Neurological Ailments Research Articles

Investigating the neuroprotective properties of Sargassum wightii extract against MPP+-induced apoptosis in SH-SY5Y human neuroblastoma cells.

This study aims to assess the neuroprotective effects of the methanolic extract of Sargassum wightii against oxidative stress and cell death induced by neurotoxins MPP + in SH-SY5Y cells. Briefly, the methanolic extract of S.wightii decreased the cytotoxicity of MPP + in SH-SY5Y cells. Treatment with S.wightii extract at a concentration of 400µg/ml resulted in a notable decrease in cell death, particularly in MPP + -induced cells. Flow cytometry analysis with annexin V/PI staining reveals apoptosis and necrosis in SH-SY5Y cell lines upon exposure to 1mM of MPP + . However, 100-400µg/ml concentrations of S.wightii extract effectively decrease apoptosis in SH-SY5Y cells. Furthermore, S.wightii inhibits caspase-3 activity, effectively shielding neuronal cells against MPP + -induced cell death. Mitochondrial membrane potential (MMP) assay using a JC-1 fluorescent probe indicates that the methanolic extract of S.wightii exhibits protective effects against MPP + -induced cell death and maintains mitochondrial membrane potential. Our results conclude that exposing SH-SY5Y cells to a methanolic extract of S.wightii could potentially increase the likelihood of inhibiting the cascade mechanism, stopping MPP+-induced apoptosis, and preventing the rupture of the mitochondrial membrane. However, the lack of low solubility and poor bioavailability reduce the therapeutic efficacy of S.wightii. Liposome-based drug delivery systems can improve the bioavailability and stability of bioactive compounds, enhancing their therapeutic potential. Hence, S.wightii may hold promise as an innovative treatment for neurological ailments.

Lead Phytomolecules for Treating Parkinson's Disease.

One percent of persons over 65 years of age suffer from Parkinson's disease, a neurological ailment marked by dopaminergic neurons in the nigrostriatal pathway gradually dying and being depleted in the striatum. Parkin and PINK1 gene mutations, which are essential for mitophagy and impair mitochondrial function, are the cause of it. Parkinson's disease is linked to a number of motor and impairment disorders, including bradykinesia, rigid muscles, tremor at rest, and imbalance. Numerous signaling pathways, including α-synuclein aggregation, lead to age-related decline in proteolytic defense systems. Parkinson's disease etiology involves oxidative stress, ferroptosis, mitochondrial failure, and neuroinflammation. Parkinson's disease is significantly influenced by neuroinflammation, which is a result of both innate and adaptive immune responses. The purpose of studying mechanisms and phytomolecules is to assist researchers in creating therapies for Parkinson's disease. Phytomolecules, like curcumin, β- amyrin, berberine, capsaicin, and gentisic acid, exert neuroprotective properties by reducing ROS levels, lessening α-synuclein-induced toxicity, and shielding the cells from apoptosis. In conclusion, the studies presented here provide valuable insights into the potential of various medications for Parkinson's disease treatment. By understanding the mechanisms behind these treatments, researchers can develop more effective treatments for PD.

Anti-Epileptic Indian Medicinal Plants: A Comprehensive Review on their Ethnopharmacological Perspectives of Recent Advancement

