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Objective: Resveratrol is a plant polyphenol known for its anti-inflammatory and pro-regenerative properties. These could be beneficial in controlling potential side effects of orthodontic treatment, such as apical root resorption. Orthodontic tooth movement occurs as part of a sterile inflammatory response. However, dysregulation of this process can result in pathologically increased osteoclast activity in the radicular vicinity, leading to unwanted root resorption. Previous studies have shown that root cementum cells can modulate recruitment of osteoclast precursors and cementum repair., Material and Methods: We investigated the effect of resveratrol on mechanically stimulated murine cementoblasts (OCCM-30) with regards to cell viability, and mRNA expression and protein levels of pro-inflammatory cytokines. Furthermore, the modulation of central related kinases was investigated., Results: Resveratrol increased viability of OCCM-30 in a time- and dose-dependent manner and significantly reduced upregulation of pERK and pAKT, upstream regulators of key cellular metabolic pathways. Furthermore, we describe for the first time that cementoblasts respond to compression with accelerated activation of STAT3 and increased translocation of NF-κB p65 into the nucleus., Conclusion: This study shows a regulation of pAKT and pERK by resveratrol in OCCM-30 cells without a negative effect on cell viability. Therefore, resveratrol may have the potential to modulate the periodontal response to mechanical stimulation., Competing Interests: Declarations. Ethics approval and consent to participate: Not applicable. Consent for publication: Not applicable. Competing interests: The authors declare no competing interests., (© 2025. The Author(s).)

As a fundamental mechanism for gene expression regulation, post-transcriptional RNA methylation plays versatile roles in various biological processes and disease mechanisms. Recent advances in single-cell technology have enabled simultaneous profiling of transcriptome-wide RNA methylation in thousands of cells, holding the promise to provide deeper insights into the dynamics, functions, and regulation of RNA methylation. However, it remains a major challenge to determine how to best analyze single-cell epitranscriptomics data. In this study, we developed SigRM, a computational framework for effectively mining single-cell epitranscriptomics datasets with a large cell number, such as those produced by the scDART-seq technique from the SMART-seq2 platform. SigRM not only outperforms state-of-the-art models in RNA methylation site detection on both simulated and real datasets but also provides rigorous quantification metrics of RNA methylation levels. This facilitates various downstream analyses, including trajectory inference and regulatory network reconstruction concerning the dynamics of RNA methylation., Competing Interests: Declaration of interests The authors declare no competing interests., (Copyright © 2024 The Author(s). Published by Elsevier Inc. All rights reserved.)