BME frontiers最新文献

Objective: To develop a high-performance reconstruction framework that enables high-quality photoacoustic tomography (PAT) imaging under limited-view and sparse-view acquisition constraints. Impact Statement: The proposed method reduces the number of required acoustic transducers while maintaining image quality comparable to full-view systems, providing a practical and cost-efficient solution for biomedical PAT imaging. Introduction: PAT offers high-resolution visualization of biological tissues. However, restrictions such as reduced transducer counts or incomplete detection geometries render the inverse problem severely ill-posed, leading to marked degradation in reconstructed images. Although diffusion models have recently shown strong promise for image restoration, existing architectures can be computationally intensive or insufficiently expressive for the complexities of PAT.Methods: We introduce a time-driven transformer-based photoacoustic diffusion model (TT-PADM) that directly restores high-quality images from limited-view and sparse-view PAT reconstructions. TT-PADM uses a time-driven transformer within a time-dependent noise-estimation network, reducing model parameters by over 80% relative to conventional transformer designs while enhancing the generative capacity of the diffusion process. Results: Simulations and experimental results show that TT-PADM delivers high-fidelity reconstructions even under severely limited acquisition conditions, producing image quality comparable to full-view PAT systems. Quantitative and qualitative analyses show that TT-PADM consistently surpasses state-of-the-art reconstruction approaches, providing notable improvements in structural accuracy and noise suppression. Conclusion: TT-PADM offers a robust, parameter-efficient, and highly effective solution for PAT image restoration under practical hardware constraints, with strong potential for deployment in resource-limited biomedical imaging scenarios.

Objective: To enhance vascular-targeted photodynamic therapy (V-PDT) efficacy by integrating real-time dosimetric monitoring and adaptive irradiance modulation based on dynamic physiological feedback. Impact Statement: This study presents a closed-loop, dual-modality optical imaging-guided V-PDT platform that enables individualized, oxygen-informed irradiance control, improving therapeutic precision and efficiency. Introduction: While V-PDT is a promising, minimally invasive treatment for tumors and vascular abnormalities, its efficacy is often hindered by rapid oxygen depletion under high irradiance, leading to treatment-limiting hypoxia. Accurate, real-time assessment of both photosensitizer concentration and blood oxygenation is essential to guide optimized therapeutic strategies, yet such capability has remained elusive in clinical settings. Methods: We developed a dual-modality imaging system integrating hyperspectral imaging (HSI) and optical-resolution photoacoustic microscopy (OR-PAM). HSI provides real-time, quantitative mapping of blood oxygen saturation and photosensitizer concentration, and OR-PAM provides high-resolution structural imaging of vascular networks. A personalized V-PDT protocol was implemented, where light irradiance was dynamically modulated in response to real-time blood oxygen feedback. Results: Real-time imaging confirmed that dynamic irradiance modulation effectively suppressed treatment-induced hypoxia while preserving therapeutic oxygen availability. The personalized-irradiation protocol significantly improved therapeutic efficacy compared with conventional fixed-irradiance protocols under identical photosensitizer dosage conditions. PAM-based structural analysis further showed that vascular damage strongly correlated with oxygen-informed irradiance adjustments. Conclusion: By integrating real-time dosimetry monitoring and feedback-controlled illumination, this study presents a closed-loop V-PDT strategy that overcomes oxygen depletion, enabling precise and efficient therapy tailored to individual tissue responses.

Objective and Impact Statement: We establish deep-ultraviolet (UV) microscopy as a fast, label-free, and simple imaging approach for assessing T cell viability, activation state, and subtype with high accuracy. Introduction: T cell characterization is critical for understanding immune function, monitoring disease progression, and optimizing cell-based therapies. Current technologies to characterize T cells, such as flow cytometry, require fluorescent labeling and are typically destructive endpoint measurements. Nondestructive, label-free imaging methods have been proposed but face limitations with throughput, specificity, and system complexity. Methods: In this work, we use static deep-UV images to characterize T cell viability and activation state and dynamic deep-UV time series to quantify intracellular activity for assessment of T cell subtype (CD4⁺ and CD8⁺). Results: T cell viability and activation state predicted from static deep-UV images showed strong agreement with flow cytometry, with a correlation of R ² > 0.97. Dynamic deep-UV images revealed unique intracellular activity that enabled accurate subtyping of CD4⁺ and CD8⁺ T cells, with a sensitivity and specificity of ~90%, corroborating recent studies on metabolic activity differences between these subtypes. Conclusion: Together, deep-UV microscopy offers a powerful tool for high-throughput immune cell characterization, with broad applications in immunology research, immune monitoring, and development of emerging cell-based therapies.

