Glial cells are present in the brain roughly in an equal proportions to neurons, although such a ratio can vary significantly between different regions. They are central in several homeostatic and developmental aspects of the central nervous system and include oligodendrocytes progenitors and mature oligodendrocytes, astrocytes, and the "immune guests" of the CNS - the microglial cells, all of which display considerable morphological and functional variability, according to several recent investigations.
Homeopathy has been causing intense debates in Italy in the past years, mainly between its supporters and the scientists and clinicians pointing out its lack of effects in comparison to placebo treatments, at least according to the most recent and influent scientific literature. This was until last september 10th, when Dr Patil and colleagues published, on Scientific Reports a paper reporting the anti-inflammatory and pain-inhibiting effects of an homeopathic preparation in rats.
Two days ago, a monday no less, James P Allison and Tasuku Honjo have been abruptly awaken (by their colleagues and relatives, before the Royal Swedish Academy of Sciences, it would seem) by the greatest new that a scientist could possibly believe to receive on a random day of his or her life: they had been awarded with the 2018 Nobel Prize for physiology or medicine.
This week, Nature published two news that try to analyse the dire political, social and scientific situation that UK is facing now, six months away from the official exit from European Union. The scenario is far from encouraging and the lack, to date, of a defined agreement between Europe and Britain is, alone, causing trouble among several scientists inside and outside UK.
Microglia, the most abundant brain-resident immune cell, are specialized - highly heterogeneous tissue macrophages that control many functional and developmental features of the central nervous system (CNS). Their homeostatic population comes mostly by yolk sac precursors during embryogenesis and maintain a rather well-defined transcriptional signature that is maintained by cytokines of the brain environment (e.g. TGF-beta) and other environmental factors (e.g. microbiota); on the other hand, different cohort of cells, displaying a slightly different signature, is represented by perypheral monocyte-derived cells that enter the brain and differentiate in loco. Given the central role played by microglia in CNS homeostasis, these cells have long be connected to the processes that either trigger or avoid neurodegeneration.
The immune network, and the host of of its modulators, represent -alongside the central nervous system- the most complex entity in human biology, which makes the full contextualization of immune responses in health an disase one of the hardest tasks of research. This picture is further complicated by sex-based differences: indeed it is a well-known fact that males and females can differ significanlty when it comes to immune processes, as well as to the prevalence of autoimmune diseases, with women being more prone to develop conditions such as rheumatoid arthritis, lupus erythematosus and multiple sclerosis (MS). An important culprit of this difference has been found in the ormonal differences that lead to immune alterations (e.g. cytokine expression, generation of pathogenetic phenotypes of immuno cells), which, in turn, are thought to trigger the pathogenesis of autoimmune conditions. In this rather vast puzzle, a research group from Chicago School of Medicine might have recently found a new piece that helped clarifying how ormonal and immune differences between sexes have an impact in MS susceptibility and development.
Most of currently used high throughput-cell sorting techniques employ the analysis of low resolution data coming from the multiparametric measurements of the light peak intensities in the emission spectra of fluorochromes that are conjugated to antibodies binding to specific phenotypic cell markers. The use of more complex approaches, such as those that rely on the analysis of image-based informations, is basically limited by the highly demanding computational power that deep learning algorithms would require to quickly process the huge amount of informations confined in these dimensional data. Nao Nitta, and colleagues, however, proposed an interesting alternative.