ALZHEIMER’S DISEASE: DELIVERY OF DRUGs THROUGH INTRANASAL ROUTE

Alzheimer’s disease (AD) is a progressive neurodegenerative disorder characterized by severe cognitive impairments. A major histopathological hallmark of AD is the presence of amyloid deposits in the parenchyma of the amygdala, hippocampus, and neocortex. β-amyloid is a small piece of a larger protein called “amyloid precursor protein†(APP). The main component of amyloid is the β-Amyloid protein (Aβ), a 39.43 amino acid peptide composed of a portion of the transmembrane domain and the extracellular domain of the APP. Aβ deposition leads to synaptic degeneration and interacts with different types of central nervous system receptors; hence, it disrupts neuronal homeostasis. Moreover, Aβ deposition along the cerebral vessels alters their tonicity and triggers some of the cerebrovascular deficits. Furthermore, its accumulation disrupts intracellular Ca2+ homeostasis which ultimately reduces neuronal Ca2+ buffering capacity and increases excitotoxicity outcomes. The emerging approach is to bypass the BBB by intranasal delivery, which provides a practical, noninvasive, rapid and simple method to deliver the therapeutic agents to the CNS. This method works the unique connection between the nose and the brain that has evolved to sense odors and other chemical stimuli. On the basis of clinical trials (Phase I and II) it is reported that the intranasal route is feasible for the transport of the drug to the CNS. Intranasal delivery does not require any modification of the therapeutic agents and does not require that drugs be coupled with any carrier like in case of drug delivery across the BBB. A wide variety of therapeutic agents, including both small molecules and macromolecules can be successfully delivered, including to the CNS, using the intranasal method.   Key Words: Alzheimer’s disease, β-amyloid, cerebrovascular deficits, excitotoxicit

Connectionist approaches to the deployment of prior knowledge for improving robustness in automatic speech recognition

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A proposed adoption model for green IT in manufacturing industries

Green information technology (IT) adoption has helped enhance the overall organization’s environmental sustainability. Developing the strategies for effective adoption of Green IT is one of the essential goals of decision-makers. This study purposes to investigate the factors that influence decision-makers’ intention to use Green IT and the proposed green IT adoption model in Malaysian manufacturing firms. The 183 valid data were obtained using survey questionnaires from Malaysia’s manufacturing industries’ industrial managers and examine collect data through two analytical techniques. Two-staged structural equation modeling and artificial neural network applied for hypotheses evaluation and finding the significance level of every factor in the model. The outcomes of hypotheses evaluation through structural equation modeling revealed that managerial interpretation and ascription of responsibility have a significant role in predicting the adoption of green information technology in manufacturing companies. Besides, the Artificial Neural Network (ANN) results showed that the managerial interpretation and ascription of responsibility are considered as the most significant factors of green information technology adoption. This study will help the decision-makers and policymakers develop policies and programs for the effective employment of green information technology in manufacturing industries