Objective: This study introduces and validates a digital twin brain framework designed to translate an individual's brain connectome into predictions of multitask neurobehavioral dynamics and personalized functional modulations. Impact Statement: We introduce a novel 2-component architecture-where a hypernetwork personalizes a main network from an individual's connectome-establishing a mechanistic platform to simulate and design personalized interventions by directly linking connectomes to behavior. Introduction: Personalized psychiatry requires digital twin models that can predict functions across multiple domains, such as affective and cognitive processing, from an individual's unique neurobiology. However, existing models struggle to bridge the gap between brain structure and complex, multitask behavior, limiting their clinical utility. Methods: A hypernetwork uses an individual's resting-state connectome to generate parameters for a main recurrent neural network that simulates participant-specific behavioral and blood-oxygen-level-dependent (BOLD) time series across tasks. Leveraging the model's end-to-end architecture linking connectomes to behavior, we used gradient backpropagation to identify connectome manipulations designed to selectively modulate affective or cognitive functions. Results: Validated on 228 individuals, the model predicted behavioral choices with over 90% accuracy, reaction times (r > 0.85), and BOLD patterns (r = 0.84) with high fidelity. Crucially, in silico interventions successfully modulated targeted functions and reproduced realistic, interindividual variability in treatment effects arising from each person's baseline connectome. Conclusion: This digital twin brain system enables high-fidelity, in silico prediction and personalized modulation of complex neurobehavioral functions, advancing the potential for individualized psychiatric care.

Objective: We report the development and validation of a deep learning-based virtual multiplexed immunostaining method for label-free tissue, enabling the simultaneous generation of ERG (ETS-related gene), PanCK (pan-cytokeratin), and hematoxylin and eosin (H&E) images for vascular invasion assessment. Impact Statement: This work delivers routine laboratory-compatible virtual multiplexed immunohistochemistry (mIHC) that reproduces ERG, PanCK, and H&E on the same tissue section without chemical staining. It addresses the cost, labor, tissue loss, and section-to-section variability of conventional IHC, as well as the practical unavailability of mIHC in most pathology laboratories, thereby improving accuracy and efficiency in assessing vascular invasion. Introduction: Traditional IHC requires one tissue section per stain, exhibits section-to-section variability, and incurs high costs and laborious staining procedures. While mIHC techniques enable simultaneous staining with multiple antibodies on a single slide, they are more tedious to perform and are currently unavailable in routine pathology laboratories. Here, we present a deep learning-based virtual multiplexed immunostaining framework that simultaneously generates ERG and PanCK, in addition to H&E virtual staining, enabling the accurate localization and interpretation of vascular invasion in thyroid cancers. Methods: This virtual mIHC technique is based on the autofluorescence microscopy images of label-free tissue sections, and its output images closely match the histochemical staining counterparts (ERG, PanCK, and H&E) of the same tissue sections. Results: Blind evaluation by board-certified pathologists demonstrated that virtual mIHC staining achieved high concordance with the histochemical staining results, accurately highlighting epithelial and endothelial cells. Virtual mIHC conducted on the same tissue section also allowed the identification and localization of small vessel invasion. Conclusion: This virtual mIHC approach can substantially improve diagnostic accuracy and efficiency in the histopathological evaluation of vascular invasion, potentially eliminating the need for traditional staining protocols and mitigating issues related to tissue loss and heterogeneity.

