Search Results (2,678)

Histotripsy is a novel, noninvasive, non-thermal technology invented in 2004 for the precise destruction of biologic tissue. It offers a powerful alternative to more conventional thermal or surgical interventions. Using short-pulse, low-duty cycle ultrasonic waves, histotripsy creates cavitation bubble clouds that selectively and precisely destroy targeted tissue in a predefined volume while sparing critical structures like bile ducts, ureters, and blood vessels. Such precision is of value when treating tumors near vital structures. The FDA has cleared histotripsy for the treatment of all liver tumors. Major medical centers are currently spearheading clinical trials, and some institutions have already integrated the technology into patient care. Histotripsy is now being studied for a host of other cancers, including primary kidney and pancreatic tumors. Preclinical murine and porcine models have already revealed promising outcomes. One of histotripsy’s primary advantages is its non-thermal mechanical actuation. This feature allows it to circumvent the limitations of heat-based techniques, including the heat sink effect and unpredictable treatment margins near sensitive tissues. In addition to its non-invasive ablative capacities, it is being preliminarily explored for its potential to induce immunomodulation and promote abscopal inhibition of distant, untreated tumors through CD8+ T cell responses. Thus, it may provide a multilayered therapeutic effect in the treatment of cancer. Histotripsy has the potential to improve precision and outcomes across a multitude of specialties, from oncology to cardiovascular medicine. Continued trials are crucial to further expand its applications and validate its long-term efficacy. Due to the speed of recent developments, the goal of this review is to provide a comprehensive and updated overview of histotripsy. It will explore its physics-based mechanisms, differentiating it from similar technologies, discuss its clinical applications, and examine its advantages, limitations, and future. Full article

Background: Clinical diagnostics, food industries, and biotechnological processes typically use an enzyme called alpha-amylase to metabolize carbohydrates. Objective: The aim of this study is to investigate how low-level laser irradiation (LLLI) affects alpha-amylase activity towards determining the usability of LLLI in non-invasive enzymatic modulation. Methods: Enzyme solutions were irradiated at 10, 20, 30, and 40 J/cm² utilizing 589 nm and 532 nm diode-pumped solid-state lasers. The iodine–starch colorimetric method was used to quantify post-irradiation enzymatic activity, with inverse correlations found between absorbance and activity levels. Modulation was determined by the wavelength and dosage. Results: Enzymatic activity significantly improved when utilizing 589 nm irradiation at lower doses, maximizing at 120% at 20 J/cm² (p < 0.01). Neutral or inhibitory effects were revealed when higher doses were applied. Enzymatic activity showed progressive inhibition when 532 nm irradiation was applied, declining to 75% at 40 J/cm² (p < 0.01). Conclusions: These outcomes indicate that conformational flexibility and catalytic efficiency occur when applying lower-energy photons at 589 nm, whilst oxidative stress and impaired enzymatic function are induced by higher-energy photons at 532 nm. This is consistent with the biphasic dose–response characteristic of photobiomodulation. Full article

Accurate diagnosis of gastrointestinal (GI) diseases typically requires invasive procedures or imaging studies that pose the risk of various post-procedural complications or involve radiation exposure. Bowel sounds (BSs), though typically described during a GI-focused physical exam, are highly inaccurate and variable, with low clinical value in diagnosis. Interpretation of the acoustic characteristics of BSs, i.e., using a phonoenterogram (PEG), may aid in diagnosing various GI conditions non-invasively. Use of artificial intelligence (AI) and improvements in computational analysis can enhance the use of PEGs in different GI diseases and lead to a non-invasive, cost-effective diagnostic modality that has not been explored before. The purpose of this work was to develop an automated AI model, You Only Listen Once (YOLO), to detect prominent bowel sounds that can enable real-time analysis for future GI disease detection and diagnosis. A total of 110 2-minute PEGs sampled at 44.1 kHz were recorded using the Eko DUO^® stethoscope from eight healthy volunteers at two locations, namely, left upper quadrant (LUQ) and right lower quadrant (RLQ) after IRB approval. The datasets were annotated by trained physicians, categorizing BSs as prominent or obscure using version 1.7 of Label Studio Software^®. Each BS recording was split up into 375 ms segments with 200 ms overlap for real-time BS detection. Each segment was binned based on whether it contained a prominent BS, resulting in a dataset of 36,149 non-prominent segments and 6435 prominent segments. Our dataset was divided into training, validation, and test sets (60/20/20% split). A 1D-CNN augmented transformer was trained to classify these segments via the input of Mel-frequency cepstral coefficients. The developed AI model achieved area under the receiver operating curve (ROC) of 0.92, accuracy of 86.6%, precision of 86.85%, and recall of 86.08%. This shows that the 1D-CNN augmented transformer with Mel-frequency cepstral coefficients achieved creditable performance metrics, signifying the YOLO model’s capability to classify prominent bowel sounds that can be further analyzed for various GI diseases. This proof-of-concept study in healthy volunteers demonstrates that automated BS detection can pave the way for developing more intuitive and efficient AI-PEG devices that can be trained and utilized to diagnose various GI conditions. To ensure the robustness and generalizability of these findings, further investigations encompassing a broader cohort, inclusive of both healthy and disease states are needed. Full article

