Chapter 1 - Introduction

How far is arterial spin labeling MRI from a clinical reality? Insights from arterial spin labeling comparative studies in Alzheimer's disease and other neurological disorders.

Reply to: Correspondence on “EASL Clinical Practice Guidelines on non-invasive tests for evaluation of liver disease severity and prognosis – 2021 update”

Central nervous system (CNS) tumors are a rare but devastating malignancy, often robbing patients of the basic quality of life. Despite advances in our understanding of the CNS tumor disease processes, the prognosis for patients with CNS tumors remains poor. Better characterization and diagnostic and monitoring approaches are necessary to assist in diagnosis and treatment of CNS tumors. One important tool in the neuro-oncology armamentarium is the use of advanced imaging techniques. We searched PubMed using the keywords neuro-oncology imaging, pseudoprogression, molecular imaging, and biomarkers. We limited our search to full-text English articles and identified other relevant articles from the reference lists of previously identified articles. Advances in imaging techniques have allowed investigators to explore various imaging modalities, from tumor characterization to differentiating pseudoprogression from tumor progression. Better imaging can result in better diagnostic approaches, greater and safer resection techniques, and improved monitoring of tumor progression. This review highlights advances in neuro-oncology imaging techniques and their clinical utility in the treatment and management of primary brain tumors.

Many patients with a diagnosis of neurological disease, such as multiple sclerosis, have symptoms or disability that is considered to be in excess of what would be expected from that disease. We aimed to describe the overall and relative frequency of symptoms 'unexplained by organic disease' in patients attending general neurology clinics with a range of neurological disease diagnoses. Newly referred outpatients attending neurology clinics in all the NHS neurological centres in Scotland, UK were recruited over a period of 15 months. The assessing neurologists recorded their initial neurological diagnoses and also the degree to which they considered the patient's symptoms to be explained by organic disease. Patients completed self report scales for both physical and psychological symptoms. The frequency of symptoms unexplained by organic disease was determined for each category of neurological disease diagnoses. 3,781 patients participated (91% of those eligible). 2,467 patients had a diagnosis of a neurological disease (excluding headache disorders). 293 patients (12%) of these patients were rated as having symptoms only "somewhat" or "not at all" explained by that disease. These patients self-reported more physical and more psychological symptoms than those with more explained symptoms. No category of neurological disease was more likely than the others to be associated with such symptoms although patients with epilepsy had fewer. A substantial proportion of new outpatients with diagnoses of neurological disease also have symptoms regarded by the assessing neurologist as being unexplained by that disease; no single neurological disease category was more likely than others to be associated with this phenomenon.

Diagnosis in Neurologic Disease

Diagnosis of herpesvirus infections of the central nervous system.

The blood-brain barrier has long been a barrier to the treatment of neurological diseases in the brain. It protects brain tissues from damage while limiting the access of drugs to the brain for drug delivery, so nanocarriers with smaller particle size are chosen to break the blood-brain barrier. This review summarizes the structural properties of nanodiamond, the interaction mechanism between nanodiamond and blood-brain barrier, and the application of nanodiamond as a carrier in the diagnosis and treatment of neurological diseases, which provides the theoretical basis for the subsequent diagnosis and treatment of 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.

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

Fabry disease is a rare X-linked inherited lysosomal storage disorder caused by the absence or reduction of alfa-galactosidase A activity in lysosomes, resulting in accumulation of glycosphingolipids in various tissues. The main organ affected is the heart, which frequently manifests as left ventricular hypertrophy and can ultimately lead to cardiac fibrosis, heart failure, valve disease, cardiac conduction abnormalities and sudden cardiac death. Today we know that myocyte damage starts before these signs and symptoms are detectable on routine studies, during the designated pre-clinical phase of Fabry disease. The initiation of specific therapy for Fabry disease during the early stages of the disease has a great impact on the prognosis of these patients avoiding progression to irreversible fibrosis and preventing cardiovascular complications. Cardiac imaging has become an essential tool in the management of Fabry disease as it can help physicians suspect the disorder, diagnose patients in the early stages and improve outcomes. The recent development of novel imaging techniques makes necessary an update on the subject. This review discusses the role of multimodal imaging in the diagnosis, staging, patient selection for treatment and prognosis of Fabry disease and discusses recent advances in imaging techniques that provide new insights into the pathogenesis of the disorder and the possibility of novel treatment targets.

Introduction. One of the newest methods that could facilitate the diagnosis and treatment of multiple sclerosis is the measurement of neurofilament levels in the blood and spinal fluid. Neurofilament chains can be found not only in multiple sclerosis but also in other neurodegenerative diseases. Latest research findings have revealed that neurofilament levels in serum along with magnetic resonance imaging and clinical evaluation could help evaluate disease relapses and prognosis. No research on neurofilament levels in multiple sclerosis patients has been done in Lithuania so far.Methods. Permit No. BE-2-73 was obtained from Kaunas Regional Biomedical Research Ethics Committee. Using random sampling we examined 28 patients with relapsing-remitting multiple sclerosis who were treated for relapse in the Multiple Sclerosis Centre of Klaipėda University Hospital. Demographic and clinical data and disease modifying therapies were evaluated. Neurofilament heavy chains levels in the blood were measured using ELISA immunoenzyme assay.Results. The research involved 28 patients: 64.3% women and 25.7% men. Neurofilament heavy chains levels in the blood were higher in patients with 3 or more functional systems affected, compared with patients with only pyramidal or cerebellar systems damaged but the difference was not statistically significant. Significantly higher neurofilament heavy chains levels were found in patients with hyperintense magnetic resonance imaging T2 lesions in both brain and spinal cord areas and with contrast-enhanced lesions. Higher neurofilament heavy chains levels were associated with positive oligoclonal bands and prolonged visual evoked potentials. No significant correlation between disease duration, age, disability, and neurofilament heavy chains levels was found. Patients with no disease modifying treatment had higher serum neurofilament heavy chains levels but the difference was not statistically significant.Conclusion. We found higher serum neurofilament heavy chains levels in patients with 3 and more functional systems affected, radiological disease activity, positive oligoclonal bands, and prolonged visual evoked potentials. These results support the hypothesis that neurofilaments could be a promising biomarker for evaluation of multiple sclerosis relapse and disease prognosis in clinical practice.

