Nerve growth factor, clinical applications and production of the recombinant protein

The mammalian neurotrophin family proteins, nerve growth factor (NGF), brain-derived neurotrophic factor (BDNF), neurotrophin-3 (NT-3) and neurotrophin-4/5 (NT-4/5) are known as neuronal survival factors. NGF, one of the most important cytokines, is composed of 118 amino acids. NGF is involved in the growth and differentiation of neural cells of the vertebrate peripheral sympathetic nerve as well as basal forebrain cholinergic neurons which degenerate in Alzheimer’s disease. In addition, it is implicated in the regulation of synaptic transmission and synaptogenesis in the central nervous system. NGF is produced by a variety of immune cells, including B cells, T cells, monocytes and mast cells as well as nervous system and binds through two distinct receptors, TrkA and p75NTR which signaling through them leads to the neuronal differentiation and cell death respectively. Considering the importance of this protein as a drug, NGF has been proposed for the treatment of neuron degenerative diseases such as Alzheimer's, Parkinson's and multiple sclerosis. To produce enough protein for research and clinical applications, genetic engineering techniques are used to produce recombinant forms. To date, there are no reports about the systems for production of the recombinant human NGF in an effective, low cost, with industrial production. Plants as a safe host generally offer major advantages such as free of animal pathogens, low costs, the ability to produce a protein similar to natural protein, and industrial production in large scale. Then they are suitable for the production of recombinant human NGF. Keywords: Nerve growth factor, Neurotrophin, Recombinant protei

‌Fame on social networks: A study of the reasons and consequences of getting famous on Instagram

With the emergence of new social media networks, the mechanisms of gaining fame have undergone massive transformations. Today, users have attained the opportunity to reach fame, which was not easily accessible before. In this regard, due to its easy-to-use capabilities and high popularity among users, Instagram has become a main platform for creating fame. Many Instagram users are inclined to become famous on this social network for various reasons, and this fame brings considerable consequences in the society, which enhances the importance of sociological analysis of the issue. By this way, the present paper tries to examine and analyze the causes and effects of fame on Instagram while reviewing the theories about economy, fame, and microcelebrity. The study was conducted with a qualitative approach and semi-structured in-depth interviews with 20 Instagram users. The findings obtained from the interviews were also extracted and analyzed with the thematic analysis technique and demonstrated 6 main themes and 12 sub-themes.  Finally, based on the interviewees' responses, significant reasons for seeking fame on Instagram include monetizing and acquiring economic capital, a shortcut to success, and an opportunity to be seen and heard, which has led to important outcomes such as the standardization of taste, the consumerism of everyday life, and changing values and norms in the society

Passage Retrieval in Log Files: An Approach Based on Query Enrichment

Abstract. The question answering systems are considered the next generation of search engines. This paper focuses on the first step of this process, which is to search for relevant passages containing answers. Passage Retrieval, can be difficult because of the complexity of data, log files in our case. Our contribution is based on the enrichment of queries by using a learning method and a novel term weighting function. This original term weighting function, used within the enrichment process, aims to assign a weight to terms according to their relatedness to the context of answers. Experiments conducted on real data show that our protocol of primitive query enrichment make it possible to retrieve relevant passages.

Measurement of serum hepatitis B surface antibody levels in Iranian autistic children and evaluation of immunological memory after booster dose injection in comparison with controls

Background: Responsiveness to hepatitis B vaccine among patients with autism spectrum disorders (ASD) has not been evaluated worldwide. We aimed to determine the anti-HBs antibody duration in autistic and healthy children few years after primary vaccination and evaluate their immunological memory against hepatitis B virus (HBV) vaccine with booster dose administration. Methods: One hundred seven and 147 HBsAg-negative children from ASD and normal population were recruited, respectively. HBV seromarkers (HBc-Ab, HBsAg, and HBs-Ab) were assessed and subsequently, molecular tests were used on all the subjects. A booster dose of vaccine was injected for those who showed low levels (10 mIU/mL) and low anti-HBs levels, respectively. Among control group, 74 (50.4) and 73 (49.6) had sufficient and low antibody levels, respectively. After injection of a booster dose for all children with low antibody, 100 of ASD and 92 (59 of 64) of control pupils contained >10 mIU/mL of antibody, respectively. In both the groups, the HBs-Ab titer increased similarly in response to the booster injection (P < 0.05). Conclusion: Despite previous investigations regarding immune impairment in individuals with autism, the immune system of these individuals was able to manage the hepatitis B vaccine challenge. © 2019 Wiley Periodicals, Inc

Automatic sleep stage identification: difficulties and possible solutions

The diagnosis of many sleep disorders is a labour intensive task that involves the specialised interpretation of numerous signals including brain wave, breath and heart rate captured in overnight polysomnogram sessions. The automation of diagnoses is challenging for data mining algorithms because the data sets are extremely large and noisy, the signals are complex and specialist's analyses vary. This work reports on the adaptation of approaches from four fields; neural networks, mathematical optimisation, financial forecasting and frequency domain analysis to the problem of automatically determing a patient's stage of sleep. Results, though preliminary, are promising and indicate that combined approaches may prove more fruitful than the reliance on a approach