Diagnosis Of Neurological Disorders Research Articles

Complete dorsal wall agenesis of the sacral canal in a Greek population: an osteological study

Introduction: The failure of closure of the dorsal wall of the sacral canal (SC) has been known since the eve of modern osteology, appearing in prehistoric times. Variants include partial or complete absence of the dorsal wall of the SC. SC presents a pathway for minimally invasive therapeutic and diagnostic procedures for spinal diseases and for ensuring analgesia and anesthesia in operations, including labor and genitourinary surgery. Aim: Our objective is to verify the incidence of complete agenesis of the SC dorsal wall in Greek population. Material and methods: We collected 155 adult dry sacra of known sex from the Third Cemetery of Athens for the study of the dorsal wall of the sacrum. Damaged or variated sacra were excluded. We also performed an exhaustive review of the relevant literature and compared our results with those of international studies. Results: Complete dorsal wall agenesis of the sacral canal was found in three cases (1.93%), two men (1.29%) and one woman (0.64%). Our review examines its incidence in other populations and explores the possibility of regional or racial correlation. Conclusion: Knowing the complete dorsal wall agenesis of the sacral canal by medical professionals is crucial for avoiding complications in spinal surgery, anesthetics and obstetrics, as well as in the differential diagnosis of neurological and urological diseases.

Spicy Recipe for At-Home Diagnostics: Smart Salivary Sensors for Point-of-Care Diagnosis of Jaundice.

Even though significant advances have been made, there is still a lack of reliable sensors capable of noninvasively monitoring bilirubin and diagnosing jaundice as the most common neonatal disease, particularly at the point-of-care (POC) where blood sampling from infants is accompanied by serious challenges and concerns. Herein, for the first time, using an easy-to-fabricate/use assay, we demonstrate the capability of curcumin embedded within paper for noninvasive optical monitoring of bilirubin in saliva. The highly selective sensing of the developed sensor toward bilirubin is attributed to bilirubin photoisomerization under blue light exposure, which can selectively restore the bilirubin-induced quenched fluorescence of curcumin. We also fabricated an IoT-enabled hand-held optoelectronic reader to measure and quantify the fluorescence and color signals of our sensor. Clinical analysis on the saliva of 18 jaundiced infants by using our developed smart salivary sensor proved that it is amenable to be widely exploited in POC applications for bilirubin monitoring as there are good correlations between its results with those of reference methods in saliva and blood. Meeting all WHO's REASSURED criteria by our developed sensor makes it a highly promising sensor for smart noninvasive diagnosis and therapeutic monitoring of jaundice, hepatitis, and other bilirubin-induced neurologic diseases at the POC.

Revolutionizing Neurology: The Role of Artificial Intelligence in Advancing Diagnosis and Treatment.

Artificial intelligence (AI) has emerged as a powerful tool in the field of neurology, significantly impacting the diagnosis and treatment of neurological disorders. Recent technological breakthroughs have given us access to a plethora of information relevant to many aspects of neurology. NeuroscienceandAIshare a long history of collaboration.Along with great potential, we encounter obstacles relating to data quality, ethics, and inherent difficulty in applying data science in healthcare. Neurological disorders pose intricate challenges due to their complex manifestations and variability.Automating image interpretation tasks, AI algorithms accurately identify brain structures and detect abnormalities. This accelerates diagnosis and reduces the workload on medical professionals. Treatment optimization benefits from AI simulations that model different scenarios and predict outcomes. These AI systems can currently perform many of the sophisticated perceptual and cognitive capacities of biological systems, such as object identification and decision making. Furthermore, AI is rapidly being used as a tool in neuroscience research, altering our understanding of brain functioning. It has the ability to revolutionize healthcare as we know it into a system in which humans and robots collaborate to deliver better care for our patients.Image analysis activities such as recognizing particular brain regions, calculating changes in brain volume over time, and detecting abnormalities in brain scans can be automated by AI systems. This lessens the strain on radiologists and neurologists while improving diagnostic accuracy and efficiency.It is now obvious that cutting-edge artificial intelligence models combined with high-quality clinical data will lead to enhanced prognostic and diagnostic models in neurological illness, permitting expert-level clinical decision aids across healthcare settings.In conclusion, AI's integration into neurology has revolutionized diagnosis, treatment, and research. As AI technologies advance, they promise to unravel the complexities of neurological disorders further, leading to improved patient care and quality of life. The symbiosis of AI and neurology offers a glimpse into a future where innovation and compassion converge to reshape neurological healthcare. This abstract provides a concise overview of the role of AI in neurology and its transformative potential.