The study explores the potential of Indian medicinal plants as botanical treasures for managing epilepsy. Epilepsy, a neurological disorder characterized by recurrent seizures, poses a significant global health challenge. Traditional medicinal practices in India have a rich history of incorporating various plant-based remedies for neurological ailments, including epilepsy. The data was collected from various scientific databases like Google Scholar, Science Direct, Elsevier, Springer and Pubmed. The collected data includes the following details - Plant part used, dose and type of extract, reference and inducing drug used and the mechanism of actions. The article thoroughly examines the phytochemical constituents found in Indian medicinal plants, elucidating their neuroprotective and anticonvulsant properties and their scrutinized bioactive compounds and therapeutic potential. The review integrates findings from preclinical and clinical studies, shedding light on the mechanisms of action and safety profiles of these plant-derived compounds. The conclusion of the manuscript emphasizes the promising role of Indian medicinal plants in epilepsy management. The identified botanical treasures offer a holistic and potentially sustainable approach to complement existing antiepileptic medications. The reviewed evidence underscores the need for further research, clinical trials, and integration of traditional knowledge into mainstream healthcare for the development of effective and accessible treatments for epilepsy. By recognizing the botanical wealth embedded in Indian traditional medicine, this review advocates for a paradigm shift toward harnessing nature's gifts in the pursuit of enhanced epilepsy care.

CRISPR/Cas9 Gene Editing: A Novel Approach Towards Alzheimer's Disease Treatment.

In defiance of the vast amount of information regarding Alzheimer's disease (AD) that has been learned over the past thirty years, progress toward developing an effective therapy has been difficult. A neurological ailment that progresses and cannot be reversed is Alzheimer's disease, which shows neurofibrillary tangles, beta-amyloid plaque, and a lack of cognitive processes that is created by tau protein clumps with hyperphosphorylation that finally advances to neuronal damage without a recognized treatment, which has stimulated research into new therapeutic strategies. The protein CAS9 is linked to CRISPR, which is a clustered Regularly Interspaced Short Palindromic Repeat that inactivates or corrects a gene by recognizing a gene sequence that produces a doublestranded break has enchanted a whole amount of interest towards its potency to cure gene sequences in AD. The novel CRISPR-Cas9 applications for developing in vitro and in vivo models to the benefit of AD investigation and therapies are thoroughly analyzed in this work. The discussion will also touch on the creation of delivery methods, which is a significant obstacle to the therapeutic use of CRISPR/Cas9 technology. By concentrating on specific genes, such as those that are significant early- onset AD risk factors and late-onset AD risk factors, like the apolipoprotein E4 (APOE4) gene, this study aims to evaluate the potential application of CRISPR/Cas9 as a possible treatment for AD.

Molecular dynamics studies reveal the structural impacts of LRRK2 R1441C and LRRK2 D1994A mutations in Parkinson's disease

Parkinson's Disease (PD) is a continuingly deteriorating neurological ailment affecting over 8.5 million patients globally as of 2019, and the numbers are expected to keep rising. To aid in identifying therapeutic targets, molecular dynamics simulations are convenient and cost-effective methods for enriching our knowledge of the molecular pathophysiology of diseases. Many proteins and their corresponding mutations have been identified to contribute to this disease, of which Leucine-rich repeat kinase 2 (LRRK2) is accountable for a significant percentage. Several mutations involving the domains in LRRK2 have been studied, which are known to interfere with various enzymatic processes, ultimately leading to trademark features of PD like aggregation of protein inclusions called Lewy Bodies (LBs), mitochondrial dysfunctions, etc. The precise molecular mechanism of the mutations' pathophysiology is still unclear. This research article looks at the structural effects of mutations, namely the R1441C and D1994A mutations, on the surrounding residues in the protein, offering novel insights into pathophysiological changes at an atomistic level. Our results indicate a gain of electrostatic interactions with a stable αβ motif within the LRR-Roc linker, amongst other changes. This article also highlights the potential involvement and importance of the αβ motif in LRRK2 associated PD.

