Machine learning and experimental analyses identified miRNA expression models associated with metastatic osteosarcoma.

Osteosarcoma (OS), as the most common primary bone cancer, has a high invasiveness and metastatic potential, therefore, it has a poor prognosis. This study identified early diagnostic biomarkers using miRNA expression profiles associated with osteosarcoma metastasis. In the first step, we used RNA-seq and online microarray data from osteosarcoma tissues and cell lines to identify differentially expressed miRNAs. Then, using seven feature selection algorithms for ranking, the first-ranked miRNAs were selected as input for five machine learning systems. Using network analysis and machine learning algorithms, we developed new diagnostic models that successfully differentiated metastatic osteosarcoma from non-metastatic samples based on newly discovered miRNA signatures. The results showed that miR-34c-3p and miR-154-3p act as the most promising models in the diagnosis of metastatic osteosarcoma. Validation for this model by RT-qPCR in benign tissue and osteosarcoma biopsies confirmed the lower expression of miR-34c-3p and miR-154-3p in OS samples. In addition, a direct correlation between miR-34c-3p expression, miR-154-3p expression and tumor grade was discovered. The combined values of miR-34c-3p and miR-154-3p showed 90 % diagnostic power (AUC = 0.90) for osteosarcoma samples and 85 % (AUC = 0.85) for metastatic osteosarcoma. Adhesion junction and focal adhesion pathways, as well as epithelial-to-mesenchymal transition (EMT) GO terms, were identified as the most significant KEGG and GO terms for the top miRNAs. The findings of this study highlight the potential use of novel miRNA expression signatures for early detection of metastatic osteosarcoma. These findings may help in determining therapeutic approaches with a quantitative and faster method of metastasis detection and also be used in the development of targeted molecular therapy for this aggressive cancer. Further research is needed to confirm the clinical utility of miR-34c-3p and miR-154-3p as diagnostic biomarkers for metastatic osteosarcoma.

3D Printing of Alginate/Chitosan-Based Scaffold Empowered by Tyrosol-Loaded Niosome for Wound Healing Applications: In Vitro and In Vivo Performances.

This study introduces a tyrosol-loaded niosome integrated into a chitosan-alginate scaffold (Nio-Tyro@CS-AL), employing advanced electrospinning and 3D printing techniques for wound healing applications. The niosomes, measuring 185.40 ± 6.40 nm with a polydispersity index of 0.168 ± 0.012, encapsulated tyrosol with an efficiency of 77.54 ± 1.25%. The scaffold's microsized porous structure (600-900 µm) enhances water absorption, promoting cell adhesion, migration, and proliferation. Mechanical property assessments revealed the scaffold's enhanced resilience, with niosomes increasing the compressive strength, modulus, and strain to failure, indicative of its suitability for wound healing. Controlled tyrosol release was demonstrated in vitro, essential for therapeutic efficacy. The scaffold exhibited significant antibacterial activity against Pseudomonas aeruginosa and Staphylococcus aureus, with substantial biofilm inhibition and downregulation of bacterial genes (ndvb and icab). A wound healing assay highlighted a notable increase in MMP-2 and MMP-9 mRNA expression and the wound closure area (69.35 ± 2.21%) in HFF cells treated with Nio-Tyro@CS-AL. In vivo studies in mice confirmed the scaffold's biocompatibility, showing no significant inflammatory response, hypertrophic scarring, or foreign body reaction. Histological evaluations revealed increased fibroblast and macrophage activity, enhanced re-epithelialization, and angiogenesis in wounds treated with Nio-Tyro@CS-AL, indicating effective tissue integration and repair. Overall, the Nio-Tyro@CS-AL scaffold presents a significant advancement in wound-healing materials, combining antibacterial properties with enhanced tissue regeneration, and holds promising potential for clinical applications in wound management.

Multiple Dipole Source Position and Orientation Estimation Using Non-Invasive EEG-like Signals.

The problem of precisely estimating the position and orientation of multiple dipoles using synthetic EEG signals is considered in this paper. After determining a proper forward model, a nonlinear constrained optimization problem with regularization is solved, and the results are compared with a widely used research code, namely EEGLAB. A thorough sensitivity analysis of the estimation algorithm to the parameters (such as the number of samples and sensors) in the assumed signal measurement model is conducted. To confirm the efficacy of the proposed source identification algorithm on any category of data sets, three different kinds of data-synthetic model data, visually evoked clinical EEG data, and seizure clinical EEG data are used. Furthermore, the algorithm is tested on both the spherical head model and the realistic head model based on the MNI coordinates. The numerical results and comparisons with the EEGLAB show very good agreement, with little pre-processing required for the acquired data.