BME frontiers最新文献

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.

Objective: The aim of this study was to develop and apply a dual-scale Capillary-Cell (CapCell) microscope to quantify spatial and temporal heterogeneity in tumor metabolism and vasculature during anti-angiogenic therapy. Impact Statement: This study introduces a dual-scale CapCell microscope, a novel imaging system to dynamically visualize metabolic and vascular adaptations in vivo. The platform reveals subregional features associated with treatment that are often missed by bulk analyses. Introduction: Tumor recurrence is often driven by microenvironmental heterogeneity in metabolism and perfusion. Given the importance of metabolic reprogramming in treatment response, the dual-scale CapCell microscope was designed to capture widefield and high-resolution images of metabolic-vascular coupling in vivo. Methods: The dual-scale CapCell microscope was implemented to image multiple endpoints including mitochondrial membrane potential and glucose uptake (widefield and high-resolution images) that are colocalized with vessel density and distance between vessels (high resolution). The CapCell was used to image 4T1 tumors grown in an orthotopic window chamber longitudinally following treatment with Combretastatin A-1 (CA1), a vascular-disrupting agent. Imaging was performed over a period of 8 days to evaluate the effects of CA1 administered on days 1 and 5. Results: Treated tumors showed a significant decrease in metabolism and vessel fraction, and a significant increase in the distance between vessels immediately following the first treatment. Within microregional areas, elevated mitochondrial activity was associated with vascular-dense regions, whereas increased glucose uptake was more apparent in less vascularized regions. Interestingly, the second treatment on day 6 had little effect on the tumor metabolism, and in fact, metabolism at this time point recovered to baseline levels despite a persistent reduction in vessel area fraction and no corresponding recovery in vascular proximity. Conclusion: The CapCell enables dual-scale, multiparametric imaging of tumor microenvironments, capturing spatial metabolic and vascular features often linked to poor therapeutic outcomes. This platform can inform therapeutic timing and guide the development of combination strategies by resolving critical tumor subpopulations.

Objective: This article describes a new method (VS-FPM) for analysis of unstained tissues based on the application of supervised machine learning to generate brightfield hematoxylin and eosin (H&E) images from phase images recovered using Fourier ptychographic microscopy (FPM). Impact Statement: VS-FPM has several advantages for label-free digital pathology. Capture of complex image information simplifies model training and allows post-capture refocusing. FPM images combine high resolution with a large field of view, and the hardware is low-cost and compatible with many existing brightfield microscope systems. Introduction: By generating realistic histologically stained images from label-free image data, virtual staining (VS) methods have the potential to streamline clinical workflows, improve image consistency, and enable new ways of visualizing and analyzing histological tissues. Methods: We trained a conditional generative adversarial network to translate high-resolution FPM images of unstained tissues to brightfield H&E images and assessed the method using diagnosis of colonic polyps as a test case. Results: We found no statistically significant difference between the spatial resolution of FPM images captured at 4× magnification and images from a pathology slide scanner at 20× magnification. Visual assessment and image similarity metrics showed that VS-FPM images of unstained tissues closely resemble images of chemically H&E-stained tissues. However, the spatial resolution of virtual H&E images was approximately 20% lower than equivalent images of chemically stained tissues. Using VS-FPM, board-certified pathologists were able to accurately distinguish normal from dysplastic tissues and derive correct pathological diagnoses. Conclusion: VS-FPM is a reliable, accessible VS method that also overcomes many other limitations inherent to histopathology microscopy.

Deep learning (DL) models have been widely applied for Alzheimer's disease (AD) stage classification. This scoping review synthesizes recent research to evaluate current performance benchmarks, identify methodological limitations, and highlight translational barriers. DL has potential to augment diagnostic accuracy and accelerate early intervention in AD, but translation requires models that generalize across datasets and integrate into real-world clinical workflows. Following scoping review methodology, 18 peer-reviewed studies published between 2018 and 2024 were analyzed. We extracted dataset sources, preprocessing strategies, model architectures, performance metrics, and translational considerations. Most studies employed convolutional neural networks (CNNs) or transfer learning (TL) backbones with accuracies frequently reported above 90%. Comparative synthesis revealed that TL and custom CNNs achieved similar headline accuracies, with differences of less than one percentage point. Reported performance was highly sensitive to task framing (cross-sectional vs. progression) and dataset provenance, with curated subsets often yielding near-ceiling internal accuracies but limited generalizability. Only one study implemented true external validation, underscoring a critical translational gap. Cost-effectiveness was rarely discussed explicitly; however, several studies indicated that open datasets reduce financial barriers, while adapting pipelines for EMR, or multisite data entails substantial resource demands. DL for AD classification shows consistent high accuracy but limited robustness, with external validation and financial cost-effectiveness remaining underreported. Future progress depends on standardized evaluation protocols, explicit reporting of financial costs, and the development of clinically interpretable, workflow-integrated models.

