ultrasound signals from teeth and analyzed the thickness of secondary dentin using machine learning. We achieved classification accuracies of 99% and 92% in grouping tooth samples by pulp-to-dentin ratio using MobileNetV2 on spectrogram images and a one-dimensional convolutional neural network on time-series data, respectively. The natural characteristics of ultrasound allow for the non-invasive acquisition of dental signals. The compact, single-element ultrasound transducers enable the acquisition of signals for machine learning without generating ultrasound images. Thus, this new method, leveraging machine learning analysis of ultrasound signals, offers a simpler and safer age estimation system that provides objective, reliable results without the risk of radiation exposure.

Acoustic tweezers use sound waves to manipulate bioparticles and cells, offering safety benefits due to ultrasound's deep penetration. However, traditional single-beam acoustic tweezers (SBAT) struggle with 3D trapping and spatial resolution due to strong axial scattering radiation forces and limited frequencies. Here, we address these challenges by developing a ring-shaped ultra-high frequency ultrasonic needle transducer (RS-UHF-NT), enabling 3D single-cell trapping, nanoparticle manipulation, and direct targeting. The transducer's ring-shaped design with a central hole minimizes near-field axial scattering radiation forces, allowing 3D trapping, while the ultra-high frequency improves spatial resolution allowing nanoparticle manipulation. Additionally, the needle configuration enhances penetration depth by reducing the contact area, providing a direct approach to the target. Experimental results confirm SBAT's clinical potential for 3D nano-drug manipulation using the RS-UHF-NT.

Metal-based nanocomposites address various challenges in chemotherapy such as biocompatibility, bioavailability, and targeted therapy. Cobalt containing nanocomposites can support multimodal therapy with its inherent catalytic ability to generate ROS, enhanced drug loading, pH responsive drug release, improved biocompatibility, and chemical stability apart from its paramagnetic property. Here, we report fabrication and characterization of nanocomposite (GCC) composed of cobalt oxide nanoparticles (Co3O4) incorporated with reduced graphene oxide (rGO) and chitosan (Cs) as substrates and functionalized with folic acid (FA) for targeted delivery and enhanced anti-cancer activity of doxorubicin (Dox) incorporated on GCC. Site-targeted delivery and apoptosis mediated cell death was observed where GCC functionalized with Dox & FA (GCC/Dox-FA) showed significantly higher cytotoxicity on A549 and MCF-7 cells compared to GCC/Dox. Site targeted delivery of GCC/ Dox was achieved with folic acid, while cytotoxicity was rendered by Dox and partly by GCC with its inherent ROS generating ability. Significant drug loading and pH dependent drug release was executed by the substrates (rGO & Cs) and Co3O4, respectively. GCC demonstrated highest loading of 0.428 mg of Dox/mg of nano-composite and at 0.4 mg/mL of Dox, the highest loading percentage was achieved (95.25 %). Biocompatibility of GCC was established in zebrafish which showed that the nanocomposite neither induced any significant abnormalities nor affected the survival rate after the microinjection. Overall, this work reports the successful combination of classical chemotherapy with Doxorubin and advanced biocompatible nano drug delivery system (GCC) for improved therapeutic management of cancer.

This study presents the design and implementation of a compact data acquisition system for immersive ultrasonic inspection of small-diameter pipelines, targeting applications where conventional systems are impractical due to size constraints. The system integrates the Eclipse Z7 platform with a customized pulser-receiver module and a rotary pipeline inspection gauge (PIG) equipped with a 5 MHz immersion-type ultrasonic transducer. The PIG module is designed to scan pipelines with an 8.18 mm wall thickness and a 200 mm inner diameter. Before deployment, real-time system calibration is performed via a connected computer interface to ensure optimal performance. Once inside the pipeline, the PIG operates autonomously, with ultrasonic data being acquired and stored locally on a Raspberry Pi. Post-inspection, the recorded data is extracted and analyzed on the computer to assess pipeline integrity. The proposed system offers a compact alternative to commercial solutions, particularly in scenarios involving limited access and small-diameter pipelines.