AN AYURVEDIC APPROACH TO ARDITA (BELL’S PALSY): A CASE REPORT

Ardita (Bell's palsy) is considered as one of the 80 Vataja Nanatmaja Vyadhis in Ayurvedic classics. In modernday science, it could be correlated with the disorder of Unilateral facial paralysis. Bell’s palsy, additionally termed as Idiopathic facial paralysis (IFP), is the common cause of unilateral facial paralysis. Facial palsy is a neurological ailment wherein the 7th cranial nerve gets affected. The facial nerve conveys sensory and motor fibres with parasympathetic nerve fibres. Damaged facial nerve (VII) consequences are functional loss of facial muscle, resulting in facial impairment. The current study has been performed to discover the effective Ayurvedic management of Bell’s palsy in a 16-year-old male patient who approached Ayurveda Mahavidyalaya and Hospital, Hubballi. He was diagnosed with Ardita (Bell's palsy) – Left-sided Bell's Palsy and managed with Karpasasthyadi taila mukhabhyanga(Facial oil massage),Panasa patra sweda(Jackfruit leaf sudation) followed by nasya(nasal drops) with Ksheera bala taila 101 for 10days and confirmed well-sized development in the signs and symptoms with no adverse effects. The house-Brackmann facial nerve grading system was used for the patient's pre-test and post-test assessment.

Time–frequency domain machine learning for detection of epilepsy using wearable EEG sensor signals recorded during physical activities

Epilepsy is a neurological ailment in which there is a disturbance in the nerve cell activity of the brain, causing recurrent seizures. The electroencephalogram (EEG) signal is widely used as a diagnostic modality to detect epilepsy ailment. The automated detection of epilepsy using wearable EEG sensor data recorded during various physical activities is interesting for continuous monitoring of brain health. This paper proposes a time–frequency (TF) domain machine learning (ML) approach for the automated detection of epilepsy using wearable sensor-based EEG signals. The Gaussian window-based Stockwell transform (GWST) is employed to evaluate the TF matrix from the EEG signal. The features such as the L1-norm and the Shannon entropy are extracted from the TF matrix of the EEG signal. The ML and deep learning (DL) models are employed to detect epilepsy using the TF domain features of EEG signals. The publicly available database containing wearable sensor-based EEG signals recorded from the subjects while performing different physical activities is used to evaluate the performance of the proposed approach. The results show that the random forest (RF) classifier coupled with GWST domain features of EEG signals has obtained an overall accuracy value of 90.74% for detecting epilepsy with hold-out validation using the EEG signals from different physical activity cases. For the 10-fold cross-validation (CV) case, the GWST domain features of EEG signal and multi-layer long short-term memory (LSTM) classifier have produced the average accuracy value of 74.44%. For jogging, running, and idle sitting activities, the GWST-based TF domain entropy features coupled with the multi-layer LSTM model have obtained accuracy values of 82.72%, 82.41%, and 87.30%, respectively. The proposed approach has achieved higher classification accuracy than existing methods to detect epilepsy using wearable sensor-based EEG signals using a 10-fold CV strategy. The suggested approach is compared with existing methods to classify seizure and seizure-free classes using resting-state EEG signals.

A deep neural network-based approach for seizure activity recognition of epilepsy sufferers.

Epilepsy is one of the most frequent neurological illnesses caused by epileptic seizures and the second most prevalent neurological ailment after stroke, affecting millions of people worldwide. People with epileptic disease are considered a category of people with disabilities. It significantly impairs a person's capacity to perform daily tasks, especially those requiring focusing or remembering. Electroencephalogram (EEG) signals are commonly used to diagnose people with epilepsy. However, it is tedious, time-consuming, and subjected to human errors. Several machine learning techniques have been applied to recognize epilepsy previously, but they have some limitations. This study proposes a deep neural network (DNN) machine learning model to determine the existing limitations of previous studies by improving the recognition efficiency of epileptic disease. A public dataset is used in this study and classified into training and testing sets. Experiments were performed to evaluate the DNN model with different dataset classification ratios (80:20), (70:30), (60:40), and (50:50) for training and testing, respectively. Results were evaluated by using different performance metrics including validations, and comparison processes that allow the assessment of the model's effectiveness. The experimental results showed that the overall efficiency of the proposed model is the highest compared with previous works, with an accuracy rate of 97%. Thus, this study is more accurate and efficient than the existing seizure detection approaches. DNN model has great potential for recognizing epileptic patient activity using a numerical EEG dataset offering a data-driven approach to improve the accuracy and reliability of seizure detection systems for the betterment of patient care and management of epilepsy.