Fatty liver disease, also known as hepatic steatosis, poses a significant global health concern due to the excessive accumulation of fat within the liver. If left untreated, this condition can give rise to severe complications. Recent advances in artificial intelligence (AI, specifically AI-based ultrasound imaging) offer promising tools for diagnosing this condition. This review endeavors to explore the current state of research concerning AI's role in diagnosing fatty liver disease, with a particular emphasis on imaging methods. To this end, a comprehensive search was conducted across electronic databases, including Google Scholar and Embase, to identify relevant studies published between January 2010 and May 2023, with keywords such as "fatty liver disease" and "artificial intelligence (AI)." The article selection process adhered to the PRISMA framework, ultimately resulting in the inclusion of 13 studies. These studies leveraged AI-assisted ultrasound due to its accessibility and cost-effectiveness, and they hailed from diverse countries, including India, China, Singapore, the United States, Egypt, Iran, Poland, Malaysia, and Korea. These studies employed a variety of AI classifiers, such as support vector machines, convolutional neural networks, multilayer perceptron, fuzzy Sugeno, and probabilistic neural networks, all of which demonstrated a remarkable level of precision. Notably, one study even achieved a diagnostic accuracy rate of 100%, underscoring AI's potential in diagnosing fatty liver disease. Nevertheless, the review acknowledged certain limitations within the included studies, with the majority featuring relatively small sample sizes, often encompassing fewer than 100 patients. Additionally, the variability in AI algorithms and imaging techniques added complexity to the comparative analysis. In conclusion, this review emphasizes the potential of AI in enhancing the diagnosis and management of fatty liver disease through advanced imaging techniques. Future research endeavors should prioritize the execution of large-scale studies that employ standardized AI algorithms and imaging techniques to validate AI's utility in diagnosing this prevalent health condition.

Autoimmune cardiovascular disease (CVD) includes myocarditis, coronary microvascular dysfunction (CMD), and heart failure with preserved ejection fraction (HFpEF). Traditional risk scores often overlook these conditions despite their links to high morbidity. Recent research highlights the significance of immunophenotyping, such as clonal hematopoiesis of indeterminate potential (CHIP), type I interferon activity, Th17/Treg imbalance, and cardiac autoantibodies, to improve risk assessment and guide treatment. This review combines mechanistic and clinical data connecting these immune signatures to endothelial dysfunction, arrhythmias, and fibrotic remodeling. It underscores the atherogenic role of CHIP and its influence on HFpEF prognosis, the vasculopathy driven by type I interferon signaling in CMD, and autoimmunity in myocarditis mediated by Th17 dominance. Cardiac-specific autoantibodies, like anti-heart and v1-adrenergic receptor antibodies, serve as early prognostic markers. Moreover, integrating immune biomarkers with advanced imaging techniques, such as cardiac magnetic resonance and positron emission tomography, could enhance risk stratification and therapy monitoring. Current immunomodulating treatments, including TNF and IL-6 inhibitors, provide cardiovascular benefits, while glucocorticoids and JAK inhibitors carry dose-related risks. In the future, personalized algorithms that incorporate CHIP genotyping, interferon scoring, and continuous biomarker and imaging assessments may transform autoimmune CVD management. This review highlights these translational insights and research priorities to bridge the gap between immune dysregulation and cardiovascular outcomes.

Functional linear regression model with randomly censored data: Predicting conversion time to Alzheimer ’s disease

In recent years, the cargo profiles of extracellular vesicles (EVs), which were inherited from their parent cells, have emerged as a reliable biomarker for liquid biopsy (LB) in disease diagnosis, prognosis, and treatment monitoring. EVs secreted by different cells exhibit distinct characteristics, particularly in terms of disease diagnosis and prediction. However, currently available techniques for the quantitative analysis of EV cargoes, including enzyme-linked immunosorbent assay (ELISA), cannot specifically identify the cellular origin of EVs, thus seriously affecting the accuracy of EV-based liquid biopsy. In light of this, we here developed ultrabright fluorescent nanosphere (FNs)-based test strips which have the unique capability to specifically assess the levels of PD-L1-positive EVs (PD-L1+ EVs) derived from both tumor cells and immune cells in bodily fluids. The levels of PD-L1+ EV subpopulations in human saliva were quantified using the ultrabright fluorescent nanosphere-based test strips with more convenience and higher efficiency (detection time <30 min). Results demonstrated that the fluorescence intensity of the test line exhibited a good linear relationship respectively with the PD-L1 levels of tumor cell- (R2 = 0.993) and immune cell-derived EVs (R2 = 0.982) in human saliva. By assessing the levels of PD-L1+ EV subpopulations, our test strips hold immense potential for advancing the application of PD-L1+ EV subpopulation-based predictions in tumor diagnosis and prognosis evaluation. In summary, by integrating the benefits of FNs and lateral flow chromatography, we here provide a strategy to accurately measure the cargo levels of EVs originating from diverse cell sources in bodily fluids.