Oral cancer (OC) is a multifactorial disease caused by isolated or combined risk factors related to tobacco, alcohol consumption, and human papillomavirus infection. It is an aggressive pathology with a low five-year survival rate after surgery, chemotherapy, and/or radiotherapy, frequently associated with severe side effects. Drugs with the highest anti-tumor effect are obtained from natural products with diverse biological and molecular activities and potential chemopreventive and anticancer properties. This review summarizes the natural products reported to have the chemopreventive and anti-tumor potential for OC treatment, showing that several of these compounds are promising candidates as chemopreventive agents, and those with the highest anti-tumor potential induce apoptosis and inhibit proliferation and metastasis-related processes. For this reason, natural products have the potential to be important preventive and therapeutic options for OC in the future.

Cervical cancer is one of the most frequent malignancies worldwide and one of the leading causes of death in women. Recently, artificial intelligence-based tools have been developed for the early diagnosis of malignancies, including cervical cancer, based on data generated in the healthcare area. This systematic review aimed to evaluate the diagnostic performance of machine learning models based on sociodemographic, epidemiologic, and clinical data for the detection of cervical cancer. A systematic literature search was performed in PubMed, Scopus, and Embase, with no time limit until July 31, 2025. The screening process was performed by two independent authors using the Rayyan® platform, as well as the risk of bias assessment process using the QUADAS-2 tool. A third author resolved cases of dispute. Meta-analysis and sensitivity analysis were performed in Stata17®. A total of 7 studies were included, in which the machine learning models selected several demographic and clinical variables for the classification of patients. Pooled diagnostic measures of these models were 0.97 (95 % CI 0.90-0.99) for sensitivity, and 0.96 (95 % CI 0.93-0.97) for specificity (I² = 48.63, Q = 35.22, p < 0.01). After sensitivity analysis, diagnostic measurements remained consistent. Machine learning models are presented as a new tool with high diagnostic performance, making their introduction into cervical cancer screening programs feasible. However, this systematic review discusses some issues that should be addressed to validate and incorporate these models into clinical practice.

The biological seal (BS) at the implant-tissue interface is essential for the success of dental implants (DIs), and the absence of a proper BS can lead to peri-implantitis. The basement membrane (BM) and junctional epithelium are critical for sealing the peri-implant mucosa, and laminin 332 is an important protein in binding the epithelium to the implant surface. The aim of this study was to evaluate the response of oral keratinocytes to titanium dental implant surfaces biofunctionalized with laminin 332. The dental implant surface was treated with a piranha solution to create hydroxyl (OH) groups, facilitating biofunctionalization with laminin 332. The modified surface underwent scanning electron microscopy, surface roughness evaluation, and chemical composition analysis. Human keratinocytes from the Cal-27 line were then cultured on the modified implants for 24 and 48 h to assess viability, morphology, cytokine secretion, and mRNA expression of tissue repair-associated genes. The results showed that laminin 332 biofunctionalization of the implant surface resulted in lower values of Ra, Rq and positive surface roughness parameters Rsk, Rku and Rv. The elemental composition showed an increase in nitrogen and carbon content corresponding to protein binding. The biofunctionalized surfaces did not affect cell viability and promoted cytokine secretion (IL-1a and IL-8) and a significant increase (p < 0.05) in MCP-1, EGF, FGF, TGF and VEGF gene expression compared to the control. In conclusion, laminin 332 coating Ti implants was shown to be effective in promoting keratinocyte adhesion, spreading, and viability. This approach could be an alternative way to improve biocompatibility.

P. gingivalis has been reported to be an endothelial cell inflammatory response inducer that can lead to endothelial dysfunction processes related to atherosclerosis; however, these studies have been carried out in vitro in cell culture models on two-dimensional (2D) plastic surfaces that do not simulate the natural environment where pathology develops. This work aimed to evaluate the pro-inflammatory response of human coronary artery endothelial cells (HCAECs) to P. gingivalis in a 3D cell culture model compared with a 2D cell culture. HCAECs were cultured for 7 days on type I collagen matrices in both cultures and were stimulated at an MOI of 1 or 100 with live P. gingivalis W83 for 24 h. The expression of the genes COX-2, eNOS, and vWF and the levels of the pro-inflammatory cytokines thromboxane A2 (TXA-2) and prostaglandin I2 (PGI2) were evaluated. P. gingivalis W83 in the 2D cell culture increased IL-8 levels at MOI 100 and decreased MCP-1 levels at both MOI 100 and MOI 1. In contrast, the 3D cell culture induced an increased gene expression of COX-2 at both MOIs and reduced MCP-1 levels at MOI 100, whereas the gene expression of eNOS, vWF, and IL-8 and the levels of TXA2 and PGI2 showed no significant changes. These data suggest that in the collagen 3D culture model, P. gingivalis W83 induces a weak endothelial inflammatory response.