Psychiatric Comorbidities in Neurologic Hospitalizations in Portugal: A Nationwide Retrospective Observational Study

Introduction: Psychiatric comorbidities have a significant impact on patients’ quality of life and often go undetected in neurologic practice. The aim of this study was to describe and characterize psychiatric comorbidities among patients hospitalized due to a neurologic disorder in mainland Portugal.Methods: A retrospective observational study was performed by analyzing hospitalization with a primary diagnosis of neurologic disorder defined as categories 76, 77, 79 - 85, 95, 109 of the Clinical Classification Software for International Classification of Diseases, Ninth Revision, Clinical Modification, occurring between 2008 and 2015 in adult patients (≥ 18 years of age). Psychiatric comorbidities were determined as the presence of a secondarydiagnosis belonging to the Clinical Classification Software categories 650 to 670.Results: A total of 294 806 hospitalization episodes with a primary diagnosis of a neurologic disorder were recorded in adult patients between 2008 - 2015 in Portuguese public hospitals. Approximately 26.9% (n = 79 442) of the episodes had a recorded psychiatric comorbidity (22.1%; 32.2%, female versus male hospitalizations). Patients with registered psychiatric comorbidities were younger (66.2 ± 16.2 vs 68.6 ± 17.2 with no psychiatric comorbidities, p < 0.001), presented lower all-cause in-hospital mortality rates, and significantly longer mean hospital stays. ‘Delirium, dementia, amnestic and other cognitive disorders’ were recorded in 7.4% (n = 21 965) of the hospitalizations, followed by alcohol-related disorders in 6.5% (n = 19 302) and mood disorders in 6.1% (n = 18 079). Epilepsy/seizures were the neurologic disorders with the highest proportion of recorded psychiatric comorbidities (39.9%).Conclusion: Psychiatric comorbidities were recorded in more than a quarter of the hospitalizations with a primary diagnosis of a Neurologic disorder. Psychiatric comorbidities varied among neurological disorders and were associated with different demographic and clinical features.

Synthetic Optical Coherence Tomography Angiographs for Detailed Retinal Vessel Segmentation Without Human Annotations.

Optical coherence tomography angiography (OCTA) is a non-invasive imaging modality that can acquire high-resolution volumes of the retinal vasculature and aid the diagnosis of ocular, neurological and cardiac diseases. Segmenting the visible blood vessels is a common first step when extracting quantitative biomarkers from these images. Classical segmentation algorithms based on thresholding are strongly affected by image artifacts and limited signal-to-noise ratio. The use of modern, deep learning-based segmentation methods has been inhibited by a lack of large datasets with detailed annotations of the blood vessels. To address this issue, recent work has employed transfer learning, where a segmentation network is trained on synthetic OCTA images and is then applied to real data. However, the previously proposed simulations fail to faithfully model the retinal vasculature and do not provide effective domain adaptation. Because of this, current methods are unable to fully segment the retinal vasculature, in particular the smallest capillaries. In this work, we present a lightweight simulation of the retinal vascular network based on space colonization for faster and more realistic OCTA synthesis. We then introduce three contrast adaptation pipelines to decrease the domain gap between real and artificial images. We demonstrate the superior segmentation performance of our approach in extensive quantitative and qualitative experiments on three public datasets that compare our method to traditional computer vision algorithms and supervised training using human annotations. Finally, we make our entire pipeline publicly available, including the source code, pretrained models, and a large dataset of synthetic OCTA images.

BAI-Net: Individualized Anatomical Cerebral Cartography Using Graph Neural Network.

