BME frontiers最新文献

Progressive neurodegenerative disease known as Parkinson's disease (PD) is characterized by both motor and nonmotor symptoms that severely reduce the quality of life. Recent developments in soft robotics provide customizable, cozy, and less intrusive assistive devices, which provide promising answers to these problems. To develop an enhanced support system for people with PD, this article explores the potential of next-generation soft robotics, specifically hydrogel materials, integrated with artificial intelligence (AI) and augmented reality (AR) to provide an innovative solution for PD management. The integration of an AI copilot allows for remote monitoring and real-time adjustments, ensuring optimal performance and personalized care. The use of AR enhances human-computer interactions, offering an intuitive and immersive experience for both patients and healthcare providers. By leveraging these advanced technologies, our approach aims to substantially improve motor function, reduce symptoms, and enhance the overall quality of life for PD patients. This review outlines the key components, benefits, and potential impact of this novel approach, highlighting the transformative potential of combining wearable robotics, AI, and AR in the treatment of PD. The potential for creating novel healthcare solutions by combining soft robotics, functional materials, the Internet-of-Things (IoT), and machine learning (ML) is highlighted by this multidisciplinary approach.

Objective: Accurate and rapid detection of disease biomarkers is critical for early diagnosis, timely intervention, and effective disease management. Impact Statement: This strategy is exemplified through the development of 2 biosensors for detecting HIV-1 DNA and HIV-1 p24, biomarkers associated with acquired immunodeficiency syndrome (AIDS). Introduction: We propose a general biosensing strategy that leverages a treble signal amplification cascade, demonstrating its versatility and applicability across diverse biomolecular targets. Methods: The method integrates multiple amplification mechanisms to achieve unparalleled sensitivity. Initially, the hybridization of 2 aided probes with the target triggers isothermal amplification facilitated by polymerase and nicking enzyme, providing a robust preliminary signal enhancement. Repeated cycles of primer extension, nicking, and signal primer dissociation then generate multiple signal primers. These primers are further amplified via rolling circle amplification (RCA), resulting in an important secondary signal boost. Finally, the amplified products activate CRISPR-Cas12a-mediated trans-cleavage, achieving a tertiary level of signal enhancement. Results: This cascade amplification approach achieves remarkable sensitivity, with detection limits of 62 aM for nucleic acids and 8.48 pg/ml for proteins, positioning it as a broadly applicable framework. Clinical samples were assayed, which indicates its capability in clinical diagnosis. Conclusion: Beyond HIV detection, the modular design of this strategy allows adaptation for various biomarkers, showcasing its potential as a universal platform for molecular diagnostics in healthcare and research.

One of the major therapeutic challenges for hepatocellular carcinoma (HCC), the most form of primary liver cancer, is how to overcome drug resistance. Due to the high failure rate of systemic therapy in treating advanced HCC patients and the increasing recurrence rate, HCC is a highly lethal malignancy. Primary and acquired drug resistance are major contributing factors to the patients with advanced HCC who do not respond effectively to long-term systemic therapy. Therefore, it is essential to look into the molecular processes that lead to drug resistance. Different studies have indicated that epithelial-to-mesenchymal transition (EMT) plays a critical part in the emergence of drug resistance. Several signaling pathways regulate this phenomenon. This review primarily concentrates on drug resistance triggered by EMT, especially in the context of HCC. The key signaling pathways that cause drug resistance in HCC, including transforming growth factor-β and epidermal growth factor receptor signaling, liver cancer stem cells, and noncoding RNAs, are highlighted in the present study, along with the most recent molecular targets discovered to prevent drug resistance. These targets could help develop novel and combinatory HCC therapy approaches. Therefore, this review aims to provide both the latest findings on molecular basis and potential solutions for HCC drug resistance.

