Anastasiya A. Polyakova

One of the important tasks of modern dentistry is to find indicators that would allow predicting the development of complications of dental implantation. However, there is currently no generally accepted set of biomarkers and assessment scales for early diagnosis of the risk of complications and monitoring of osseointegration processes. Accordingly, the possibility of early prevention and treatment in an adequate time frame is reduced to prevent the development of implant rejection. To improve the forecasting and evaluation of the effectiveness of dental implantation based on the molecular profile of patients an analytical review of publications over the past decade devoted to the use of biomarkers for predicting and evaluating the effectiveness of dental implantation was conducted. The tissue response to an implant in peri-implantitis is a complex pathophysiological process that involves the interaction between the implant, surrounding tissues, and the body’s immune system. Based on the analysis of literature data, a panel of biomarkers has been substantiated that can be used to predict complications after dental implantation and assess the severity of peri-implantitis from the perspective of modern views on the pathophysiological molecular and cellular mechanisms of peri-implantitis: α-tubulin, β-tubulin, cyclooxygenase-1,2,3, vascular endothelial growth factor and its receptor, melatonin and its receptors, nerve cell nuclear protein, nitric oxide, vascular cell adhesion molecule-1, neuron-specific enolase, claudine-1 and E-cadherin. The use of this panel of molecular markers will improve the accuracy of predictions, individualize treatment approaches, and ensure the long-term stability of implants.

The article analyzes the data of elderly patients with airway cancer before and after surgery. To reduce the mortality of cancer patients in the postoperative period, it is necessary to identify markers that can predict the deterioration of the patient's condition. The study involved 83 patients, who recorded 216 parameters before and 47 parameters after surgery. The data contain not only general laboratory parameters, but also blood gas parameters, hemodynamic data and autonomic indices, metabolic parameters and cardiorespiratory stress tests at various levels of exercise. Statistical data analysis is carried out using the SPSS program and is based on the following methods: one-sided Kolmogorov-Smirnov test, Mann-Whitney test, chi-square test and the use of logistic regression to build a multivariate model. The investigated variable is the result of the operation, which can take the following values: no complications, complications after surgery, death. The result of this analysis, namely the determination of mortality markers, will determine which of the patients should not undergo surgery, and who need special medical supervision in the postoperative period.