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Thakral S, Yadav A, Singh V, Kumar M, Kumar P, Narang R, Sudhakar K, Verma A, Khalilullah H, Jaremko M, Emwas AH. Alzheimer's disease: Molecular aspects and treatment opportunities using herbal drugs. Ageing Res Rev 2023; 88:101960. [PMID: 37224884 DOI: 10.1016/j.arr.2023.101960] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2023] [Revised: 05/12/2023] [Accepted: 05/19/2023] [Indexed: 05/26/2023]
Abstract
Alzheimer's disease (AD), also called senile dementia, is the most common neurological disorder. Around 50 million people, mostly of advanced age, are suffering from dementia worldwide and this is expected to reach 100-130 million between 2040 and 2050. AD is characterized by impaired glutamatergic and cholinergic neurotransmission, which is associated with clinical and pathological symptoms. AD is characterized clinically by loss of cognition and memory impairment and pathologically by senile plaques formed by Amyloid β deposits or neurofibrillary tangles (NFT) consisting of aggregated tau proteins. Amyloid β deposits are responsible for glutamatergic dysfunction that develops NMDA dependent Ca2+ influx into postsynaptic neurons generating slow excitotoxicity process leading to oxidative stress and finally impaired cognition and neuronal loss. Amyloid decreases acetylcholine release, synthesis and neuronal transport. The decreased levels of neurotransmitter acetylcholine, neuronal loss, tau aggregation, amyloid β plaques, increased oxidative stress, neuroinflammation, bio-metal dyshomeostasis, autophagy, cell cycle dysregulation, mitochondrial dysfunction, and endoplasmic reticulum dysfunction are the factors responsible for the pathogenesis of AD. Acetylcholinesterase, NMDA, Glutamate, BACE1, 5HT6, and RAGE (Receptors for Advanced Glycation End products) are receptors targeted in treatment of AD. The FDA approved acetylcholinesterase inhibitors Donepezil, Galantamine and Rivastigmine and N-methyl-D-aspartate antagonist Memantine provide symptomatic relief. Different therapies such as amyloid β therapies, tau-based therapies, neurotransmitter-based therapies, autophagy-based therapies, multi-target therapeutic strategies, and gene therapy modify the natural course of the disease. Herbal and food intake is also important as preventive strategy and recently focus has also been placed on herbal drugs for treatment. This review focuses on the molecular aspects, pathogenesis and recent studies that signifies the potential of medicinal plants and their extracts or chemical constituents for the treatment of degenerative symptoms related to AD.
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Affiliation(s)
- Samridhi Thakral
- Department of Pharmaceutical Sciences, Guru Jambheshwar University of Science and Technology, Hisar 125001, Haryana, India
| | - Alka Yadav
- Department of Pharmaceutical Sciences, Guru Jambheshwar University of Science and Technology, Hisar 125001, Haryana, India
| | - Vikramjeet Singh
- Department of Pharmaceutical Sciences, Guru Jambheshwar University of Science and Technology, Hisar 125001, Haryana, India.
| | - Manoj Kumar
- Department of Pharmaceutical Sciences, Guru Jambheshwar University of Science and Technology, Hisar 125001, Haryana, India
| | - Pradeep Kumar
- Department of Pharmaceutical Sciences and Natural Products, Central University of Punjab, Bathinda 151401, Punjab, India
| | - Rakesh Narang
- Institute of Pharmaceutical Sciences, Kurukshetra University, Kurukshetra 136119, Haryana, India
| | - Kalvatala Sudhakar
- School of Pharmaceutical Sciences, Lovely Professional University, Phagwara, India
| | - Amita Verma
- Bioorganic and Medicinal Chemistry Research Laboratory, Department of Pharmaceutical Sciences, Sam Higginbottom University of Agriculture, Technology and Sciences, Prayagraj 211007, India.
