Diagnosis Of Neurological Disorders Research Articles

Evaluation of glutamic acid decarboxylase (GAD) 65 antibody detection methods for neurological and diabetic investigation in an Australian diagnostic laboratory

The role of anti-glutamic acid decarboxylase (GAD) 65 autoantibodies in autoimmune neurological conditions is evolving, but testing recommendations remain unchanged in Australia with GAD enzyme-linked immunosorbent assay (ELISA) and immunoblot as the only two Therapeutic Goods Administration approved testing methods available in Australia. Common practice is for use of ELISA in diagnosis of type 1 diabetes mellitus (T1DM) and use of immunoblot for diagnosis of GAD65-associated neurological disease. We observed a cohort of patients with negative immunoblot results and positive ELISA in the context of GAD-associated neurological disease without T1DM. In the absence of robust consensus guidelines on preferred testing modalities, we sought to determine if ELISA could have a superior role in the diagnosis of GAD-associated neurological disease when compared to immunoblot in this paper. We tested for anti-GAD65 autoantibodies on 55 patient samples, 40 samples requested for neurological disease and 15 type 1 diabetes samples with detectable anti-GAD65, using two testing platforms: Euroimmun anti-GAD enzyme-linked immunosorbent assay (ELISA) and. Euroimmun EuroLine immunoblot for paraneoplastic neurologic syndromes. These results were correlated against the clinical scenario. Positive ELISA results had a sensitivity of 100% and specificity of 91% for GAD65-related neurological disease. Immunoblot showed sensitivity of 43% and specificity of 76% for GAD65-related neurological disease. ELISA proved more sensitive and specific for GAD65-related neurological disease compared to immunoblot, raising questions about the role of this testing modality in neurological disease. We propose that ELISA should be used as a sole diagnostic method for all GAD65 antibody-related neurological disease over immunoblot. The presence of anti-GAD65 antibody on immunoblot is of doubtful clinical significance.

Ultrasensitive and Simple Dopamine Electrochemical Sensor Based on the Synergistic Effect of Cu-TCPP Frameworks and Graphene Nanosheets

Dopamine (DA) is an important neurotransmitter. Abnormal concentration of DA can result in many neurological diseases. Developing reliable determination methods for DA is of great significance for the diagnosis and monitoring of neurological diseases. Here, a novel and simple electrochemical sensing platform for quantitative analysis of DA was constructed based on the Cu-TCPP/graphene composite (TCPP: Tetrakis(4-carboxyphenyl)porphyrin). Cu-TCPP frameworks were selected in consideration of their good electrochemical sensing potential. The graphene nanosheets with excellent conductivity were then added to further improve the sensing efficiency and stability of Cu-TCPP frameworks. The electrochemical properties of the Cu-TCPP/graphene composite were characterized, showing its large electrode active area, fast electron transfer, and good sensing performance toward DA. The signal enhancement mechanism of DA was explored. Strong accumulation ability and high electrocatalytic rate were observed on the surface of Cu-TCPP/graphene-modified glassy carbon electrode (Cu-TCPP/graphene/GCE). Based on the synergistic sensitization effect, an ultrasensitive and simple DA electrochemical sensor was developed. The linear range is 0.02-100 and 100-1000 µM, and the detection limit is 3.6 nM for the first linear range. It was also successfully used in detecting DA in serum samples, and a satisfactory recovery was obtained.

Convergence of Artificial Intelligence and Neuroscience towards the Diagnosis of Neurological Disorders-A Scoping Review.