Extrusion-based 3D printing has been rapidly advancing as a key technique for fabricating tissue engineering scaffolds. However, 3D printing complex structures with appropriate mechanical strength and biocompatibility remains a challenge. Suspended 3D printing is an emerging fabrication strategy that enables the creation of tissues or organs by a support medium that provides a stable printing environment without the need for additional support structures. This study presents a novel strategy for fabricating intricate scaffolds using suspended 3D printing of bioinks incorporating dissolved PCL (dPCL) and hydroxyapatite (HA). The optimized dPCL/HA bioink demonstrated up to an 85% reduction of print errors compared to conventional methods, significantly improving 3D printability. Moreover, mechanical assessments revealed a compressive Young's modulus approximately 50 MPa higher in dPCL/HA scaffolds than dPCL scaffolds. Furthermore, dPCL/HA scaffolds outperformed both PCL and dPCL scaffolds in cell proliferation tests. Complex 3D shapes, including helices, saddles, multi-curvature structures, hollow hemispheres, and zygomatic bones, were successfully 3D printed, demonstrating the ability to mimic natural and intricate anatomical structures of the human body. These approaches pave the way for 3D printing patient-specific and structurally robust bone constructs with enhanced mechanical and biological properties.

Recent breakthroughs in mRNA therapeutics have transformed vaccine development, largely powered by lipid nanoparticle (LNP) based delivery systems. However, these systems exhibit a strong hepatic tropism, making them suboptimal for targeting extrahepatic organs such as the brain, lungs, pancreas, heart, and tumor tissues critical to non-vaccine therapeutic applications. This review explores next-generation delivery strategies designed to overcome liver centric distribution. We highlight emerging platforms, including pKa-tuned LNPs, polymeric and peptide-based carriers, exosomes, and biomimetic vesicles, along with physical enhancement techniques such as ultrasound, laser, and MRI-guided systems. Nonetheless, researchers are achieving more precise delivery to deep seated tissues by integrating these technologies with targeted ligands and responsive release mechanisms. Applications in oncology, cardiology, pulmonology, and neurology are discussed with a focus on preclinical and early clinical outcomes. Regulatory considerations, including immunogenicity, biodistribution, and manufacturing scalability, are also reviewed. Ultimately, this article presents a forward-looking perspective on engineering safe, organ specific mRNA delivery platforms beyond the liver, enabling the advancement of precision therapeutics. This review will provide a timely and comprehensive overview of innovative strategies to overcome these challenges, focusing on non-vaccine applications.

Male infertility is a significant societal issue, contributing to 40-50 % of all infertility cases and impacting declining birth rates globally. However, traditional semen analysis methods, including hemocytometry, computer-aided semen analysis (CASA), and microfluidic chips often involve invasive procedures and can lead to variations in sample quality and are time-consuming. Here, we investigate the application of high-frequency, ultrasound and wavelength feature extraction for accurate sperm quantification, eliminating the need for preprocessing. A high-frequency ultrasound-based system provides a non-destructive approach for sperm concentration measurement. Our results demonstrated that the application of a wavelength detection algorithm achieved classification accuracy up to 99 %. Unlike conventional methods that require extensive preprocessing of semen samples, our ultrasound-based approach removes this step entirely. This makes the process more efficient and causes less physical damage to sperm. These findings suggest that ultrasound technology offers an accurate method for sperm concentration measurement, providing a potential non-destructive alternative for reproductive health diagnostics.

Photoacoustic brain imaging (PABI) has emerged as a promising biomedical imaging modality, combining high contrast of optical imaging with deep tissue penetration of ultrasound imaging. This review explores the application of photoacoustic imaging in brain tumor imaging, highlighting the synergy between nanomaterials and state of the art optical techniques to achieve high-resolution imaging of deeper brain tissues. PABI leverages the photoacoustic effect, where absorbed light energy causes thermoelastic expansion, generating ultrasound waves that are detected and converted into images. This technique enables precise diagnosis, therapy monitoring, and enhanced clinical screening, specifically in the management of complex diseases such as breast cancer, lymphatic disorder, and neurological conditions. Despite integration of photoacoustic agents and ultrasound radiation, providing a comprehensive overview of current methodologies, major obstacles in brain tumor treatment, and future directions for improving diagnostic and therapeutic outcomes. The review underscores the significance of PABI as a robust research tool and medical method, with the potential to revolutionize brain disease diagnosis and treatment.