Intelligent bell facial paralysis assessment: a facial recognition model using improved SSD network

With the continuous progress of technology, the subject of life science plays an increasingly important role, among which the application of artificial intelligence in the medical field has attracted more and more attention. Bell facial palsy, a neurological ailment characterized by facial muscle weakness or paralysis, exerts a profound impact on patients’ facial expressions and masticatory abilities, thereby inflicting considerable distress upon their overall quality of life and mental well-being. In this study, we designed a facial attribute recognition model specifically for individuals with Bell’s facial palsy. The model utilizes an enhanced SSD network and scientific computing to perform a graded assessment of the patients’ condition. By replacing the VGG network with a more efficient backbone, we improved the model’s accuracy and significantly reduced its computational burden. The results show that the improved SSD network has an average precision of 87.9% in the classification of light, middle and severe facial palsy, and effectively performs the classification of patients with facial palsy, where scientific calculations also increase the precision of the classification. This is also one of the most significant contributions of this article, which provides intelligent means and objective data for future research on intelligent diagnosis and treatment as well as progressive rehabilitation.

EEGDiR: Electroencephalogram denoising network for temporal information storage and global modeling through Retentive Network

Electroencephalogram (EEG) signals are pivotal in clinical medicine, brain research, and neurological disorder studies. However, their susceptibility to contamination from physiological and environmental noise challenges the precision of brain activity analysis. Advances in deep learning have yielded superior EEG signal denoising techniques that eclipse traditional approaches. In this research, we deploy the Retentive Network architecture – initially crafted for large language models (LLMs) – for EEG denoising, exploiting its robust feature extraction and comprehensive modeling prowess. Furthermore, its inherent temporal structure alignment makes the Retentive Network particularly well-suited for the time-series nature of EEG signals, offering an additional rationale for its adoption. To conform the Retentive Network to the unidimensional characteristic of EEG signals, we introduce a signal embedding tactic that reshapes these signals into a two-dimensional embedding space conducive to network processing. This avant-garde method not only carves a novel trajectory in EEG denoising but also enhances our comprehension of brain functionality and the accuracy in diagnosing neurological ailments. Moreover, in response to the labor-intensive creation of deep learning datasets, we furnish a standardized, preprocessed dataset poised to streamline deep learning advancements in this domain.

A Comprehensive Review and Androgen Deprivation Therapy and Its Impact on Alzheimer's Disease Risk in Older Men with Prostate Cancer.

Prostate cancer (PCa) is one of the most prevalent malignancies affecting males worldwide. Despite reductions in mortality rates due to advances in early identification and treatment methods, PCa remains a major health concern. Recent research has shed light on a possible link between PCa and Alzheimer's disease (AD), which is a significant neurological ailment that affects older males all over the world. Androgen deprivation therapy (ADT), a cornerstone therapeutic method used in conjunction with radiation and palliative care in advanced metastatic PCa cases, is critical for disease management. Evidence reveals a relationship between ADT and cognitive impairment. Hormonal manipulation may cause long-term cognitive problems through processes such as amyloid beta (Aβ) aggregation and neurofibrillary tangles (NFTs). Fluctuations in basal androgen levels can upset the delicate balance of genes that are sensitive to androgen levels, contributing to cognitive impairment. This detailed review dives into the various aspects of PCa aetiology and its relationship with cognitive decline. It investigates the discovery of particular biomarkers, as well as microRNAs (miRNAs), which play important roles in pathogenic progression. The review attempts to identify potential biomarkers associated with ADT-induced cerebral changes, including Aβ oligomer buildup, NFT formation, and tauopathy, which can contribute to early-onset dementia and cognitive impairment. Besides it further aims to provide insights into innovative diagnostic and therapeutic avenues for alleviating PCa and ADT-related cognitive sequelae by unravelling these complicated pathways and molecular mechanisms.

