Current Update on Categorization of Migraine Subtypes on the Basis of Genetic Variation: a Systematic Review

Migraine is a complex neurovascular disorder that is characterized by severe behavioral, sensory, visual, and/or auditory symptoms. It has been labeled as one of the ten most disabling medical illnesses in the world by the World Health Organization (Aagaard et al Sci Transl Med 6(237):237ra65, 2014). According to a recent report by the American Migraine Foundation (Shoulson et al Ann Neurol 25(3):252-9, 1989), around 148 million people in the world currently suffer from migraine. On the basis of presence of aura, migraine is classified into two major subtypes: migraine with aura (Aagaard et al Sci Transl Med 6(237):237ra65, 2014) and migraine without aura. (Aagaard K et al Sci Transl Med 6(237):237ra65, 2014) Many complex genetic mechanisms have been proposed in the pathophysiology of migraine but specific pathways associated with the different subtypes of migraine have not yet been explored. Various approaches including candidate gene association studies (CGAS) and genome-wide association studies (Fan et al Headache: J Head Face Pain 54(4):709-715, 2014). have identified the genetic markers associated with migraine and its subtypes. Several single nucleotide polymorphisms (Kaur et al Egyp J Neurol, Psychiatry Neurosurg 55(1):1-7, 2019) within genes involved in ion homeostasis, solute transport, synaptic transmission, cortical excitability, and vascular function have been associated with the disorder. Currently, the diagnosis of migraine is majorly behavioral with no focus on the genetic markers and thereby the therapeutic intervention specific to subtypes. Therefore, there is a need to explore genetic variants significantly associated with MA and MO as susceptibility markers in the diagnosis and targets for therapeutic interventions in the specific subtypes of migraine. Although the proper characterization of pathways based on different subtypes is yet to be studied, this review aims to make a first attempt to compile the information available on various genetic variants and the molecular mechanisms involved with the development of MA and MO. An attempt has also been made to suggest novel candidate genes based on their function to be explored by future research.

Biosynthesis of JC-La2CoO4 magnetic nanoparticles explored in catalytic and SMMs properties

We have reported the synthesis of JC-La2CoO4 magnetic nanoparticles from Jatropha Curcas L. leaf extract in aqueous medium and potential application study in catalytic & Single Molecule Magnets (SMMs). Several techniques were used to investigate the structural, morphological, and elemental composition, particle size, optical properties, catalytic and magnetic properties by XRD, FTIR, SEM, EDAX, XPS, UV-visible and squid magnetic measurement. It was found that the crystallite sizes and grain sizes of JC-La2CoO4 NPs were 11.3 ± 1 and 24.1 ± 1 nm respectively and surface morphology of the nanoparticles looks spherical shape with good surface area. The band gap of JC-La2CoO4 was found to be 4.95 eV indicates good semiconductor in nature. XPS studies shows that La and Co present in + 3 and + 2 oxidation state respectively and suggest the composition formula is La2CoO4 with satisfied all the valency of metal ions. The photocatalytic efficiency of La2CoO4 shows good result against methylene blue (MB) compared to other dyes like MO, NO, RhB in presence of sunlight with rate constant 56.73 × 10-3 min-1 and completely degraded within 115 mints. The importance of JC-La2CoO4 has magnetic properties with antiferromagnetic coupling and SMMs properties with nature.

Randomness in the choice of neighbours promotes cohesion in mobile animal groups

Classic computational models of collective motion suggest that simple local averaging rules can promote many observed group-level patterns. Recent studies, however, suggest that rules simpler than local averaging may be at play in real organisms; for example, fish stochastically align towards only one randomly chosen neighbour and yet the schools are highly polarized. Here, we ask-how do organisms maintain group cohesion? Using a spatially explicit model, inspired from empirical investigations, we show that group cohesion can be achieved in finite groups even when organisms randomly choose only one neighbour to interact with. Cohesion is maintained even in the absence of local averaging that requires interactions with many neighbours. Furthermore, we show that choosing a neighbour randomly is a better way to achieve cohesion than interacting with just its closest neighbour. To understand how cohesion emerges from these random pairwise interactions, we turn to a graph-theoretic analysis of the underlying dynamic interaction networks. We find that randomness in choosing a neighbour gives rise to well-connected networks that essentially cause the groups to stay cohesive. We compare our findings with the canonical averaging models (analogous to the Vicsek model). In summary, we argue that randomness in the choice of interacting neighbours plays a crucial role in achieving cohesion.