Objective: This study aimed to investigate the protective effects and underlying mechanisms of baicalein against iron overload-induced osteoblast dysfunction and bone loss. Impact Statement: This research is the first to demonstrate that baicalein, a natural flavonoid, functions as a dual-action agent combining iron chelation and antioxidation to prevent iron overload-induced ferroptosis in osteoblasts, offering a novel therapeutic strategy for iron overload-related osteoporosis. Introduction: Iron overload contributes to osteoblast damage and osteoporosis through ferroptosis, an iron-dependent cell death pathway. Current treatments fail to simultaneously address iron accumulation and bone loss, highlighting the need for effective dual-function therapies. Methods: Using iron dextran-treated MC3T3-E1 osteoblasts and a murine iron overload model, we assessed the effects of baicalein on cell viability, osteogenic differentiation, ferroptosis markers, and the nuclear factor erythroid 2-related factor 2 (Nrf2)/glutathione peroxidase 4 (GPX4) pathway via biochemical assays, Western blot, and micro-computed tomography. Genetic and pharmacological inhibition of Nrf2 were applied to validate the mechanism. Results: Baicalein chelated iron, scavenged reactive oxygen species, and suppressed ferroptosis in osteoblasts, restoring differentiation under iron overload. It activated Nrf2 nuclear translocation and upregulated GPX4/solute carrier family 7-member 11 (SLC7A11) expression. In mice, baicalein reduced iron deposition, oxidative stress, and bone loss, and these effects were abolished by Nrf2 inhibition. Conclusion: Baicalein alleviates iron overload-induced osteoblast ferroptosis and osteoporosis by activating the Nrf2/GPX4 pathway, supporting its clinical potential as a therapeutic agent for iron-related bone disorders.

Objective: The aim of this study was to investigate multiomics (MO) integration with stacked-ensemble learning for predicting neoadjuvant chemotherapy (NAC) response and recurrence risk in breast cancer (BC). Impact Statement: This study demonstrates that a stacked-ensemble learning model integrating clinicopathologic and magnetic resonance imaging (MRI)-based intratumoral heterogeneity biomarkers effectively predicts NAC response and postoperative recurrence risk in BC patients. These findings underscore MO and machine learning's potential to optimize clinical decision-making. Introduction: Selecting BC patients who will benefit from NAC remains challenging. Methods: We retrospectively analyzed 124 BC patients receiving NAC (3 to 8 cycles) prior to mastectomy. Two radiomics signatures-RadS_ET and RadS_ITH-were derived from pre-NAC high-resolution dynamic MRI to track entire-tumor and intratumoral heterogeneous characteristics, respectively. These signatures were integrated with clinicopathologic indicators using stacked-ensemble learning algorithms to predict pathological complete response (pCR) and 3-year disease-free survival (DFS). Results: Among the 124 patients, the pCR rate was 26.6%. For pCR prediction, RadS_ITH and RadS_ET yielded areas under the curve (AUCs) of 0.798 and 0.770, respectively. The MO-integrated model, combining RadS_ITH, RadS_ET, clinical N stage, and molecular subtype, achieved a significantly higher AUC (0.917; 95% confidence interval [CI], 0.860 to 0.958; P < 0.05) than individual models. Postoperative recurrence occurred in 13.6% of patients. The elastic-net Cox model achieved a DFS concordance index of 0.78 (95% CI, 0.72 to 0.83) using pre-NAC variables (MO-predicted pCR, Response Evaluation Criteria in Solid Tumors response, RadS_ITH), and 0.81 (95% CI, 0.76 to 0.92) with post-NAC variables (pathologic grade, pCR status, pT stage, and pN stage). Conclusion: The MO integration with stacked-ensemble learning effectively predicts NAC response and recurrence risk in BC.

