Diagnosis Of Neurological Disorders Research Articles

Brain disorder prediction with dynamic multivariate spatio-temporal features: Application to Alzheimer's disease and autism spectrum disorder.

The dynamic functional connectivity (dFC) in functional magnetic resonance imaging (fMRI) is beneficial for the analysis and diagnosis of neurological brain diseases. The dFCs between regions of interest (ROIs) are generally delineated by a specific template and clustered into multiple different states. However, these models inevitably fell into the model-driven self-contained system which ignored the diversity at spatial level and the dynamics at time level of the data. In this study, we proposed a spatial and time domain feature extraction approach for Alzheimer’s disease (AD) and autism spectrum disorder (ASD)-assisted diagnosis which exploited the dynamic connectivity among independent functional sub networks in brain. Briefly, independent sub networks were obtained by applying spatial independent component analysis (SICA) to the preprocessed fMRI data. Then, a sliding window approach was used to segment the time series of the spatial components. After that, the functional connections within the window were obtained sequentially. Finally, a temporal signal-sensitive long short-term memory (LSTM) network was used for classification. The experimental results on Alzheimer’s Disease Neuroimaging Initiative (ADNI) and Autism Brain Imaging Data Exchange (ABIDE) datasets showed that the proposed method effectively predicted the disease at the early stage and outperformed the existing algorithms. The dFCs between the different components of the brain could be used as biomarkers for the diagnosis of diseases such as AD and ASD, providing a reliable basis for the study of brain connectomics.

Magnetic Nanoparticles: Current Advances in Nanomedicine, Drug Delivery and MRI

Magnetic nanoparticles (MNPs) have evolved tremendously during recent years, in part due to the rapid expansion of nanotechnology and to their active magnetic core with a high surface-to-volume ratio, while their surface functionalization opened the door to a plethora of drug, gene and bioactive molecule immobilization. Taming the high reactivity of the magnetic core was achieved by various functionalization techniques, producing MNPs tailored for the diagnosis and treatment of cardiovascular or neurological disease, tumors and cancer. Superparamagnetic iron oxide nanoparticles (SPIONs) are established at the core of drug-delivery systems and could act as efficient agents for MFH (magnetic fluid hyperthermia). Depending on the functionalization molecule and intrinsic morphological features, MNPs now cover a broad scope which the current review aims to overview. Considering the exponential expansion of the field, the current review will be limited to roughly the past three years.

NGS-driven molecular diagnosis of heterogeneous hereditary neurological disorders reveals novel and known variants in disease-causing genes.

Hereditary neurological disorders (HNDs) are a clinically and genetically heterogeneous group of disorders. These disorders arise from the impaired function of the central or peripheral nervous system due to aberrant electrical impulses. More than 600 various neurological disorders, exhibiting a wide spectrum of overlapping clinical presentations depending on the organ(s) involved, have been documented. Owing to this clinical heterogeneity, diagnosing these disorders has been a challenge for both clinicians and geneticists and a large number of patients are either misdiagnosed or remain entirely undiagnosed. Contribution of genetics to neurological disorders has been recognized since long; however, the complete picture of the underlying molecular bases are under-explored. The aim of this study was to accurately diagnose 11 unrelated Pakistani families with various HNDs deploying NGS as a first step approach. Using exome sequencing and gene panel sequencing, we successfully identified disease-causing genomic variants these families. We report four novel variants, one each in, ECEL1, NALCN, TBR1 and PIGP in four of the pedigrees. In the rest of the seven families, we found five previously reported pathogenic variants in POGZ, FA2H, PLA2G6 and CYP27A1. Of these, three families segregate a homozygous 18bp in-frame deletion of FA2H, indicating a likely founder mutation segregating in Pakistani population. Genotyping for this mutation can help low-cost population wide screening in the corresponding regions of the country. Our findings not only expand the existing repertoire of mutational spectrum underlying neurological disordersbut will also help in genetic testing of individuals with HNDs in other populations.

