Sonic Hedgehog (Shh) signalling cascade is one of the intricate signal transduction mechanisms that govern the precisely regulated developmental processes of multicellular organisms. Along with establishing the patterns of cellular differentiation to direct complex organ formation, it also has an important role in post-embryonic tissue regeneration and repair processes. Especially, Shh signalling is implicated in the induction of multifarious neuronal populations in central nervous system. There is compelling evidence of the involvement of Shh protein in the signalling network that regulates various morphogenetic processes such as the exquisite neural tube pattern formation. In the morphogenetic field, the activation of Shh signalling processes is intricately linked to the alterations at the molecular level in the structure of Shh protein that leads to its altered biophysical and biochemical reactivity. This brief article gives an overview of such complex cascade of events in Shh signalling and its transduction pathways.

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Online self-customisation (OSC) enables customers to tailor their preferences to certain product features via a brand-hosted online platform. Recent literature has given increasing attention to how consumers value OSC. However, extant research is characterised by a scarcity of understanding the effects of brand identity and individual differences on consumer responses to OSC. The purpose of this paper is to examine the mediating role of trust and the moderating role of need for uniqueness on the effects of brand identity prestige and brand identity similarity on consumer perceived usefulness of OSC. A field survey, through mall intercept, was conducted to test this conceptual framework. Our findings advance this field by finding that, not only the brand identity and consumer need for uniqueness, but also the interaction between them may affect consumers’ evaluation of OSC

Nitric oxide is a key regulating factor for physiological functions, when elevated during inflammatory conditions, NO, can lower endogenous antioxidants level. Naringenin, a bioflavonoid has shown to possess anti-inflammatory action. However, its role in NO-mediated responses has not been elucidated till date. This study was designed to investigate antioxidant potential of naringenin against inflammation-mediated nitric oxide overproduction and antioxidant status with an improved glycemic profile in diabetic rats. From total rats, Group 1 received normal saline, while remaining received single intraperitoneal injection of alloxan and were then equally divided into group 2, 3, and 4, which latter received no-treatment, metformin (50 mg kg −1 day −1) and naringenin (50 mg kg −1 day −1), respectively, for 1 month. Results showed that naringenin significantly downregulated levels of glucose (p < .05), lipid profile, inflammatory biomarkers, and nitric oxide (p < .01) in alloxan-induced diabetic rats. It also improved SOD level as compared to that of metformin treatment. This work delivers that naringenin exerts antioxidant effect by downregulating inflammation-mediated nitric oxide overproduction. How to cite this article: Rehman K, Khan II, Akash MSH, Jabeen K, Haider K. Naringenin downregulates inflammation-mediated nitric oxide overproduction and potentiates endogenous antioxidant status during hyperglycemia.

Enhancers are short motifs that contain high position variability and free scattering. Identifying these non-coding DNA fragments and their strength is vital because they play an important role in the control of gene regulation. Enhancer identification is more complicated than other genetic factors due to free scattering and their very high amount of locational variation. To classify this biological difficulty, several computational tools in bioinformatics have been created over the last few years as current learning models are still lacking. To overcome these limitations, we introduce iEnhancer-Deep, a deep learning-based framework that uses One-Hot Encoding and a convolutional neural network for model construction, primarily for the identification of enhancers and secondarily for the classification of their strength. Parallels between the iEnhancer-Deep and existing state-of-the-art methodologies were drawn to evaluate the performance of the proposed model. Furthermore, a cross-species test was carried out to assess the generalizability of the proposed model. In general, the results show that the proposed model produced comparable results with the state-of-the-art models.