Objective: This study aims to develop methoxy poly(ethylene glycol) (mPEG) and silver-modified magnetite nanoparticles termed Fe₃O₄@mPEG-Ag NPs as efficient non-antibiotic antibacterial agents to address the growing challenge of drug-resistant bacterial infections. Impact Statement: This work demonstrates a synergistic nanomaterial design that achieves high antibacterial efficacy, stability, and biocompatibility, positioning it as a promising alternative to conventional antibiotics in combating antimicrobial resistance. Introduction: Infectious diseases caused by drug-tolerant bacteria present a serious global health risk. Fe₃O₄@mPEG-Ag NPs were developed as synthetic bactericides that integrate the antibacterial properties of Ag with an excellent stability and dispersibility of mPEG-modified Fe₃O₄. Methods: Fe₃O₄@mPEG-Ag NPs were fabricated via a serial coprecipitation technique. A series of structural and functional characterizations was performed, and antibacterial activity was tested. Additional assessments included minimum inhibitory concentration (MIC) determination, detailed mechanistic evaluation, cytocompatibility assays, and in silico molecular docking studies. Results: Fe₃O₄@mPEG-Ag NPs demonstrate superior antibacterial activity at a MIC as low as 50 μg·ml^-1 and achieved an efficacy similar to ciprofloxacin. The improved bactericidal effect is attributed to strong electrostatic interactions, membrane disruption through enhanced reactive oxygen species generation under visible light, and intracellular damage via NP penetration and controlled Ag⁺ leaching. Surface functionalization improves colloidal stability and bioactivity while simultaneously maintaining >80% cell viability. Molecular docking further supports the experimental findings by confirming the inhibition of Staphylococcus aureus DNA gyrase and Escherichia coli β-lactamase enzymes. Conclusion: Fe₃O₄@mPEG-Ag NPs demonstrate synergistic antibacterial mechanisms with high biocompatibility, highlighting their potential as effective nanotherapeutics for bacterial control, and represent a promising alternative to conventional antibiotics to combat antimicrobial resistance.

Objective: This work aims to develop Prussian blue (PB) nanoparticles that mitigate bone marrow mesenchymal stem cell (BMSC) senescence and alleviate bone loss in type 2 diabetes (T2D). Impact Statement: PB nanozymes are established as a targeted therapeutic strategy for maintaining bone quality in T2D-addressing an unmet clinical need through innovative nanomaterial design. Introduction: Diabetes is associated with a higher risk of fractures through distinct mechanisms. Elevated blood sugar levels and excessive nutrition in T2D trigger reactive oxygen species (ROS) overproduction that impairs mitochondrial function, induces BMSC senescence, and compromises osteogenic potential. Engineered as artificial enzyme counterparts, nanozymes effectively eliminate ROS while circumventing the inherent constraints of natural antioxidant enzymes. Methods: PB nanoparticles were synthesized and fully characterized. BMSCs treated with high glucose plus palmitate-bovine serum albumin served as the diabetic cell model. The nanoparticles were evaluated for their capacity to scavenge ROS, modulate mitochondrial function, counteract cellular senescence, and restore osteogenic potential. Finally, their ability to attenuate bone loss was verified in a T2D mouse model. Results: We demonstrated that PB nanoparticles efficiently scavenge ROS, rebalance mitochondrial dynamics by up-regulating fusion proteins and down-regulating fission proteins, and restore membrane potential. These actions suppress BMSC senescence and revive osteogenic capacity, culminating in substantial attenuation of T2D-associated bone loss in vivo. Conclusion: These findings introduce a promising and innovative approach for managing bone quality in patients with T2D.

Objective: This study proposed a transmissive-detected hyperspectral imaging (TD-HSI) strategy for blood oxygen mapping in order to address the limitation of reflective HSI in obtaining high-resolution blood oxygen information from deep tissues. Impact Statement: This innovative TD-HSI has great potential in promoting noninvasive, high-resolution in vivo blood oxygen monitoring and provides a powerful tool for the study of tissue oxygenation and microcirculation diseases. Introduction: Oxygen saturation (SO₂) served as a critical indicator reflecting physiological states. However, strong scattering of tissue prevents accurate SO₂ mapping with promising resolution, which also limited the depth of reflective HSI. Methods: Monte Carlo simulations were employed to theoretically evaluate the deep-tissue measurement of SO₂ between conventional reflective-detected HSI (RD-HSI) and TD-HSI. Then, in vivo TD-HSI system was used to observe the impact of hypoxia on individual arteries and veins at various locations in mice, and monitor the SO₂ fluctuations during subcutaneous tumor growth over a 1-week period. Results: The simulations showed that TD-HSI remarkably extended the depth of accurate SO₂ detection and boasted approximately 6-fold greater precision in detecting SO₂ variations. In vivo experiments validated the efficacy of TD-HSI, demonstrating its capability to achieve SO₂ mapping in mice skin with single-vessel resolution, a feat not possible with RD-HSI. Conclusion: We conducted a comprehensive evaluation of the capability of TD-HSI strategy for deep-tissue blood oxygen imaging. Our data demonstrated that TD-HSI offered substantial improvements over conventional RD-HSI in noninvasively acquiring blood oxygen information in deep tissue.