Sensing lysozyme fibrils by salicylaldimine substituted BODIPY dyes - A correlation with molecular structure

Quick and efficient detection of protein fibrils has enormous impact on the diagnosis and treatment of amyloid related neurological diseases. Among several methods, fluorescence based techniques have garnered most importance in the detection of amyloid fibrils due to its high sensitivity and extreme simplicity. Among other classes of molecular probes, BODIPY derivatives have been employed extensively for the detection of amyloid fibrils. However, there are very few studies on the relationship between the molecular structure of BODIPY dyes and their amyloid sensing activity. Here in a BODIPY based salicylaldimine Schiff base and its corresponding boron complex have been evaluated for their ability to sense amyloid fibrils from hen-egg white lysozyme using steady state and time-resolved spectroscopic techniques. Both dyes show fluorescence enhancement as well as increase in their excited state lifetime upon their binding with lysozyme fibrils. However, the BODIPY derivative which shows more emission enhancement in fibrillar solution has much lower affinity towards amyloid fibrils as compared to other derivative. This contrasting behaviour in the emission enhancement and binding affinity has been explained on the basis of differences in their photophysical properties in water and amyloid fibril originating from the difference in their molecular structure. Such correlation between the amyloid sensitivity and the molecular structure of the probe can open up a new strategy for designing new efficient amyloid probes.

Computer-Aided Detection and Diagnosis of Neurological Disorder.

Nowadays, neurological problems are more regular, representing a worry to pregnant ladies, guardians, healthy babies, and kids. Neurological problems emerge in a wide assortment of structures, each with its arrangement of beginnings, inconveniences, and results. The conclusion of neurological illnesses is an evolving concern and predominantly troublesome difficulty for current medication. Current diagnosis advancements (e.g., MRI and EEG) produce immense information (in proportion and aspect) as location, checking, and therapy of nervous system illnesses. As a common rule, investigation of that enormous clinical information is performed physically by specialists to distinguish and figure out the irregularities. It is a genuinely troublesome errand for an individual to collect, make due, investigate, and absorb enormous amounts of information through visible review. As an outcome, the specialist has been requesting electronic conclusion frameworks known as “computer-aided diagnosis” that can consequently identify the nervous system irregularities utilizing the essential clinical information. This framework further develops uniformity of findings, builds treatment outcomes, protects lives, and lessens price and time. As of late, few examinations have improved the computer-aided design frameworks for the executives of enormous clinical information for determination appraisal. This paper investigates the difficulties of tremendous clinical information giving. This article fundamentally evaluated and looked at the exhibition of existing AI and comprehensive learning approaches for identifying nervous system illnesses. A far-reaching piece of this concentration also shows different modalities and illness-determined datasets that identify and record pictures, signs, addresses, and so forth. Restricted related works are additionally summed up on nervous system illnesses, as this space has essentially less work zeroed in on illness and recognition rules. A portion of the standard assessment measurements is likewise introduced in this review for improved outcome examination.

SANE (Easy Gait Analysis System): Towards an AI-Assisted Automatic Gait-Analysis.

The gait cycle of humans may be influenced by a range of variables, including neurological, orthopedic, and pathological conditions. Thus, gait analysis has a broad variety of applications, including the diagnosis of neurological disorders, the study of disease development, the assessment of the efficacy of a treatment, postural correction, and the evaluation and enhancement of sport performances. While the introduction of new technologies has resulted in substantial advancements, these systems continue to struggle to achieve a right balance between cost, analytical accuracy, speed, and convenience. The target is to provide low-cost support to those with motor impairments in order to improve their quality of life. The article provides a novel automated approach for motion characterization that makes use of artificial intelligence to perform real-time analysis, complete automation, and non-invasive, markerless analysis. This automated procedure enables rapid diagnosis and prevents human mistakes. The gait metrics obtained by the two motion tracking systems were compared to show the effectiveness of the proposed methodology.

