Browsing by Author "Dhushyandhun, ME"

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    Bifunctional cysteine gold nanocluster for β-amyloid fibril inhibition and fluorescence imaging: A distinctive approach to manage Alzheimer's disease
    (Journal of Materials Chemistry B, 2023-04) Resmi, AN; Rekha, CR; Dhushyandhun, ME; Sarathkumar, E; Shenoy, SJ; Gulia, KK; Jayasree, RS
    Alzheimer's disease (AD) is a progressive complex neurodegenerative disorder affecting millions of individuals worldwide. Currently, there is no effective treatment for AD. AD is characterized by the deposition of amyloid plaques/fibrils. One major strategy for managing this disease is by slowing the progression of AD using different drugs which could potentially limit free-radical formation, oxidative stress and lipid peroxidation and promote the survival of neurons exposed to β-amyloid. Inhibition of amyloid fibrillization and clearance of amyloid plaques/fibrils are essential for the prevention and treatment of AD. The thiophilic interaction between the side chain of an aromatic residue in a polypeptide and a sulphur atom of the compound can effectively inhibit amyloid fibril formation. In this work, we have synthesized cysteine-capped gold nanoclusters (Cy-AuNCs) which exhibit inherent red emission and can disintegrate amyloid fibrils through the aforementioned thiophilic interactions. Herein, we also used molecular docking to study the thiophilic interactions between the sulphur atom of Cy-AuNCs and the aromatic rings of the protein. Finally, the gold cluster was functionalized with a brain targeting molecule, Levodopa (AuCs-LD), to specifically target the brain and to facilitate passage through the blood brain barrier (BBB). Both Cy-AuNCs and AuCs-LD showed good biocompatibility and the inherent fluorescence properties of nanoclusters enabled real time imaging. The efficacy of the nanoclusters to disintegrate amyloid fibrils and their ability to cross the BBB were demonstrated both in vitro and in vivo in the BBB model and the AD animal model respectively. Our results imply that nanoparticle-based artificial molecular chaperones may offer a promising therapeutic approach for AD.
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    Ultrasensitive Detection of Blood-Based Alzheimer’s Disease Biomarkers: A Comprehensive SERSImmunoassay Platform Enhanced by Machine Learning
    (ACS Chemical Neuroscience, 2024-11) Resmi, AN; Nazeer, SS; Dhushyandhun, ME; Paul, Willi; Chacko, BP; Menon, RN; Jayasree, RS
    Accurate and early disease detection is crucial for improving patient care, but traditional diagnostic methods often fail to identify diseases in their early stages, leading to delayed treatment outcomes. Early diagnosis using blood derivatives as a source for biomarkers is particularly important for managing Alzheimer’s disease (AD). This study introduces a novel approach for the precise and ultrasensitive detection of multiple core AD biomarkers (Aβ40, Aβ42, p-tau, and t-tau) using surface-enhanced Raman spectroscopy (SERS) combined with machine-learning algorithms. Our method employs an antibody-immobilized aluminum SERS substrate, which offers high precision, sensitivity, and accuracy. The platform achieves an impressive detection limit in the attomolar (aM) range and spans a wide dynamic range from aM to micromolar (μM) concentrations. This ultrasensitive and specific SERS immunoassay platform shows promise for identifying mild cognitive impairment (MCI), a potential precursor to AD, from blood plasma. Machine-learning algorithms applied to the spectral data enhance the differentiation of MCI from AD and healthy controls, yielding excellent sensitivity and specificity. Our integrated SERS-machine-learning approach, with its interpretability, advances AD research and underscores the effectiveness of a cost-efficient, easy-to-prepare Al-SERS substrate for clinical AD detection.