Diabetes mellitus is a chronic disorder characterized by persistent hyperglycemia that damages multiple organs. With global prevalence rising, accurate, convenient, and non-invasive glucose monitoring is urgently needed. However, current diagnostic methods—such as point-sample tests and continuous glucose monitoring (CGM)—remain limited by invasiveness and potential inaccuracy. In this study, we conduct an in vitro feasibility evaluation toward non-invasive glucose monitoring using an 80 MHz high-frequency ultrasound (HFU) transducer paired with a convolutional neural network (CNN). Signals were converted into time-frequency representations and analyzed using CNNs to classify blood samples by glucose concentration. Despite the inherent heterogeneity and noise in whole blood, the system achieved approximately 68% multi-class accuracy across glucose levels. These results indicate that ultrasound-based, AI-driven signal analysis offers a promising alternative for continuous, non-invasive glucose assessment, supporting improved diabetes management in clinical and home settings.

In this review, we focus on advances in drug delivery systems (DDSs) pertaining to modern therapeutics, with a particular emphasis on the role of ultrasound (US)-mediated drug delivery (UMDD). We highlight the need for advanced systems in response to several challenges, such as the diversity of pharmacological agents and individual patient variations, over traditional methodologies. We detail the mechanisms of UMDD (thermal and mechanical), and discuss various material formulations suitable for UMDD. We also discuss new perspectives on the potential of US to innovate drug delivery methodologies and improve patient outcomes to emphasize the importance of development to enhance treatment effectiveness.

Diabetes management requires frequent blood glucose monitoring, yet current methods remain invasive and inconvenient. We present a novel non-invasive approach for classifying blood glucose levels using ultrasound and deep learning. The proposed method employs a single-element ultrasound transducer to capture acoustic signals from flowing whole blood, which are then analyzed by a convolutional neural network (CNN) to determine the glucose concentration category. This approach combines ultrasound's non-invasive blood glucose monitoring capabilities with CNN pattern recognition to achieve high classification accuracy without preprocessing blood samples. In contrast to prior techniques, our approach can analyze unprocessed whole blood in real time. We validated the system on blood samples spanning a wide range of glucose concentrations. Experimental results demonstrate that the CNN can reliably distinguish multiple clinically relevant glycemic ranges directly from the raw ultrasound waveforms. The key advantages of this method are its non-invasive nature, the high accuracy enabled by artificial intelligence (AI)-based signal analysis, and the capability to operate on whole blood directly. This integrated ultrasound & CNN-based glucose classification system promises a convenient, needle-free solution for diabetes monitoring.

This paper highlights technological advancements in non-invasive blood glucose monitoring against the backdrop of increasing global prevalence of diabetes. Traditional monitoring methods, primarily invasive methods face limitations in providing continuous glucose level data, which is essential for effective and timely diagnosis of disease stage and for determining the optimal therapeutic strategy. Recent non-invasive technologies encompass optical, acoustic, electromagnetic, and chemical approaches. These technologies exploit the intrinsic properties of glucose, such as its optical absorption coefficients, to offer promising avenues for less intrusive blood glucose monitoring. Despite these advancements, challenges in achieving high accuracy persist due to interference from substances like water and other blood components. This underlines the need for sophisticated algorithms and sensor designs for accurate glucose estimation. Further research is required to integrate various sensing techniques and advanced data processing to enhance accuracy and user-friendliness. In conclusion, while significant progress has been made, developing a reliable, convenient, and accessible method for non-invasive glucose monitoring is crucial for transforming diabetes management and improving patients' quality of life.