Peripheral nerve injury (PNI) poses a major clinical challenge, frequently resulting in chronic pain, muscle atrophy, and long-term functional impairment. While autologous nerve grafting remains the gold standard for repairing long-gap defects, its application is limited by donor-site morbidity and limited tissue availability. Nerve guidance conduits (NGCs) have emerged as promising alternatives; however, their efficacy remains suboptimal, primarily because most fail to recapitulate the spatiotemporally coordinated regenerative microenvironment required for robust axonal extension, timely remyelination, and durable neurovascular integration. Key limitations of current designs include an inability to balance the bioactivity of natural materials with the tunability of synthetic polymers, insufficient nutrient and oxygen delivery for long-gap repair, and a lack of dynamic, stage-specific regulation of the healing process. Consequently, microenvironment reconstruction represents the central bottleneck to achieving effective regeneration. This review synthesizes recent advances in purposefully rebuilding the NGC microenvironment across three interdependent dimensions: (i) activation and functional regulation of Schwann cells; (ii) immunomodulation to resolve inflammation while promoting repair; (iii) angiogenesis to ensure metabolic support. We place special emphasis on biomaterial strategies, particularly advanced hydrogels that integrate physical, biochemical, and dynamic cues for spatiotemporally programmed regeneration. Finally, we outline design principles and translational considerations for next-generation NGCs aimed at closing the efficacy gap with autografts.

Accumulating evidence indicates that the dysregulation of small nucleolar RNAs (snoRNAs) is relevant with diseases. Identifying snoRNA-disease associations by computational methods is desired for biologists, which can save considerable costs and time compared biological experiments. However, it still faces some challenges as followings: (i) Many snoRNAs are detected in recent years, but only a few snoRNAs have been proved to be associated with diseases; (ii) Computational predictors trained with only a few known snoRNA-disease associations fail to accurately identify the snoRNA-disease associations. In this study, we propose a ranking framework, called iSnoDi-MDRF, to identify potential snoRNA-disease associations based on multiple biological data, which has the following highlights: (i) iSnoDi-MDRF integrates ranking framework, which is not only able to identify potential associations between known snoRNAs and diseases, but also can identify diseases associated with new snoRNAs. (ii) Known gene-disease associations are employed to help train a mature model for predicting snoRNA-disease association. Experimental results illustrate that iSnoDi-MDRF is very suitable for identifying potential snoRNA-disease associations. The web server of iSnoDi-MDRF predictor is freely available at http://bliulab.net/iSnoDi-MDRF/ .

Exploring disease mechanisms at the lncRNA level provides valuable guidance for disease prognosis and treatment. Recently, there has been a surge of interest in exploring disease mechanisms via computational methods to overcome the challenge of tremendous manpower and material resources in biological experiments. However, current computational methods suffer from two main limitations: simple data structures that do not consider the close association between multiple types of data, and the lack of a systematic pathogenesis analysis that identified disease-associated lncRNAs are not applied to the downstream disease prognosis and therapeutic analysis from the perspective of data analysis. In this end, we present a systemic pipeline including disease-associated lncRNAs identification and downstream pathogenesis analysis on how the predicted lncRNAs are involved in the disease prognosis and therapy. Due to the importance of identifying disease-associated lncRNAs and the weak interpretability of existing computational identification methods, we propose a novel approach named iLncDA-PT to identify disease-associated lncRNAs considering the interactions between various bio-entities outperforming the other state-of-the-art methods, and then we conduct a systematically subsequent analysis on prognosis and therapy for a specific disease, hepatocellular carcinoma (HCC), as an example. Finally, we reveal a significant association between immune checkpoint expression, tumor microenvironment, and drug treatment.

Circular RNA (circRNA) plays a significant role in gene expression, splicing, and transcription. More and more evidence indicates that circRNA is related to the pathogenesis and development of diseases. In this paper, a non-negative matrix factorization algorithm based on circRNA expression profiles data and disease similarity data (EDNMF) is proposed to predict circRNA-disease associations. The EDNMF algorithm firstly preprocesses the circRNA-disease associations to solve the impact of too little the number of known circRNA-disease associations. Then, the EDNMF algorithm converts circRNA expression profile and cancer similarity data into constraints. Finally, we can obtain the final scores for circRNA-disease associations by improved NMF algorithm based on pre-processed circRNA-disease associations. The performance results of 5-fold and 10-fold cross-validation indicate that the EDNMF algorithm achieves satisfactory performance comparing with other algorithms. Besides, the case study shows that EDNMF can mine new circRNA-disease associations very well, which can provide a reference for studying circRNA-disease associations.

Characterizing the cellular origin and disease‐driven dynamics of cfRNA is essential for integrating cfRNA profiling into clinical workflows and precision‐medicine strategies. Most cfRNA studies are restricted to bulk‐level analyses, which preclude detailed analysis of alterations in the cellular origins of cfRNA. Single‐cell RNA sequencing reveals cellular heterogeneity and communication, but its application to cfRNA is limited by diverse cellular origins, leaving a critical gap in understanding functional alterations in cfRNA biomarker‐originating cells. In this work, we propose CellFreeGMF, a tool designed to enable diagnosis classification of clinical samples, identify cfRNA biomarkers, and analyze the alterations in their originating cells based on graph matrix factorization. Furthermore, by utilizing cell–cell communication analysis, CellFreeGMF investigates the functional alterations occurring in the cfRNA originating cells under disease conditions. We validate CellFreeGMF on diverse cell‐free RNA transcriptome clinical datasets. In the case of pancreatic ductal adenocarcinoma (PDAC), CellFreeGMF not only identified cfRNA biomarkers but also traced their cellular origins to myeloid and T‐cell populations. Further analysis revealed significant transcriptomic differences in these cell populations between disease and normal groups. Our user‐friendly CellFreeGMF toolkit ( https://cellfreegmf.readthedocs.io/ ) enables identifying cfRNA biomarkers and elucidating pathophysiological changes in their originating cells.