Evaluation of the blood neutrophil-to-lymphocyte ratio as a biomarker for meningoencephalitis of unknown etiology in dogs.

BackgroundThe neutrophil‐to‐lymphocyte ratio (NLR) has been identified as a biomarker in several inflammatory and autoimmune diseases. Multiple sclerosis (MS) has been found to be associated with changes in the NLR in humans.ObjectivesTo examine the diagnostic value of the NLR in meningoencephalitis of unknown etiology (MUE) in dogs.AnimalsThirty‐eight MUE dogs, 20 hydrocephalic dogs, 10 brain tumor (BT) dogs, 32 idiopathic epilepsy (IE) dogs, and 41 healthy dogs.MethodsRetrospective study. Medical records were reviewed to identify dogs with a diagnosis of neurologic disease. The NLR was determined in all dogs.ResultsThe median NLR was significantly higher in MUE dogs (6.08) than in healthy (1.78, P < .001), IE (2.50, P < .05), and hydrocephalic dogs (1.79, P < .05). The area under the receiver operating characteristic curve of the NLR for differentiation between MUE and healthy dogs was 0.96, and between the MUE dogs and dogs with other forebrain diseases was 0.86. An optimal cutoff of 4.16 for the NLR had a sensitivity of 71.1% and specificity of 83.9% to differentiate the MUE dogs from the dogs with other forebrain diseases.Conclusions and Clinical ImportanceThe NLR could be a biomarker for diagnosing MUE and distinguishing it from other intracranial diseases in dogs.

Artificial Intelligence Models in the Diagnosis of Adult-Onset Dementia Disorders: A Review.

Background: The progressive aging of populations, primarily in the industrialized western world, is accompanied by the increased incidence of several non-transmittable diseases, including neurodegenerative diseases and adult-onset dementia disorders. To stimulate adequate interventions, including treatment and preventive measures, an early, accurate diagnosis is necessary. Conventional magnetic resonance imaging (MRI) represents a technique quite common for the diagnosis of neurological disorders. Increasing evidence indicates that the association of artificial intelligence (AI) approaches with MRI is particularly useful for improving the diagnostic accuracy of different dementia types. Objectives: In this work, we have systematically reviewed the characteristics of AI algorithms in the early detection of adult-onset dementia disorders, and also discussed its performance metrics. Methods: A document search was conducted with three databases, namely PubMed (Medline), Web of Science, and Scopus. The search was limited to the articles published after 2006 and in English only. The screening of the articles was performed using quality criteria based on the Newcastle–Ottawa Scale (NOS) rating. Only papers with an NOS score ≥ 7 were considered for further review. Results: The document search produced a count of 1876 articles and, because of duplication, 1195 papers were not considered. Multiple screenings were performed to assess quality criteria, which yielded 29 studies. All the selected articles were further grouped based on different attributes, including study type, type of AI model used in the identification of dementia, performance metrics, and data type. Conclusions: The most common adult-onset dementia disorders occurring were Alzheimer’s disease and vascular dementia. AI techniques associated with MRI resulted in increased diagnostic accuracy ranging from 73.3% to 99%. These findings suggest that AI should be associated with conventional MRI techniques to obtain a precise and early diagnosis of dementia disorders occurring in old age.

Methods to capture proteomic and metabolomic signatures from cerebrospinal fluid and serum of healthy individuals

Discovery of reliable signatures for the empirical diagnosis of neurological diseases—both infectious and non-infectious—remains unrealized. One of the primary challenges encountered in such studies is the lack of a comprehensive database representative of a signature background that exists in healthy individuals, and against which an aberrant event can be assessed. For neurological insults and injuries, it is important to understand the normal profile in the neuronal (cerebrospinal fluid) and systemic fluids (e.g., blood). Here, we present the first comparative multi-omic human database of signatures derived from a population of 30 individuals (15 males, 15 females, 23–74 years) of serum and cerebrospinal fluid. In addition to empirical signatures, we also assigned common pathways between serum and CSF. Together, our findings provide a cohort against which aberrant signature profiles in individuals with neurological injuries/disease can be assessed—providing a pathway for comprehensive diagnostics and therapeutics discovery.

