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The autonomic nervous system and cancer

Makale M. T., Kesari S., Wrasidlo W.

Biocybernetics and Biomedical Engineering

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2017

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Vol. 37, no. 3

443--542

EN

Recent data have demonstrated extensive autonomic nervous system (ANS) neural participation in malignant tumors and infiltration of nerve fibers in and around malignant tumors. ANS cybernetic imbalances deriving from central nervous system (CNS) stress are associated with poorer patient outcome and may play a key role in tumor expansion. The ANS modulates and can destabilize tissue stem cells, and it drives the expression of neurotransmitter receptors on tumor cells. Disruption of tumor innervation and pharmacological ANS blockade have abrogated cancer growth in preclinical models. The present review interprets recent key findings with respect to the ANS and cancer. We highlight new data from animal models addressing specific cancers suggesting that unbalanced autonomic cybernetic control loops are associated with tissue instability which in turn promotes, (1) cancer stem cell based tumor initiation and growth, and (2) metastasis. We posit that identifying the sources of neural control loop dysregulation in specific tumors may reveal potential targets for antitumor therapy. Given the striking tumor regression results obtained with gastric vagotomy in gastric cancer models, and the effects of b-adrenergic blockade in pancreatic tumor models, it may be feasible to improve cancer outcomes with therapeutics targeted to the nervous system.

2

Full-automatic computer aided system for stem cell clustering using content-based microscopic image analysis

Li C., Huang X., Jiang T., Xu N.

Biocybernetics and Biomedical Engineering

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2017

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Vol. 37, no. 3

540--558

EN

Stem cells are very original cells that can differentiate into other cells, tissues and organs, which play a very important role in biomedical treatments. Because of the importance of stem cells, in this paper we propose a full-automatic computer aided clustering system to assist scientists to explore potential co-occurrence relations between the cell differentiation and their morphological information in phenotype. In this proposed system, a multi-stage Content-based Microscopic Image Analysis (CBMIA) framework is applied, including image segmentation, feature extraction, feature selection, feature fusion and clustering techni-ques. First, an Improved Supervised Normalized Cuts (ISNC) segmentation algorithm is newly introduced to partition multiple stem cells into individual regions in an original microscopic image, which is the most important contribution in this paper. Then, based on the seg-mented stem cells, 11 different feature extraction approaches are applied to represent the morphological characteristics of them. Thirdly, by analysing the robustness and stability of the extracted features, Hu and Zernike moments are selected. Fourthly, these two selected features are combined by an early fusion approach to further enhance the properties of the feature representation of stem cells. Finally, k-means clustering algorithm is chosen to classify stem cells into different categories using the fused feature. Furthermore, in order to prove the effectiveness and usefulness of this proposed system, we carry out a series of experiments to evaluate our methods. Especially, our ISNC segmentation obtains 92.4% similarity, 96.0% specificity and 107.8% ration of accuracy, showing the potential of our work.

3

Automatic tracking of neural stem cells in sequential digital images

Zhang T., Jia W., Zhu Y., Yang J.

Biocybernetics and Biomedical Engineering

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2016

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Vol. 36, no. 1

66--75

EN

Neural stem cells are the cells that give rise to the main cell types of the nervous system. Due to their varying size and shape, and random movement, the tracking of these cells in suspension in video sequences is challenging. This paper develops an automatic tracking system for neural stem cells. The system first detects and localizes cells in the image sequence, followed by a feature extraction step for the subsequent cell tracking. Then, the system tracks inactive cells using an improved mean shift algorithm, divisive cells through a context-based technique, and active cells by means of dynamic local prediction (DLP) and gray prediction (GP) algorithms. Experimental results show that the proposed system not only improves the accuracy of fast moving tracking, but also constructs accurately the trajectories of the cell movement and reduces the iterations during the center searching.

4

Hematopoietic stem cell based therapy of immunosuppressive viral infection — Numerical simulations

Korpusik A.

Biocybernetics and Biomedical Engineering

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2014

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Vol. 34, no. 2

125--131

EN

In the light of recent advantages in stem cell research and gene based therapies of viral infections, we present a numerical experiment referring to hematopoietic stem cell based therapy of immunosuppressive viral infection. We use a variation of basic mathematical model for impairment of help to simulate immune impairing infection. Next, we increase virus-specific CTL production (as in therapy) in different stages of infection. Obtained results are analyzed and compared with results from recent in vivo experiment.