Artificial intelligence (AI) is a field of computer science that deals with the simulation of human intelligence using machines so that such machines gain problem-solving and decision-making capabilities similar to that of the human brain. Neuroscience is the scientific study of the struczture and cognitive functions of the brain. Neuroscience and AI are mutually interrelated. These two fields help each other in their advancements. The theory of neuroscience has brought many distinct improvisations into the AI field. The biological neural network has led to the realization of complex deep neural network architectures that are used to develop versatile applications, such as text processing, speech recognition, object detection, etc. Additionally, neuroscience helps to validate the existing AI-based models. Reinforcement learning in humans and animals has inspired computer scientists to develop algorithms for reinforcement learning in artificial systems, which enables those systems to learn complex strategies without explicit instruction. Such learning helps in building complex applications, like robot-based surgery, autonomous vehicles, gaming applications, etc. In turn, with its ability to intelligently analyze complex data and extract hidden patterns, AI fits as a perfect choice for analyzing neuroscience data that are very complex. Large-scale AI-based simulations help neuroscientists test their hypotheses. Through an interface with the brain, an AI-based system can extract the brain signals and commands that are generated according to the signals. These commands are fed into devices, such as a robotic arm, which helps in the movement of paralyzed muscles or other human parts. AI has several use cases in analyzing neuroimaging data and reducing the workload of radiologists. The study of neuroscience helps in the early detection and diagnosis of neurological disorders. In the same way, AI can effectively be applied to the prediction and detection of neurological disorders. Thus, in this paper, a scoping review has been carried out on the mutual relationship between AI and neuroscience, emphasizing the convergence between AI and neuroscience in order to detect and predict various neurological disorders.

A novel framework for in-vivo diffusion tensor distribution MRI of the human brain

Neural tissue microstructure plays an important role in developmental, physiological and pathophysiological processes. Diffusion tensor distribution (DTD) MRI helps probe subvoxel heterogeneity by describing water diffusion within a voxel using an ensemble of non-exchanging compartments characterized by a probability density function of diffusion tensors. In this study, we provide a new framework for acquiring multiple diffusion encoding (MDE) images and estimating DTD from them in the human brain in vivo. We interfused pulsed field gradients (iPFG) in a single spin echo to generate arbitrary b-tensors of rank one, two, or three without introducing concomitant gradient artifacts. Employing well-defined diffusion encoding parameters we show that iPFG retains salient features of a traditional multiple-PFG (mPFG/MDE) sequence while reducing the echo time and coherence pathway artifacts thereby extending its applications beyond DTD MRI. Our DTD is a maximum entropy tensor-variate normal distribution whose tensor random variables are constrained to be positive definite to ensure their physicality. In each voxel, the second-order mean and fourth-order covariance tensors of the DTD are estimated using a Monte Carlo method that synthesizes micro-diffusion tensors with corresponding size, shape, and orientation distributions to best fit the measured MDE images. From these tensors we obtain the spectrum of diffusion tensor ellipsoid sizes and shapes, and the microscopic orientation distribution function (μODF) and microscopic fractional anisotropy (μFA) that disentangle the underlying heterogeneity within a voxel. Using the DTD-derived μODF, we introduce a new method to perform fiber tractography capable of resolving complex fiber configurations. The results revealed microscopic anisotropy in various gray and white matter regions and skewed MD distributions in cerebellar gray matter not observed previously. DTD MRI tractography captured complex white matter fiber organization consistent with known anatomy. DTD MRI also resolved some degeneracies associated with diffusion tensor imaging (DTI) and elucidated the source of diffusion heterogeneity which may help improve the diagnosis of various neurological diseases and disorders.

A Gender-Based Point of View in Pediatric Neurology.

While the significance of gender has only recently been recognized, gender assigned at birth has long been understood to have a significant influence on a number of illnesses. Due to the paucity of data in this regard in pediatrics, the purpose of this narrative review is to frame the most recent knowledge about the role of gender assigned at birth in the neurological development and neuropsychiatric disorders among young people. Literature analysis showed that gender disparities exist in neurologic and neuropsychiatric disorders among the pediatric population and supported the fact that new guidelines should take this into account. However, there is an urgent need for specific studies focused on gender role among children and adolescents in order to better understand how this can relate to diagnosis, development and treatment of different neurologic and neuropsychiatric diseases. Moreover, further efforts should be directed to identify unique risks linked to gender disorders and gender dysphoria as well as taking into account a gender point of view when approaching a pediatric patient.