Brain atlas is an important tool in the diagnosis and treatment of neurological disorders. However, due to large variations in the organizational principles of individual brains, many challenges remain in clinical applications. Brain atlas individualization network (BAI-Net) is an algorithm that subdivides individual cerebral cortex into segregated areas using brain morphology and connectomes. The presented method integrates group priors derived from a population atlas, adjusts areal probabilities using the context of connectivity fingerprints derived from the fiber-tract embedding of tractography, and provides reliable and explainable individualized brain areas across multiple sessions and scanners. We demonstrate that BAI-Net outperforms the conventional iterative clustering approach by capturing significantly heritable topographic variations in individualized cartographies. The topographic variability of BAI-Net cartographies has shown strong associations with individual variability in brain morphology, connectivity as well as higher relationship on individual cognitive behaviors and genetics. This study provides an explainable framework for individualized brain cartography that may be useful in the precise localization of neuromodulation and treatments on individual brains.

Assistive tools for classifying neurological disorders using fMRI and deep learning: A guide and example.

Deep-learning (DL) methods are rapidly changing the way researchers classify neurological disorders. For example, combining functional magnetic resonance imaging (fMRI) and DL has helped researchers identify functional biomarkers of neurological disorders (e.g., brain activation and connectivity) and pilot innovative diagnostic models. However, the knowledge required to perform DL analyses is often domain-specific and is not widely taught in the brain sciences (e.g., psychology, neuroscience, and cognitive science). Conversely, neurological diagnoses and neuroimaging training (e.g., fMRI) are largely restricted to the brain and medical sciences. In turn, these disciplinary knowledge barriers and distinct specializations can act as hurdles that prevent the combination of fMRI and DL pipelines. The complexity of fMRI and DL methods also hinders their clinical adoption and generalization to real-world diagnoses. For example, most current models are not designed for clinical settings or use by nonspecialized populations such as students, clinicians, and healthcare workers. Accordingly, there is a growing area of assistive tools (e.g., software and programming packages) that aim to streamline and increase the accessibility of fMRI and DL pipelines for the diagnoses of neurological disorders. In this study, we present an introductory guide to some popular DL and fMRI assistive tools. We also create an example autism spectrum disorder (ASD) classification model using assistive tools (e.g., Optuna, GIFT, and the ABIDE preprocessed repository), fMRI, and a convolutional neural network. In turn, we provide researchers with a guide to assistive tools and give an example of a streamlined fMRI and DL pipeline. We are confident that this study can help more researchers enter the field and create accessible fMRI and deep-learning diagnostic models for neurological disorders.

Classification of Human Concentration Levels Based on Electroencephalography Signals

Concentration denotes the capability to direct one's attention to a specific subject matter. Presently, within the era characterized by an overwhelming abundance of information inundating human existence, distractions frequently impede human concentration, thereby influencing the depth of knowledge acquisition. Various elements contribute to the decline in human concentration, including diminished metabolic states, inadequate sleep, and engaging in multiple tasks simultaneously. The cognitive state of an individual during the process of thinking can be assessed through the analysis of electroencephalography signals. The primary objective of this investigation is to facilitate experts' interpretation of electroencephalography signal outcomes for categorizing concentration levels. The dataset utilized in this examination comprises unprocessed EEG data obtained from observing individuals in both relaxation and concentration states. After data preprocessing, feature extraction is executed, and classification is performed using the Support Vector Machine technique. The outcome of this study reveals an accuracy rate of 84%. These developments allow for continual monitoring of brain function, an enhanced comprehension of cerebral activities, and increased operational efficacy of end-effectors. The implications of these advancements on prospective research opportunities are evident in the potential for more accurate diagnosis of neurological disorders and the progression of sophisticated BCI applications designed to support healthcare and monitor cognitive states. The evolution of EEG technology is paving the way for novel research pathways in neuroscience and human-computer interaction.