The increasing demand for noninvasive inspection (NII) of complex civil infrastructures requires overcoming the limitations of traditional ground-penetrating radar (GPR) systems in addressing diverse and large-scale applications. The solution proposed in this study focuses on an initial design that integrates a low-SWaP (Size, Weight, and Power) ultra-wideband (UWB) impulse radar with realistic electromagnetic modeling for deployment on unmanned aerial vehicles (UAVs). The system incorporates ultra-realistic antenna and propagation models, utilizing Finite Difference Time Domain (FDTD) solvers and multilayered media, to replicate realistic airborne sensing geometries. Verification and calibration are performed by comparing simulation outputs with laboratory measurements using varied material samples and target models. Custom signal processing algorithms are developed to extract meaningful features from complex electromagnetic environments and support anomaly detection. Additionally, machine learning (ML) techniques are trained on synthetic data to automate the identification of structural characteristics. The results demonstrate accurate agreement between simulations and measurements, as well as the potential for deploying this design in flight tests within realistic environments featuring complex electromagnetic interference. Full article

Dexamethasone, widely used as an exogenous glucocorticoid in clinical and animal practice, has recently been recognized as an environmental contaminant of concern. Existing evidence documents its ability to induce persistent dyslipidemia in adult offspring. In this study, plasma cholesterol levels in male rats exposed to dexamethasone prenatally (PDE) were increased. Meanwhile, developmental tracking revealed a reduction in hepatic low-density lipoprotein receptor (LDLR) promoter H3K27 acetylation (H3K27ac) and corresponding transcriptional activity across gestational-to-postnatal stages. Mechanistic investigations established glucocorticoid receptor/histone deacetylase2 (GR/HDAC2) axis-mediated epigenetic programming of LDLR through H3K27ac modulation in PDE offspring, potentiating susceptibility to hypercholesterolemia. Additionally, in peripheral blood mononuclear cells (PBMC) of PDE male adult offspring, LDLR H3K27ac level and expression were also decreased and positively correlated with those in the liver. Clinical studies further substantiated that male newborns prenatally treated with dexamethasone exhibited increased serum cholesterol levels and consistent reductions in LDLR H3K27ac levels and corresponding transcriptional activity in PBMC. This study establishes a complete evidence chain linking PDE with epigenetic programming and cholesterol metabolic dysfunction, proposing PBMC epigenetic biomarkers as a novel non-invasive monitoring tool for assessing the developmental toxicity of chemical exposures during pregnancy. This has significant implications for improving environmental health risk assessment systems. Full article

Purpose: Predicting colorectal cancer liver metastasis (CRLM) is essential for prognostic assessment. This study aims to develop and validate an interpretable multimodal machine learning framework based on multiparametric MRI for predicting CRLM, and to enhance the clinical interpretability of the model through SHapley Additive exPlanations (SHAP) analysis and deep learning visualization. Methods: This multicenter retrospective study included 463 patients with pathologically confirmed colorectal cancer from two institutions, divided into training (n = 256), internal testing (n = 111), and external validation (n = 96) sets. Radiomics features were extracted from manually segmented regions on axial T2-weighted imaging (T2WI) and diffusion-weighted imaging (DWI). Deep learning features were obtained from a pretrained ResNet101 network using the same MRI inputs. A least absolute shrinkage and selection operator (LASSO) logistic regression classifier was developed for clinical, radiomics, deep learning, and combined models. Model performance was evaluated by AUC, sensitivity, specificity, and F1-score. SHAP was used to assess feature contributions, and Grad-CAM was applied to visualize deep feature attention. Results: The combined model integrating features across the three modalities achieved the highest performance across all datasets, with AUCs of 0.889 (training), 0.838 (internal test), and 0.822 (external validation), outperforming single-modality models. Decision curve analysis (DCA) revealed enhanced clinical net benefit from the integrated model, while calibration curves confirmed its good predictive consistency. SHAP analysis revealed that radiomic features related to T2WI texture (e.g., LargeDependenceLowGrayLevelEmphasis) and clinical biomarkers (e.g., CA19-9) were among the most predictive for CRLM. Grad-CAM visualizations confirmed that the deep learning model focused on tumor regions consistent with radiological interpretation. Conclusions: This study presents a robust and interpretable multiparametric MRI-based model for noninvasively predicting liver metastasis in colorectal cancer patients. By integrating handcrafted radiomics and deep learning features, and enhancing transparency through SHAP and Grad-CAM, the model provides both high predictive performance and clinically meaningful explanations. These findings highlight its potential value as a decision-support tool for individualized risk assessment and treatment planning in the management of colorectal cancer. Full article

