scholarly journals Proceedings of Chemistry, Pharmacology, Pharmacokinetics and Synthesis of Biflavonoids

Molecules ◽  
2021 ◽  
Vol 26 (19) ◽  
pp. 6088
Author(s):  
Xinqian He ◽  
Fan Yang ◽  
Xin’an Huang
Keyword(s):  
Alzheimer’S Disease ◽  
Parkinson’S Disease ◽  
Alzheimer's Disease ◽  
Parkinson's Disease ◽  
Pharmacological Activities ◽  
Wide Range ◽  
Linear Fragment ◽  
Anti Inflammatory

Biflavonoids, composed of two monoflavonoid residues, occur naturally in angiosperms, bryophytes, ferns, and gymnosperms. More than 592 biflavonoids have been structurally elucidated, and they can be classified into two groups of C-C and C-linear fragments-C, based on whether the linker between the two residues contains an atom. As the linker can be established on two arbitrary rings from different residues, the C-C type contains various subtypes, as does the C-linear fragment-C type. Biflavonoids have a wide range of pharmacological activities, including anti-inflammatory, antioxidant, antibacterial, antiviral, antidiabetic, antitumor, and cytotoxic properties, and they can be applied in Alzheimer’s disease and Parkinson’s disease. This review mainly summarizes the distribution and chemistry of biflavonoids; additionally, their bioactivities, pharmacokinetics, and synthesis are discussed.

scholarly journals Non-Steroidal Anti-Inflammatory Drugs in Alzheimer's Disease and Parkinson's Disease: Reconsidering the Role of Neuroinflammation

2010 ◽  
Vol 3 (6) ◽  
pp. 1812-1841 ◽  
Author(s):  
Amy H. Moore ◽  
Matthew J. Bigbee ◽  
Grace E. Boynton ◽  
Colin M. Wakeham ◽  
Hilary M. Rosenheim ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Parkinson’S Disease ◽  
Alzheimer's Disease ◽  
Parkinson's Disease ◽  
Inflammatory Drugs ◽  
Anti Inflammatory

Deep Brain Stimulation

2021 ◽  
Author(s):  
Tipu Aziz ◽  
Holly Roy
Keyword(s):  
Alzheimer’S Disease ◽  
Parkinson’S Disease ◽  
Alzheimer's Disease ◽  
Parkinson's Disease ◽  
Deep Brain Stimulation ◽  
Brain Stimulation ◽  
Brain Regions ◽  
Clinical Aspects ◽  
Wide Range ◽  
Deep Brain

Deep brain stimulation (DBS) is a neurosurgical technology that allows the manipulation of activity within specific brain regions through delivery of electrical stimulation via implanted electrodes. The growth of DBS has led to research around the development of novel interventions for a wide range of neurological and neuropsychiatric conditions, including Parkinson’s disease, dystonia, chronic pain, Tourette’s syndrome, treatment-resistant depression, anorexia nervosa, and Alzheimer’s disease. Some of these treatment approaches have a high level of efficacy as well as an established place in the clinical armamentarium for the diseases in question, such as DBS for movement disorders, including Parkinson’s disease. Other interventions are at a more developmental stage, such as DBS for depression and Alzheimer’s disease. Success both in clinical aspects of DBS and new innovations depends on a close-knit multidisciplinary team incorporating experts in the underlying condition (often neurologists and psychiatrists); neurosurgeons; nurse specialists, who may be involved in device programming and other aspects of patient care; and researchers including neuroscientists, imaging specialists, engineers, and signal analysts. Directly linked to the growth of DBS as a specialty is allied research around neural signals analysis and device development, which feed directly back into further clinical progress. The close links between clinical DBS and basic and translational research make it an exciting and fast-moving area of neuroscience.

scholarly journals Cerebrospinal fluid Aβ42 and fractalkine are associated with Parkinson’s disease with freezing of gait

2020 ◽  
Author(s):  
J. M. Hatcher-Martin ◽  
J.L. McKay ◽  
B. Sommerfeld ◽  
J.C. Howell ◽  
F. C. Goldstein ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Parkinson’S Disease ◽  
Alzheimer's Disease ◽  
Parkinson's Disease ◽  
Cerebrospinal Fluid ◽  
Linear Models ◽  
Inflammatory Marker ◽  
Freezing Of Gait ◽  
Cross Sectional ◽  
Anti Inflammatory