Preparation and application of pH-responsive drug delivery systems.

Microenvironment-responsive drug delivery systems (DDSs) can achieve targeted drug delivery, reduce drug side effects and improve drug efficacies. Among them, pH-responsive DDSs have gained popularity since the pH in the diseased tissues such as cancer, bacterial infection and inflammation differs from a physiological pH of 7.4 and this difference could be harnessed for DDSs to release encapsulated drugs specifically to these diseased tissues. A variety of synthetic approaches have been developed to prepare pH-sensitive DDSs, including introduction of a variety of pH-sensitive chemical bonds or protonated/deprotonated chemical groups. A myriad of nano DDSs have been explored to be pH-responsive, including liposomes, micelles, hydrogels, dendritic macromolecules and organic-inorganic hybrid nanoparticles, and micron level microspheres. The prodrugs from drug-loaded pH-sensitive nano DDSs have been applied in research on anticancer therapy and diagnosis of cancer, inflammation, antibacterial infection, and neurological diseases. We have systematically summarized synthesis strategies of pH-stimulating DDSs, illustrated commonly used and recently developed nanocarriers for these DDSs and covered their potential in different biomedical applications, which may spark new ideas for the development and application of pH-sensitive nano DDSs.

The Immersive Cleveland Clinic Virtual Reality Shopping Platform for the Assessment of Instrumental Activities of Daily Living.

A decline in the performance of instrumental activities of daily living (IADLs) has been proposed as a prodromal marker of neurological disease. Existing clinical and performance-based IADL assessments are not feasible for integration into clinical medicine. Virtual reality (VR) is a powerful yet underutilized tool that could advance the diagnosis and treatment of neurological disease. An impediment to the adoption and scaling of VR in clinical neurology is VR-related sickness resulting from sensory inconsistencies between the visual and vestibular systems (i.e., locomotion problem). The Cleveland Clinic Virtual Reality Shopping (CC-VRS) platform attempts to solve the locomotion problem by coupling an omnidirectional treadmill with high-resolution VR content, enabling the user to physically navigate a virtual grocery store to simulate shopping. The CC-VRS consists of Basic and Complex shopping experiences; both require walking 150 m and retrieving five items. The Complex experience has additional scenarios that increase the cognitive and motor demands of the task to better represent the continuum of activities associated with real-world shopping. The CC-VRS platform provides objective and quantitative biomechanical and cognitive outcomes related to the user's IADL performance. Initial data indicate that the CC-VRS results in minimal VR-sickness and is feasible and tolerable for older adults and patients with Parkinson's disease (PD). The considerations underlying the development, design, and hardware and software technology are reviewed, and initial models of integration into primary care and neurology are provided.

Epilepsi Tespitinde Gürbüz Yerel Ortalama Ayrışım ve Ampirik Kip Ayrışım Yöntemlerinin Performans Analizi

Electroencephalography (EEG) data, which provides information about the electrical activity of the brain, are widely used in the diagnosis of neurological diseases, EEG signals also provide important information in the detection of epilepsy, which is one of the diseases affecting approximately 1% of the world’s population. In this study, it was aimed to detect the epileptic seizure before the seizure by using EEG signals. For this purpose, after preprocessing steps were performed by using EEG signals in different situations from epilepsy and healthy individuals, features were extracted from EEG signals from subband signals obtained by using Robust Local Mean Decomposition (RLMD) and Empirical Mode Decomposition (AKA) methods. Classification studies were carried out with the obtained features and Artificial Neural Networks (ANN). In line with the studies, the classification results of the different states of the EEG signals were revealed using the performance parameters of accuracy, sensitivity, specificity, precision and f1 score.