Personalized medicine and artificial intelligence in neurology: an individual approach to diagnosis and treatment

The article considers an individual approach to the diagnosis and treatment of neurological diseases within the framework of personalized medicine and the use of artificial intelligence capabilities. The main focus is on an individual approach to the diagnosis and treatment of neurological diseases. Modern methods of data analysis and technologies allowing to adapt treatment to a specific patient are considered. Examples of successful applications of machine learning and artificial intelligence algorithms for the prediction, diagnosis and treatment of neurological disorders are also being investigated. The authors also emphasize the importance of collecting and analyzing big data in the development of personalized medicine in neurology and identify prospects for further research and application of this methodology. In addition, the advantages of personalized medicine and the use of artificial intelligence in neurology, such as improving diagnostic accuracy, optimizing treatment and improving the effectiveness of results, were analyzed. The challenges and limitations faced by researchers and doctors when implementing personalized approaches in neurological practice are studied, as well as ethical issues related to the use of patient data and decision-making based on machine learning algorithms are analyzed.

Interaction of MRI Contrast Agent [Gd(DOTA)]- with Lipid Membranes: A Molecular Dynamics Study.

Contrast agents are important imaging probes in clinical MRI, allowing the identification of anatomic changes that otherwise would not be possible. Intensive research on the development of new contrast agents is being made to image specific pathological markers or sense local biochemical changes. The most widely used MRI contrast agents are based on gadolinium(III) complexes. Due to their very high charge density, they have low permeability through tight biological barriers such as the blood-brain barrier, hampering their application in the diagnosis of neurological disorders. In this study, we explore the interaction between the widely used contrast agent [Gd(DOTA)]- (Dotarem) and POPC lipid bilayers by means of molecular dynamics simulations. This metal complex is a standard reference where several chemical modifications have been introduced to improve key properties such as bioavailability and targeting. The simulations unveil detailed insights into the agent's interaction with the lipid bilayer, offering perspectives beyond experimental methods. Various properties, including the impact on global and local bilayer properties, were analyzed. As expected, the results indicate a low partition coefficient (KP) and high permeation barrier for this reference compound. Nevertheless, favorable interactions are established with the membrane leading to moderately long residence times. While coordination of one inner-sphere water molecule is maintained for the membrane-associated chelate, the physical-chemical attributes of [Gd(DOTA)]- as a MRI contrast agent are affected. Namely, increases in the rotational correlation times and in the residence time of the inner-sphere water are observed, with the former expected to significantly increase the water proton relaxivity. This work establishes a reference framework for the use of simulations to guide the rational design of new contrast agents with improved relaxivity and bioavailability and for the development of liposome-based formulations for use as imaging probes or theranostic agents.

Direct Clinical Applications of Natural Language Processing in Common Neurological Disorders: Scoping Review

Background Natural language processing (NLP), a branch of artificial intelligence that analyzes unstructured language, is being increasingly used in health care. However, the extent to which NLP has been formally studied in neurological disorders remains unclear. Objective We sought to characterize studies that applied NLP to the diagnosis, prediction, or treatment of common neurological disorders. Methods This review followed the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) standards. The search was conducted using MEDLINE and Embase on May 11, 2022. Studies of NLP use in migraine, Parkinson disease, Alzheimer disease, stroke and transient ischemic attack, epilepsy, or multiple sclerosis were included. We excluded conference abstracts, review papers, as well as studies involving heterogeneous clinical populations or indirect clinical uses of NLP. Study characteristics were extracted and analyzed using descriptive statistics. We did not aggregate measurements of performance in our review due to the high variability in study outcomes, which is the main limitation of the study. Results In total, 916 studies were identified, of which 41 (4.5%) met all eligibility criteria and were included in the final review. Of the 41 included studies, the most frequently represented disorders were stroke and transient ischemic attack (n=20, 49%), followed by epilepsy (n=10, 24%), Alzheimer disease (n=6, 15%), and multiple sclerosis (n=5, 12%). We found no studies of NLP use in migraine or Parkinson disease that met our eligibility criteria. The main objective of NLP was diagnosis (n=20, 49%), followed by disease phenotyping (n=17, 41%), prognostication (n=9, 22%), and treatment (n=4, 10%). In total, 18 (44%) studies used only machine learning approaches, 6 (15%) used only rule-based methods, and 17 (41%) used both. Conclusions We found that NLP was most commonly applied for diagnosis, implying a potential role for NLP in augmenting diagnostic accuracy in settings with limited access to neurological expertise. We also found several gaps in neurological NLP research, with few to no studies addressing certain disorders, which may suggest additional areas of inquiry. Trial Registration Prospective Register of Systematic Reviews (PROSPERO) CRD42021228703; https://www.crd.york.ac.uk/PROSPERO/display_record.php?RecordID=228703

