Neurological Ailments Research Articles

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.

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.

A study of third and fourth ventricular sizes in Bangladeshi adults using computed tomography scan

Background: The assessment of normal measurements of cerebral ventricles in living humans holds significant importance for diagnosing and monitoring various pathologies. Current digital computed tomography (CT) scan machines enable the direct visualization of ventricles in cross-sectional images, allowing real-time measurement of different ventricular dimensions, thereby aiding in comprehending their anatomy. This research aims to create baseline reference values for the sizes of the third and fourth ventricles in normal Bangladeshi adults using CT scans. Methods: This was a retrospective study conducted in the department of radiology and imaging, Shaheed Monsur Ali Medical college hospital, Dhaka, Bangladesh, from January 2023 to December 2023. A total of 408 adult patients with clinical symptoms or history suggestive of neurological ailment were analyzed. CT scans of the brain of all the patients were performed in Supria 128 Fujifilm multi-slice CT scanner and the studies were analyzed retrospectively on Synapse 3D workstation. Measurements were taken with inbuilt linear calipers. The greatest width of the third ventricle (3V) and fourth ventricle (4V) on axial images using a linear approach were measured. Statistical analysis was performed by SPSS version 22.0. Result: The study found that the mean measurements of the 3V were 4.27±1.22 mm in males and 3.73±1.03 mm in females, with an overall mean of 3.92±1.13 mm. For the 4V, the mean measurements were 11.73±1.27 mm in males and 10.67±1.07 mm in females, resulting in an overall mean of 11.03±1.25 mm. The largest dimensions in both ventricles were consistently observed in individuals aged 60 years and above, regardless of gender. Additionally, on average, the measurements of both ventricles were higher in males compared to females. Statistical analysis revealed significant differences in the sizes of both ventricles between male and female subjects. Conclusions: This study concludes normal third and fourth ventricular sizes in Bangladeshi adults using CT scans. These can serve as baseline reference values in our hospital in routine practice.

Potential Therapeutic Role of Cannabinoid System in Alzheimer’s Disease: A Review

Abstract: The purpose of the study was to investigate the role of the endocannabinoid system and the therapeutic potential benefits of cannabinoids in Alzheimer’s disease. The most frequent form of dementia in older individuals is Alzheimer's disease (AD), a progressive neurological ailment that gradually deprives people of their memory, cognitive abilities, and, ultimately, their ability to perform daily tasks. Alzheimer's disease is distinguished by a progressive deterioration in cognitive function, which includes a decline in memory, learning capacity, and thinking power. The human body's endocannabinoid system functions as a channel for cell communication. It is a neurotransmission system that is present in various regions of the body and tissues as well as assists in the control of a number of metabolic processes. Endocannabinoids, enzymes, and cannabinoid receptors all play a role in managing a variety of biological functions. In this review, we aimed to summarize the endocannabinoid system, the effect of beta-amyloid, tau-protein accumulation and neuroinflammation, potential therapeutic benefits of cannabinoids, current pre-clinical and clinical evidence for the potential therapeutic benefits, limitations, and challenges, and potential future research in the field. Emerging scientific data suggests that the ECS (Endocannabinoid system) is linked to neurodegenerative illnesses and that altering its tone may be a useful therapeutic strategy for the treatment of AD (Alzheimer’s disease).

Neurotrophin growth factors and their receptors as promising blood biomarkers for Alzheimer's Disease: a gene expression analysis study.

Alzheimer's disease (AD) is a multifaceted neurological ailment affecting more than 50million individuals globally, distinguished by a deterioration in memory and cognitive abilities. Investigating neurotrophin growth factors could offer significant contributions to understanding AD progression and prospective therapeutic interventions. The present investigation collected blood samples from 50 patients diagnosed with AD and 50 healthy individuals serving as controls. The mRNA expression levels of neurotrophin growth factors and their receptors were measured using quantitative PCR. A Bayesian regression model was used in the research to assess the relationship between gene expression levels and demographic characteristics such as age and gender. The correlations between variables were analyzed using Spearman correlation coefficients, and the diagnostic potential was assessed using a Receiver Operating Characteristic curve. NTRK2, TrkA, TrkC, and BDNF expression levels were found to be considerably lower (p-value < 0.05) in the blood samples of AD patients compared to the control group. The expression of BDNF exhibited the most substantial decrease in comparison to other neurotrophin growth factors. Correlation analysis indicates a statistically significant positive association between the genes. The ROC analysis showed that BDNF exhibited the greatest Area Under the Curve (AUC) value of 0.76, accompanied by a sensitivity of 70% and specificity of 66%. TrkC, TrkA, and NTRK2 demonstrated considerable diagnostic potential in distinguishing between cases and controls. The observed decrease in the expression levels of NTRK2, TrkA, TrkC, and BDNF in AD patients, along with the identified associations between specific genes and their diagnostic capacity, indicate that these expressions have the potential to function as biomarkers for the diagnosis and treatment of AD.

An innovative transportable immune device for the recognition of α-synuclein using KCC-1-nPr-CS2 modified silver nano-ink: integration of pen-on-paper technology with biosensing toward early-stage diagnosis of Parkinson's disease.