ContourTL-Net: Contour-Based Transfer Learning Algorithm for Early-Stage Brain Tumor Detection.

Brain tumors are critical neurological ailments caused by uncontrolled cell growth in the brain or skull, often leading to death. An increasing patient longevity rate requires prompt detection; however, the complexities of brain tissue make early diagnosis challenging. Hence, automated tools are necessary to aid healthcare professionals. This study is particularly aimed at improving the efficacy of computerized brain tumor detection in a clinical setting through a deep learning model. Hence, a novel thresholding-based MRI image segmentation approach with a transfer learning model based on contour (ContourTL-Net) is suggested to facilitate the clinical detection of brain malignancies at an initial phase. The model utilizes contour-based analysis, which is critical for object detection, precise segmentation, and capturing subtle variations in tumor morphology. The model employs a VGG-16 architecture priorly trained on the "ImageNet" collection for feature extraction and categorization. The model is designed to utilize its ten nontrainable and three trainable convolutional layers and three dropout layers. The proposed ContourTL-Net model is evaluated on two benchmark datasets in four ways, among which an unseen case is considered as the clinical aspect. Validating a deep learning model on unseen data is crucial to determine the model's generalization capability, domain adaptation, robustness, and real-world applicability. Here, the presented model's outcomes demonstrate a highly accurate classification of the unseen data, achieving a perfect sensitivity and negative predictive value (NPV) of 100%, 98.60% specificity, 99.12% precision, 99.56% F1-score, and 99.46% accuracy. Additionally, the outcomes of the suggested model are compared with state-of-the-art methodologies to further enhance its effectiveness. The proposed solution outperforms the existing solutions in both seen and unseen data, with the potential to significantly improve brain tumor detection efficiency and accuracy, leading to earlier diagnoses and improved patient outcomes.

An exploration of causal relationships between nine neurological diseases and the risk of breast cancer: a Mendelian randomization study.

Some preceding researches have observed that certain neurological disorders, such as Alzheimer's disease and multiple sclerosis, may affect breast cancer risk. However, whether there are causal relationships between these neurological conditions and breast cancer is inconclusive. This study was designed to explore whether neurological disorders affected the risks of breast cancer overall and of the two subtypes (ER+ and ER-). In the course of this study, genome-wide association study (GWAS) data for nine neurological diseases (Alzheimer's disease, multiple sclerosis, Parkinson's disease, myasthenia gravis, generalized epilepsy, intracerebral haemorrhage, cerebral atherosclerosis, brain glioblastoma, and benign meningeal tumour) were collected from the Complex Trait Genetics lab and the MRC Integrative Epidemiology Unit, and single-nucleotide polymorphisms (SNPs) extensively associated with these neurological ailments had been recognized as instrumental variables (IVs). GWAS data on breast cancer were collected from the Breast Cancer Association Consortium (BCAC). Two-sample Mendelian randomization (MR) analyses as well as multivariable MR analyses were performed to determine whether these SNPs contributed to breast cancer risk. Additionally, the accuracy of the results was evaluated using the false discovery rate (FDR) multiple correction method. Both heterogeneity and pleiotropy were evaluated by analyzing sensitivities. According to the results of two-sample MR analyses, Alzheimer's disease significantly reduced the risks of overall (OR 0.925, 95% CI [0.871-0.982], P = 0.011) and ER+ (OR 0.912, 95% CI [0.853-0.975], P = 0.007) breast cancer, but there was a negative result in ER- breast cancer. However, after multiple FDR corrections, the effect of Alzheimer's disease on overall breast cancer was not statistically significant. In contrast, multiple sclerosis significantly increased ER+ breast cancer risk (OR 1.007, 95% CI [1.003-1.011], P = 0.001). In addition, the multivariable MR analyses showed that Alzheimer's disease significantly reduced the risk of ER+ breast cancer (IVW: OR 0.929, 95% CI [0.864-0.999], P=0.047; MR-Egger: OR 0.916, 95% CI [0.846-0.992], P=0.031); however, multiple sclerosis significantly increased the risk of ER+ breast cancer (IVW: OR 1.008, 95% CI [1.003-1.012], P=4.35×10-4; MR-Egger: OR 1.008, 95% CI [1.003-1.012], P=5.96×10-4). There were no significant associations between the remainder of the neurological diseases and breast cancer. This study found the trends towards a decreased risk of ER+ breast cancer in patients with Alzheimer's disease and an increased risk in patients with multiple sclerosis. However, due to the limitations of Mendelian randomization, we cannot determine whether there are definite causal relationships between neurological diseases and breast cancer risk. For conclusive evidences, more prospective randomized controlled trials will be needed in the future.