AbstractObjectiveTo evaluate the association of Alzheimer’s disease-related and inflammation-related cerebrospinal fluid (CSF) markers with freezing of gait (FOG) in patients with Parkinson’s disease (PD).MethodThe study population included well-characterized PD patients with FOG (PD-FOG), without FOG (PD-NoFOG) and healthy controls (HC). CSF was collected using standard protocols. Three Alzheimer’s disease-related markers and 10 inflammation-related markers were measured in a Luminex 200 platform. Differences in marker expression across groups were evaluated with multivariate linear models.ResultsCSF was collected from PD-FOG (N=12), PD-NoFOG (N=20) and HC (N=11) for analysis. Age was not significantly different between the three groups. Duration of PD was not significantly different between the two PD groups. After adjusting for covariates and multiple comparisons, the anti-inflammatory marker, fractalkine, was significantly decreased in the PD groups compared to HC (P=0.022), and further decreased in PD-FOG compared to PD-NoFOG or HC (P=0.032). The Alzheimer’s disease-related protein, Aβ42, was increased in PD-FOG compared to PD-NoFOG or HC (P=0.004). p-Tau181 was also decreased in both PD groups compared to HC (P=0.010).ConclusionsWe found high levels of Aβ42 in PD-FOG patients and cross-sectional data which supports an increase over time from early to advanced state. We also found low levels of fractalkine which might suggest anti-inflammatory effect. This is the first time an association between fractalkine and FOG has been shown. Whether these changes are specific to FOG requires further exploration.

scholarly journals Cerebrospinal fluid biomarkers in Parkinson’s disease with freezing of gait: an exploratory analysis

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
J. M. Hatcher-Martin ◽  
J. L. McKay ◽  
A. F. Pybus ◽  
B. Sommerfeld ◽  
J. C. Howell ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Parkinson’S Disease ◽  
Alzheimer's Disease ◽  
Parkinson's Disease ◽  
Inflammatory Marker ◽  
Freezing Of Gait ◽  
Least Squares Regression ◽  
Cerebrospinal Fluid Biomarkers ◽  
Study Population ◽  
Anti Inflammatory

AbstractWe explore the association between three Alzheimer’s disease-related and ten inflammation-related CSF markers and freezing of gait (FOG) in patients with Parkinson’s disease (PD). The study population includes PD patients with FOG (PD-FOG, N = 12), without FOG (PD-NoFOG, N = 19), and healthy controls (HC, N = 12). Age and PD duration are not significantly different between groups. After adjusting for covariates and multiple comparisons, the anti-inflammatory marker, fractalkine, is significantly decreased in the PD groups compared to HC (P = 0.002), and further decreased in PD-FOG compared to PD-NoFOG (P = 0.007). The Alzheimer’s disease-related protein, Aβ42, is increased in PD-FOG compared to PD-NoFOG and HC (P = 0.001). Group differences obtained in individual biomarker analyses are confirmed with multivariate discriminant partial least squares regression (P < 0.001). High levels of Aβ42 in PD-FOG patients supports an increase over time from early to advanced state. Low levels of fractalkine might suggest anti-inflammatory effect. These findings warrant replication.

Neurobehavioral Consequences Associated with Long Term Tramadol Utilization and Pathological Mechanisms

2020 ◽  
Vol 18 (10) ◽  
pp. 758-768 ◽  
Author(s):  
Khadga Raj ◽  
Pooja Chawla ◽  
Shamsher Singh
Keyword(s):  
Alzheimer’S Disease ◽  
Parkinson’S Disease ◽  
Alzheimer's Disease ◽  
Parkinson's Disease ◽  
Serotonin Reuptake ◽  
Selective Serotonin Reuptake Inhibitors ◽  
Serotonin Syndrome ◽  
Dopamine Synthesis ◽  
Synthetic Analog

: Tramadol is a synthetic analog of codeine used to treat pain of moderate to severe intensity and is reported to have neurotoxic potential. At therapeutic dose, tramadol does not cause major side effects in comparison to other opioid analgesics, and is useful for the management of neurological problems like anxiety and depression. Long term utilization of tramadol is associated with various neurological disorders like seizures, serotonin syndrome, Alzheimer’s disease and Parkinson’s disease. Tramadol produces seizures through inhibition of nitric oxide, serotonin reuptake and inhibitory effects on GABA receptors. Extensive tramadol intake alters redox balance through elevating lipid peroxidation and free radical leading to neurotoxicity and produces neurobehavioral deficits. During Alzheimer’s disease progression, low level of intracellular signalling molecules like cGMP, cAMP, PKC and PKA affect both learning and memory. Pharmacologically tramadol produces actions similar to Selective Serotonin Reuptake Inhibitors (SSRIs), increasing the concentration of serotonin, which causes serotonin syndrome. In addition, tramadol also inhibits GABAA receptors in the CNS has been evidenced to interfere with dopamine synthesis and release, responsible for motor symptoms. The reduced level of dopamine may produce bradykinesia and tremors which are chief motor abnormalities in Parkinson’s Disease (PD).

