Neuroprotective Role of Phytochemicals

Neurodegenerative diseases are normally distinguished as disorders with loss of neurons. Various compounds are being tested to treat neurodegenerative diseases (NDs) but they possess solitary symptomatic advantages with numerous side effects. Accumulative studies have been conducted to validate the benefit of phytochemicals to treat neurodegenerative diseases including Alzheimer's disease (AD) and Parkinson's disease (PD). In this present review we explored the potential efficacy of phytochemicals such as epigallocatechin-3-galate, berberin, curcumin, resveratrol, quercetin and limonoids against the most common NDs, including Alzheimer's disease (AD) and Parkinson's disease (PD). The beneficial potentials of these phytochemicals have been demonstrated by evidence-based but more extensive investigation needs to be conducted for reducing the progression of AD and PD.

Glutathione in the Nervous System as a Potential Therapeutic Target to Control the Development and Progression of Amyotrophic Lateral Sclerosis

Amyotrophic lateral sclerosis (ALS) is a rare neurological disorder that affects the motor neurons responsible for regulating muscle movement. However, the molecular pathogenic mechanisms of ALS remain poorly understood. A deficiency in the antioxidant tripeptide glutathione (GSH) in the nervous system appears to be involved in several neurodegenerative diseases characterized by the loss of neuronal cells. Impaired antioxidant defense systems, and the accumulation of oxidative damage due to increased dysfunction in GSH homeostasis are known to be involved in the development and progression of ALS. Aberrant GSH metabolism and redox status following oxidative damage are also associated with various cellular organelles, including the mitochondria and nucleus, and are crucial factors in neuronal toxicity induced by ALS. In this review, we provide an overview of the implications of imbalanced GSH homeostasis and its molecular characteristics in various experimental models of ALS.

Potential therapeutic strategies of phytochemicals in neurodegenerative disorders

Neurodegeneration is a syndrome that occurs through the loss of the neuronal system's structure and function. In the 21st century, major health issues are related to cognitive impairment and neurological disorders such as autism, learning disabilities, Huntington's, cerebral palsy, schizophrenia, Alzheimer's, neuromuscular, lateral sclerosis, and Parkinson's disease may be life-threatening. Various experimental and epidemiological studies reveal the risk factors associated with the disease, like oxidative stress, hypertension, antioxidant enzyme abnormalities, metabolic toxicity, advanced age, cytoskeletal abnormalities, genetic defects, autoimmunity, mineral deficiencies, and other vascular disorders. Various compounds have been screened for the treatment of neurodegenerative diseases (NDs), but, due to their side effects, they have solitary symptomatic benefits. Phytochemicals play a crucial role in maintaining the chemical balance of the brain by affecting the receptor function of specific inhibitory neurotransmitters. This review highlights the importance of phytochemicals for neurodegenerative diseases, in particular the possible mechanism of action of these natural compounds used for the treatment.

Neuropsychopathologie de la maladie de Parkinson (1/2)

PARKINSON'S DISEASE NEUROPSYCHOPATHOLOGY (1/2).: A progressive, neurodegenerative disease of unknown aetiology, Parkinson's disease is characterised mainly by motor disorders. As the disease progresses, other disorders appear: speech, swallowing, perspiration, genitourinary, etc., as well as cognitive and psychological conditions. The consequences are significant, both on a social level and in terms of autonomy. The treatment is specialised and multi-professional.

Advancement in biosensor: "Telediagnosis" and "remote digital imaging"

Current developments in sensors and actuators are heralding a new era to facilitate things to happen effortlessly and efficiently with proper communication. On the other hand, Internet of Things (IoT) has been boomed up with er potential and occupies a wide range of disciplines. This study has choreographed to design of an algorithm and a smart data-processing scheme to implement the obtained data from the sensing system to transmit to the receivers. Technically, it is called "telediagnosis" and "remote digital monitoring," a revolution in the field of medicine and artificial intelligence. For the proof of concept, an algorithmic approach has been implemented for telediagnosis with one of the degenerative diseases, that is, Parkinson's disease. Using the data acquired from an improved interdigitated electrode, sensing surface was evaluated with the attained sensitivity of 100 fM (n = 3), and the limit of detection was calculated with the linear regression value coefficient. By the designed algorithm and data processing with the assistance of IoT, further validation was performed and attested the coordination. This proven concept can be ideally used with all sensing strategies for immediate telemedicine by end-to-end communications.