A coupled-GAN architecture to fuse MRI and PET image features for multi-stage classification of Alzheimer’s disease

Alzheimer’s disease (AD) is a degenerative neurological ailment that begins with memory loss and ultimately leads to a total loss of mental capacity. Researchers are interested in using magnetic resonance imaging (MRI) and positron emission tomography (PET) to find people with mild cognitive impairment (MCI), which is a stage before Alzheimer’s disease (AD). Significant hippocampal loss and temporal lobe atrophy characterize the transition from MCI to AD, which can be visualized using T1-W structural MRI. PET visualizes brain glucose metabolism, which indicates neuronal activity, making it a viable neuroimaging method for AD diagnosis. The extraction and fusion of structural and metabolite information about brain alterations contained in multimodal data is crucial for achieving an appropriate classification result. Therefore, in this work a new end-to-end coupled-GAN (CGAN) architecture is introduced. The proposed CGANC network consists of two sub-models: a CGAN for extraction of fused features from multimodal data, and a CNN classifier to classify these features. The proposed CGAN model is trained to encode MRI and PET images into a shared latent space. The fused features are extracted from this shared latent space and then are classified according to particular stage of AD. In order to test the effectiveness of the suggested approach, experiments are done on the publicly available ADNI dataset and compared with state-of-the-art methods. The proposed method’s source code will be made freely available at https://github.com/ChandrajitChoudhury/CGAN-AD.

Effect of quercetin against pilocarpine-induced epilepsy in mice.

Globally, an estimated 50 million people are affected by epilepsy, a persistent, noncommunicable neurological ailment. Quercetin (QR) is a prevalent flavonoid substance extensively dispersed throughout agricultural life. In a pilocarpine (PILO)-induced epilepsy model in mice, this investigation aimed to determine whether QR has an antiepileptic effect and explore its putative mechanism of action. Fifty mice were allocated into seven groups, with six in every group. The first group received physiological saline, the second group was given diazepam (1 mg/kg), and four groups were administered QR at 50, 100, 150, and 200 mg/kg, respectively. The seventh group (the induction group) received normal saline. After 30 min, all groups were injected intraperitoneally with PILO. The impact of QR on motor coordination was assessed using the rotarod test, while measures such as latency to first seizure, generalized tonic-clonic seizures (GTCS), number of convulsions, and mortality were recorded. Serum samples were collected through the retro-orbital route to measure prostaglandin E2 (PGE2) and interleukin 1 beta (IL-1β) levels. QR showed no significant difference in motor impairment, but increased duration until the initial seizure occurred and declined the mortality rate, duration of GTCS, and incidence of convulsions. All doses of QR significantly reduced PGE2 levels (P ≤ 0.05). However, QR's effect on IL-1β reduction was statistically insignificant (P > 0.05). QR's capacity to inhibit PILO-induced epilepsy by decreasing IL-1 and PGE2 levels is supported by this study. The results of this work indicate that QR could have a function to treat acute epilepsy.