Purpose: This study aimed to develop a predictive nomogram integrating AI-based BI-RADS lexicons and lesion-to-nipple distance (LND) ultrasound features to differentiate mass-type ductal carcinoma in situ (DCIS) from invasive ductal carcinoma (IDC) visible on ultrasound. Methods: The final study cohort consisted of 170 women with 175 pathologically confirmed malignant breast lesions, including 26 cases of DCIS and 149 cases of IDC. LND and AI-based features from the S-Detect system (BI-RADS lexicons) were analyzed. Rare features were consolidated into broader categories to enhance model stability. Data were split into training (70%) and validation (30%) sets. Logistic regression identified key predictors for an LND nomogram. Model performance was evaluated using receiver operating characteristic (ROC) curves, 1000 bootstrap resamples, and calibration curves to assess discrimination and calibration. Results: Multivariate logistic regression identified smaller lesion size, irregular shape, LND ≤ 3 cm, and non-hypoechoic echogenicity as independent predictors of DCIS. These variables were integrated into the LND nomogram, which demonstrated strong discriminative performance (AUC = 0.851 training; AUC = 0.842 validation). Calibration was excellent, with non-significant Hosmer-Lemeshow tests (p = 0.127 training, p = 0.972 validation) and low mean absolute errors (MAE = 0.016 and 0.034, respectively), supporting the model’s accuracy and reliability. Conclusions: The AI-based comprehensive nomogram demonstrates strong reliability in distinguishing mass-type DCIS from IDC, offering a practical tool to enhance non-invasive breast cancer diagnosis and inform preoperative planning. Full article

Milk lactose concentration has been proposed as a noninvasive indicator of metabolic health in dairy cows, particularly during early lactation when metabolic demands are elevated. This study aimed to investigate the relationship between milk lactose levels and physiological, biochemical, and behavioral parameters in early-lactation Holstein cows. Twenty-eight clinically healthy cows were divided into two groups: Group 1 (milk lactose < 4.70%, n = 14) and Group 2 (milk lactose ≥ 4.70%, n = 14). Both groups were monitored over a 21-day period using the Brolis HerdLine in-line milk analyzer (Brolis Sensor Technology, Vilnius, Lithuania) and SmaXtec intraruminal boluses (SmaXtec Animal Care Technology^®, Graz, Austria). Parameters including milk yield, milk composition (lactose, fat, protein, and fat-to-protein ratio), blood biomarkers, and behavior were recorded. Cows with higher milk lactose concentrations (≥4.70%) produced significantly more milk (+12.76%) and showed increased water intake (+15.44%), as well as elevated levels of urea (+21.63%), alanine aminotransferase (ALT) (+22.96%), glucose (+4.75%), magnesium (+8.25%), and iron (+13.41%) compared to cows with lower lactose concentrations (<4.70%). A moderate positive correlation was found between milk lactose and urea levels (r = 0.429, p < 0.01), and low but significant correlations were observed with other indicators. These findings support the use of milk lactose concentration as a practical biomarker for assessing metabolic and physiological status in dairy cows, and highlight the value of integrating real-time monitoring technologies in precision livestock management. Full article

Background: Community-acquired pneumonia (CAP) is the leading cause of mortality in children from middle- to low-income countries; diagnosing CAP includes clinical evaluation, laboratory testing and pulmonary imaging. Lung ultrasound (LUS) is a sensitive, accessible, non-invasive, non-radiant method for accurately evaluating the lung involvement in acute diseases. Whether LUS findings can be correlated with CAP’s severity or sepsis risk remains debatable. This study aimed to increase the importance of LUS in diagnosing and monitoring CAP. We analyzed 102 children aged 1 month up to 18 years, hospital admitted with CAP. Mean age was 5.71 ± 4.85 years. Underweight was encountered in 44.11% of children, especially below 5 years, while overweight was encountered in 11.36% of older children and adolescents. Patients with CAP presented with fever (79.41%), cough (97.05%), tachypnea (18.62%), respiratory failure symptoms (20.58%), chest pain (12.74%) or poor feeding. Despite the fact that 21.56% had clinically occult CAP and six patients (5.88%) experienced radiologically occult pneumonia, CAP diagnosis was established based on anomalies detected using LUS. Conclusions: Detailed clinical examination with abnormal/modified breath sounds and/or tachypnea is suggestive of acute pneumonia. LUS is a sensitive diagnostic tool. A future perspective of including LUS in the diagnosis algorithm of CAP should be taken into consideration. Full article

