BME frontiers最新文献

Objective and Impact Statement: Human epidermal growth factor receptor 2 (HER2) is a critical protein in cancer cell growth that signifies the aggressiveness of breast cancer (BC) and helps predict its prognosis. Here, we introduce a deep learning-based approach utilizing pyramid sampling for the automated classification of HER2 status in immunohistochemically (IHC) stained BC tissue images. Introduction: Accurate assessment of IHC-stained tissue slides for HER2 expression levels is essential for both treatment guidance and understanding of cancer mechanisms. Nevertheless, the traditional workflow of manual examination by board-certified pathologists encounters challenges, including inter- and intra-observer inconsistency and extended turnaround times. Methods: Our deep learning-based method analyzes morphological features at various spatial scales, efficiently managing the computational load and facilitating a detailed examination of cellular and larger-scale tissue-level details. Results: This approach addresses the tissue heterogeneity of HER2 expression by providing a comprehensive view, leading to a blind testing classification accuracy of 84.70%, on a dataset of 523 core images from tissue microarrays. Conclusion: This automated system, proving reliable as an adjunct pathology tool, has the potential to enhance diagnostic precision and evaluation speed, and might substantially impact cancer treatment planning.

Objective: The objective of this work is to design and fabricate a novel multifunctional nanocarrier combining thrombus-targeted imaging and ultrasound-mediated drug delivery for the theranostics of thrombotic diseases. Impact Statement: This study develops a new technology that can accurately visualize the thrombus and deliver drugs with controllable properties to diagnose and treat thrombotic diseases. Introduction: Thrombotic diseases are a serious threat to human life and health. The diagnosis and treatment of thrombotic diseases have always been a challenge. In recent years, nanomedicine has brought new ideas and new methods for the theranostics of thrombotic diseases. However, there are also many problems need to be solved, such as biosafety and stability of nanocarriers, early diagnosis, and timely treatment of thrombotic diseases, difficulty in clinical translation. Methods: The S1P@CD-PLGA-rtPA nanobubbles (NBs) were prepared by integrating sulfur hexafluoride (SF₆)-loaded poly (D, L-lactide-co-glycolide) (PLGA) NBs, cyclodextrin (CD), sphingosine-1-phosphate (S1P), and recombinant tissue plasminogen activator (rtPA). Results: S1P@CD-PLGA-rtPA NBs had rapid and excellent thrombosis targeting imaging performance based on the specific interaction of S1P-S1PR1 (sphingosine-1-phosphate receptor 1). Furthermore, S1P@CD-PLGA-rtPA NBs that specifically targeting to the thrombosis regions could also respond to external ultrasound to achieve accurate and efficient delivery of rtPA to enhance the thrombolysis effectiveness and efficiency. Conclusion: This study proposes a new idea and strategy of targeting thrombus in rats via the specific interaction of S1P-S1PR1. On this basis, the acoustic response properties of bubble carriers could be fully utilized by combining thrombus-specific targeted imaging and ultrasound-mediated drug delivery for effective thrombolysis, which is expected to be applied in targeted diagnosis and treatment of thrombotic diseases in the future.

With the recent advances in neoantigen identification, peptide-based cancer vaccines offer substantial potential in the field of immunotherapy. However, rapid clearance, low immunogenicity, and insufficient antigen-presenting cell (APC) uptake limit the efficacy of peptide-based cancer vaccines. This review explores the barriers hindering vaccine efficiency, highlights recent advancements in synthetic delivery systems, and features strategies for the key delivery steps of lymph node (LN) drainage, APC delivery, cross-presentation strategies, and adjuvant incorporation. This paper also discusses the design of preclinical studies evaluating vaccine efficiency, including vaccine administration routes and murine tumor models.

Spatial monoomics has been recognized as a powerful tool for exploring life sciences. Recently, spatial multiomics has advanced considerably, which could contribute to clarifying many biological issues. Spatial monoomics techniques in epigenomics, genomics, transcriptomics, proteomics, and metabolomics can enhance our understanding of biological functions and cellular identities by simultaneously measuring tissue structures and biomolecule levels. Spatial monoomics technology has evolved from monoomics to spatial multiomics. Moreover, the spatial resolution, high-throughput detection capability, capture efficiency, and compatibility with various sample types of omics technology have considerably advanced. Despite the technological advances in this field, data analysis frameworks have stagnated. Current challenges include incomplete spatial monoomics data analysis pipeline, overly complex data analysis tasks, and few established spatial multiomics data analysis strategies. In this review, we systematically summarize recent developments of various spatial monoomics techniques and improvements in related data analysis pipeline. On the basis of the spatial multiomics technology, we propose a data integration strategy with cross-platform, cross-slice, and cross-modality. We summarize the potential applications of spatial monoomics technology, aiming to provide researchers and clinicians with a better understanding of how such applications have advanced. Spatial multiomics technology is expected to substantially impact biology and precision medicine through measurements of cellular tissue structures and the extraction of biomolecular features.

Objective: The objective of this work is to investigate the mapping relationship between transcranial ultrasound image quality and transcranial acoustic metamaterial parameters using inverse design methods. Impact Statement: Our study provides insights into inverse design methods and opens the route to guide the preparation of transcranial acoustic metamaterials. Introduction: The development of acoustic metamaterials has enabled the exploration of cranial ultrasound, and it has been found that the influence of the skull distortion layer on acoustic waves can be effectively eliminated by adjusting the parameters of the acoustic metamaterial. However, the interaction mechanism between transcranial ultrasound images and transcranial acoustic metamaterial parameters is unknown. Methods: In this study, 1,456 transcranial ultrasound image datasets were used to explore the mapping relationship between the quality of transcranial ultrasound images and the parameters of transcranial acoustic metamaterials. Results: The multioutput parameter prediction model of transcranial metamaterials based on deep back-propagation neural network was built, and metamaterial parameters under transcranial image evaluation indices are predicted using the prediction model. Conclusion: This inverse big data design approach paves the way for guiding the preparation of transcranial metamaterials.