Parkinson's disease (PD), the second most frequent neurodegenerative illness, is a neurological ailment that produces unintentional or uncontrolled body movements, which should be diagnosed in its early stages to hinder the progression. Monitoring the concentration of α-synuclein (α-Syn) in body fluids can be one of the most efficient ways for PD early detection. In this work, a paper-based electrochemical immunosensor was designed for α-Syn bio-assay in human plasma samples based on encapsulation of the biotinylated antibody on novel dendritic fibrous nanosilica ((KCC-1-nPr-CS2)-Ab). For this purpose, a three-electrode system was prepared using stabilization of silver nano-ink on photographic paper. Then, the (KCC-1-NH-CS2)-Ab was immobilized on its surface and used to detect the target antigen (α-Syn). After characterization of the prepared substrate by FE-SEM and EDS, the redox behavior of the biosensor was evaluated using chronoamperometry techniques. Under optimal experimental conditions and using a label-free strategy, the engineered immunosensor showed a linear relationship between peak current and antigen concentration in the linear range from 0.002 to 128 ng mL-1 with the lower limit of quantification of 0.002 ng mL-1. Moreover, this work involves unprecedented use of conductive nano-inks for the manufacture of α-Syn immunosensor, which is aided by the use of a mesoporous silicate dendrimer in encapsulating the α-Syn antibody, thus offering a robust and simple point-of-care device for early PD diagnosis. The ability of the proposed platform to detect small amounts of α-Syn offers a promising approach to developing low-cost, sensitive, and transportable biosensors for Parkinson's disease screening in its early stages.

The Greater Omentum: Multifaceted Interactions in Neurological Recovery and Disease Progression.

The greater omentum, a unique anatomical structure composed of adipocytes, loose connective tissue, and a dense vascular network. Plays a pivotal role beyond its traditional understanding. It houses specialized immunological units known as 'Milky spots,' making it a key player in immune response. Moreover, the omentum's capacity to enhance tissue perfusion, absorb edema fluid, boost acetylcholine synthesis, and foster neuron repair have rendered it a topic of interest in the context of various diseases, especially neurological disorders. This review provides a comprehensive overview of the intricate anatomy and histology of the greater omentum, casting light on its multifaceted functions and its associations with a spectrum of diseases. With a specific focus on neurological ailments, we delineate the intricate relationship that the omentum shares with other pathologies like stroke and we underly its contribution to serving as a therapeutic agent in neurological disorders. By deciphering the underlying mechanisms and emphasizing areas that demand further investigation. This review aims to spark renewed interest and pave the way for comprehensive studies exploring the greater omentum's potential in neurology and broader medicine overall. Given these diverse interactions that yet remain elusive, we must investigate and understand the nuanced relationship between the greater omentum and pathologies, especially its role in stroke's pathophysiology and therapeutic interventions so as to enhance patient care.

Potential role of herbal plants and beta sitosterol as a bioactive constituent in circumventing Alzheimer’s Disease

Alzheimer's Disease (AD), a neurological ailment, mostly affects the older population all around the world. The rational therapies show limited efficacy, adverse effects, and poor patient compliance; therefore, herbal drugs are considered a suitable supplement to the drug therapy for the treatment of AD. According to research, herbal drugs reduce symptoms of AD and also improve brain functioning through the inhibition of beta amyloid, gamma-secretase, and acetylcholine, along with the regulation of antioxidants and the activation of alpha-secretase. Various herbal plants like Salvia officinalis L., Bertholletia excelsa L., Withania somnifera L., and Urtica dioica L. help slow down the progression of AD by scavenging free radicals, inhibiting lipid peroxidation, beta amyloid and tau phosphorylation. Beta sitosterol, a phytosterol found abundantly in plants, has the ability to cross the Blood Brain Barrier and thus acts as a bioactive constituent in circumventing various neurological disorders. Numerous in vitro and in vivo investigations indicate that beta sitosterol shows immunomodulatory, lipid-lowering, as well as antioxidant properties. The plant sterol, beta sitosterol, has the capacity to decrease beta-amyloid platelet synthesis, indicating that it might be helpful in the treatment and prevention of AD. Treatment with beta-sitosterol can lessen plaque burden and also enhance spatial learning and recognition abilities in patients suffering from AD.

Use of probiotics in treating psychiatric and neurological disorders.

To the Editor, The gut-brain axis is a bidirectional pathway through which the central nervous system and gut microbiota communicate. Metabolites from the gut microbiota can interact with ANS gut synapses, similarly, gut physiology can be controlled by the ANS. Visceral sensory signals from the gut travel to the CNS via the vagus nerve and can influence mood changes. Any alterations in this complex pathway can lead to the development of several gastrointestinal, psychiatric, and neurological disorders that include depression, anxiety, Alzheimer’s disease and Parkinson’s disorder [1-2]. As the relationship between psychiatric and gastrointestinal symptoms becomes well-established with 60% of patients with gastroenterological complaints having psychiatric origins [3], the question arises of whether it would be beneficial to focus on the gut microbiota and use probiotics to treat psychiatric and neurological ailments. Recent research has opened a new door to targeting the gut microbiota to treat disorders such as depression, anxiety, Alzheimer's disease, Parkinson's disease, and autism spectrum disorder [1]. The effect of modifying gut microbiota to treat psychiatric illnesses that include depression, anxiety, and schizophrenia has been studied, showing that the use of probiotics can alleviate depression but has no effect on schizophrenia. The gut microbiota influences the hypothalamic-pituitary-adrenal axis, allowing probiotics to alter the stress response of cortisol; however, sufficient data is not available to comment on the effect of probiotics on anxiety [4]. Furthermore, the scope of probiotics seems to extend beyond mood disorders to relieving symptoms of neurological disorders that include Parkinson’s, migraine and epilepsy [5] The close relationship of gastrointestinal and neuropsychiatric health often results in disorders of both systems appearing concomitantly and GIT manifestations are often dismissed as being caused by the psychiatric complain; however, we may look at the other side of the picture which is that gastrointestinal dysbiosis may be a cause or an exacerbating factor of the neuropsychiatric symptoms in which case probiotics can be a reliable treatment. More evidence needs to be obtained on their effectiveness and whether they may be used as stand-alone or along with other medications.