Background/Objectives: Mammary carcinoma is a common disease in female dogs. Cannabidiol (CBD) can inhibit cell proliferation and induce apoptosis in human cancer cells. However, its low solubility in aqueous media requires solvents such as ethanol or dimethylsulfoxide that limit their dosage. Incorporating CBD into oil-in-water nanoemulsions (Nem) can improve its aqueous dispersibility. This study aimed to develop a CBD-Nem formulation and evaluate its effects on canine mammary cancer cell lines (CF41.Mg and IPC366) and non-cancer cells (MDCK). Methods: CBD-Nem was prepared with Miglyol 812 oil and Epikuron 145 V as the surfactant, and was characterized by analyzing size, morphology, zeta potential, release profile, and uptake/internalization. Moreover, the antitumor effects of CBD-Nem were evaluated in cancer cells through viability, proliferation, cell cycle, and migration–invasion assays. Results: CBD-Nem exhibited a monodisperse nanometric population (~150 nm), spherical shape, and negative zeta potential (~−50 mV). The in vitro release kinetics showed slow and sustained delivery at both pH 5.5 and pH 7.4. Rhodamine-Nem, as a fluorescent model of CBD-Nem, was taken up and homogenously internalized in CF41.Mg cells. CBD-Nem decreased the viability of cancer cells with a maximum effect at 50 µM and showed a lower toxicity in MDCK cells. Long-term efficacy (20 days) was evidenced by CBD-Nem at inhibiting colony formation in cancer cells. Furthermore, CBD-Nem reduced the proportion of cells in the G2-M phase, induced apoptosis, and inhibited the migration and invasion of CF41.Mg cells. Conclusions: CBD-Nem exhibited an in vitro antitumor effect, which supports its study in dogs with mammary carcinoma. Full article

Non-thermal plasma technology is used in the food sector due to its many advantages such as low operating costs, fast and efficient processing at low temperatures, minimal environmental impact, and preservation of sensory and nutritional properties. In this article, the plasma was generated using a high-voltage electrical discharge (HVED) with argon at a voltage of 35 kV and a frequency of 60 Hz. Plasma monitoring and diagnostics were performed using optical emission spectroscopy (OES) to optimise the process parameters and for quality control. OES was used as a non-invasive sensor to collect useful information about the properties of the plasma and to identify excited species. The values obtained for electron temperature and electron density (up to $2.3 \mathrm{eV}$ and up to ${10}^{23} {\mathrm{m}}^{-3}$) confirmed that the generated plasma is a non-thermal plasma. Therefore, the use of OES is recommended in the daily control of food processing, as this is necessary to confirm that the processes are non-thermal and suitable for the food sector. Full article

Although intracranial hypertension (ICH) has traditionally been framed as simply a numerical escalation of intracranial pressure (ICP) and usually dealt with in its clinical form and not in terms of its complex underlying pathophysiology, an emerging body of evidence indicates that ICH is not simply an elevated ICP process but a complex process of molecular dysregulation, glymphatic dysfunction, and neurovascular insufficiency. Our aim in this paper is to provide a complete synthesis of all the new thinking that is occurring in this space, primarily on the intersection of glymphatic dysfunction and cerebral vein physiology. The aspiration is to review how glymphatic dysfunction, largely secondary to aquaporin-4 (AQP4) dysfunction, can lead to delayed cerebrospinal fluid (CSF) clearance and thus the accumulation of extravascular fluid resulting in elevated ICP. A range of other factors such as oxidative stress, endothelin-1, and neuroinflammation seem to significantly impair cerebral autoregulation, making ICH challenging to manage. Combining recent studies, we intend to provide a revised conceptualization of ICH that recognizes the nuance and complexity of ICH that is understated by previous models. We wish to also address novel diagnostics aimed at better capturing the dynamic nature of ICH. Recent advances in non-invasive imaging (i.e., 4D flow MRI and dynamic contrast-enhanced MRI; DCE-MRI) allow for better visualization of dynamic changes to the glymphatic and cerebral blood flow (CBF) system. Finally, wearable ICP monitors and AI-assisted diagnostics will create opportunities for these continuous and real-time assessments, especially in limited resource settings. Our goal is to provide examples of opportunities that exist that might augment early recognition and improve personalized care while ensuring we realize practical challenges and limitations. We also consider what may be therapeutically possible now and in the future. Therapeutic opportunities discussed include CRISPR-based gene editing aimed at restoring AQP4 function, nano-robotics aimed at drug targeting, and bioelectronic devices purposed for ICP modulation. Certainly, these proposals are innovative in nature but will require ethically responsible confirmation of long-term safety and availability, particularly to low- and middle-income countries (LMICs), where the burdens of secondary ICH remain preeminent. Throughout the review, we will be restrained to a balanced pursuit of innovative ideas and ethical considerations to attain global health equity. It is not our intent to provide unequivocal answers, but instead to encourage informed discussions at the intersections of research, clinical practice, and the public health field. We hope this review may stimulate further discussion about ICH and highlight research opportunities to conduct translational research in modern neuroscience with real, approachable, and patient-centered care. Full article

