Machine Learning for personalised stress detection: Inter-individual variability of EEG-ECG markers for acute-stress response.

Stress appears as a response for a broad variety of physiological stimuli. It does vary among individuals in amplitude, phase and frequency. Thus, the necessity for personalised diagnosis is key to prevent stress-related diseases. In order to evaluate stress levels, a multi-sensing system is proposed based on non-invasive EEG and ECG signals. A target population of 24 individuals which age range between 18-23 years old are intentionally exposed to control-induced stress tests while EEG and ECG are simultaneously recorded. The acquired signals are processed by using semisupevised Machine Learning techniques as those provide a patient-specific approach due to key characteristics such as adaptiveness and robustness. In here, a stress metric is proposed that jointly with each individual medical history provide mechanisms to prevent and avoid possible chronic-health issues for individuals whom are more sensitive to stressors. Finally, supervised learning techniques are used to classify the obtained featured clusters to evaluate specific and general subject models in order to pave the way for real time stress monitoring.

An update on the biology of cancer stem cells in breast cancer

Breast cancer stem cells are defined as cancer cells with self-renewal capacity. These cells represent a small subpopulation endowed with the ability to form new tumours when injected in nude mice. Markers of differentiation have been used to identify these cancer cells. In the case of breast cancer, CD44+/CD24- select a population with stem cell properties. The fact that these cells have self-renewal ability has suggested that this population could be responsible for new tumour formation and cancer relapse. These cells have been shown to be more resistant to chemotherapy and radiotherapy than normal cancer cells. The identification of the molecular druggable alterations responsible for the initiation and maintenance of cancer stem cells is an important goal. In this article we will review all these points with special emphasis on the possible role of new drugs designed to interact with molecular pathways of cancer stem cells (AU)

No disponible