Computational Insights into Chromene/pyran Derivatives: Molecular Docking, ADMET Studies, DFT Calculations, and MD Simulations as Promising Candidates for Parkinson's Disease DOI
Anjali Rani, M. Aslam,

Javed Khan

и другие.

Chemistry & Biodiversity, Год журнала: 2024, Номер 21(8)

Опубликована: Май 31, 2024

Parkinson's disease (PD) is a neurodegenerative condition characterized by both motor and non-motor symptoms. Although PD commonly associated with decline of dopaminergic neurons in the substantia nigra, other diagnostic criteria biomarkers also exist. In search for novel therapeutic agents, chromene pyran derivatives have shown potential due to their diverse pharmacological activities. This study utilizes comprehensive computational approach investigate viability chromene/pyran compounds as treatments PD. The drug-likeness characteristics these molecules were analyzed using ADMET (Absorption, Distribution, Metabolism, Excretion, Toxicity) studies. Molecular docking was performed against PDB ID: 2V5Z. best three chosen compound 7, 24, 67 binding energy -6.7, -8.6, -10.9 kcal/mol. Molecules demonstrating positive blood-brain barrier permeability, good solubility, favorable affinity further evaluated Density Functional Theory (DFT) calculations Dynamics (MD) simulations assess electronic structure stability. DFT indicated that molecule 82 has dipole moment 15.70 D. RMSD RMSF results confirmed stability complexes over 100 ns simulation, maximum 3 hydrogen bonds formed.

Язык: Английский

A Review on a Deep Learning Perspective in Brain Cancer Classification DOI Open Access
Gopal S. Tandel, Mainak Biswas, O. G. Kakde

и другие.

Cancers, Год журнала: 2019, Номер 11(1), С. 111 - 111

Опубликована: Янв. 18, 2019

A World Health Organization (WHO) Feb 2018 report has recently shown that mortality rate due to brain or central nervous system (CNS) cancer is the highest in Asian continent. It of critical importance be detected earlier so many these lives can saved. Cancer grading an important aspect for targeted therapy. As diagnosis highly invasive, time consuming and expensive, there immediate requirement develop a non-invasive, cost-effective efficient tools characterization grade estimation. Brain scans using magnetic resonance imaging (MRI), computed tomography (CT), as well other modalities, are fast safer methods tumor detection. In this paper, we tried summarize pathophysiology cancer, modalities automatic computer assisted machine deep learning paradigm. Another objective paper find current issues existing engineering also project future Further, have highlighted relationship between disorders like stroke, Alzheimer’s, Parkinson’s, Wilson’s disease, leukoriaosis, neurological context

Язык: Английский

Процитировано

386
Multiple system atrophy DOI

Alessandra Fanciulli,

Iva Stanković, Florian Krismer

и другие.

International review of neurobiology, Год журнала: 2019, Номер unknown, С. 137 - 192

Опубликована: Янв. 1, 2019

Язык: Английский

Процитировано

222
An update on blood-based biomarkers for non-Alzheimer neurodegenerative disorders DOI
Nicholas J. Ashton, Abdul Hye, Anto P. Rajkumar

и другие.

Nature Reviews Neurology, Год журнала: 2020, Номер 16(5), С. 265 - 284

Опубликована: Апрель 22, 2020

Язык: Английский

Процитировано

163
Multiple system atrophy DOI
Werner Poewe, Iva Stanković, Glenda M. Halliday

и другие.

Nature Reviews Disease Primers, Год журнала: 2022, Номер 8(1)

Опубликована: Авг. 25, 2022

Язык: Английский

Процитировано

163
Biomarkers for Parkinson’s Disease: How Good Are They? DOI
Tianbai Li, Weidong Le

Neuroscience Bulletin, Год журнала: 2019, Номер 36(2), С. 183 - 194

Опубликована: Окт. 23, 2019

Язык: Английский

Процитировано

126
A new MR imaging index for differentiation of progressive supranuclear palsy-parkinsonism from Parkinson's disease DOI
Aldo Quattrone, Maurizio Morelli, Salvatore Nigro

и другие.

Parkinsonism & Related Disorders, Год журнала: 2018, Номер 54, С. 3 - 8

Опубликована: Июль 25, 2018

Язык: Английский

Процитировано

123
Establishing a framework for neuropathological correlates and glymphatic system functioning in Parkinson’s disease DOI Creative Commons
Saranya Sundaram, Rachel Hughes,

Eric Peterson

и другие.

Neuroscience & Biobehavioral Reviews, Год журнала: 2019, Номер 103, С. 305 - 315

Опубликована: Май 24, 2019

Язык: Английский

Процитировано

120
A clinical-anatomical signature of Parkinson's disease identified with partial least squares and magnetic resonance imaging DOI
Yashar Zeighami, Seyed‐Mohammad Fereshtehnejad, Mahsa Dadar

и другие.

NeuroImage, Год журнала: 2017, Номер 190, С. 69 - 78

Опубликована: Дек. 19, 2017

Язык: Английский

Процитировано

103
Imaging the Substantia Nigra in Parkinson Disease and Other Parkinsonian Syndromes DOI Creative Commons
Yun Jung Bae, Jong‐Min Kim, Chul‐Ho Sohn

и другие.

Radiology, Год журнала: 2021, Номер 300(2), С. 260 - 278

Опубликована: Июнь 8, 2021

Parkinson disease is characterized by dopaminergic cell loss in the substantia nigra of midbrain. There are various imaging markers for disease. Recent advances MRI have enabled elucidation underlying pathophysiologic changes nigral structure. This has contributed to accurate and early diagnosis improved progression monitoring. article aims review recent developments other parkinsonian syndromes, including nigrosome imaging, neuromelanin quantitative iron mapping, diffusion-tensor imaging. In particular, this examines using 7-T 3-T susceptibility-weighted Finally, discusses volumetry its clinical importance related symptom manifestation. will improve understanding advancements Published under a CC BY 4.0 license.

Язык: Английский

Процитировано

99
Classification of PPMI MRI scans with voxel-based morphometry and machine learning to assist in the diagnosis of Parkinson’s disease DOI Creative Commons
Gabriel Solana-Lavalle, Roberto Rosas-Romero

Computer Methods and Programs in Biomedicine, Год журнала: 2020, Номер 198, С. 105793 - 105793

Опубликована: Окт. 15, 2020

Background and objectives: Qualitative quantitative analyses of Magnetic Resonance Imaging (MRI) scans are carried out to study understand Parkinson's Disease, the second most common neurodegenerative disorder in people at their 60's. Some based on application voxel-based morphometry (VBM) magnetic resonance images determine regions interest, within gray matter, where there is a loss nerve cells that generate dopamine. This dopamine indicative disease. The purpose this research introduction new method classify 3-D an individual, as assisting tool for diagnosis disease by using largest MRI dataset (Parkinson's Progression Markers Initiative) from population patients with control individuals. A contribution separate studies conducted men women since gender plays significant role Neurobiology, which demonstrated fact more prone than are. Methods: Previous classification, VBM detect features extracted first- second-order statistics methods. Furthermore, number considerably reduced feature selection techniques. Seven classifiers used we conducting experiments women. Results: best detection performance achieved 99.01% accuracy, 99.35% sensitivity, 100% specificity, precision. 96.97% 96.15% 97.22% During classification images, corresponding computational complexity few selected. Conclusions: proposed provides high disease, While previous works have focused analysis striatum region brain (the nuclear complex basal ganglia), approach over whole looking decreases tissue thickness, consequence finding other interest such cortex.

Язык: Английский

Процитировано

97