This narrative review explores the potential effects of Propolis and its bioactive compounds on bone health. Propolis, a resinous product collected by bees, is renowned for its antimicrobial, anti-inflammatory, and antioxidant properties. Recent research emphasizes its positive role in osteogenesis, primarily through the modulation of osteoclast and osteoblast activity via molecular pathways. Key mechanisms include reducing inflammatory cytokines, protecting against oxidative stress, and upregulating growth factor essential for bone formation. While compounds such as Caffeic Acid Phenethyl Ester, Apigenin, Quercetin, and Ferulic Acid have been well-documented, emerging evidence points to the significant roles of less-studied compounds like Pinocembrin, Kaempferol, p-Coumaric acid, and Galangin. This review synthesizes the current literature, focusing on the mechanisms by which these bioactive compounds influence osteogenesis. Firstly, it explores the techniques for characterizing bioactive compounds presented in propolis, the chemogeographic variations in its composition, and the effects of both crude extracts and isolated compounds on bone tissue, offering a comprehensive analysis of recent findings across different experimental models. Further, it discusses the effects of Propolis compounds on bone health. In summary, these compounds modulate signaling pathways, including nuclear factor kappa beta, wingless-related integration site, mitogen-activated protein kinase, vascular endothelial growth factor, and reactive oxygen species. These pathways influence the receptor activator of nuclear factor kappa-β/receptor activator of nuclear factor kappa-β ligand/osteoprotegerin system, fostering bone cell differentiation. This regulation mitigates excessive osteoclast formation, stimulates osteoblast activity, and ultimately contributes to the restoration of bone homeostasis by maintaining a balanced bone remodeling process.

Background/Objectives: Myocardial revascularization surgery (MR) is routinely performed in hospitals. However, there is a lack of an algorithm in the scientific literature aimed at predicting intraoperative parameters, such as total surgery time (TST) and cardiopulmonary bypass time (CBT), based on preoperative MR parameters. Therefore, the objective of the present study is to apply a complex network model to predict parameters associated with TST and CBT. Methods: Retrospective data from 124 patients who underwent MR, including medical history, vital signs, and laboratory/biochemical tests, were used, with 30 patients contributing to the construction of the network. Three complex networks were created to study the targets (TST and CBT). The Eigenvector metric was employed to investigate the parameters most relevant to these targets. Results: Regardless of the target, parameters derived from the blood gas analysis followed by erythrogram displayed greater relevance according to the eigenvector metric. However, for TST, the most prominent parameter was Red Blood Cells, while, for CBT, Diastolic Blood Pressure emerged as the most important variable. Conclusion: The targeted complex network model revealed that pulmonary, hemodynamic, and perfusion factors are relevant to the intraoperative parameters of MR. The networks also demonstrated that, although the targets show significant correlation with each other (TST and CBT-r = 0.76; p = 0.000), the importance of the parameters in the networks does not follow the same order. This reiterates the strength of the network in revealing specific information when a particular target is selected.