Anti-Parkinsonian Activity of Mushrooms Against Orofacial Dyskinesia Induced by Reserpine in Wistar Rats

A neurological ailment called Parkinson’s disease (PD) which distresses roughly 1.5% of people above age 65 in the world. Dopamine nerve cell death (DA) in the Substantia nigra parc compacta (SNc) with the development of a striatal DA deficiency are two features of PD.As of now, the focalization of PD is uncertain. The goal was to assess the neuroprotective efficacy of ethanolic extracts from different mushroom species viz. P. ostreatus, A. bisporus and C. cibarius are against orofacial dyskinesia (OD) by reserpine and their in vivo antioxidant status. In this study, reserpine (1-mg/kg) was repeatedly administered on alternate days over a five-day interval to produce vacuous chewing movements (VCMs) and tongue protrusions (TPs) in rats. Evaluation of reserpine-induced catalepsy was done. Catalase (CAT), glutathione reductase (GSH), superoxide dismutase (SOD) and lipid peroxidation (LPO) concentrations were examined in prosencephalon in response to an ethanolic extract of mushrooms. The histological evaluation of attenuating oxidative stress also shows the effectiveness of all these mushroom extracts. In animal models of Parkinson’s disease produced by reserpine, P. ostreatus, A. bisporus and C. cibarius were found to have a therapeutic impact against the disease.

The Interplay of Mitochondrial Bioenergetics and Dopamine Agonists as an Effective Disease-Modifying Therapy for Parkinson's Disease.

Parkinson's disease (PD) is a progressive neurological ailment with a slower rate of advancement that is more common in older adults. The biggest risk factor for PD is getting older, and those over 60 have an exponentially higher incidence of this condition. The failure of the mitochondrial electron chain, changes in the dynamics of the mitochondria, and abnormalities in calcium and ion homeostasis are all symptoms of Parkinson's disease (PD). Increased mitochondrial reactive oxygen species (mROS) and an energy deficit are linked to these alterations. Levodopa (L-DOPA) is a medication that is typically used to treat most PD patients, but because of its negative effects, additional medications have been created utilizing L-DOPA as the parent molecule. Ergot and non-ergot derivatives make up most PD medications. PD is successfully managed with the use of dopamine agonists (DA). To get around the motor issues produced by L-DOPA, these dopamine derivatives can directly excite DA receptors in the postsynaptic membrane. In the past 10years, two non-ergoline DA with strong binding properties for the dopamine D2 receptor (D2R) and a preference for the dopamine D3 receptor (D3R) subtype, ropinirole, and pramipexole (PPx) have been developed for the treatment of PD. This review covers the most recent research on the efficacy and safety of non-ergot drugs like ropinirole and PPx as supplementary therapy to DOPA for the treatment of PD.

Deciphering the Association of Epstein-Barr Virus and Its Glycoprotein M Peptide with Neuropathologies in Mice.

The reactivation of ubiquitously present Epstein-Barr virus (EBV) is known to be involved with numerous diseases, including neurological ailments. A recent in vitro study from our group unveiled the association of EBV and its 12-amino acid peptide glycoprotein M146-157 (gM146-157) with neurodegenerative diseases, viz., Alzheimer's disease (AD) and multiple sclerosis. In this study, we have further validated this association at the in vivo level. The exposure of EBV/gM146-157 to mice causes a decline in the cognitive ability with a concomitant increase in anxiety-like symptoms through behavioral assays. Disorganization of hippocampal neurons, cell shrinkage, pyknosis, and apoptotic appendages were observed in the brains of infected mice. Inflammatory cytokines such as tumor necrosis factor-α (TNF-α) and interleukin-6 (IL-6) were found to be elevated in infected mouse brain tissue samples, whereas TNF-α exhibited a decline in the serum of these mice. Further, the altered levels of nuclear factor-kappa B (NF-kB) and neurotensin receptor 2 affirmed neuroinflammation in infected mouse brain samples. Similarly, the risk factor of AD, apolipoprotein E4 (ApoE4), was also found to be elevated at the protein level in EBV/gM146-157 challenged mice. Furthermore, we also observed an increased level of myelin basic protein in the brain cortex. Altogether, our results suggested an integral connection of EBV and its gM146-157 peptide to the neuropathologies.