Segmented solenoid RF coils for MRI of ex vivo brain samples at ultra-high field preclinical and clinical scanners

Magnetic resonance imaging (MRI) is a well-known and widespread imaging modality for neuroscience studies and the clinical diagnoses of neurological disorders, mainly due to its capability to visualize brain microstructures and quantify various metabolites. Additionally, its noninvasive nature makes possible the correlation of high-resolution MRI from ex vivo brain samples with histology, supporting the study of neurodegenerative disorders such as Alzheimer's or Parkinson's disease. However, the quality and resolution of ex vivo MRI highly depend on the availability of specialized radiofrequency coils with maximized filling factors for the different sizes and shapes of the samples to be studied. For instance, small, dedicated radiofrequency (RF) coils are not always commercially available in ultrahigh field whole-body MRI scanners. Even for ultrahigh field preclinical scanners, specific RF coils for ex vivo MRI are expensive and not always available. Here, we describe the design and construction of two RF coils based on the solenoid geometry for ex vivo MRI of human brain tissues in a 7T whole-body scanner and for ex vivo MRI of marmoset brain samples in a 9.4T preclinical scanner. We designed the 7T solenoid RF coil to maximize the filling factor of human brain samples conditioned on cassettes for histology, while the 9.4T solenoid was constructed to accommodate marmoset brain samples conditioned in 50 ml centrifuge tubes. Both solenoid designs operate in transceiver mode. The measured B1+ maps show a high level of homogeneity in the imaging volume of interest, with a high signal-to-noise ratio over the imaging volume. High-resolution (80 µm in plane, 500 µm slice thickness) images of human brain samples were acquired with the 7T solenoid, while marmoset brain samples were acquired with an isotropic resolution of 60 µm using the 9.4T solenoid coil.

Clinical impact and misdiagnosis of functional ophthalmological symptoms: a case report

Introduction The disease burden due to non-specific, functional, and somatoform disorders is high. An unknown part of these medically unexplained symptoms belongs to factitious disorders. When it comes to deciding whether a patient is able to work, it is essential to differentiate a somatoform disorder from a factitious disorder.ObjectivesThe aim is to highlight, on the one hand, the differences between somatoform disorder and factitious disorder and, on the other hand, the potential impact of misdiagnosis in medical expertise regarding eligibility for a disability income.MethodsA case report of Ms A. a 42-year-old Caucasian woman. She worked as a 100% fiduciary accountant until the age of 32 when she was placed on medical leave due to persistent trigeminal neuralgia. Subsequently, she developed total blindness, accompanied by distress in a crucial emotional context. A diagnosis of factitious disorder was retained by an expert psychiatrist, with severe consequences for her, such as disability income suspension and family conflict. We evaluated Ms. A. in our multidisciplinary medical expertise service for a disability income review.ResultsThe patient reported a total absence of light perception in both eyes (subjective), not confirmed by objective ocular examination and spécific neuro-ophthalmological examination.Psychiatric examination revealed that Ms. A. had been sexually assaulted at age of 7 and sexually abused for a year by her teacher at age of 14. Regarding the identity of the first abuser, she describes that she “can’t see his face” and that the multiple sexual assaults during her teenage years took place in the classroom after school, with the teacher “pulling down the window shades so it was totally dark.” She explains that, defensively, to avoid thoughts related to the traumatic experience, she was heavily invested in her studies. But, at the age of 30, after separation with her first boyfriend, diffuse pain and progressively total blindness appeared.We concluded the diagnoses of pain disorders related to psychological factors and a dissociative neurological symptom disorder with visual disturbance.Conclusions Blindness not explained by a physiologic process may accompany trauma and psychological distress, with the search for the link between the onset of symptoms and significant unconscious psychic conflicts being crucial in the psychiatric investigation. A new diagnosis of dissociative neurological symptom disorder with visual disturbance (6B60.0) is included in the ICD-11. It is characterized by visual symptoms such as blindness, tunnel vision, diplopia, visual distortions, or hallucinations that are not consistent with a recognized disease of the nervous system, other mental, behavioral, or neurodevelopmental disorders. Differentiating this pathology from factitious disorder or simulation is essential from an insurance medicine point of view, but also for its treatment.Disclosure of InterestNone Declared

Real-Time Gait Phase Detection on Wearable Devices for Real-World Free-Living Gait.