Objective: This study presents a mathematical model and finite element simulations to investigate interstitial fluid flow and nanodrug transport in a solid tumor, incorporating transvascular exchange, convection-diffusion-reaction dynamics, and intratumoral injection mechanisms. Impact Statement: Optimizing nanodrug distribution remains a critical challenge in cancer therapy. The proposed model advances nanomedicine by enhancing the mechanistic understanding of nanodrug transport in a solid tumor. Introduction: Cancer, a global threat, often manifests as solid tumors driven by uncontrolled cell growth. The heterogeneous microenvironment, lymphatic drainage, nano-bio interactions, and elevated interstitial fluid pressure (IFP) hinder effective nanodrug delivery. Nanoparticle (NP)-based drug delivery systems offer a promising solution, with FES providing an effective approach to model and simulate the complex delivery process. Methods: The model considered a spherical and symmetrical tumor architecture comprising a central necrosis region, viable tumor, and surrounding healthy tissue with functional lymphatic dynamics. Substantial nanodrug carriers (dextran, liposomal, polyethylene glycol (PEG)-coated gold, and magnetic) and conventional doxorubicin are evaluated in the tumor. The governing fluid flow and solute transport equation along with the specified boundary conditions are solved using the finite element method through the Galerkin approach. Results: Simulations show that IFP peaks in the necrotic core and sharply declines at the viable-healthy tissue interface. Both fluid pressure and velocity are sensitive when fluid flow resistance drops below 5. Necrotic core size influences IFP, and critical necrotic radius (R _CN) marks pressure stabilization and defines the threshold for effective nanodrug delivery. Vascular normalization and functional lymphatic dynamics show marginal impact. Smaller NPs (~10 nm) diffuse faster but undergo rapid degradation, while larger particles (>30 nm) exhibit prolonged retention at the injection site. Liposomal, PEG-coated gold, and magnetic variants demonstrate superior therapeutic action compared to conventional doxorubicin. Conclusion: The findings of the study highlight its strong potential for optimizing nanodrug delivery and design, as well as hyperthermia treatment, enhancing personalized cancer therapy.

Upconversion nanoparticles (UCNPs) are emerging as highly promising nanomaterials due to their exceptional optical properties, enabling diverse applications in biosensing, bioimaging, photodynamic therapy, and drug delivery. However, their potential toxicity should be comprehensively investigated for the safe utilization of UCNPs in several biomedical and environmental applications. This review systematically evaluates the current knowledge on UCNP toxicity from 2008 to 2024, focusing on key toxicological pathways, such as oxidative stress, reactive oxygen species (ROS) production, inflammatory responses, and apoptosis/necrosis, alongside their absorption, distribution, metabolism, and excretion processes and kinetics. Distinctively, this review introduces a bibliometric analysis of UCNP toxicity and biodistribution research, providing a quantitative assessment of publication trends, influential authors, leading institutions, funding agencies, and keyword occurrences. This approach offers a macroscopic perspective on the evolution and current landscape of UCNP safety research, a dimension largely unexplored in existing literature. Furthermore, the review combines mechanistic insights into UCNP toxicity with a critical evaluation of surface modifications, physicochemical properties, and administration routes, presenting a holistic framework for understanding UCNP biosafety. By combining bibliometric data with mechanistic insights, this review provides a data-driven perspective on UCNP-associated risks, actionable strategies for enhancing biosafety through surface engineering, and a forward-looking discussion on regulatory challenges and future directions for UCNP-based technologies. These findings bridge existing gaps in the literature and offer a comprehensive resource for researchers, clinicians, and policymakers, facilitating the safe development and utilization of UCNP-based technologies while establishing robust safety guidelines to mitigate adverse effects on human health and the environment.

Globally, increased illness and disorders have gained importance in improvising therapeutics to help extend the lifespan of an individual. In this scenario, understanding the mechanism of bacterial pathogenicity linked to the interaction between the host and the pathogen focusing on essential metal ions is necessary. Numerous studies indicate that the severity of a disease might be due to the reduced availability of iron, linked to abnormal production or lack of acquisition systems. However, several microbes produce siderophores as virulence factors, low-molecular-weight organic compounds for acquisition of iron by iron-chelating systems. In medical applications, siderophores are employed in novel strategies in order to design effective new drugs and vaccines, targeting and delivering antibiotics to target sites in multidrug-resistant pathogens. Meanwhile, some types of siderophores are used as drug delivery modalities and antimalarial, anticancer, and antibacterial agents, for example, by employing conjugation techniques such as Trojan horse delivery. Hence, the current review integrates several applications of siderophores with an overview covering taxonomy, organisms producing iron affinity carriers, and their acquisition mechanism. This understanding may delineate newer opportunities to adapt possible therapies and/or treatments against several multidrug-resistant pathogens, representing a crucial solution for public health problems worldwide.

Objective: This is an initial study to validate central venous pressure (CVP) measurements derived from quantitative compression ultrasound (QCU). Impact Statement: This study is the first gold standard invasive validation of CVP estimation from QCU. Introduction: QCU finds the collapse force-the force required for complete occlusion-of the short axis of the internal jugular vein (IJV) to estimate CVP. Methods: We captured QCU data as well as the noninvasive clinical standard jugular venous pulsation height (JVP) on cardiac intensive care unit (CICU) patients at Massachusetts General Hospital (MGH). We compared these data to ground truth invasive CVP data from the MGH CICU. Results: Using linear regression, we correlated invasive CVP with collapse force (r ²: 0.82, error: 1.08 mmHg) and with JVP (r ²: 0.45, error: 1.39 mmHg). To directly compare our method to JVP, we measured the percentage of patients whose uncertainty estimates for QCU methods and for JVP overlapped with their invasive CVP counterparts. We found that the CVP overlap accuracy of collapse force (77.8%) and of collapse force and hydrostatic offset (88.9%) are higher than that of JVP (12.5%). Finally, we input QCU image segmentation data of the short-axis cross-sections of the IJV and carotid artery into an inverse finite element model to predict the invasive CVP waveform. Conclusion: These results validate the noninvasive technique for estimating CVP, namely, QCU, indicating that it may provide a desirable, middle-ground alternative to invasive catheterization and to visual inspection of the JVP.