| | - Habibullah Khalilullah
- Department of Pharmaceutical Chemistry and Pharmacognosy, Unaizah College of Pharmacy, Qassim University, Unayzah 51911, Saudi Arabia
| | - Mariusz Jaremko
- Smart-Health Initiative (SHI) and Red Sea Research Center (RSRC), Division of Biological and Environmental Sciences and Engineering (BESE), King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Saudi Arabia
| | - Abdul-Hamid Emwas
- Core Labs, King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Saudi Arabia
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Ossowska A, Kusiak A, Świetlik D. Progression of Selected Parameters of the Clinical Profile of Patients with Periodontitis Using Kohonen's Self-Organizing Maps. J Pers Med 2023; 13:346. [PMID: 36836580 PMCID: PMC9958729 DOI: 10.3390/jpm13020346] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2023] [Revised: 02/12/2023] [Accepted: 02/14/2023] [Indexed: 02/19/2023] Open
Abstract
(1) Background: Periodontitis is an inflammatory condition that affects the tissues surrounding the tooth and causes clinical attachment loss, which is the loss of periodontal attachment (CAL). Periodontitis can advance in various ways, with some patients experiencing severe periodontitis in a short period of time while others may experience mild periodontitis for the rest of their lives. In this study, we have used an alternative methodology to conventional statistics, self-organizing maps (SOM), to group the clinical profiles of patients with periodontitis. (2) Methods: To predict the periodontitis progression and to choose the best treatment plan, we can use artificial intelligence, more precisely Kohonen's self-organizing maps (SOM). In this study, 110 patients, both genders, between the ages of 30 and 60, were included in this retrospective analysis. (3) Results: To discover the pattern of patients according to the periodontitis grade and stage, we grouped the neurons together to form three clusters: Group 1 was made up of neurons 12 and 16 that represented a percentage of slow progression of almost 75%; Group 2 was made up of neurons 3, 4, 6, 7, 11, and 14 in which the percentage of moderate progression was almost 65%; and Group 3 was made up of neurons 1, 2, 5, 8, 9, 10, 13, and 15 that represented a percentage of rapid progression of almost 60%. There were statistically significant differences in the approximate plaque index (API), and bleeding on probing (BoP) versus groups (p < 0.0001). The post-hoc tests showed that API, BoP, pocket depth (PD), and CAL values were significantly lower in Group 1 relative to Group 2 (p < 0.05) and Group 3 (p < 0.05). A detailed statistical analysis showed that the PD value was significantly lower in Group 1 relative to Group 2 (p = 0.0001). Furthermore, the PD was significantly higher in Group 3 relative to Group 2 (p = 0.0068). There was a statistically significant CAL difference between Group 1 relative to Group 2 (p = 0.0370). (4) Conclusions: Self-organizing maps, in contrast to conventional statistics, allow us to view the issue of periodontitis advancement by illuminating how the variables are organized in one or the other of the various suppositions.
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Affiliation(s)
- Agata Ossowska
- Department of Periodontology and Oral Mucosa Diseases, Medical University of Gdansk, 80-208 Gdańsk, Poland
| | - Aida Kusiak
- Department of Periodontology and Oral Mucosa Diseases, Medical University of Gdansk, 80-208 Gdańsk, Poland
| | - Dariusz Świetlik
- Division of Biostatistics and Neural Networks, Medical University of Gdansk, 80-211 Gdańsk, Poland
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QSAR, Molecular Docking, Dynamic Simulation and Kinetic Study of Monoamine Oxidase B Inhibitors as Anti-Alzheimer Agent. CHEMISTRY AFRICA 2022. [DOI: 10.1007/s42250-022-00561-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
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Zhang Q, Yang P, Pang X, Guo W, Sun Y, Wei Y, Pang C. Preliminary exploration of the co-regulation of Alzheimer's disease pathogenic genes by microRNAs and transcription factors. Front Aging Neurosci 2022; 14:1069606. [PMID: 36561136 PMCID: PMC9764863 DOI: 10.3389/fnagi.2022.1069606] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2022] [Accepted: 11/15/2022] [Indexed: 12/12/2022] Open
Abstract
