Publications by authors named "D C Nayak"

Artificial intelligence (AI) is a rapidly transforming drug discovery and development process, significantly impacting the pharmaceutical industry and enhancing human health. This review article examines the tremendous role of AI in analyzing complex biological data, optimizing research processes, and reducing costs of production. Implementation of AI in the pharmaceutical sector can store a vast dataset of manufacturing processes, identify potential disease targets, simulate physiological conditions, and predict drug interactions.

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Transposable element (TE) silencing in the germline is crucial for preserving genome integrity; its absence results in sterility and diminished developmental robustness. The Piwi-interacting RNA (piRNA) pathway is the primary small non-coding RNA mechanism by which TEs are silenced in the germline. Three piRNA binding proteins promote the piRNA pathway function in the germline- P-element-induced wimpy testis (Piwi), Aubergine (Aub), and Argonaute 3 (Ago3).

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The transdermal route is one of the effective routes for delivering drugs. It also overcomes many limitations associated with oral delivery. One of the limitations of this route is the drug's poor skin permeability-stratum corneum, the skin's outermost layer that also acts as a barrier for the drug to penetrate.

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Background: Primary aldosteronism can be treated medically but there is no standardised method to evaluate treatment outcomes. We aimed to develop criteria for assessing the outcomes of targeted medical treatment of primary aldosteronism, analyse outcomes across an international cohort, and identify factors associated with a complete treatment response.

Methods: An international panel of 31 primary aldosteronism experts used the Delphi method to reach consensus on the definition of complete, partial, or absent biochemical and clinical outcomes of medical treatment of primary aldosteronism.

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The emergence of infectious disease and antibiotic resistance in bacteria like Escherichia coli (E. coli) shows the necessity for novel computational techniques for identifying essential genes that contribute to resistance. The task of identifying resistant strains and multi-drug patterns in E.

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