Human papillomavirus (HPV) is the most common virus for genital tract infections. Cervical cancer ranks as the fourth most prevalent cancer globally, with over 99% of cases in women attributed to HPV infection. This infection continues to pose an ongoing threat to public health. Therefore, the development of rapid, high-throughput, and sensitive HPV detection platforms is important, especially in regions with limited access to advanced medical resources. CRISPR-based biosensors, a promising new method for nucleic acid detection, are now rapidly and widely used in basic and applied research and have received much attention in recent years for HPV diagnosis and treatment. In this review, we discuss the mechanisms and functions of the CRISPR-Cas system, focusing on its applications in HPV diagnostics. The review covers CRISPR technologies such as CRISPR-Cas9, CRISPR-Cas12, and CRISPR-Cas13, along with nucleic acid amplification methods, CRISPR-based signal output systems, and point-of-care testing (POCT) strategies. This comprehensive overview highlights the versatility and potential of CRISPR technologies in HPV detection. We also discuss the numerous CRISPR biosensors developed since the introduction of CRISPR to detect HPV. Finally, we discuss some of the challenges faced in HPV detection by the CRISPR-Cas system.

Objective: We developed 3-dimensional spatially resolved gene neighborhood network embedding (3D-spaGNN-E) to find subcellular gene proximity relationships and identify key subcellular motifs in cell-cell communication (CCC). Impact Statement: The pipeline combines 3D imaging-based spatial transcriptomics and graph-based deep learning to identify subcellular motifs. Introduction: Advancements in imaging and experimental technology allow the study of 3D spatially resolved transcriptomics and capture better spatial context than approximating the samples as 2D. However, the third spatial dimension increases the data complexity and requires new analyses. Methods: 3D-spaGNN-E detects single transcripts in 3D cell culture samples and identifies subcellular gene proximity relationships. Then, a graph autoencoder projects the gene proximity relationships into a latent space. We then applied explainability analysis to identify subcellular CCC motifs. Results: We first applied the pipeline to mesenchymal stem cells (MSCs) cultured in hydrogel. After clustering the cells based on the RNA count, we identified cells belonging to the same cluster as homotypic and those belonging to different clusters as heterotypic. We identified changes in local gene proximity near the border between homotypic and heterotypic cells. When applying the pipeline to the MSC-peripheral blood mononuclear cell (PBMC) coculture system, we identified CD4⁺ and CD8⁺ T cells. Local gene proximity and autoencoder embedding changes can distinguish strong and weak suppression of different immune cells. Lastly, we compared astrocyte-neuron CCC in mouse hypothalamus and cortex by analyzing 3D multiplexed-error-robust fluorescence in situ hybridization (MERFISH) data and identified regional gene proximity differences. Conclusion: 3D-spaGNN-E distinguished distinct CCCs in cell culture and tissue by examining subcellular motifs.

Objective: The objective of this work is to develop a framework based on large language models (LLMs) to predict postoperative acute kidney injury (AKI) outcomes in elderly patients. Impact Statement: Our study demonstrates that LLMs have the potential to address the issues of poor generalization and weak interpretability commonly encountered in disease prediction using traditional machine learning (ML) models. Introduction: AKI is a severe postoperative complication, especially in elderly patients with declining renal function. Current AKI prediction models rely on ML, but their lack of interpretability and generalizability limits clinical use. LLMs, with extensive pretraining and text generation capabilities, offer a new solution. Methods: We applied prompt engineering and knowledge distillation based on instruction fine-tuning to optimize LLMs for AKI prediction. The framework was tested on 2,649 samples from 2 private Chinese hospitals and one public South Korean dataset, which were divided into internal and external datasets. Results: The LLM framework showed robust external performance, with accuracy rates: commercial LLMs (internal: 63.73%, external: 68.73%), open-source LLMs (internal: 63.70%, external: 64.24%), and ML models (internal: 63.93%, external: 58.27%). LLMs also provided human-readable explanations for better clinical understanding. Conclusion: The proposed framework showcases the potential of LLMs to enhance generalization and interpretability in postoperative AKI prediction, paving the way for more robust and transparent predictive solutions in clinical settings.