Background Alzheimer's disease (AD) is the most common form of age-related neurodegenerative disease. Unfortunately, due to the complexity of pathological types and clinical heterogeneity of AD, there is a lack of satisfactory treatment for AD. Previous studies have shown that microRNAs and transcription factors can modulate genes associated with AD, but the underlying pathophysiology remains unclear. Methods The datasets GSE1297 and GSE5281 were downloaded from the gene expression omnibus (GEO) database and analyzed to obtain the differentially expressed genes (DEGs) through the "R" language "limma" package. The GSE1297 dataset was analyzed by weighted correlation network analysis (WGCNA), and the key gene modules were selected. Next, gene ontology (GO) and Kyoto encyclopedia of genes and genomes (KEGG) pathway enrichment analysis for the key gene modules were performed. Then, the protein-protein interaction (PPI) network was constructed and the hub genes were identified using the STRING database and Cytoscape software. Finally, for the GSE150693 dataset, the "R" package "survivation" was used to integrate the data of survival time, AD transformation status and 35 characteristics, and the key microRNAs (miRNAs) were selected by Cox method. We also performed regression analysis using least absolute shrinkage and selection operator (Lasso)-Cox to construct and validate prognostic features associated with the four key genes using different databases. We also tried to find drugs targeting key genes through DrugBank database. Results GO and KEGG enrichment analysis showed that DEGs were mainly enriched in pathways regulating chemical synaptic transmission, glutamatergic synapses and Huntington's disease. In addition, 10 hub genes were selected from the PPI network by using the algorithm Between Centrality. Then, four core genes (TBP, CDK7, GRM5, and GRIA1) were selected by correlation with clinical information, and the established model had very good prognosis in different databases. Finally, hsa-miR-425-5p and hsa-miR-186-5p were determined by COX regression, AD transformation status and aberrant miRNAs. Conclusion In conclusion, we tried to construct a network in which miRNAs and transcription factors jointly regulate pathogenic genes, and described the process that abnormal miRNAs and abnormal transcription factors TBP and CDK7 jointly regulate the transcription of AD central genes GRM5 and GRIA1. The insights gained from this study offer the potential AD biomarkers, which may be of assistance to the diagnose and therapy of AD.
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Affiliation(s)
- Qi Zhang
- School of Computer Science, Sichuan Normal University, Chengdu, China
| | - Ping Yang
- School of Computer Science, Sichuan Normal University, Chengdu, China
| | - Xinping Pang
- West China School of Basic Medical Sciences and Forensic Medicine, Sichuan University, Chengdu, China
| | - Wenbo Guo
- School of Computer Science, Sichuan Normal University, Chengdu, China
| | - Yue Sun
- School of Computer Science, Sichuan Normal University, Chengdu, China
| | - Yanyu Wei
- National Key Laboratory of Science and Technology on Vacuum Electronics, School of Electronic Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China
| | - Chaoyang Pang
- School of Computer Science, Sichuan Normal University, Chengdu, China
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Ossowska A, Kusiak A, Świetlik D. Evaluation of the Progression of Periodontitis with the Use of Neural Networks. J Clin Med 2022; 11:4667. [PMID: 36012906 PMCID: PMC9409699 DOI: 10.3390/jcm11164667] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2022] [Revised: 08/01/2022] [Accepted: 08/07/2022] [Indexed: 11/16/2022] Open
Abstract
Periodontitis is an inflammatory disease of the tissues surrounding the tooth that results in loss of periodontal attachment detected as clinical attachment loss (CAL). The mildest form of periodontal disease is gingivitis, which is a necessary condition for periodontitis development. We can distinguish also some modifying factors which have an influence on the rate of development of periodontitis from which the most important are smoking and poorly controlled diabetes. According to the new classification from 2017, we can identify four stages of periodontitis and three grades of periodontitis. Grades tell us about the periodontitis progression risk and may be helpful in treatment planning and motivating the patients. Artificial neural networks (ANN) are widely used in medicine and in dentistry as an additional tool to support clinicians in their work. In this paper, ANN was used to assess grades of periodontitis in the group of patients. Gender, age, nicotinism approximal plaque index (API), bleeding on probing (BoP), clinical attachment loss (CAL), and pocket depth (PD) were taken into consideration. There were no statistically significant differences in the clinical periodontal assessment in relation to the neural network assessment. Based on the definition of the sensitivity and specificity in medicine we obtained 85.7% and 80.0% as a correctly diagnosed and excluded disease, respectively. The quality of the neural network, defined as the percentage of correctly classified patients according to the grade of periodontitis was 84.2% for the training set. The percentage of incorrectly classified patients according to the grade of periodontitis was 15.8%. Artificial neural networks may be useful tool in everyday dental practice to assess the risk of periodontitis development however more studies are needed.