Detecting gait phases with wearables unobtrusively and reliably in real-time is important for clinical gait rehabilitation and early diagnosis of neurological diseases. Due to hardware limitations of microcontrollers in wearable devices (e.g., memory and computation power), reliable real-time gait phase detection on the microcontrollers remains a challenge, especially for long-term real-world free-living gait. In this work, a novel algorithm based on a reduced support vector machine (RSVM) and a finite state machine (FSM) is developed to address this. The RSVM is developed by exploiting the cascaded K-means clustering to reduce the model size and computation time of a standard SVM by 88% and a factor of 36, with only minor degradation in gait phase prediction accuracy of around 4%. For each gait phase prediction from the RSVM, the FSM is designed to validate the prediction and correct misclassifications. The developed algorithm is implemented on a microcontroller of a wearable device and its real-time (on the fly) classification performance is evaluated by twenty healthy subjects walking along a predefined real-world route with uncontrolled free-living gait. It shows a promising real-time performance with an accuracy of 91.51%, a sensitivity of 91.70%, and a specificity of 95.77%. The algorithm also demonstrates its robustness with varying walking conditions.

A Video is Worth a Thousand Words: The Use of Home Videos in Pediatric Neurology.

The use of home video recordings (HVRs) may aid in the diagnosis of neurological disorders. However, this practice remains underutilized. Through an anonymous survey, we sought to understand the perspectives of healthcare providers regarding the sharing of HVRs alongside referrals for responsive and economical pediatric neurology care. This was timely given COVID-19 has worsened wait times for diagnosis and consequently treatment. Most providers agree that sharing of HVRs improves patient care (93.1%: 67/73) and prevents both additional investigations (67%: 49/73) and hospital admissions (68.5%: 50/73). However, a minority of providers (21.9 %: 16/73) currently share HVRs alongside their referrals.

Machine learning as a new tool in neurological disease prevention, diagnosis, and treatment

More than 600 different neurological diseases affect the human population. Some of these are genetic and can emerge even before birth, and some are caused by defects, infections, trauma, degeneration, inflammation, and cancer. However, they all share disabilities caused by damage to the nervous system. In the last decades, the burden of almost all neurological disorders has increased in terms of absolute incidence, prevalence, and mortality, largely due to the population’s growth and aging. This represents a dangerous trend and should become our priority for the future. But what new goals are we going to set and reach now, and how will we exploit thought-provoking technological skills for making these goals feasible? Machine learning can be at the root of the problem. Indeed, most recently, there has been a push towards medical data analysis by machine learning, and a great improvement in the training capabilities particularly of artificial deep neural networks (DNNs) inspired by the biological neural networks characterizing the human brain. This has generated competitive results for applications such as biomolecular target and protein structure prediction, structure-based rational drug design, and repurposing, all exerting a major impact on neuroscience and human well-being. By approaching early risks for diseases, non-invasive diagnosis, personalized treatment assessment, drug discovery, and automated science, the machine learning arena has thus the potential of becoming the new frontier for empowering neuroscience research and clinical practice in the years ahead.

Effective Epileptic Seizure Detection by Classifying Focal and Non-focal EEG Signals using Human Learning Optimization-based Hidden Markov Model

Electroencephalogram (EEG) recordings are analyzed to make a diagnosis of neurological diseases like epilepsy before surgical intervention. It is essential to examine the EEG signals of individuals with epilepsy to separate between focal and non-focal seizure sources. In this research, a train-optimized hidden Markov model (HMM) based on human learning optimization is proposed to automatically detect the epileptic seizure by distinguishing the focal and non-focal epileptic EEG signals. Human learning optimization (HLO) plays a crucial part in electrode selection, which aids in the subsequent feature extraction process, reduces the model’s complexity, and chooses the features with the best discriminating properties. The HLO-train optimized HMM model receives the feature vector made up of frequency band-based features, which is then tuned to optimum using the proposed model to improve detection performance. The searching and learning skills of the human is included in the development of the HLO algorithm, that helps in boosting the convergence of the model adds value to the research. The Epileptic seizure is effectively recognized using the model and the efficiency of the research is proved by measuring the evaluation metrices. The HLO-train optimized HMM model attained for the CHB-MIT scalp EEG database with an accuracy of 99.158 %, sensitivity of 98.086 %, and specificity of 99.155 %, while the accuracy of 94.231 %, sensitivity of 92.075 %, and specificity of 99.926 % with the Siena scalp dataset, which is efficient, when compared with state of art methods.

Accurate module induced brain network construction for mild cognitive impairment identification with functional MRI.