MRIO: the Magnetic Resonance Imaging Acquisition and Analysis Ontology.

Magnetic resonance imaging of the brain is a useful tool in both the clinic and research settings, aiding in the diagnosis and treatments of neurological disease and expanding our knowledge of the brain. However, there are many challenges inherent in managing and analyzing MRI data, due in large part to the heterogeneity of data acquisition. To address this, we have developed MRIO, the Magnetic Resonance Imaging Acquisition and Analysis Ontology. MRIO provides well-reasoned classes and logical axioms for the acquisition of several MRI acquisition types and well-known, peer-reviewed analysis software, facilitating the use of MRI data. These classes provide a common language for the neuroimaging research process and help standardize the organization and analysis of MRI data for reproducible datasets. We also provide queries for automated assignment of analyses for given MRI types. MRIO aids researchers in managing neuroimaging studies by helping organize and annotate MRI data and integrating with existing standards such as Digital Imaging and Communications in Medicine and the Brain Imaging Data Structure, enhancing reproducibility and interoperability. MRIO was constructed according to Open Biomedical Ontologies Foundry principles and has contributed several classes to the Ontology for Biomedical Investigations to help bridge neuroimaging data to other domains. MRIO addresses the need for a "common language" for MRI that can help manage the neuroimaging research, by enabling researchers to identify appropriate analyses for sets of scans and facilitating data organization and reporting.

Harnessing Graphdiyne for Selective Cu2+ Detection: A Promising Tool for Parkinson's Disease Diagnostics and Pathogenesis.

Cu2+ accelerates the viral-like propagation of α-synuclein fibrils and plays a key role in the pathogenesis of Parkinson's disease (PD). Therefore, the accurate detection of Cu2+ is essential for the diagnosis of PD and other neurological diseases. The Cu2+ detection process is impeded by substances that have similar electrochemical properties. In this study, graphdiyne (GDY), a new kind of carbon allotrope with strong electron-donating ability, was utilized for the highly selective detection of Cu2+ by taking advantage of its outstanding adsorption capacity for Cu2+. Density functional theory (DFT) calculations show that Cu atoms are adsorbed in the cavity of GDY, and the absorption energy between Cu and C atoms is higher than that of graphene (GR), indicating that the cavity of GDY is favorable for the adsorption of Cu atoms and electrochemical sensing. The GDY-based electrochemical sensor can effectively avoid the interference of amino acids, metal ions and neurotransmitters and has a high sensitivity of 9.77 μA·μM-1·cm-2, with a minimum detectable concentration of 200 nM. During the investigating pathogenesis and therapeutic process of PD with α-synuclein as the diagnostic standard, the concentration of Cu2+ in cells before and after L-DOPA and GSH treatments were examined, and it was found that Cu2+ exhibits high potential as a biomarker for PD. This study not only harnesses the favorable adsorption of the GDY and Cu2+ to improve the specificity of ion detection but also provide clues for deeper understanding of the role of Cu2+ in neurobiology and neurological diseases.