Objective: This study explores the role of methoxy polyethylene glycol@Elabela-11 (mPEG@ELA-11), a pH-responsive ELA-11 conjugate, in modulating macrophage function and attenuating atherosclerosis, focusing on the protein kinase B (AKT)-mediated endoplasmic reticulum (ER) stress pathway as a molecular target. Impact Statement: We reveal that ELA-11 alleviates atherosclerosis by suppressing macrophage foam cell formation, M1 polarization, and apoptosis via the AKT-ER stress pathway. We also develop mPEG@ELA-11, a novel pH-responsive nanocarrier, to enhance targeted drug delivery and therapeutic efficacy, offering a breakthrough for peptide-based cardiovascular nanomedicine. Introduction: Atherosclerosis, driven by macrophage dysfunction and lipid accumulation, is a major global killer. ELA-11, a fragment of Elabela peptide, shows cardiovascular protective effects, but its role in atherosclerosis and optimal delivery remain unstudied. Methods: Elabela mRNA (APELA) expression was analyzed in human carotid atherosclerotic plaques using real-time quantitative PCR analysis, and serum ELA levels were quantified via enzyme-linked immunosorbent assay in patients with carotid stenosis. In vitro studies on RAW264.7 macrophages evaluated mPEG@ELA-11 effects on oxidized low-density lipoprotein-induced foam cell formation, polarization, and apoptosis. In vivo efficacy was tested in ApoE^-/- mice, comparing mPEG@ELA-11 with free ELA-11, and its pH-responsive release mechanism was characterized. Results: APELA was down-regulated in human atherosclerotic plaques, especially unstable lesions. mPEG@ELA-11 suppressed foam cell formation, M1 polarization, and apoptosis by inhibiting the AKT-ER stress pathway in vitro. In mice, it reduced plaque area more effectively than free ELA-11 attributed to pH-triggered release. Conclusion: The pH-responsive mPEG@ELA-11 alleviates atherosclerosis by modulating macrophages via the AKT-ER stress pathway, with favorable targeting and safety, representing a promising targeted peptide nanomedicine for atherosclerosis.

Objective: Metabolic dysfunction-associated steatotic liver disease (MASLD) is a complex, progressive disorder involving multiple cell types, ranging from simple steatosis to metabolic dysfunction-associated steatohepatitis (MASH), characterized by pro-inflammatory macrophage activation, and can eventually advance to fibrosis, initiated by hepatic stellate cells (HSCs). In vitro multi-cell coculture models are vital tools for elucidating the mechanisms underlying MASLD. Impact Statement: Existing in vitro models for MASLD, including traditional 2-dimensional (2D) cultures and advanced organ-on-a-chip and organoid systems, face challenges in representing multiple cell types and analyzing them individually. Here, utilizing a cell carrier developed in our laboratory, we introduce a series of 3D dynamic coculture models that simulate different stages of MASLD progression and enable individual cell type analysis. Introduction: Currently, no single system provides an optimal balance of control, reproducibility, and analytical convenience. Most in vitro models lack the ability to isolate and analyze individual cell types post-culture, making it difficult to study cell-specific responses in MASLD progression. Methods: The 3D hollow porous sphere cell carrier allows cells to grow on its surface, while the culture device (mini-bioreactor) creates a dynamic environment. The 3 distinct MASLD models were established based on cocultured cell types: steatosis (hepatocytes only), MASH (hepatocytes and macrophages in a 4:1 ratio), and fibrosis (hepatocytes, macrophages, and HSCs in an 8:2:1 ratio). Well-established MASLD mouse models were employed to validate our in vitro 3D dynamic MASLD models, using 7-week-old male C57BL/6J mice fed a high-fat diet. Results: Our models demonstrate a progressive decline in hepatocyte viability and increased lipid accumulation, mirroring in vivo pathology. Additionally, gene expression profiles of our models align with those observed in MASLD-affected mouse livers. Notably, comparative analysis highlights the role of pro-inflammatory macrophages in disrupting hepatocyte lipid metabolism. Conclusion: These models offer a robust platform for investigating MASLD mechanisms and show potential for screening anti-MASLD therapeutics.