Functional brain networks (FBNs) estimated from functional magnetic resonance imaging (fMRI) data has become a potentially useful way for computer-aided diagnosis of neurological disorders, such as mild cognitive impairment (MCI), a prodromal stage of Alzheimer's Disease (AD). Currently, Pearson's correlation (PC) is the most widely-used method for constructing FBNs. Despite its popularity and simplicity, the conventional PC-based method usually results in dense networks where regions-of-interest (ROIs) are densely connected. This is not accordance with the biological prior that ROIs may be sparsely connected in the brain. To address this issue, previous studies proposed to employ a threshold or l_1-regularizer to construct sparse FBNs. However, these methods usually ignore rich topology structures, such as modularity that has been proven to be an important property for improving the information processing ability of the brain. To this end, in this paper, we propose an accurate module induced PC (AM-PC) model to estimate FBNs with a clear modular structure, by including sparse and low-rank constraints on the Laplacian matrix of the network. Based on the property that zero eigenvalues of graph Laplacian matrix indicate the connected components, the proposed method can reduce the rank of the Laplacian matrix to a pre-defined number and obtain FBNs with an accurate number of modules. To validate the effectiveness of the proposed method, we use the estimated FBNs to classify subjects with MCI from healthy controls. Experimental results on 143 subjects from Alzheimer's Disease Neuroimaging Initiative (ADNI) with resting-state functional MRIs show that the proposed method achieves better classification performance than previous methods.

Myositis Autoantibodies in Patients with Suspected Post-Treatment Lyme Disease Syndrome.

Most patients suffering from Lyme disease are effectively treated with antibiotics. In some patients, however, problems persist for a long time despite appropriate therapy. The term post-treatment Lyme disease syndrome (PTLDS) is currently used for this condition in scientific literature. The pathogenesis is still not precisely known, but the involvement of immunopathological mechanisms is assumed. In our study, we analyzed the presence of autoantibodies including myositis-specific (MSA) and myositis-associated autoantibodies (MAA) in patients with laboratory proven history of Lyme disease and with clinical symptoms of PTLDS. A total of 59 patients meeting the criteria for PTLDS were enrolled in this study. The control group consisted of 40 patients undergoing differential diagnosis of neurological disorders without clinical and/or laboratory-proven history of Lyme disease. The presence of autoantibodies was determined by immunoblot methods and positive samples were further tested for serum creatine kinase (CK) and myoglobin levels. The presence of myositis autoantibodies was detected in 18 subjects with suspected PTLDS (30.5%), but only in 5% of control subjects exhibiting no evidence of Lyme disease history. The difference was statistically significant (p = 0.002). The subsequent biochemical analysis of muscle-damage markers in positive subjects found a mild elevation in six MSA/MAA-positive PTLDS patients. The study detected raised MSA/MAA autoantibodies formation in the group of PTLDS patients raising the question about their involvement in the pathogenesis of this syndrome.

Magnetic transferrin nanoparticles (MTNs) assay as a novel isolation approach for exosomal biomarkers in neurological diseases

BackgroundBrain-derived exosomes released into the blood are considered a liquid biopsy to investigate the pathophysiological state, reflecting the aberrant heterogeneous pathways of pathological progression of the brain in neurological diseases. Brain-derived blood exosomes provide promising prospects for the diagnosis of neurological diseases, with exciting possibilities for the early and sensitive diagnosis of such diseases. However, the capability of traditional exosome isolation assays to specifically isolate blood exosomes and to characterize the brain-derived blood exosomal proteins by high-throughput proteomics for clinical specimens from patients with neurological diseases cannot be assured. We report a magnetic transferrin nanoparticles (MTNs) assay, which combined transferrin and magnetic nanoparticles to isolate brain-derived blood exosomes from clinical samples.MethodsThe principle of the MTNs assay is a ligand-receptor interaction through transferrin on MTNs and transferrin receptor on exosomes, and electrostatic interaction via positively charged MTNs and negatively charged exosomes to isolate brain-derived blood exosomes. In addition, the MTNs assay is simple and rapid (< 35 min) and does not require any large instrument. We confirmed that the MTNs assay accurately and efficiently isolated exosomes from serum samples of humans with neurodegenerative diseases, such as dementia, Parkinson's disease (PD), and multiple sclerosis (MS). Moreover, we isolated exosomes from serum samples of 30 patients with three distinct neurodegenerative diseases and performed unbiased proteomic analysis to explore the pilot value of brain-derived blood protein profiles as biomarkers.ResultsUsing comparative statistical analysis, we found 21 candidate protein biomarkers that were significantly different among three groups of neurodegenerative diseases.ConclusionThe MTNs assay is a convenient approach for the specific and affordable isolation of extracellular vesicles from body fluids for minimally-invasive diagnosis of neurological diseases.

