A thorough understanding of the role of lncRNA in prostate cancer pathogenesis; Current knowledge and future research directions.

In the entire world, prostate cancer (PCa) is one of the most common and deadly cancers. Treatment failure is still common among patients, despite PCa diagnosis and treatment improvements. Inadequate early diagnostic markers and the emergence of resistance to conventional therapeutic approaches, particularly androgen-deprivation therapy, are the causes of this. Long non-coding RNAs (lncRNAs), as an essential group of regulatory molecules, have been reported to be dysregulated through prostate tumorigenesis and hold great promise as diagnostic targets. Besides, lncRNAs regulate the malignant features of PCa cells, such as proliferation, invasion, metastasis, and drug resistance. These multifunctional RNA molecules interact with other molecular effectors like miRNAs and transcription factors to modulate various signaling pathways, including AR signaling. This study aimed to compile new knowledge regarding the role of lncRNA through prostate tumorigenesis in terms of their effects on the various malignant characteristics of PCa cells; in light of these characteristics and the significant potential of lncRNAs as diagnostic and therapeutic targets for PCa. AVAILABILITY OF DATA AND MATERIALS: Not applicable.

Effect of selegiline as a monomine oxidase B inhibitor on the expression of neurotrophin mRNA levels in a contusion rat model of spinal cord injury.

OBJECTIVE: Spinal cord injury (SCI) is followed by a cascade of events at the site of injury, including vascular ischemia, an increase in free radicals, inflammation, and neuronal death. In these individuals, protection of nerves and supporting cells, as well as prevention of neuronal damage, may improve recovery opportunities. Neurotrophins are a family of polypeptides that regulate nerve differentiation, growth, and survival. Selegiline is a selective monoamine oxidase B (MAO-B) inhibitor used to treat Parkinson's disease. Selegiline has been found to have neuroprotective properties and may be useful for the expression of neurotrophins. The aim of this study was to evaluate the expression levels of neurotrophin genes in spinal cord rats treated with selegiline. METHODS: Rats were divided into four groups: injury (control), laminectomy, sham (injured rat received 1 ml saline intraperitoneally) and treatment (injured rat received 5 mg/kg selegiline intraperitoneally for 7 days; once a day). The BBB scale (Basso, Beattie and Bresnahan) was performed once a week for 4 weeks to assess motor function after a spinal cord injury. On day 28 after SCI, the rat was sacrificed and the spinal cord lesion removed. A real-time PCR approach was used to assess neurotrophin gene expression. RESULTS: The results showed that administration of selegiline improves locomotor function and increases mRNA levels of BDNF, GDNF, NT-3, and NT-4. CONCLUSION: In summary, the results of this study suggest that selegiline may be an appropriate treatment for spinal cord injuries.

Spatial regression modelling of particulate pollution in Calgary, Canada.

The study presents a spatial analysis of particulate pollution, which includes not only particulate matter, but also black carbon, a pollutant of growing concern for human health. We developed land use regression (LUR) models for two particulate matter size fractions, PM2.5 and PM10, and for δC, an index calculated from black carbon (BC)-a component of PM2.5-which indicates the portion of organic versus elemental BC. LUR models were estimated over Calgary (Canada) for summer 2015 and winter 2016. As all samples exhibited significant spatial autocorrelation, spatial autoregressive lag (SARlag) and error (SARerr) models were computed. SARlag models were preferred for all pollutants in both seasons, and yielded goodness of fit aligned with or higher than values reported in the literature. LUR models yielded consistent sets of predictors, representing industrial activities, traffic, and elevation. The obtained model coefficients were then combined with local land use variables to compute fine-scale concentration predictions over the entire city. The predicted concentrations were slightly lower and less dispersed than the observed ones. Consistent with observed pollution records, prediction maps exhibited higher concentration over the road network, industrial areas, and the eastern quadrants of the city. Lastly, results of a corresponding study of PM in summer 2010 and winter 2011 were considered. While the small size of the 2010-2011 sample hampered a multi-temporal analysis, we cautiously note comparable seasonal patterns and consistent association with land use variables for both PM fine fractions over the 5-year interval.