As regards their morphology and biology, tumours consist of heterogeneous cell populations. The cancer stem cell (CSC) hypothesis assumes that a tumour is hierarchically organized and not all of the cells are equally capable of generating descendants, similarly to normal tissue. The only cells being able to self-renew and produce a heterogeneous tumour cell population are cancer stem cells. CSCs probably derive from normal stem cells, although progenitor cells may be taken into consideration as the source of cancer stem cells. CSCs reside in the niche defined as the microenvironment formed by stromal cells, vasculature and extracellular matrix. The CSC assays include FACS sorting, xenotransplantation to immunodeficient mice (SCID), incubation with Hoechst 33342 dye, cell culture in non-adherent conditions, cell culture with bromodeoxyuridine. CSCs have certain properties that make them resistant to anticancer therapy, which suggests they may be the target for potential therapeutic strategies.

Our study shows a significant difference in results and methods of evaluation of PI between pathologists.

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The proliferation index (PI) is crucial in histopathologic diagnostics, in particular tumors. It is calculated based on Ki-67 protein expression by immunohistochemistry. PI is routinely evaluated by a visual assessment of the sample by a pathologist. However, this approach is far from ideal due to its poor intra- and interobserver variability and time-consuming. These factors force the community to seek out more precise solutions. Virtual pathology as being increasingly popular in diagnostics, armed with artificial intelligence, may potentially address this issue. The proposed solution calculates the Ki-67 proliferation index by utilizing a deep learning model and fuzzy-set interpretations for hot-spots detection. The obtained region-of-interest is then used to segment relevant cells via classical methods of image processing. The index value is approximated by relating the total surface area occupied by immunopositive cells to the total surface area of relevant cells. The achieved results are compared to the manual calculation of the Ki-67 index made by a domain expert. To increase results reliability, we trained several models in a threefold manner and compared the impact of different hyper-parameters. Our best-proposed method estimates PI with 0.024 mean absolute error, which gives a significant advantage over the current state-of-the-art solution.

Abstract:The aim of the experiment was to determine if possible changes in connective tissue induced by massage could have a positive effect justifing the use of massage in all post-traumatic connective tissue conditions, e.g. tendon injuries. The investigations were performed in a group of 18 Buffalo rats. The rats were divided into two groups (experimental and control). To standardize the massage procedure, it was performed with an algometer probe of 0.5 cm 2 with constant pressure force of 1 kG (9,81 N). To analyse the number and diameter of collagen fibrils, two electron micrographs were performed for each rat of the collected segments of tendons of rat tail lateral extensor muscle. After image digitalization and calibration, the measurements were carried out using iTEM 5.0 software. The number of fibrils, their diameter and area were measured in a cross-sectional area. An increase of the number of collagen fibrils was observed in the tendons of massaged animals compared to the control group. Our study demonstrated that massage may cause a beneficial effect on metabolic activity of tendon's fibroblasts and, in consequence, may be applied for more effective use of massage for the prevention of tendon injury as well as after the injury has occurred.