Abstract: Background: Sentinel lymph node biopsy (SLNB) plays a crucial role in the management of patients with malignant melanoma. However, limited studies have been conducted in SLNB for lower extremity malignant melanoma in Asians.Objectives: We used coaxial projective lymphography to demonstrate a technique for SLNB in lower extremity malignant melanoma and evaluated the accuracy and efficacy of this new technique.Methods: Twenty patients with lower extremity malignant melanoma who underwent SLNB were enrolled in this prospective study. All the patients had SLNB performed under the guidance of Indocyanine Green (ICG) based coaxial projective lymphography. Tissue samples of suspicious lymph nodes with or without ICG labeling were excised, and analyzed by intraoperative frozen section ex

Abstract: Purpose:In the clinical treatment of bone defects that exceed the critical size threshold, traditional methods using metal fixation devices, autografts, and allografts exhibit significant limitations. Meanwhile, bone scaffolds with minimal risks of secondary injury, low immune rejection are emerging as a promising alternative. The effective design of porosity, pore size, and trabecular thickness in bone scaffolds is critical; however, current strategies often struggle to optimally balance these parameters. Here, we propose a bionic bone scaffold design method that mimics multiple properties of natural cancellous bone using a diffusion model.MethodsFirst, we develop a classifier-free conditional diffusion model and train it on a Micro-CT (μCT) image dataset of porcine vertebral ca

Abstract: Medical image segmentation is crucial for accurate diagnosis and effective treatment planning. However, in cross-domain semi-supervised segmentation, the scarcity of labeled data often leads to suboptimal performance and poor generalization across diverse medical imaging domains. Moreover, pseudo-labels generated from unlabeled data are inherently noisy, introducing confirmation bias that destabilizes training and hinders the model’s ability to accurately capture complex anatomical structures. To address these challenges, we propose HARP: Harmonization and Adaptive Refinement of Pseudo-Labels for Cross-Domain Medical Image Segmentation, a framework designed to enhance segmentation performance by integrating two novel modules: the Adaptive Pseudo-label Selection (APS) module and t

Abstract: Diabetic microvascular complications result from complex vascular remodeling influenced by hyperglycemia and hypoxia. However, there is currently no comprehensive method for systematically studying their combined effects on overall vascular morphology and perfusion function. To address this, a dual-modal ex vivo vascular culture and imaging system is developed. First, an ex vivo chick chorioallantoic membrane (CAM) model is established as a vascular culture platform. Then a dual-modal imaging system integrating incoherent bright-field imaging and laser speckle imaging is constructed for vascular imaging. Following this, a vascular structure and perfusion analysis algorithm is employed to achieve full-scale characterization of vascular structural and perfusion changes in the CAM m

Abstract:Objective:Large language models (LLMs) have exhibited remarkable efficacy in natural language processing (NLP) tasks, with fine-tuning for Biomedical Named Entity Recognition (BioNER) receiving significant research attention. However, the substantial computational demands associated with fine-tuning large-scale models constrain their development and deployment. Consequently, this study investigates parameter-efficient fine-tuning (PEFT) techniques to optimize LLMs for BioNER under limited computational resources. By leveraging these methods, competitive model performance is maintained while preserving in-domain generalization capability.Methods:In this study, we employed the PEFT method QLoRA to fine-tune the open-source Llama3.1 model, developing the NERLlama3.1 model specificall

Abstract: Metabolic disorders could cause dysregulated glucose and lipid at the systemic level, but how inter-tissue/organ communications contribute to glucolipotoxicity is difficult to dissect in animal models. To solve this problem, myocardium and nerve tissues were modelled by 3D engineered heart tissues (EHTs) and neural organoids (NOs), which were co-cultured in a generalised medium with normal or elevated glucose/fatty acid contents. Morphology, gene expression, cell death and functional assessments detected no apparent alterations of EHTs and NOs in co-culture under normal conditions. By contrast, NOs significantly ameliorated glucolipotoxicity in EHTs. Transcriptomic and protein secretion assays identified the extracellular matrix protein versican as a key molecule that was transfe

