Abstract:Spatial deposition and patterning of microparticles are crucial in chemistry, medicine, and biology. Existing technologies like electric force manipulation, despite precise trajectory control, struggle with complex and personalized patterns. Key challenges include adjusting the quantity of particles deposited in different areas and accurately depositing particles in non-continuous patterns. Here, we present a rational process termed combinatory electric-field-guided deposition (CED) for achieving spatially regulated microparticle deposition on insulative substrates. This process involves coating the substrates with insulating materials like PVP and positioning it on a relief-patterned negative electrode. The negative electric field generated by the electrode attracts microparticle

Abstract:The development of precision personalized medicine poses a significant need for the next generation of advanced diagnostic and therapeutic technologies, and one of the key challenges is the development of highly time-, space-, and dose-controllable drug delivery systems that respond to the complex physiopathology of patient populations. In response to this challenge, an increasing number of stimuli-responsive smart materials are integrated into biomaterial systems for precise targeted drug delivery. Among them, responsive microcapsules prepared by droplet microfluidics have received much attention. In this study, we present a UV-visible light cycling mediated photoswitchable microcapsule (PMC) with dynamic permeability-switching capability for precise and tailored drug release. Th

Abstract：Pressure ulcers present a significant human and economic challenge, lacking a reliable method for early detection. To address this, we developed a system capable of early detection by using cooling stimulation and dynamic data acquisition techniques to monitor blood perfusion and skin temperature. The system consists of laser speckle perfusion imaging and thermal imaging. And we performed simulations to demonstrate that the system is capable of detect tissue damage across multiple layers, from superficial to deep. Testing on a rabbit ear model demonstrated that this approach, which combines dynamic perfusion and temperature parameters, effectively distinguishes early pressure ulcer areas from normal skin with a significant p value of 0.0015. This distinction was more precise compa

Abstract:Optical coherence tomography (OCT) has been extensively utilized in the field of biomedical imaging due to its non-invasive nature and its ability to provide high-resolution, in-depth imaging of biological tissues. However, the use of low-coherence light can lead to unintended interference phenomena within the sample, which inevitably introduces speckle noise into the imaging results. This type of noise often obscures key features in the image, thereby reducing the accuracy of medical diagnoses. Existing denoising algorithms, while removing noise, tend to also damage the structural details of the image, affecting the quality of diagnosis. To overcome this challenge, we have proposed a speckle noise (PSN) framework. The core of this framework is an innovative dual-module noise gene

Abstract:Tissue vascularization plays a critical role in the regeneration and repair of damaged tissues. However, in certain instances of tissue injury, the pace and effectiveness of vascularization can be limited. Innovative strategies leveraging magnetic fields and magnetic nanoparticles (MNPs) are devised to enhance the efficacy of tissue vascularization. This review explores the potential of magnetic field-assisted strategies in augmenting tissue vascularization and repair. Direct application of static or dynamic magnetic fields, alone or in combination with MNPs, offers a means to modulate cellular behaviors and gene expression, thereby promoting angiogenesis and tissue regeneration. Techniques such as cell labeling, gene delivery using MNPs, and magnetic targeting have shown promise

Abstract：In the field of tissue engineering, local hypoxia in large-cell structures (larger than 1 mm3) poses a significant challenge. Oxygen-releasing biomaterials supply an innovative solution through oxygen ⁠ delivery in a sustained and controlled manner. Compared to traditional methods such as emulsion, sonication, and agitation, microfluidic technology offers distinct benefits for oxygen-releasing material production, including controllability, flexibility, and applicability. It holds enormous potential in the production of smart oxygen-releasing materials. This review comprehensively covers the fabrication and application of microfluidic-enabled oxygen-releasing biomaterials. To begin with, the physical mechanism of various microfluidic technologies and their differences in oxygen ca

Abstract：Deep convolutional neural networks often perform poorly when faced with datasets that suffer from quantity imbalances and classification difficulties. Despite advances in the field, existing two-stage approaches still exhibit dataset bias or domain shift. To counter this, a phased progressive learning schedule has been proposed that gradually shifts the emphasis from representation learning to training the upper classifier. This approach is particularly beneficial for datasets with larger imbalances or fewer samples. Another new method a coupling-regulation-imbalance loss function is proposed, which combines three parts: a correction term, focal loss, and LDAM loss. This loss is effective in addressing quantity imbalances and outliers, while regulating the focus of attention on sa

Abstract：Cervical abnormal cell detection plays a crucial role in the early screening of cervical cancer. In recent years, some deep learning-based methods have been proposed. However, these methods rely heavily on large amounts of annotated images, which are time-consuming and laborintensive to acquire, thus limiting the detection performance. In this paper, we present a novel Semi-supervised Cervical Abnormal Cell detector (SCAC), which effectively utilizes the abundant unlabeled data. We utilize Transformer as the backbone of SCAC to capture long-range dependencies to mimic the diagnostic process of pathologists. In addition, in SCAC, we design a Unified Strong and Weak Augment strategy (USWA) that unifies two data augmentation pipelines, implementing consistent regularization in semisu

Abstract：Macular edema, a prevalent ocular complication observed in various retinal diseases, can lead to significant vision loss or blindness, necessitating accurate and timely diagnosis. Despite the potential of deep learning for segmentation of macular edema, challenges persist in accurately identifying lesion boundaries, especially in low-contrast and noisy regions, and in distinguishing between Inner Retinal Fluid (IRF), Sub-Retinal Fluid (SRF), and Pigment Epithelial Detachment (PED) lesions. To address these challenges, we present a novel approach, termed Semantic Uncertainty Guided Cross-Transformer Network (SuGCTNet), for the simultaneous segmentation of multi-class macular edema. Our proposed method comprises two key components, the semantic uncertainty guided attention module (S

Abstract：Multispectral imaging, a non-invasive technique to measure retinal oxygen saturation levels, is limited due to its time-consuming and expensive nature. In this paper, we present R3ST (Retinex-based Semantic Spectral Separation Transformer), an innovative end-to-end method for RGB fundus image spectral reconstruction. Our proposed model leverages RGB images to reconstruct multispectral images, making retinal oximetry more accessible for diagnostics.Existing spectral reconstruction methods face challenges in achieving high accuracy and generalizing across camera sources, affecting diagnostic results. R3ST addresses these issues through an unsupervised semantic spectral separation module that strives to encourage the separate reconstruction of pixels with different semantics as much

Abstract: The UNet architecture, based on Convolutional Neural Networks (CNN), has demonstrated its remarkable performance in medical image analysis. However, it faces challenges in capturing long-range dependencies due to the limited receptive fields and inherent bias of convolutional operations. Recently, numerous transformer-based techniques have been incorporated into the UNet architecture to overcome this limitation by effectively capturing global feature correlations. However, the integration of the Transformer modules may result in the loss of local contextual information during the global feature fusion process. To overcome these challenges, we propose a 2D medical image segmentation model called Multi-scale Cross Perceptron Attention Network (MCPA). The MCPA consists of three main

Abstract: BACKGROUND: Recent advances in artificial intelligence and digital image processing have inspired the use of deep neural networks for segmentation tasks in multimodal medical imaging. Unlike natural images, multimodal medical images contain much richer information regarding different modal properties and therefore present more challenges for semantic segmentation. However, there is no report on systematic research that integrates multi-scaled and structured analysis of single-modal and multimodal medical images. METHODS: We propose a deep neural network, named as Modality Preserving U-Net (MPU-Net), for modality-preserving analysis and segmentation of medical targets from multimodal medical images. The proposed MPU-Net consists of a modality preservation encoder (MPE) module that