A Comprehensive Review of the Impact of Machine Learning and Omics on Rare Neurological Diseases

Background: Rare diseases, predominantly caused by genetic factors and often presenting neurological manifestations, are significantly underrepresented in research. This review addresses the urgent need for advanced research in rare neurological diseases (RNDs), which suffer from a data scarcity and diagnostic challenges. Bridging the gap in RND research is the integration of machine learning (ML) and omics technologies, offering potential insights into the genetic and molecular complexities of these conditions. Methods: We employed a structured search strategy, using a combination of machine learning and omics-related keywords, alongside the names and synonyms of 1840 RNDs as identified by Orphanet. Our inclusion criteria were limited to English language articles that utilized specific ML algorithms in the analysis of omics data related to RNDs. We excluded reviews and animal studies, focusing solely on studies with the clear application of ML in omics data to ensure the relevance and specificity of our research corpus. Results: The structured search revealed the growing use of machine learning algorithms for the discovery of biomarkers and diagnosis of rare neurological diseases (RNDs), with a primary focus on genomics and radiomics because genetic factors and imaging techniques play a crucial role in determining the severity of these diseases. With AI, we can improve diagnosis and mutation detection and develop personalized treatment plans. There are, however, several challenges, including small sample sizes, data heterogeneity, model interpretability, and the need for external validation studies. Conclusions: The sparse knowledge of valid biomarkers, disease pathogenesis, and treatments for rare diseases presents a significant challenge for RND research. The integration of omics and machine learning technologies, coupled with collaboration among stakeholders, is essential to develop personalized treatment plans and improve patient outcomes in this critical medical domain.

Brain Expression Levels of Commonly Measured Blood Biomarkers of Neurological Damage Differ with Respect to Sex, Race, and Age

It is increasingly evident that blood biomarkers have potential to improve the diagnosis and management of both acute and chronic neurological conditions. The most well-studied candidates, and arguably those with the broadest utility, are proteins that are highly enriched in neural tissues and released into circulation upon cellular damage. It is currently unknown how the brain expression levels of these proteins is influenced by demographic factors such as sex, race, and age. Given that source tissue abundance is likely a key determinant of the levels observed in the blood during neurological pathology, understanding such influences is important in terms of identifying potential clinical scenarios that could produce diagnostic bias. In this study, we leveraged existing mRNA sequencing data originating from 2,642 normal brain specimens harvested from 382 human donors to examine potential demographic variability in the expression levels of genes which code for 28 candidate blood biomarkers of neurological damage. Existing mass spectrometry data originating from 26 additional normal brain specimens harvested from 26 separate human donors was subsequently used to tentatively assess whether observed transcriptional variance was likely to produce corresponding variance in terms of protein abundance. Genes associated with several well-studied or emerging candidate biomarkers including neurofilament light chain (NfL), ubiquitin carboxyl-terminal hydrolase isozyme L1 (UCH-L1), neuron-specific enolase (NSE), and synaptosomal-associated protein 25 (SNAP-25) exhibited significant differences in expression with respect to sex, race, and age. In many instances, these differences in brain expression align well with and provide a mechanistic explanation for previously reported differences in blood levels.

Artificial Intelligence in Neurology: Current Applications and Future Prospects

Artificial intelligence (AI) is reshaping the field of neurology, enhancing diagnosis, treatment, and management of neurological disorders. This article explores AI's role in neurology, highlighting its ability to process vast amounts of data to improve diagnostic accuracy and personalize treatments. AI applications, from neuroimaging to clinical decision support, have shown promising results in enhancing patient care. However, challenges such as data security, ethical concerns, and the need for stringent regulatory frameworks remain significant. The potential of AI in neurology continues to grow, promising revolutionary changes in patient outcomes and healthcare practices, provided these challenges are effectively managed.

Clinical research on neurological and psychiatric diagnosis and monitoring using wearable devices: A literature review

AbstractWearable devices have opened up exciting possibilities for monitoring and managing home health, particularly in the realm of neurological and psychiatric diseases. These devices capture signals related to physiological and behavioral changes, including heart rate, sleep patterns, and motor functions. Their emergence has resulted in significant advancements in the management of such conditions. Traditional clinical diagnosis and assessment methods heavily rely on patient reports and evaluations conducted by healthcare professionals, often leading to a detachment of patients from their home environment and creating additional burdens for both patients and healthcare providers. The increasing popularity of wearable devices offers a potential solution to these challenges. This review focuses on the clinical utility of wearable devices in diagnosing and monitoring neurological and psychiatric diseases. Through research findings and practical examples, we highlight the potential role of wearable devices in diagnosing and monitoring conditions such as autism spectrum disorder, depression, epilepsy, stroke prognosis, Parkinson's disease, dementia, and other diseases. Additionally, the review discusses the benefits and limitations of wearable devices in clinical applications, while highlighting the challenges they face. Finally, it provides prospects for enhancing the value of wearable devices in the monitoring and diagnosis of neurological and psychiatric diseases.