Estimating high-order brain functional networks by correlation-preserving embedding.

Brain functional network (FN) has emerged as a potential tool for identifying mental and neurological diseases. Traditional FN estimation methods such as Pearson's correlation (PC) and sparse representation (SR), despite their popularity, can only model low-order relationships between brain regions (i.e., nodes of FN), thus failing to capture more complex interaction in the brain. Recently, researchers proposed to estimate high-order FN (HoFN) and successfully used them in the early diagnosis of neurological diseases. In practice, however, such HoFN is constructed by directly considering the columns (or rows) of the adjacency matrix of low-order FN (LoFN) as node feature vectors that may contain some redundant or noisy information. In addition, it is not really reflected whether the original low-order relationship is maintained during the construction of the HoFN. To address these problems, we propose correlation-preserving embedding (COPE) to re-code the LoFN prior to constructing HoFN. Specifically, we first use SR to construct traditional LoFN. Then, we embed the LoFN via COPE to generate the new node representation for removing the potentially redundant/noisy information in original node feature vectors and simultaneously maintaining the low-order relationship between brain regions. Finally, the expected HoFN is estimated by SR based on the new node representation. To verify the effectiveness of the proposed scheme, we conduct experiments on 137 subjects from the public Alzheimer's Disease Neuroimaging Initiative (ADNI) database to identify subjects with mild cognitive impairment (MCI) from normal controls. Experimental results show that the proposed scheme can achieve better performance than the baseline method. Brain functional network (FN) has emerged as a potential tool for identifying mental and neurological diseases. Traditional FN estimation methods such as Pearson's correlation (PC) and sparse representation (SR), despite their popularity, can only model low-order relationships between brain regions (i.e., nodes of FN), thus failing to capture more complex interaction in the brain. Recently, researchers proposed to estimate high-order FN (HoFN) and successfully used them in the early diagnosis of neurological diseases. In practice, however, such HoFN is constructed by directly considering the columns (or rows) of the adjacency matrix of low-order FN (LoFN) as node feature vectors that may contain some redundant or noisy information. In addition, it is not really reflected whether the original low-order relationship is maintained during the construction of the HoFN. To address these problems, we propose correlation-preserving embedding (COPE) to re-code the LoFN prior to constructing HoFN. Specifically, we first use SR to construct traditional LoFN. Then, we embed the LoFN via COPE to generate the new node representation for removing the potentially redundant/noisy information in original node feature vectors and simultaneously maintaining the low-order relationship between brain regions. Finally, the expected HoFN is estimated by SR based on the new node representation. To verify the effectiveness of the proposed scheme, we conduct experiments on 137 subjects from the public Alzheimer's Disease Neuroimaging Initiative (ADNI) database to identify subjects with mild cognitive impairment (MCI) from normal controls. Experimental results show that the proposed scheme can achieve better performance than the baseline method.

A Deep Spatiotemporal Attention Network for Mild Cognitive Impairment Identification.

Mild cognitive impairment (MCI) is a nervous system disease, and its clinical status can be used as an early warning of Alzheimer's disease (AD). Subtle and slow changes in brain structure between patients with MCI and normal controls (NCs) deprive them of effective diagnostic methods. Therefore, the identification of MCI is a challenging task. The current functional brain network (FBN) analysis to predict human brain tissue structure is a new method emerging in recent years, which provides sensitive and effective medical biomarkers for the diagnosis of neurological diseases. Therefore, to address this challenge, we propose a novel Deep Spatiotemporal Attention Network (DSTAN) framework for MCI recognition based on brain functional networks. Specifically, we first extract spatiotemporal features between brain functional signals and FBNs by designing a spatiotemporal convolution strategy (ST-CONV). Then, on this basis, we introduce a learned attention mechanism to further capture brain nodes strongly correlated with MCI. Finally, we fuse spatiotemporal features for MCI recognition. The entire network is trained in an end-to-end fashion. Extensive experiments show that our proposed method significantly outperforms current baselines and state-of-the-art methods, with a classification accuracy of 84.21%.

