Introduction: In the course of evolution, humans developed a number of complex multi-step wound healing mechanisms which limit the infectious agents access to the bloodstream, protect the organism from blood loss, and restore skin integrity. The process of skin wound healing includes the following stages: haemostasis, inflammation, proliferation, and remodeling. These processes are possible because of modulators, growth factors, cytokines, matrix metalloproteinases and cellular receptors, as well as some trace elements like zinc. Materials and Methods: The presented data was analyzed and compiled using all relevant articles describing the role of zinc in blood coagulation, proliferation, damaged tissues regeneration and angiogenesis. Results and Discussion: There are some on-going studies about zinc effects on blood coagulation, proliferation, damaged tissues regeneration and angiogenesis. However, molecular mechanisms of these processes are not yet fully understood and require further study. The analysis of scientific efforts to investigate the role of zinc in wound healing molecular mechanisms is especially relevant to the understanding of treatment of skin wound injuries. Conclusion: Wound healing is a complex multi-phase process consisting of several phases. Each stage involves metal ions, primarily zinc, which stimulates re-epithelialization, decreases inflammation and bacterial growth. The use of known zinc-based drugs is accompanied by side effects and low efficacy due to low skin absorption. These factors significantly limit use of such drugs and highlight the urgency of finding new, more effective and safe treatment. The emerging field of nanobiotechnology may provide an alternative platform to develop new therapeutic agents for the wound healing process. Graphical Abstract

Introducing the concept of digital twins in healthcare, medical education, and research is a complex multistage challenge requiring participation of multidisciplinary teams. In pursuing this goal, we have created a validated database of scans of colorectal tumor slides associated with relevant clinical and histological information. This database is also linked to the blood bank, which opens a wide range of opportunities for further research. Herein, we present our experience within the scope of the digital twins initiative.

Evaluation of the parameters such as tumor microenvironment (TME) and tumor budding (TB) is one of the most important steps in colorectal cancer (CRC) diagnosis and cancer development prognosis. In recent years, artificial intelligence (AI) has been successfully used to solve such problems. In this paper, we summarize the latest data on the use of artificial intelligence to predict tumor microenvironment and tumor budding in histological scans of patients with colorectal cancer. We performed a systematic literature search using 2 databases (Medline and Scopus) with the following search terms: ("tumor microenvironment" OR "tumor budding") AND ("colorectal cancer" OR CRC) AND ("artificial intelligence" OR "machine learning " OR "deep learning"). During the analysis, we gathered from the articles performance scores such as sensitivity, specificity, and accuracy of identifying TME and TB using artificial intelligence. The systematic review showed that machine learning and deep learning successfully cope with the prediction of these parameters. The highest accuracy values in TB and TME prediction were 97.7% and 97.3%, respectively. This review led us to the conclusion that AI platforms can already be used as diagnostic aids, which will greatly facilitate the work of pathologists in detection and estimation of TB and TME as instruments and second-opinion services. A key limitation in writing this systematic review was the heterogeneous use of performance metrics for machine learning models by different authors, as well as relatively small datasets used in some studies.

Background: bladder cancer is one of the most common urinary tract malignancies. Establishment of robust predictors of disease progression and outcome is important for personalizing treatment of non-muscular invasive bladder carcinoma (NMIBC). In this study we evaluated association of PD-L1 expression with other prognostic biomarkers, such as expression of miRNA-145 and miRNA-200a, FGFR3 gene expression, and mutation status in tissue specimens of the luminal subtype of newly diagnosed high and low grade NMIBC. Methods: twenty patients with primary luminal NMIBC were enrolled in the study. Tumor grade and risk level were determined in accordance with European Organization for Research and Treatment of Cancer (EORTC) guidelines and World Health Organization (WHO) classification. Neoplasm molecular subtype and PD-L1 expression level were assessed by immunohistochemistry. We used real-time PCR to evaluate the expression of microRNAs and FGFR3. We detected FGFR3 hotspot mutations in codons 248 and 249 by Sanger sequencing. Results: high grade primary luminal NMIBC showed comparatively higher expression of PD-L1 and microRNA-145 than a low grade tumor, whereas the latter had a higher FGFR3 expression and hotspot mutation rate. The tumor grade (HR = 571.72 [11.03–2.96] p = 0.002), PD-L1 expression (HR = 2.33 [0.92–1.92] p = 0.012), and FGFR3 expression (HR = 0.08 [0.17–0.42] p = 0.003) were associated with relapse-free survival. Conclusions: tumor grade in association with PD-L1 and FGFR3 expression can be considered as a complex predictor for primary luminal NMIBC progression.