Alzheimer’s disease (AD) is one of the most common forms of dementia. AD is associated with memory loss and cognitive decline. Several research works have been carried out to treat AD. However, currently available treatment options are only useful in the treatment of the individual pathology of AD but not useful in disease modification. Recent research works have identified the associated effects of neuroinflammation, oxidative stress, and glial cell dysfunction in AD pathology. Aspirin is one of the most commonly used NSAIDs in the treatment of several inflammatory diseases. Aspirin inhibits cyclooxygenase (COX) enzymes through an irreversible pathway. However, aspirin also exhibits other important pharmacological properties. This review aims to highlight the potential of aspirin-based multi-target directed ligands in the regulation of AD pathology through the regulation of neuroinflammation and oxidative stress. Schematic overview of aspirin and aspirin-derived multi-target-directed ligands (MTDLs) targeting interconnected pathological processes in Alzheimer’s disease, including neuroinflammation, oxidative stress, glial activation, and amyloid-β–associated dysfunction.
New epoxy-based composites reinforced with Prosopis Juliflora / Borassus Flabellifer (PJ/BF) fibers fabricated through hand layup technique with compression molding process were studied for their mechanical, wear properties and water absorption, as a function of weight
This research aims to explore the mechanical and microstructural properties of AZ91 magnesium alloy, which is reinforced with ZrC nanoparticles and produced through stir casting enhanced by ultrasonic assistance. The material’s inherent limitations in overall mechanical performance and toughness under harsh conditions are meant to be solved by the addition of ZrC nanoparticles. Tensile strength, hardness, and varied ZrC concentrations (1.5
Most research on the mode II fracture toughness of fiber-reinforced concrete (FRC) has intentionally avoided bridging fibers at pre-notch surfaces by using a through-thickness crack (TTC) that cuts the entire thickness, including fibers in this region. The objective of the present research is to accurately measure mode II fracture toughness (KIIC) using double-notched cube (DNC) specimens on steel fiber reinforced self-compacting concrete (SFRSCC). The effects of precrack-to-specimen width ratios (a/w), i.e., a/w = 0.3, 0.4, and 0.5, and fiber volume fraction percentage (Vf%), i.e., Vf% = 1% and 1.5% were investigated. A comparison between KIIC measured through specimens having the TTC concept, i.e., the absence of fiber bridging on the surfaces of the pre-notch, and those with the presence of fiber bridging on the surfaces of the pre-notch, i.e., the matrix crack (MC) concept. For greater clarity, the SCC specimens were cast without fibers with (MC/C) or without fiber bridging on the pre-crack surfaces to determine the unique effect of the presence of fiber bridging on the pre-crack surfaces on enhancing KIIC. The results showed that DNC specimens with MC consistently obtained the highest mode KIIC for all values of a/w, indicating the greatest resistance to crack growth. KIIC increased as the a/w ratio increased. MC/C method, i.e., the presence of fibers behind the crack front only, showed more effectiveness on the KIIC than the TTC, i.e., the presence of fibers ahead of the crack front only. In general, the MC is an accurate method for measuring KIIC of FRC.
Artificial intelligence (AI) and machine learning (ML) are causing a revolution in the pharmaceutical industry by improving drug discovery, disease diagnoses, and medication adherence. Studies showing how cutting-edge gadgets like motion sensors, ingestible sensors, and computerized pillboxes boost prescription adherence by ensuring that patients follow their prescribed regimens reveal the effects of artificial intelligence in various domains. By employing machine learning and deep learning algorithms to enable the early and accurate identification of conditions like diabetic retinopathy and systemic sclerosis, artificial intelligence (AI) outperforms traditional methods in the diagnosis of disease. Furthermore, by employing tools like MolAICal and NLP to predict chemical characteristics, optimize formulations, and find new therapeutic targets, AI speeds up the drug development process. Even with these developments, there are still issues with data accessibility, transparency, and ethics. To fully realize AI's promise and make sure it can continue to transform pharmaceutical practice