scholarly journals Application of deep learning in detecting neurological disorders from magnetic resonance images: a survey on the detection of Alzheimer’s disease, Parkinson’s disease and schizophrenia

2020 ◽  
Vol 7 (1) ◽  
Author(s):  
Manan Binth Taj Noor ◽  
Nusrat Zerin Zenia ◽  
M Shamim Kaiser ◽  
Shamim Al Mamun ◽  
Mufti Mahmud
Keyword(s):  
Alzheimer’S Disease ◽  
Parkinson’S Disease ◽  
Alzheimer's Disease ◽  
Parkinson's Disease ◽  
Deep Learning ◽  
Magnetic Resonance ◽  
Neurological Disorders ◽  
Magnetic Resonance Images ◽  
Future Research ◽  
Comparative Performance

Abstract Neuroimaging, in particular magnetic resonance imaging (MRI), has been playing an important role in understanding brain functionalities and its disorders during the last couple of decades. These cutting-edge MRI scans, supported by high-performance computational tools and novel ML techniques, have opened up possibilities to unprecedentedly identify neurological disorders. However, similarities in disease phenotypes make it very difficult to detect such disorders accurately from the acquired neuroimaging data. This article critically examines and compares performances of the existing deep learning (DL)-based methods to detect neurological disorders—focusing on Alzheimer’s disease, Parkinson’s disease and schizophrenia—from MRI data acquired using different modalities including functional and structural MRI. The comparative performance analysis of various DL architectures across different disorders and imaging modalities suggests that the Convolutional Neural Network outperforms other methods in detecting neurological disorders. Towards the end, a number of current research challenges are indicated and some possible future research directions are provided.

Generalized Rhythmic Delta Activity Frontally Predominant Differentiates Dementia With Lewy Bodies From Alzheimer's Disease and Parkinson's Disease Dementia: A Conventional Electroencephalography Visual Analysis

2021 ◽  
pp. 155005942199714
Author(s):  
Lucia Zinno ◽  
Anna Negrotti ◽  
Chiara Falzoi ◽  
Giovanni Messa ◽  
Matteo Goldoni ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Parkinson’S Disease ◽  
Alzheimer's Disease ◽  
Parkinson's Disease ◽  
Cognitive Impairment ◽  
Dementia With Lewy Bodies ◽  
Visual Analysis ◽  
Lewy Bodies ◽  
Delta Activity ◽  
Rhythmic Delta Activity

Introduction. An easily accessible and inexpensive neurophysiological technique such as conventional electroencephalography may provide an accurate and generally applicable biomarker capable of differentiating dementia with Lewy bodies (DLB) from Alzheimer’s disease (AD) and Parkinson’s disease-associated dementia (PDD). Method. We carried out a retrospective visual analysis of resting-state electroencephalography (EEG) recording of 22 patients with a clinical diagnosis of 19 probable and 3 possible DLB, 22 patients with probable AD and 21 with PDD, matched for age, duration, and severity of cognitive impairment. Results. By using the grand total EEG scoring method, the total score and generalized rhythmic delta activity frontally predominant (GRDAfp) alone or, even better, coupled with a slowing of frequency of background activity (FBA) and its reduced reactivity differentiated DLB from AD at an individual level with an high accuracy similar to that obtained with quantitative EEG (qEEG). GRDAfp alone could also differentiate DLB from PDD with a similar level of diagnostic accuracy. AD differed from PDD only for a slowing of FBA. The duration and severity of cognitive impairment did not differ between DLB patients with and without GRDAfp, indicating that this abnormal EEG pattern should not be regarded as a disease progression marker. Conclusions. The findings of this investigation revalorize the role of conventional EEG in the diagnostic workup of degenerative dementias suggesting the potential inclusion of GRDAfp alone or better coupled with the slowing of FBA and its reduced reactivity, in the list of supportive diagnostic biomarkers of DLB.

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