Ran Liu

A 3D-printed flexible TiO ₂ NP-PDMS waveguide patch conforms to irregular psoriatic lesions for precise 308 nm UV phototherapy, ensuring robust therapeutic outcomes while strictly protecting healthy skin.

We present a lateral ventricular brain-computer interface (LV-BCI) that deploys an expandable, flexible electrode into the lateral ventricle through a minimally invasive external ventricular drainage pathway. Inspired by the framework of traditional Chinese lanterns, the electrode expands uniformly within the ventricle and conforms to the ependymal wall. Compared with conventional subdural ECoG electrodes, the LV-BCI shows superior signal stability and immunocompatibility. Resting-state spectral analyses revealed a maximum effective bandwidth comparable to subdural ECoG. In evoked potential tests, the LV-BCI maintained a consistently higher signal-to-noise ratio over 112 days without the decline typically associated with scarring or other immune responses. Immunohistochemistry showed only a transient, early microglial activation after implantation, returning to control levels and remaining stable through 168 days. We further designed an "action-memory T-maze" task and developed a microstate sequence classifier (MSSC) to predict rats' turn decisions. The LV-BCI achieved prediction accuracy up to 98%, significantly outperforming subdural ECoG, indicating enhanced access to decision-related information from deep structures such as the hippocampus. These results establish the lateral ventricle as a viable route for neural signal acquisition. Using a lantern-inspired flexible electrode, we achieve long-term stable recordings and robust memory decision decoding from within the ventricular system, opening new directions for BCI technology and systems neuroscience.

Sperm head morphology has been identified as a characteristic that can be used to predict a male's semen quality. Here, harnessing the close relationship considering sperm head shape to quality and morphology, we propose a joint learning model for sperm head segmentation and morphological category prediction. In the model, the sperm category prediction and the ellipticity, calculated by using the segmented sperm head profile, are used to synthesize the morphology to which the sperm belongs. In traditional clinical testing, fertility experts analyze sperm morphology by 2D images of sperm samples, which cannot represent the whole character of their quality and morphological category. To overcome the problem that single-angle 2D images cannot accurately identify sperm morphology, we use a deep-learning-based tracking and detection system to dynamically acquire sperm images with multiple frames and angles and then use the multi-frame and multi-angle time-series images of sperm to determine sperm morphology based on the multi-task model proposed in this study. Performing better than 3D sperm reconstruction and traditional computer-assisted sperm assessment systems, this approach enables end-to-end analysis of viable spermatozoa, requiring minimal computing power and utilizing equipment already available in most embryology laboratories.

In this study, we introduce a brain-computer interface (BCI) framework incorporating MXene microneedle EEG electrodes, tailored for versatile deployment. The dry electrodes, configured as 1 mm² microneedles, underwent meticulous processing to establish a cohesive integration with the MXene conductive material. The microneedle architecture facilitates epidermal penetration, yielding low contact impedance, enabling the recording of spontaneous EEG and induced brain activity, and ensuring high precision in steady-state visual evoked potential (SSVEP) speller. Simultaneously, the microneedle electrode demonstrates commendable biological compatibility and superior nuclear magnetic resonance compatibility. It exhibits minimal artifact generation and manifests no heating-related adaptations in nuclear magnetic environments. The inherent microneedle electrode structure endows it with robust anti-interference capabilities. In vibrational environments, the SSVEP text input accuracy of the microneedle electrode remains comparable to that of gel electrodes, maintaining consistent impedance and delivering high-fidelity EEG acquisition during real-motion scenarios. The microneedle electrode devised in this study serves as a reliable signal acquisition tool, thereby advancing the development of BCI systems tailored for practical usage scenarios.

Immune responses, and their dysregulation are crucial in various diseases, influencing therapeutic outcomes. However, the underlying molecular mechanisms are still not fully understood. Therefore, continuous monitoring of cytokine concentrations is essential for managing immune‐related conditions. Traditional methods, which rely on frequent blood extracts, are invasive and uncomfortable. Thus, there is an urgent need for non‐invasive alternatives for sequential protein monitoring in serum. Recently, researchers have focused on sampling cytokines directly from human interstitial fluid (ISF), as cytokines diffuse from serum to ISF. Microneedle patches have gained attention for their ability to minimize pain and facilitate frequent sampling. However, collecting large molecular biomarkers like cytokines from ISF remains challenging. In this study, an array of Voltage‐gated Ion Channels Inspired Artificial Micro‐electric Driving Fluid Channels (VGICs‐AFCs) is developed. Inspired by Voltage‐gated Ion Channels (VGICs), and by integrating hollow microneedles with iontophoresis, this device facilitates the selective enrichment of cytokines within the hollow structure. The VGICs‐AFCs are successfully validated in mouse models, demonstrating a strong correlation of cytokine concentrations in ISF and in serum samples for four key cytokines. This method could advance disease mechanism research and therapeutic interventions, and offer a practical way for high‐frequency cytokine monitoring in clinical diagnostics.