Molecular Basis of <i>Sida cordifolia</i> (L.) Induced Apoptosis in Melanoma Cell Line

Sida cordifolia of the family Malvaceae is widely used in traditional medicine for treating inflammation, respiratory and neurological ailments and wound healing. Its extract was found to possess effective antitumor activity in hepatocellular carcinoma and HeLa cell lines. This study was aimed at screening the anticancer activity of S. cordifolia and to investigate its mechanism of action. Aerial parts of the plant were subjected to hot continuous extraction by Soxhlet apparatus with ethanol as solvent. Cytotoxicity of the extract was assessed in various cancer cell lines viz. breast, ovarian, colon, skin, and liver cancer by MTT assay. For each cell line, the IC50 value was calculated. The mechanism of anticancer activity of the extract was studied in melanoma cells by exposing them to 12.5 and 25 μg/ml extract and comparing results with the control. Gel electrophoresis was used to analyse DNA laddering. Expression of TP53, Bcl and Caspase gene family proteins were determined by SDS-PAGE. Mitochondrial membrane potential was studied by the JC-1 kit. Cell cycle analysis was performed by using a flow cytometer. Statistical analysis was done by ANNOVA, and significant values were further analysed by Tucky post-hoc analysis. P value less than 0.05 was considered statistically significant. MTT assay revealed maximum cytotoxicity of the extract against melanoma with an IC50 value of 16.51μg/ml. Melanoma cells treated with the extract demonstrated dose-dependent DNA laddering. The extract also exhibited a dose-dependent increase in the level of Bax, Caspase 3, Caspase 9 and p53 proteins. Expression of Bcl2 protein was significantly reduced. Treatment of melanoma cells with the extract showed significant loss of mitochondrial membrane potential. Melanoma cell population in subG0 and G2/S was significantly elevated. From these results, we conclude that ethanol extract of S. cordifolia is cytotoxic to melanoma cells. It acts by inducing apoptosis via an intrinsic mechanism. The extract also arrests melanoma cells in the G2/M phase.

Combined laser-activated SVF and PRP remodeled spinal sclerosis via activation of Olig-2, MBP, and neurotrophic factors and inhibition of BAX and GFAP

A single injection of platelet-rich plasma (PRP) or stromal vascular fraction (SVF) in treating neurological ailments suggests promise; however, there is limited evidence of the efficacy of combination therapy. This trial aimed to determine whether combining SVF and PRP could provide further therapeutic effects in treating multiple sclerosis (MS). Fifteen Persian cats were separated into three groups (n = 5): group I (control negative), and group II (control positive); EB was injected intrathecally into the spinal cord and then treated 14 days later with intrathecal phosphate buffered saline injection, and group III (SVF + PRP), cats were injected intrathecally with EB through the spinal cord, followed by a combination of SVF and PRP 14 days after induction. Therapeutic effects were evaluated using the Basso–Beattie–Bresnahan scale throughout the treatment timeline and at the end. Together with morphological, MRI scan, immunohistochemical, transmission electron microscopy, and gene expression investigations. The results demonstrated that combining SVF and PRP successfully reduced lesion intensity on gross inspection and MRI. In addition to increased immunoreactivity to Olig2 and MBP and decreased immunoreactivity to Bax and GFAP, there was a significant improvement in BBB scores and an increase in neurotrophic factor (BDNF, NGF, and SDF) expression when compared to the positive control group. Finally, intrathecal SVF + PRP is the most promising and safe therapy for multiple sclerosis, resulting in clinical advantages such as functional recovery, MRI enhancement, and axonal remyelination.