Periodontitis is accompanied by the proliferation of small blood vessels in the gingival lamina propria. Specialized postcapillary venules, termed periodontal high endothelial-like venules, are also present, and demonstrate morphological and functional traits similar to those of high endothelial venules (HEVs) in lymphatic organs. The suggested role of HEVs in the pathogenesis of chronic periodontitis involves participation in leukocyte transendothelial migration and therefore proinflammatory effects appear. Recent observations suggest that chronic periodontitis is an independent risk factor for systemic vascular disease and may result in stimulation of the synthesis of acute phase protein by cytokines released by periodontal high endothelial cells (HECs). However, tissue expression of HEV-linked adhesion molecules has not been evaluated in the gingiva of patients with chronic periodontitis. This is significant in relation to potential therapy targeting expression of the adhesion molecules. In this review, current knowledge of HEV structure and the related expression of four surface adhesion molecules of HECs [CD34, platelet endothelial cell adhesion molecule 1, endoglin and intercellular adhesion molecule 1 (ICAM-1)], involved in the key steps of the adhesion cascade in periodontal diseases, are discussed. Most studies on the expression of adhesion molecules in the development and progression of periodontal diseases pertain to ICAM-1 (CD54). Studies by the authors demonstrated quantitatively similar expression of three of four selected surface markers in gingival HEVs of patients with chronic periodontitis and in HEVs of reactive lymph nodes, confirming morphological and functional similarity of HEVs in pathologically altered tissues with those in lymphoid tissues.

Prostate cancer (PCa) is the main cause of cancer-related mortality in males and the diagnosis, treatment, and care of these patients places a great burden on healthcare systems globally. Clinically, PCa is highly heterogeneous, ranging from indolent tumors to highly aggressive disease. In many cases treatment—generally either radiotherapy (RT) or surgery—can be curative. Several key genetic and demographic factors such as age, family history, genetic susceptibility, and race are associated with a high incidence of PCa. While our understanding of PCa, which is mainly based on the tools of molecular biology—has improved dramatically in recent years, efforts to better understand this complex disease have led to the identification of a new type of PCa–oligometastatic PCa. Oligometastatic disease should be considered an individual, heterogeneous entity with distinct metastatic phenotypes and, consequently, wide prognostic variability. In general, patients with oligometastatic disease typically present less biologically aggressive tumors whose metastatic potential is more limited and which are slow-growing. These patients are good candidates for more aggressive treatment approaches. The main aim of the presented review was to evaluate the utility of liquid biopsy for diagnostic purposes in PCa and for use in monitoring disease progression and treatment response, particularly in patients with oligometastatic PCa. Liquid biopsies offer a rapid, non-invasive approach whose use t is expected to play an important role in routine clinical practice to benefit patients. However, more research is needed to resolve the many existing discrepancies with regard to the definition and isolation method for specific biomarkers, as well as the need to determine the most appropriate markers. Consequently, the current priority in this field is to standardize liquid biopsy-based techniques. This review will help to improve understanding of the biology of PCa, particularly the recently defined condition known as “oligometastatic PCa”. The presented review of the body of evidence suggests that additional research in molecular biology may help to establish novel treatments for oligometastatic PCa. In the near future, the treatment of PCa will require an interdisciplinary approach involving active cooperation among clinicians, physicians, and biologists.

The aim of this study was to quantify the variability of pre-treatment lung tumor motion during a single breathing period for 55 non-small cell lung cancer (NSCLC) targets. The influence of breathing on the volume and position of lung tumor was examined by comparing the information about tumor from respiratory-correlated four-dimensional computed tomography (4DCT) and three-dimensional computed tomography (3DCT) obtained without respiratory monitoring. The impact of age, gender, lung volume changes and immobilization device on tumor respiratory motion was evaluated. Based on the performed analysis, the significant differences were found between tumor volumes on 3DCT and 4DCT, although the comparison of volumes between 4DCT bins showed no statistically significant dependency. The significant differences between tumor center of mass coordinates in the cranial-caudal (CC) and anterior-posterior (AP) directions were found. According to the results of statistical testing, there was no impact of gender and immobilization device on detected tumor respiratory motion. The impact was found for patient's age, lung volume changes, tumor volume and its location in different lung segments. The dominant lung cancer motion was observed for smaller tumors (up to 20 cc) located in posterior, caudal segments. This effect was also associated with a large variation in the lung volume during one respiratory cycle, observed for older patients. The important finding of the study is connected with the description of different patterns of tumor motion in AP and CC directions.

The paper describes a computer tool dedicated to the comprehensive analysis of lung changes in computed tomography (CT) images. The correlation between the dose delivered during radiotherapy and pulmonary fibrosis is offered as an example analysis. The input data, in DICOM (Digital Imaging and Communications in Medicine) format, is provided from CT images and dose distribution models of patients. The CT images are processed using convolution neural networks, and next, the selected slices go through the segmentation and registration algorithms. The results of the analysis are visualized in graphical format and also in numerical parameters calculated based on the images analysis.