Wearable ultrasound presents a promising technological solution for continuous, noninvasive monitoring of deep-tissue structures and physiological functions, addressing the key limitation of traditional wearable sensors that are largely confined to superficial signal detection. In recent years, wearable ultrasound transducers have advanced from early flexible and stretchable array designs to high-performance platforms that combine rigid functional components with flexible architectures. This hybrid design enables more stable coupling with the body surface and supports multimodal imaging capabilities. These developments highlight the strong potential of wearable ultrasound for long-term physiological monitoring and personalized medicine. However, several critical challenges remain before fully continuous and practical applications can be realized. This perspective reviews recent progress in wearable ultrasound technology and discusses future directions, such as the development of compact wireless systems, the incorporation of artificial intelligence, the expansion to 2-dimensional array configurations, and the creation of multimodal functional platforms.

Objective: This study aims to engineer a tigecycline–hemoglobin nanoparticle (TIG–HBNP) system to enhance the targeting effect of tigecycline (TIG) on Klebsiella pneumoniae, thereby improving its antibacterial efficacy. Impact Statement: This study represents the first report of loading TIG onto hemoglobin (HB) nanoparticles. This system utilizes the iron dependence of K. pneumoniae to enhance the targeting ability of TIG and increase the drug concentration at the infection site, thereby enhancing the antibacterial activity and providing a promising strategy for combating drug-resistant gram-negative bacterial pneumonia. Introduction:K. pneumoniae is a gram-negative bacillus that causes severe primary pneumonia with high pathogenicity and increasing drug resistance. TIG is a key therapeutic option, but its clinical effectiveness is limited by extensive systemic distribution and insufficient drug concentration at infectious foci. HB serves as a promising protein nanocarrier and provides iron, an essential nutrient for K. pneumoniae growth, suggesting great potential for targeted antibiotic delivery. Methods: Molecular docking was performed to analyze the binding affinity and interaction between HB and TIG. TIG–HBNP was fabricated via a drug–protein self-assembly approach. The nanoparticles were characterized in terms of particle size, zeta potential, drug-loading efficiency, morphology using transmission electron microscopy and atomic force microscopy, stability, and biocompatibility. Targeting capability was evaluated. Pharmacokinetic profiles and antibacterial activity were further assessed. Results: Molecular docking verified stable binding between TIG and HB. TIG–HBNP exhibited a uniform spherical morphology, a particle size of approximately 200 nm, a negative surface charge, and a drug-loading efficiency exceeding 20%. The nanoparticles showed favorable stability and safety. Enhanced targeting to K. pneumoniae was confirmed in both in vitro and in vivo models. Improved pharmacokinetic behavior and enhanced antibacterial activity against K. pneumoniae were also observed. Conclusion: TIG–HBNP enhances TIG’s therapeutic efficacy against K. pneumoniae infections. Furthermore, HB holds promise as a versatile carrier for diverse antibiotics, offering a scalable platform to combat multidrug-resistant pathogens.

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: 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—RadSET and RadSITH—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, RadSITH and RadSET yielded areas under the curve (AUCs) of 0.798 and 0.770, respectively. The MO-integrated model, combining RadSITH, RadSET, 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, RadSITH), 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 and Impact Statement: This study examines prefrontal cortex (PFC) hemodynamic responses in children with idiopathic central precocious puberty (ICPP) versus normals and constructs a noninvasive diagnostic model using functional near-infrared spectroscopy (fNIRS) augmented by machine learning. Introduction: Current ICPP diagnosis relies on invasive and time-consuming gonadotropin-releasing hormone stimulation tests. While fNIRS offers a noninvasive alternative, the neural mechanisms underlying ICPP remain unclear, and reliable automated diagnostic tools distinguishing patients from healthy peers are lacking. Methods: fNIRS data were acquired from 167 participants (82 ICPP and 85 normal) during a mental arithmetic (MA) task. General linear models and statistical tests were employed to analyze group and gender-specific activation patterns. Multidimensional features were extracted from hemodynamic signals, and a conditional denoising diffusion probabilistic model (C-DDPM) was introduced for data augmentation. Results: Analysis revealed gender-specific disparities, with the normal group exhibiting more extensive PFC activation than the ICPP group. In classification, a decision tree model using features from key negatively correlated channels achieved 86.57% accuracy. Notably, integrating C-DDPM-generated synthetic data further improved classifier performance metrics. Conclusion: The study elucidates the mechanisms of PFC activation in both normative and ICPP-affected cohorts during MA tasks and validates the effectiveness of machine learning in distinguishing between normal and ICPP children. This study provides a scientific basis for the development of automated, noninvasive rapid diagnostic tools for ICPP.

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: 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.

[This corrects the article DOI: 10.34133/bmef.0214.].

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.