Alternative and complementary medicine in neurological disorders and neurological disability patients: Prevalence, factors, opinions and reasons

ObjectivesThis cross-sectional study is aimed to investigate the prevalence of the use of complementary and alternative medicine (CAM), types of CAMs used, as well as reasons and factors found in patients with neurological diseases or neurological disability. MethodsAn anonymous, self-administered survey was designed to collect information about demographics, personal medical history, CAM usage habits, and different opinions about CAM vs. conventional medicine (CM). The survey was applied by non-medical personnel in a rehabilitation hospital to patients older than 18 years, with a confirmed diagnosis of neurological disorder or neurological disability. Results453 patients were included, 59.7 % were female. Mean age: 49 years (SD: 18.2). 200 patients (44.2 %) had a history of chronic diseases. Depression was found in 139 patients (30.7 %). Neurological disorders were: peripheral neuropathy: 97 (21.4 %), headache or migraine: 78 (17.2 %), neurodegenerative diseases: 69 (15.2 %) and stroke: 58 (12.8 %). 60.2 % (n = 273) had some degree of disability. Prevalence of CAM use was 36 % (n = 163). Most common CAMs were: Biological therapies: 133 (71.1 %), energy-based therapies: 63 (33.7 %) and body manipulation therapies: 51 (27.3 %). Reasons to use CAM were: “to complement the pharmacological treatment” (45 = 27.6 %), and “treatment failure with CM” (43 = 26.4 %). Finally, older age (p = 0.0280) and having a chronic disease (p = 0.0229) or depression (p < 0.001) were the factors associated with the current use of CAM. ConclusionOne third of patients with neurological pathologies use CAM, the most popular type of CAM is biologic therapies. As in other pathologies, CAM use was associated with older age, history of depression, and chronic diseases. Therefore, it is suggested that physicians routinely ask patients about the use of CAM, if they need more information about CAM, and even verify the possibility of drug interactions or adverse effects.

Optimized three-dimensional ultrashort echo time: Magnetic resonance fingerprinting for myelin tissue fraction mapping.

Quantitative assessment of brain myelination has gained attention for both research and diagnosis of neurological diseases. However, conventional pulse sequences cannot directly acquire the myelin-proton signals due to its extremely short T2 and T2* values. To obtain the myelin-proton signals, dedicated short T2 acquisition techniques, such as ultrashort echo time (UTE) imaging, have been introduced. However, it remains challenging to isolate the myelin-proton signals from tissues with longer T2. In this article, we extended our previous two-dimensional ultrashort echo time magnetic resonance fingerprinting (UTE-MRF) with dual-echo acquisition to three dimensional (3D). Given a relatively low proton density (PD) of myelin-proton, we utilized Cramér-Rao Lower Bound to encode myelin-proton with the maximal SNR efficiency for optimizing the MR fingerprinting design, in order to improve the sensitivity of the sequence to myelin-proton. In addition, with a second echo of approximately 3ms, myelin-water component can be also captured. A myelin-tissue (myelin-proton and myelin-water) fraction mapping can be thus calculated. The optimized 3D UTE-MRF with dual-echo acquisition is tested in simulations, physical phantom and in vivo studies of both healthy subjects and multiple sclerosis patients. The results suggest that the rapidly decayed myelin-proton and myelin-water signal can be depicted with UTE signals of our method at clinically relevant resolution (1.8 mm isotropic) in 15 min. With its good sensitivity to myelin loss in multiple sclerosis patients demonstrated, our method for the whole brain myelin-tissue fraction mapping in clinical friendly scan time has the potential for routine clinical imaging.