In bioanalysis, precisely isolating liquid reactions in distinct systems or at different temporal sequences is vital for ensuring accurate results devoid of crosstalk. However, passive liquid isolation is unattainable through existing microfluidic valves. Here, liquid bridge cutting valves (LBCVs) are introduced to automatically segregate liquids by establishing airlocks, offering an innovative microfluidic structure for liquid distribution. The principle of liquid bridge breakup is studied and applied to the design of LBCVs. Additionally, monolithic chips connecting units with LBCVs in different topologies facilitate sequential sampling and reactions, achieving the detection of sweat glucose and lactate in wearable applications, as well as cortisol ELISA on the chips. As a missing puzzle piece of microfluidic elements in liquid separation, LBCVs can be seamlessly integrated with maturing microfluidic structures, creating a lab‐on‐a‐chip device to enable complex fluid manipulation for individual healthcare monitoring and clinical scenarios.

Tendon and ligament ruptures are prevalent, and severe sports injuries require surgical repair. In clinical practice, monitoring of tissue strain is critical to alert severe postoperative complications such as graft reinjury and loosening. Here, we present a sensor system that integrates a strain sensor and communication coil onto surgical silk sutures, enabling in situ monitoring and wireless readout of tissue strains via surgical implantation. The flexible sensor shows excellent adaptability to soft tissues, providing a strain monitoring range of 0 to 10% with a minimum detection threshold of 0.25% and maintaining stability more than 300,000 stretching cycles. The wireless sensor could be integrated with complex structures in surgical scenarios involving lateral collateral ligament injury and anterior cruciate ligament reconstruction, enabling distinct responses to graft stretching, reinjury, and loosening. Animal experiments demonstrate that the sensor can acquire real-time, clinical-grade strain data while exhibiting high biocompatibility. The sensor system shows considerable potential in evaluating preclinical implant performance and monitoring implant-related surgical complications.

Background: Understanding how artificial intelligence (AI) is employed to predict, diagnose, and perform relevant analyses in Alzheimer’s disease research is a rapidly evolving field. This study integrated and analyzed the relevant literature from the Science Citation Index (SCI) and Social Science Citation Index (SSCI) on the application of AI in Alzheimer’s disease (AD), covering publications from 2004 to 2023.Objective: This study aims to identify the key research hotspots and trends of the application of AI in AD over the past 20 years through a bibliometric analysis.Methods: Using the Web of Science Core Collection database, we conducted a comprehensive visual analysis of literature on AI and AD published between January 1, 2004, and December 31, 2023. The study utilized Excel, Scimago Graphica, VOSviewer, and CiteSpace software to visualize trends in annual publications and the distribution of research by countries, institutions, journals, references, authors, and keywords related to this topic.Results: A total of 2,316 papers were obtained through the research process, with a significant increase in publications observed since 2018, signaling notable growth in this field. The United States, China, and the United Kingdom made notable contributions to this research area. The University of London led in institutional productivity with 80 publications, followed by the University of California System with 74 publications. Regarding total publications, the Journal of Alzheimer’s Disease was the most prolific while Neuroimage ranked as the most cited journal. Shen Dinggang was the top author in both total publications and average citations. Analysis of reference and keyword highlighted research hotspots, including the identification of various stages of AD, early diagnostic screening, risk prediction, and prediction of disease progression. The “task analysis” keyword emerged as a research frontier from 2021 to 2023.Conclusion: Research on AI applications in AD holds significant potential for practical advancements, attracting increasing attention from scholars. Deep learning (DL) techniques have emerged as a key research focus for AD diagnosis. Future research will explore AI methods, particularly task analysis, emphasizing integrating multimodal data and utilizing deep neural networks. These approaches aim to identify emerging risk factors, such as environmental influences on AD onset, predict disease progression with high accuracy, and support the development of prevention strategies. Ultimately, AI-driven innovations will transform AD management from a progressive, incurable state to a more manageable and potentially reversible condition, thereby improving healthcare, rehabilitation, and long-term care solutions.

The view of embodied emotion believes that emotions are the emotions of the body. While emotion-specific patterns of self-reported bodily sensation have been previously reported, the physiological bodily representation across emotions remains to be addressed. The present study aimed to investigate the effectiveness of multi-site bodily electrodermal representations of emotions. A multi-channel electrodermal measurement device was designed to record electrodermal activities from nine body sites (neck, back, chest, bilateral abdomen, bilateral wrist, and bilateral ankle) from thirty-six college students (all male), while they were presented with a series of emotional pictures. Using the integral skin conductance response feature and a random forest classification method, the classification of high and low arousal levels achieved an average classification accuracy of 80.4±8.1%, and the classification of positive, neutral, and negative states reached an average classification accuracy of 76.4±10.2%. The classification models for arousal and valence were found to rely on distinct bodily representations. Meanwhile, the classification results of multi-site measurement were significantly better than single-site results. Our findings for the first time illustrate the bodily electrodermal representations of emotion and suggest the feasibility of affective computing using bodily electrodermal signals.