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Since gold nanoparticles (AuNPs) have great potential to bring improvements to the biomedical field, their impact on biological systems should be better understood, particularly over the long term, using realistic doses of exposure. MicroRNAs (miRNAs) are small noncoding RNAs that play key roles in the regulation of biological pathways, from development to cellular stress responses. In this study, we performed genome-wide miRNA expression profiling in primary human dermal fibroblasts 20 weeks after chronic and acute (non-chronic) treatments to four AuNPs with different shapes and surface chemistries at a low dose. The exposure condition and AuNP surface chemistry had a significant impact on the modulation of miRNA levels. In addition, a network-based analysis was employed to provide a more complex, systems-level perspective of the miRNA expression changes. In response to the stress caused by AuNPs, miRNA co-expression networks perturbed in cells under non-chronic exposure to AuNPs were enriched for target genes implicated in the suppression of proliferative pathways, possibly in attempt to restore cell homeostasis, while changes in miRNA co-expression networks enriched for target genes related to activation of proliferative and suppression of apoptotic pathways were observed in cells chronically exposed to one specific type of AuNPs. In this case, miRNA dysregulation might be contributing to enforce a new cell phenotype during stress. Our findings suggest that miRNAs exert critical roles in the cellular responses to the stress provoked by a low dose of NPs in the long term and provide a fertile ground for further targeted experimental studies.
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http://dx.doi.org/10.1039/d0nr04701e | DOI Listing |
Bioinform Adv
August 2025
Mount Sinai Institute for Systems Biomedicine, Icahn School of Medicine at Mount Sinai, New York, NY 10029, United States.
Motivation: Advances in high-throughput technologies have shifted the focus from bulk to single cell or spatial transcriptomic and proteomic analysis of tissues and cell cultures. The resulting increase in gene and/or protein lists leads to the subsequent growth of up- and downregulated pathways lists. This trend creates the need for pathway-network based integration strategies that allow quick exploration of shared and distinct mechanisms across datasets.
View Article and Find Full Text PDFAn integrated approach is proposed to rapidly evaluate the effects of anticancer treatments in 3D models, combining a droplet-based microfluidic platform for spheroid formation and single-spheroid chemotherapy application, label-free morphological analysis, and machine learning to assess treatment response. Morphological features of spheroids, such as size and color intensity, are extracted and selected using the multivariate information-based inductive causation algorithm, and used to train a neural network for spheroid classification into viability classes, derived from metabolic assays performed within the same platform as a benchmark. The model is tested on Ewing sarcoma cell lines and patient-derived xenograft (PDX) cells, demonstrating robust performance across datasets.
View Article and Find Full Text PDFAnn Plast Surg
September 2025
Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN.
Background: The generation of intelligible speech is the single most important outcome after cleft palate repair. The development of velopharyngeal dysfunction (VPD) compromises the outcome, and the burden of VPD remains largely unknown in low- and middle-income countries (LMICs). To scale up VPD care in these areas, we continue to explore the use of artificial intelligence (AI) and machine learning (ML) for automatic detection of VPD from speech samples alone.
View Article and Find Full Text PDFSci Adv
September 2025
Shanghai Key Laboratory of Atmospheric Particle Pollution and Prevention (LAP3), National Observations and Research Station for Wetland Ecosystems of the Yangtze Estuary, Department of Environmental Science & Engineering, Fudan University, Shanghai 200443, China.
Fentanyl and its analogs are a global concern, making their accurate identification essential for public health. Here, we introduce Fentanyl-Hunter, a screening platform that uses a machine learning classifier and multilayer molecular network to select and annotate fentanyl compounds using mass spectrometry (MS). Our classification model, based on 772 fentanyl spectra and spectral binning feature engineering, achieved an score of 0.
View Article and Find Full Text PDFRev Sci Instrum
September 2025
Hefei University of Technology, School of Mechanical Engineering, Hefei 230009, China.
In unstructured environments, robots face challenges in efficiently and accurately grasping irregular, fragile objects. To address this, this paper introduces a soft robotic hand tailored for such settings and enhances You Only Look Once v5s (YOLOv5s), a lightweight detection algorithm, to achieve efficient grasping. A rapid pneumatic network-based soft finger structure, broadly applicable to various irregularly placed objects, is designed, with a mathematical model linking the bending angle of the fingers to input gas pressure, validated through simulations.
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