Electron transfer-based norepinephrine detection with high sensitivity regulated by polyethyleneimine molecular weight

Monitoring the concentration of norepinephrine (NE) plays a vital role in studying and diagnosing neurological diseases, but the sensitivity and portability in monitoring remain challenges. In this study, the fluorescent probe, polyethyleneimine and bovine serum albumin co-modified gold nanoclusters (PEI/BSA-GNC), was constructed for sensitive ratiometric fluorescence detection and portable smartphone-based analysis of NE. Compared to the fluorescence generated from NE autoxidation, the fluorescence of NE reacting with PEI was much stronger and blue-shifted. This was owing to the weakened electron-giving ability originated from the reaction products of PEI and NE. As the PEI molecular weights increased, the sensitivity of fluorescent response showed a non-monotonic trend which were highest at PEI 10,000. The detection ranges of NE detection were 0.01–50 μM both in aqueous solution and in serum, and the detection limit in serum (5 nM) was lower than in aqueous solution (45 nM) and most previously reported probes, revealing the high sensitivity in complicated environment. Furthermore, the smartphone application was designed and applied in serum with good recoveries. The fluorescent probe shows great potential for quantitative detection of NE and is suitable for the early diagnosis of neurological diseases.

Bibliometric research on the developments of artificial intelligence in radiomics toward nervous system diseases.

The growing interest suggests that the widespread application of radiomics has facilitated the development of neurological disease diagnosis, prognosis, and classification. The application of artificial intelligence methods in radiomics has increasingly achieved outstanding prediction results in recent years. However, there are few studies that have systematically analyzed this field through bibliometrics. Our destination is to study the visual relationships of publications to identify the trends and hotspots in radiomics research and encourage more researchers to participate in radiomics studies. Publications in radiomics in the field of neurological disease research can be retrieved from the Web of Science Core Collection. Analysis of relevant countries, institutions, journals, authors, keywords, and references is conducted using Microsoft Excel 2019, VOSviewer, and CiteSpace V. We analyze the research status and hot trends through burst detection. On October 23, 2022, 746 records of studies on the application of radiomics in the diagnosis of neurological disorders were retrieved and published from 2011 to 2023. Approximately half of them were written by scholars in the United States, and most were published in Frontiers in Oncology, European Radiology, Cancer, and SCIENTIFIC REPORTS. Although China ranks first in the number of publications, the United States is the driving force in the field and enjoys a good academic reputation. NORBERT GALLDIKS and JIE TIAN published the most relevant articles, while GILLIES RJ was cited the most. RADIOLOGY is a representative and influential journal in the field. "Glioma" is a current attractive research hotspot. Keywords such as "machine learning," "brain metastasis," and "gene mutations" have recently appeared at the research frontier. Most of the studies focus on clinical trial outcomes, such as the diagnosis, prediction, and prognosis of neurological disorders. The radiomics biomarkers and multi-omics studies of neurological disorders may soon become a hot topic and should be closely monitored, particularly the relationship between tumor-related non-invasive imaging biomarkers and the intrinsic micro-environment of tumors.

NIR quantum dot construction of a fluorescence anisotropy signal amplification biosensor for sensitive, rapid and separation-free detection of dopamine in serum.

Dopamine (DA) is an important small-molecule neurotransmitter, which is closely related to the development of many neurological diseases and has received increasing attention in the diagnosis of neurological diseases. Currently, the assays of the detection of dopamine such as electrochemical and colorimetric methods have low sensitivity, poor selectivity and susceptibility to interference, which limit the accurate quantification of dopamine. Fluorescence anisotropy immunoassay is a traditional analytical method in which the quantification is based on the change in fluorescence anisotropy values observed when fluorescence molecules are bound to a certain volume and mass of the material. Since dopamine is a small molecule with small volume and mass, we took advantage of the good photostability of the second near-infrared window (NIR-II) quantum dots (QDs) and the low spontaneous interference of the substrate, and designed a biosensor dopamine fluorescence anisotropy probe streptavidin biosensor (DFAP-SAB) based on the NIR-II QDs combined with streptavidin signal amplification to achieve rapid and separation-free detection of dopamine in human serum. The detection signal has a good linearity between 50 nM and 3000 nM with a detection limit of 11.2 nM. The application of NIR-II QDs provides the possibility of biosensor applications for complex samples. The construction of the streptavidin signal amplification device provides a new idea for small molecule detection.

Utility of next-generation sequencing in the diagnosis of neurological disorders

Utility of next-generation sequencing in the diagnosis of neurological disorders