Soft Attention Based DenseNet Model for Parkinson's Disease Classification Using SPECT Images.

ObjectiveDeep learning algorithms have long been involved in the diagnosis of severe neurological disorders that interfere with patients’ everyday tasks, such as Parkinson’s disease (PD). The most effective imaging modality for detecting the condition is DaTscan, a variety of single-photon emission computerized tomography (SPECT) imaging method. The goal is to create a convolutional neural network that can specifically identify the region of interest following feature extraction.MethodsThe study comprised a total of 1,390 DaTscan imaging groups with PD and normal classes. The architecture of DenseNet-121 is leveraged with a soft-attention block added before the final classification layer. For visually analyzing the region of interest (ROI) from the images after classification, Soft Attention Maps and feature map representation are used.OutcomesThe model obtains an overall accuracy of 99.2% and AUC-ROC score 99%. A sensitivity of 99.2%, specificity of 99.4% and f1-score of 99.1% is achieved that surpasses all prior research findings. Soft-attention map and feature map representation aid in highlighting the ROI, with a specific attention on the putamen and caudate regions.ConclusionWith the deep learning framework adopted, DaTscan images reveal the putamen and caudate areas of the brain, which aid in the distinguishing of normal and PD cohorts with high accuracy and sensitivity.

Application of Video-Based Deep Learning for Early Diagnosis of Neurological Disorders

Daniel Groos, MS; Lars Adde, PT, PhD; Sindre Aubert, MS; Lynn Boswell, PT, MS; Raye-Ann de Regnier, MD; Toril Fjørtoft, PT, PhD; Deborah Gaebler-Spira, MD; Andreas Haukeland, MS; Marianne Loennecken, PT; Michael Msall, MD; Unn Inger Möinichen, PT, MS; Aurelie Pascal, PT, PhD; Colleen Peyton, PT, DPT; Heri Ramampiaro, PhD; Michael D. Schreiber, MD; Inger Elisabeth Silberg, MD; Nils Thomas Songstad, MD, PhD; Niranjan Thomas, MD, PhD; Christine Van den Broeck, PT, PhD; Gunn Kristin Øberg, PT, PhD; Espen A.F. Ihlen, PhD; Ragnhild Støen, MD, PhD

Diversity of Astrovirus in Goats in Southwest China and Identification of Two Novel Caprine Astroviruses.

ABSTRACTA total of 232 goat fecal samples (124 diarrheic and 108 nondiarrheic) collected from 12 farms in Southwest China were tested for astrovirus using RT-PCR. A total of 16.9% (21/124) of diarrheic and 20.4% (22/108) of nondiarrheic samples were astrovirus-positive, and no statistical difference was found in the detection rate between healthy and sick goats. Furthermore, 28 obtained complete ORF2 sequences could be classified into six genotypes according to the species classification criteria of the International Committee on Taxonomy of Viruses (ICTV). It is worth noting that, in addition to four known caprine astrovirus genotypes (MAstV–33, MAstV–34, Caprine Astrovirus G5.1, and Caprine Astrovirus G3.1), MAstV–13 and MAstV–24 genotypes were identified in goats. Interestingly, five of 19 ORF2 sequences in the Caprine Astrovirus G3.1 genotype showed possible intragenotypic recombination events. Furthermore, nearly complete caprine astrovirus genomes of MAstV–13 and MAstV–24 genotypes were obtained. The genome of the SWUN/ECJK3/2021 strain shared the highest similarity (62.0% to 73.9%) with astrovirus in MAstV–13, and clustered in the so-called human-mink-ovine (HMO) clade, which contained the majority of the neurotropic astrovirus strains. Moreover, the SWUN/LJK2-2/2020 strain showed the highest similarity (69.7% to 78.6%) and the closest genetic relationship to the known porcine and bovine astroviruses in MAstV–24. In conclusion, this study confirmed six genotypes of astrovirus circulating among goats in Southwest China, including MAstV–13 and MAstV–24 genotypes. These findings enhance our knowledge of the prevalence and diversity of astroviruses.IMPORTANCE Caprine astrovirus is a newly emerging virus, and information regarding its prevalence and molecular characteristics remains limited. In this study, six genotypes of astrovirus, including MAstV–13 and MAstV–24, were identified in goats, adding two novel caprine astrovirus genotypes to the four previously known genotypes, thereby enriching the diversity of the caprine astrovirus. Moreover, genomes of MAstV–13 SWUN/ECJK3/2021 and MAstV–24 SWUN/LJK2-2/2020 strains were obtained from goats, which aids in the understanding of the infection spectrum and host range of the two genotypes. This study is the first to demonstrate the presence of neurotropic-like astrovirus (MAstV–13) in goats, which has significant implications for the diagnosis of neurological diseases in goats.

