New Analysis Tool Identified for Predicting the Efficacy of AChE Inhibitors in Treating Alzheimer’s Disease

New Analysis Tool Identified for Predicting the Efficacy of AChE Inhibitors in Treating Alzheimer’s Disease
In a recent study published in the journal Computational and Mathematical Methods in Medicine, a team of researchers found that the use of multiscale entropy analysis (MSE), of EEG signals, especially Slope 2, provides a potential tool for predicting the efficacy of AChE inhibitors prior to therapy in patients with Alzheimer’s Disease (AD). Alzheimer’s disease (AD) is a dementia condition that causes a progressive decline in cognitive functioning. AD neurodegeneration is thought to be caused by deposition of amyloid beta-peptide in plaques or formation of neurofibrillary tangle in brain tissue. Symptoms of AD are related to a cholinergic deficit in the cerebral cortex and other areas of the brain. Acetylcholinesterase (AChE) inhibitors inhibit the acetylcholinesterase enzyme from breaking down acetylcholine and cause an increase in the level and duration of neurotransmitter acetylcholine activity. AChEs have been found to be an effective therapy for patients with AD. Pharmacoeconomic studies have shown that therapies can postpone dementia from progressing to more severe stages, however, clinicians still debate that AChE inhibitors have an effect on only 25–50% of patients with AD, which cannot be identified before the start of the treatment. Moreover, the time scale for assessing the effect of AChE inhibitors can last between several months to several years. Tracing the activation of the neurotransmitter ACh, which is released from the presynaptic vesicles that are triggered
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