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Affiliation(s)
- Agata Ossowska
- Department of Periodontology and Oral Mucosa Diseases, Medical University of Gdansk, Orzeszkowej 18 St., 80-208 Gdansk, Poland
| | - Aida Kusiak
- Department of Periodontology and Oral Mucosa Diseases, Medical University of Gdansk, Orzeszkowej 18 St., 80-208 Gdansk, Poland
| | - Dariusz Świetlik
- Division of Biostatistics and Neural Networks, Medical University of Gdansk, Debinki 1 St., 80-211 Gdansk, Poland
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Petralia MC, Mangano K, Quattropani MC, Lenzo V, Nicoletti F, Fagone P. Computational Analysis of Pathogenetic Pathways in Alzheimer’s Disease and Prediction of Potential Therapeutic Drugs. Brain Sci 2022; 12:brainsci12070827. [PMID: 35884634 PMCID: PMC9313152 DOI: 10.3390/brainsci12070827] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2022] [Revised: 06/17/2022] [Accepted: 06/22/2022] [Indexed: 11/16/2022] Open
Abstract
Background. Alzheimer’s disease (AD) is a chronic and progressive neurodegenerative disease which affects more than 50 million patients and represents 60–80% of all cases of dementia. Mutations in the APP gene, mostly affecting the γ-secretase site of cleavage and presenilin mutations, have been identified in inherited forms of AD. Methods. In the present study, we performed a meta-analysis of the transcriptional signatures that characterize two familial AD mutations (APPV7171F and PSEN1M146V) in order to characterize the common altered biomolecular pathways affected by these mutations. Next, an anti-signature perturbation analysis was performed using the AD meta-signature and the drug meta-signatures obtained from the L1000 database, using cosine similarity as distance metrics. Results. Overall, the meta-analysis identified 1479 differentially expressed genes (DEGs), 684 downregulated genes, and 795 upregulated genes. Additionally, we found 14 drugs with a significant anti-similarity to the AD signature, with the top five drugs being naftifine, moricizine, ketoconazole, perindopril, and fexofenadine. Conclusions. This study aimed to integrate the transcriptional profiles associated with common familial AD mutations in neurons in order to characterize the pathogenetic mechanisms involved in AD and to find more effective drugs for AD.
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Affiliation(s)
- Maria Cristina Petralia
- Department of Clinical and Experimental Medicine, University of Messina, 98122 Messina, Italy;
| | - Katia Mangano
- Department of Biomedical and Biotechnological Sciences, University of Catania, Via S. Sofia 97, 95123 Catania, Italy; (K.M.); (P.F.)
| | | | - Vittorio Lenzo
- Department of Social and Educational Sciences of the Mediterranean Area, University for Foreigners “Dante Alighieri” of Reggio Calabria, 89125 Reggio Calabria, Italy;
| | - Ferdinando Nicoletti
- Department of Biomedical and Biotechnological Sciences, University of Catania, Via S. Sofia 97, 95123 Catania, Italy; (K.M.); (P.F.)
- Correspondence:
| | - Paolo Fagone
- Department of Biomedical and Biotechnological Sciences, University of Catania, Via S. Sofia 97, 95123 Catania, Italy; (K.M.); (P.F.)
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