Black Phosphorus Nanosheets Protect Neurons by Degrading Aggregative α-syn and Clearing ROS in Parkinson's Disease.

Although evidence indicates that the abnormal accumulation of α-synuclein (α-syn) in dopamine neurons of the substantia nigra is the main pathological feature of Parkinson's disease (PD), no compounds that have both α-syn antiaggregation and α-syn degradation functions have been successful in treating the disease in the clinic. Here, it is shown that black phosphorus nanosheets (BPNSs) interact directly with α-syn fibrils to trigger their disaggregation for PD treatment. Moreover, BPNSs have a specific affinity for α-syn through van der Waals forces. And BPNSs are found to activate autophagy to maintain α-syn homeostasis, improve mitochondrial dysfunction, reduce reactive oxygen species levels, and rescue neuronal death and synaptic loss in PC12 cells. It is also observed that BPNSs penetrate the blood-brain barrier and protect against dopamine neuron loss, alleviating behavioral disorders in 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP) induced mouse model and hA53T α-syn transgenic mice. Together, the study reveals that BPNSs have the potential as a novel integrated nanomedicine for clinical diagnosis and treatment of neurological diseases.

Functional Neurological Disorder Presenting After Concussion: A Retrospective Case Series.

Although a majority of individuals recover from a concussion within weeks of the index injury, a substantial minority of patients report persistent postconcussion symptoms. Some of these symptoms may reflect a diagnosis of functional neurological disorder (FND). The authors evaluated the relationship between persistent postconcussion symptoms and FND symptoms. In this retrospective chart review, the authors characterized demographic and clinical information from 50 patients with a confirmed diagnosis of FND whose functional neurological symptoms started after a concussion. Patients who developed FND after a concussion had high rates of baseline risk factors for both persistent postconcussion symptoms and FND. After the concussive event, functional neurological symptoms presented abruptly or developed insidiously over time. Functional neurological symptoms ranged widely and included gait symptoms, seizures, speech and language symptoms, weakness, sensory symptoms, tremors, and vision and oculomotor symptoms. Functional neurological symptoms can arise after a concussion. FND should be considered in the differential diagnosis of individuals presenting with neurological symptoms beginning after a concussion. By failing to recognize functional symptoms, clinicians may inadvertently reinforce negative health-related beliefs regarding a patient's injured brain.

Identification and validation of N-acetylputrescine in combination with non-canonical clinical features as a Parkinson’s disease biomarker panel

Parkinson’s disease is a progressive neurodegenerative disorder in which loss of dopaminergic neurons in the substantia nigra results in a clinically heterogeneous group with variable motor and non-motor symptoms with a degree of misdiagnosis. Only 3–25% of sporadic Parkinson’s patients present with genetic abnormalities that could represent a risk factor, thus environmental, metabolic, and other unknown causes contribute to the pathogenesis of Parkinson’s disease, which highlights the critical need for biomarkers. In the present study, we prospectively collected and analyzed plasma samples from 194 Parkinson’s disease patients and 197 age-matched non-diseased controls. N-acetyl putrescine (NAP) in combination with sense of smell (B-SIT), depression/anxiety (HADS), and acting out dreams (RBD1Q) clinical measurements demonstrated combined diagnostic utility. NAP was increased by 28% in Parkinsons disease patients and exhibited an AUC of 0.72 as well as an OR of 4.79. The clinical and NAP panel demonstrated an area under the curve, AUC = 0.9 and an OR of 20.4. The assessed diagnostic panel demonstrates combinatorial utility in diagnosing Parkinson’s disease, allowing for an integrated interpretation of disease pathophysiology and highlighting the use of multi-tiered panels in neurological disease diagnosis.