ants and Neoplasms

Early detection of cancer is critical in medical sciences, as the sooner a cancer is diagnosed, the higher are the chances of recovery. Tumour cells are characterized by specific volatile organic compounds (VOCs) that can be used as cancer biomarkers. Through olfactory associative learning, animals can be trained to detect these VOCs. Insects such as ants have a refined sense of smell, and can be easily and rapidly trained with olfactory conditioning. Using urine from patient-derived xenograft mice as stimulus, we demonstrate that individual ants can learn to discriminate the odour of healthy mice from that of tumour-bearing mice and do so after only three conditioning trials. After training, they spend approximately 20% more time in the vicinity of the learned odour than beside the other stimulus. Chemical analyses confirmed that the presence of the tumour changed the urine odour, supporting the behavioural results. Our study demonstrates that ants reliably detect tumour cues in mice urine and have the potential to act as efficient and inexpensive cancer bio-detectors.

Topics: Animals; Ants; Heterografts; Humans; Learning; Mice; Neoplasms; Odorants; Smell

Targeted drug delivery system is believed as one of the most promising solutions for cancer treatment due to its low-dose requirement and less side effects. However, both passive targeting and active targeting rely on systemic blood circulation and diffusion, which is actually not the real "active" drug delivery. In this paper, an ant-behavior inspired nanonetwork composing of intelligent nanomachines is proposed. A big intelligent nanomachine take small intelligent nanomachines and drugs to the vicinity of of the tumor area. The small intelligent nanomachines can coordinate with each other to find the most effective path to the tumor cell for drug transportation. The framework and mechanism of this cooperative network are proposed. The route finding algorithm is presented. The convergence performance is analytically analyzed where the influence of the factors such as molecule degradation rate, home-destination distance, number of small nanomachines to the convergence is presented. Finally the simulation results validate the effectiveness of the proposed mechanism and analytical analysis.

Topics: Algorithms; Animals; Ants; Behavior, Animal; Computer Simulation; Drug Delivery Systems; Models, Biological; Nanomedicine; Neoplasms

It is increasingly appreciated that phenotypic stochasticity plays fundamental roles in biological systems at the cellular level and that a variety of mechanisms generates phenotypic interconversion over a broad range of time scales. The ensuing dynamic heterogeneity can be used to understand biological and clinical processes involving diverse phenotypes in different cell populations. The same principles can be applied, not only to populations composed of cells, but also to populations composed of molecules, tissues, and multicellular organisms. Stochastic units generating dynamic heterogeneity can be integrated across various length scales. We propose that a graphical tool we have developed, called a metronomogram, will allow us to identify factors that suitably influence the restoration of homeostatic heterogeneity so as to modulate the consequences of dynamic heterogeneity for desired outcomes.

Topics: Animals; Ants; Behavior, Animal; Biofilms; Cryptococcus neoformans; Gene Expression Regulation, Neoplastic; Humans; Models, Biological; Neoplasm Metastasis; Neoplasms; Oncogenes; Phenotype; Proteome; Receptor, ErbB-2; Stochastic Processes