Proposing a Framework to Understand the Role of Imaging in Degenerative Cervical Myelopathy: Enhancement of MRI Protocols Needed for Accurate Diagnosis and Evaluation.

Proposing a Framework to Understand the Role of Imaging in Degenerative Cervical Myelopathy: Enhancement of MRI Protocols Needed for Accurate Diagnosis and Evaluation.

Prospects of Single Cell Omics (SCO) Analysis for Investigating Nervous System Disorders

Single Cell Omics (SCO) is an evolving field in biomedical research which offers prime application in neurosciences. The human nervous system is complex and shows variability in cell types. It includes neurons (functional components), glial cells (supporting cells), astrocytes (provide nourishment), oligodendrocytes (synthesize myelin sheath), and microglia (defense mechanism). To understand the functional and disease states of the nervous system, it is essential to investigate them at the single-cell level. It has been estimated that every 1 in 9 people is affected by mental or neurological disorders, including psychological disorders (generally referred to as mental disorders), psychotic disorders (involving psychosis), and neurological disorders (involving neurological factors), globally. These disorders have multifactorial etiology and are caused by genetic and environmental factors. Every disorder has distinct pathophysiology affecting multiple brain regions. SCO has excellent potential to provide insight regarding the diagnosis, pathophysiology, and treatment of neurological disorders. The stringent well directed SCO methods enhance the understanding of complex nervous system disorders, such as meningitis, stroke, schizophrenia, Parkinson’s disease, and Alzheimer’s disease, which paves the way for future research.

A new optical fiber biosensor for acetylcholine detection based on pH sensitive fluorescent carbon quantum dots

Acetylcholine (Ach) is an important neurotransmitter and plays a vital role in the diagnosis and treatment of many neurological diseases. A new acetylcholine optical fiber biosensor is reported based on the nitrogen doped fluorescent carbon quantum dots/ acetylcholinesterase (N-CQDs/AchE) compound, where AchE was immobilized on the surface of N-CQDs by the cross-linking method. N-CQDs were synthesized by the hydrothermal method as fluorescent probe and can be responsible for pH value. The optical properties, morphology, particle size distribution and surface groups of N-CQDs were characterized by UV-Vis spectrophotometer, Fluorescence spectrometer, High-resolution transmission electron microscope (TEM) and Fourier transform infrared spectrometer (FTIR), respectively. The relationship between acetylcholine chloride solution and fluorescence intensity of N-CQDs/AchE complex was detected in detailed. Good linear relationship between fluorescence intensity and the concentration of acetylcholine from 20–200 µM and 1–100 nM with the lower detection limits of 16.28 µM and 0.29 nM, respectively. Compared with other sensors, this new optic fiber sensor immobilized (N-CQDs/AchE) complex is more simple and operable.