Abstract: Accurate wavelength calibration is crucial for the reliable performance of transmission holographic spectrometers, which are widely used in analytical chemistry, materials science, and biomedical research. However, inherent optical aberrations in the focusing components often cause systematic wavelength errors—particularly when the available emission lines from standard calibration sources are unevenly distributed over the measurement range. To address this challenge, we propose a novel error calibration method that leverages the inherent symmetry of aberration-induced errors in transmission optics. Our approach involves generating an error calibration function by combining polynomial fitting with symmetry-based averaging, thereby compensating for systematic residual errors acros

Abstract: Low-quality fundus images pose significant challenges for both ophthalmologists and computer-aided diagnosis systems. While many existing deep learning-based image quality enhancement algorithms require low- and high-quality image pairs for training, such pairs are often difficult to obtain in practice. On the other hand, unpaired image enhancement algorithms tend to struggle in preserving small structures and suppressing artefacts, which are crucial for medical applications. To address these issues, we propose an unpaired structure-preserving cycle quality alternating network for low-quality fundus image enhancement. Our method consists of three main components: (1) a cycle quality alternating framework to provide pixel-wise supervision for unpaired image enhancement, (2) a qual

Abstract: Accurate detection of abnormal cells is essential for early screening and precise diagnosis of cervical cancer. Despite the recent advances in deep learning-based methods for cervical cancer detection, their broad clinical applications are hindered by several technical challenges. On the one hand, gradually evolved abnormal cells are visually similar to normal cells. On the other hand, single cells and cell clusters exhibit significant appearance variations, overlooking those between normal and abnormal cells. In order to overcome these challenges, we propose a novel dual-branch multi-granularity hierarchical contrast and cross attention network, called DMCA-Net. Specifically, DMCA-Net utilizes dual branches to detect abnormal and normal cells, respectively. Meanwhile, an inter-c

Abstract: Vascular channels embedded within tissue-engineered hydrogels play a crucial role in replicating natural physiological environments and facilitating the delivery of nutrients and removal of metabolic byproducts. Although present techniques provide diverse strategies to create vascular channels, the flexible and scalable construction of these channels within hydrogels remains a challenge. Here, inspired by Lego assembly, an innovative modular construction strategy is introduced for developing perfusable vascular channels within hydrogels. This approach involves the customized design and fabrication of individual modules featuring diverse vascular channel architectures, which can be flexibly assembled into large-scale hydrogel constructs with hierarchical vascular channels through

Abstract: Background: Stroke presents a substantial health burden, emphasizing the crucial necessity for robust screening tools to gauge its severity and assess associated biomarkers. The identification of retinal biomarkers for stroke is a pivotal pursuit, enabling both early-stage risk and subsequent prognosis prediction for personalized intervention strategies. This study aims to analyze retinal blood vessel oxygen saturation (SO2) and structure across ischemic and hemorrhagic stroke subtypes compared to healthy controls. Additionally, it seeks to examine adjusted odds ratios between retinal vascular features and two stroke subtypes, employing these metrics for the classification of stroke.Methods: This study assessed retinal images from 29 ischemic stroke patients, 23 hemorrhagic strok

Abstract: The incidence and burden of skin wounds, especially chronic and complex wounds, have a profound impact on healthcare. Effective wound healing strategies require a multidisciplinary approach, and advances in materials science and bioengineering have paved the way for the development of novel wound healing dressing. In this context, electrospun nanofibers can mimic the architecture of the natural extracellular matrix and provide new opportunities for wound healing. Inspired by the bioelectric phenomena in the human body, electrospun nanofibrous scaffolds with electroactive characteristics are gaining widespread attention and gradually emerging. To this end, this review first summarizes the basic process of wound healing, the causes of chronic wounds, and the current status of clini