To address the limitations of low detection efficiency and poor spatial resolution of traditional cable insulation diagnosis methods, a novel cable insulation diagnosis method based on impedance spectroscopy has been proposed. An impedance spectroscopy analysis model of the frequency response of high-voltage single-core cables under different aging conditions has been established. The initial classification of insulation condition is achieved based on the impedance phase deviation between the test cable and the reference cable. Under localized aging conditions, the impedance phase spectroscopy is more than twice as sensitive to dielectric changes as the amplitude spectroscopy. Leveraging this advantage, a multi-parameter diagnostic framework is developed that integrates key spectral features such as the first phase angle zero-crossing frequency, initial phase, and resonance peak amplitude. The proposed method enables quantitative estimation of aging severity, spatial extent, and location. This technique offers a non-invasive, high-resolution solution for advanced cable health diagnostics and provides a foundation for practical deployment of power system asset management. Full article

Exosomes are nanoscale extracellular vesicles (EVs) that carry biomolecular signatures reflective of their parent cells, making them powerful tools for non-invasive diagnostics and therapeutic monitoring. Despite their potential, clinical application is hindered by challenges such as low abundance, heterogeneity, and the complexity of biological samples. To address these limitations, plasmonic biosensing technologies—particularly propagating surface plasmon resonance (PSPR), localized surface plasmon resonance (LSPR), and surface-enhanced Raman scattering (SERS)—have been developed to enable label-free, highly sensitive, and multiplexed detection at the single-vesicle level. This review outlines recent advancements in nanoplasmonic platforms for exosome detection and profiling, emphasizing innovations in nanostructure engineering, microfluidic integration, and signal enhancement. Representative applications in oncology, neurology, and immunology are discussed, along with the increasingly critical role of artificial intelligence (AI) in spectral interpretation and diagnostic classification. Key technical and translational challenges—such as assay standardization, substrate reproducibility, and clinical validation—are also addressed. Overall, this review highlights the synergy between exosome biology and plasmonic nanotechnology, offering a path toward real-time, precision diagnostics via sub-femtomolar detection of exosomal miRNAs through next-generation biosensing strategies. Full article

Background/Objectives: Magnetic resonance imaging (MRI) is a powerful, non-invasive diagnostic tool capable of capturing detailed anatomical and physiological information. MRI contrast agents enhance image contrast but, especially linear gadolinium-based compounds, have been associated with safety concerns. This has prompted interest in alternative contrast agents. Manganese-based contrast agents offer a promising substitute, owing to manganese’s favorable magnetic properties, natural biological role, and strong T1 relaxivity. This review aims to critically assess the structure, mechanisms, applications, and challenges of manganese-based contrast agents in MRI. Methods: This review synthesizes findings from preclinical and clinical studies involving various types of manganese-based contrast agents, including small-molecule chelates, nanoparticles, theranostic platforms, responsive agents, and controlled-release systems. Special attention is given to pharmacokinetics, biodistribution, and safety evaluations. Results: Mn-based agents demonstrate promising imaging capabilities, with some achieving relaxivity values comparable to gadolinium compounds. Targeted uptake mechanisms, such as hepatocyte-specific transport via organic anion-transporting polypeptides, allow for enhanced tissue contrast. However, concerns remain regarding the in vivo release of free Mn²⁺ ions, which could lead to toxicity. Preliminary toxicity assessments report low cytotoxicity, but further comprehensive long-term safety studies should be carried out. Conclusions: Manganese-based contrast agents present a potential alternative to gadolinium-based MRI agents pending further validation. Despite promising imaging performance and biocompatibility, further investigation into stability and safety is essential. Additional research is needed to facilitate the clinical translation of these agents. Full article