Bioinformatics and Deep Learning Approach to Discover Food-Derived Active Ingredients for Alzheimer's Disease Therapy

Alzheimer's disease (AD) prevention is a critical challenge for aging societies, necessitating the exploration of food ingredients and whole foods as potential therapeutic agents. This study aimed to identify natural compounds (NCs) with therapeutic potential in AD using an innovative bioinformatics-integrated deep neural analysis approach, combining computational predictions with molecular docking and in vitro experiments for comprehensive evaluation. We employed the bioinformatics-integrated deep neural analysis of NCs for Disease Discovery (BioDeepNat) application in the data collected from chemical databases. Random forest regression models were utilized to predict the IC50 (pIC50) values of ligands interacting with AD-related target proteins, including acetylcholinesterase (AChE), amyloid precursor protein (APP), beta-secretase 1 (BACE1), microtubule-associated protein tau (MAPT), presenilin-1 (PSEN1), tumor necrosis factor (TNF)-α, and valosin-containing protein (VCP). Their activities were then validated through a molecular docking analysis using Autodock Vina. Predictions by the deep neural analysis identified 166 NCs with potential effects on AD across seven proteins, demonstrating outstanding recall performance. The top five food sources of these predicted compounds were black walnut, safflower, ginger, fig, corn, and pepper. Statistical clustering methodologies segregated the NCs into six well-defined groups, each characterized by convergent structural and chemical signatures. The systematic examination of structure-activity relationships uncovered differential molecular patterns among clusters, illuminating the sophisticated correlation between molecular properties and biological activity. Notably, NCs with high activity, such as astragalin, dihydromyricetin, and coumarin, and medium activity, such as luteolin, showed promising effects in improving cell survival and reducing lipid peroxidation and TNF-α expression levels in PC12 cells treated with lipopolysaccharide. In conclusion, our findings demonstrate the efficacy of combining bioinformatics with deep neural networks to expedite the discovery of previously unidentified food-derived active ingredients (NCs) for AD intervention.

Sensitivity and specificity of the Amyotrophic Lateral Sclerosis Functional Rating Scale-Revised to detect dysarthria in individuals with amyotrophic lateral sclerosis

INTRODUCTION/AIMS

Given the widespread use of the Amyotrophic Lateral Sclerosis Functional Rating Scale-Revised (ALSFRS-R) to measure disease progression in ALS and recent reports demonstrating its poor sensitivity, we aimed to determine the sensitivity and specificity of the ALSFRS-R bulbar subscale and speech item to detect validated clinical ratings of dysarthria in individuals with ALS.

METHODS

Paired ALSFRS-R and validated Speech Intelligibility Test (SIT) data from individuals with ALS were analyzed. Trained raters completed duplicate, independent, and blinded ratings of audio recordings to obtain speech intelligibility (%) and speaking rate (words per minute, WPM). Binary dysarthria profiles were derived (dysarthria ≤96% intelligible and/or <150 WPM). Data were obtained using the Kruskal-Wallis test, receiver-operating characteristic (ROC) curve, area under the curve (AUC), sensitivity and specificity percentages, and positive/negative predictive values (PPV/NPV).

RESULTS

A total of 250 paired SIT and ALSFRS-R data points were analyzed. Dysarthria was confirmed in 72.4% (n = 181). Dysarthric speakers demonstrated lower ALSFRS-R bulbar subscale (8.9 vs. 11.2) and speech item (2.7 vs. 3.7) scores (P < .0001). The ALSFRS-R bulbar subscale score had an AUC of 0.81 (95% confidence interval [CI] 0.75 to 0.86). A subscale score of ≤11 yielded a sensitivity of 86%, specificity of 57%, PPV of 84%, and NPV of 60% to correctly identify dysarthria status. The ALSFRS-R speech item score demonstrated an AUC of 0.81 to detect dysarthria (95% CI 0.76 to 0.85), with sensitivity of 79%, specificity of 75%, PPV of 89%, and NPV of 58% for a speech item cutpoint of ≤3.

DISCUSSION

The ALSFRS-R bulbar and speech item subscale scores may be useful, inexpensive, and quick tools for monitoring dysarthria status in ALS.