Controlling and monitoring temperature at the single cell level has become pivotal in biology and medicine. Indeed, temperature influences many intracellular processes and is also involved as an activator in novel therapies. Aiming to assist such developments, several approaches have recently been proposed to probe cell temperature in vitro. None of them have so far been extended to a living organism. Here we present the first in vivo intracellular temperature imaging. Our technique relies on measuring the fluorescence polarization anisotropy of green fluorescent protein (GFP) on a set of GFP expressing neurons in Caenorhabditis elegans (C. elegans). We demonstrate fast and noninvasive monitoring of subdegree temperature changes on a single neuron induced by local photoheating of gold nanoparticles. This simple and biocompatible technique is envisioned to benefit several fields including hyperthermia treatment, selective drug delivery, thermal regulation of gene expression and neuron laser ablation.

There is an important relationship between probiotics, psychobiotics and cognitive and behavioral processes, which include neurological, metabolic, hormonal and immunological signaling pathways; the alteration in these systems may cause alterations in behavior (mood) and cognitive level (learning and memory). Psychobiotics have been considered key elements in affective disorders and the immune system, in addition to their effect encompassing the regulation of neuroimmune regulation and control axes (the hypothalamic-pituitary-adrenal axis or HPA, the sympathetic-adrenal-medullary axis or SAM and the inflammatory reflex) in diseases of the nervous system. The aim of this review is to summarize the recent findings about psychobiotics, the brain-gut axis and the immune system. The review focuses on a very new and interesting field that relates the microbiota of the intestine with diseases of the nervous system and its possible treatment, in neuroimmunomodulation area. Indeed, although probiotic bacteria will be concentrated after ingestion, mainly in the intestinal epithelium (where they provide the host with essential nutrients and modulation of the immune system), they may also produce neuroactive substances which act on the brain-gut axis.

Genomic signal processing (GSP) refers to the use of signal processing for the analysis of genomic data. GSP methods require the transformation or mapping of the genomic data to a numeric representation. To date, several DNA numeric representations (DNR) have been proposed; however, it is not clear what the properties of each DNR are and how the selection of one will affect the results when using a signal processing technique to analyze them. In this paper, we present an experimental study of the characteristics of nine of the most frequently-used DNR. The objective of this paper is to evaluate the behavior of each representation when used to measure the similarity of a given pair of DNA sequences.

Human papillomavirus (HPV) is considered the aetiological agent for cervical cancer. Several reports have addressed a relationship with HPV and breast cancer, as different HPVs have been identified. The purpose of this study was to detect HPV DNA in 67 breast cancer patients and 40 non-malignant disease breast tissues by means of Polymerase Chain Reaction with consensus primers. The frequency of HPV in the cases group were 4.4% (3/67) and no positive samples among the reference group were identified. From the 3 positive samples, HPV types 16, 18 and 33 were identified by restriction patterns and direct sequencing. The high diversity among detection in the related studies shows that population genomic heterogeneity plays an important role in the disease. The low frequency detected in the present study suggests that HPV does not play an important role in breast cancer.

Genomic signal processing (GSP) methods which convert DNA data to numerical values have recently been proposed, which would offer the opportunity of employing existing digital signal processing methods for genomic data. One of the most used methods for exploring data is cluster analysis which refers to the unsupervised classification of patterns in data. In this paper, we propose a novel approach for performing cluster analysis of DNA sequences that is based on the use of GSP methods and the K-means algorithm. We also propose a visualization method that facilitates the easy inspection and analysis of the results and possible hidden behaviors. Our results support the feasibility of employing the proposed method to find and easily visualize interesting features of sets of DNA data.