Search Results (55)

The COVID-19 pandemic has revealed the profound and lasting impact of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) on the nervous system. Beyond acute infection, SARS-CoV-2 acts as a potent immunomodulatory agent, disrupting immune homeostasis and contributing to persistent inflammation, autoimmunity, and neurodegeneration. Long COVID, or post-acute sequelae of SARS-CoV-2 infection (PASC), is characterized by a spectrum of neurological symptoms, including cognitive dysfunction, fatigue, neuropathy, and mood disturbances. These are linked to immune dysregulation involving cytokine imbalance, blood–brain barrier (BBB) disruption, glial activation, and T-cell exhaustion. Key biomarkers such as interleukin-6 (IL-6), tumor necrosis factor-alpha (TNF-α), glial fibrillary acidic protein (GFAP), and neurofilament light chain (NFL) correlate with disease severity and chronicity. This narrative review examines the immunopathological mechanisms underpinning the neurological sequelae of long COVID, focusing on neuroinflammation, endothelial dysfunction, and molecular mimicry. We also assess the role of viral variants in shaping neuroimmune outcomes and explore emerging diagnostic and therapeutic strategies, including biomarker-guided and immune-targeted interventions. By delineating how SARS-CoV-2 reshapes neuroimmune interactions, this review aims to support the development of precision-based diagnostics and targeted therapies for long COVID-related neurological dysfunction. Emerging approaches include immune-modulatory agents (e.g., anti-IL-6), neuroprotective drugs, and strategies for repurposing antiviral or anti-inflammatory compounds in neuro-COVID. Given the high prevalence of comorbidities, personalized therapies guided by biomarkers and patient-specific immune profiles may be essential. Advancements in vaccine technologies and targeted biologics may also hold promise for prevention and disease modification. Finally, continued interdisciplinary research is needed to clarify the complex virus–immune–brain axis in long COVID and inform effective clinical management. Full article

Pediatric central nervous system (CNS) tumors, including gliomas, medulloblastomas, and diffuse midline gliomas (previously diffuse intrinsic pontine gliomas), remain a major clinical challenge due to their complex biology, limited treatment effectiveness, and generally poor prognosis. Standard treatments are often aggressive and associated with substantial toxicity, particularly in advanced stages. This review highlights recent developments in radiopharmaceuticals for molecular imaging and targeted radiotherapy. A comprehensive literature analysis was conducted, focusing on radiotracers with clinical relevance in pediatric neuro-oncology, including metabolic, peptide receptor-based, and antibody-based agents. Radiopharmaceuticals such as ¹⁸F-FLT, ⁶⁴CuCl₂, and 1-L-18F-FETrp have improved the ability to monitor tumor biology, proliferation, and treatment response, aiding in diagnosis at an early stage, assessment of tumor behavior, and detection of recurrence or progression. Additionally, peptide receptor-based radiotracers, such as ⁶⁸Ga-DOTATATE and ¹⁷⁷Lu-DOTATATE, are already used for both diagnostic purposes and targeted radiotherapy, particularly in neuroblastomas and gliomas. Antibody-based radiotracers like ¹³¹I-omburtamab, targeting B7-H3, are emerging as promising tools for addressing difficult-to-treat tumors such as diffuse midline glioma. Collectively, these advances provide new hope for children afflicted by these devastating malignancies, offering promising solutions for more specific and precise diagnosis and, additionally, for more effective, personalized, and less toxic tumor therapies. Full article

Charcot Neuro-Osteoarthropathy (CNO) is a debilitating complication predominantly affecting individuals with diabetes and peripheral neuropathy. Radiological assessment plays a central role in the diagnosis, staging, and management of CNO. While plain radiographs remain the cornerstone of initial imaging, advanced modalities such as Magnetic Resonance Imaging (MRI) and Computed Tomography (CT) have significantly enhanced diagnostic accuracy. Nuclear imaging, including bone scintigraphy, radiolabeled leukocyte scans, and FDG-PET/CT, offers additional diagnostic precision in complex cases, especially when differentiating CNO from infections or evaluating patients with metal implants. This review underscores the importance of a multimodal imaging approach suited to the clinical stage and specific diagnostic challenges of CNO. It highlights the critical need for standardized imaging protocols and integrated diagnostic algorithms that combine radiological, clinical, and laboratory findings. Advances in imaging biomarkers and novel techniques such as diffusion-weighted MRI hold promise for improving early detection and monitoring treatment efficacy. In conclusion, the effective management of CNO in diabetic foot patients requires a multidisciplinary approach that integrates advanced imaging technologies with clinical expertise. Timely and accurate diagnosis not only prevents debilitating complications but also facilitates the development of personalized therapeutic strategies, ultimately improving patient outcomes. Full article

Nowadays, the diagnosis of Parkinson’s disease (PD) remains essentially clinical, based on the subjective observations of clinicians. In addition, misdiagnosis with other neuro disorders, such as Alzheimer’s (AD), can occur. Herein, an untargeted lipidomic analysis of 75 plasma samples was performed to identify lipid species capable of discriminating between these two neuro groups. Therefore, PLS-DA and OPLS-DA analysis revealed significant differences in patient profiles in the sphingolipid and glycerophospholipid categories. As a result, a putative lipid biomarker panel was developed, which included HexCer (40:1; O2) and PC (O-32:0), with an area under the receiver operating characteristic curve (AUC) > 80, respectively. This panel was effective in discriminating between diseased and healthy subjects, but most importantly, it could discriminate between two neurodegenerative disorders that can present similar symptoms, namely PD and AD. Together, these findings suggest that the dysregulated metabolism of lipids plays a critical role in AD and PD pathology and may represent a valuable clinical tool for their diagnosis. Thus, further targeted studies are encouraged to better understand the underlying mechanisms of PD and confirm the diagnostic potency of the identified lipid metabolites. Full article

The bacterium Xanthomonas campestris poses a significant threat to global agriculture due to its ability to infect leaves, fruits, and stems under various climatic conditions. Its rapid spread across large crop areas results in economic losses, compromises agricultural productivity, increases management costs, and threatens food security, especially in small-scale agricultural systems. To address this issue, this study developed a model that combines fuzzy logic and neural networks, optimized with intelligent algorithms, to detect symptoms of this foliar disease in 15 essential crop species under different environmental conditions using images. For this purpose, Sugeno-type fuzzy inference systems and adaptive neuro-fuzzy inference systems (ANFIS) were employed, configured with rules and clustering methods designed to address cases where diagnostic uncertainty arises due to the imprecision of different agricultural scenarios. The model achieved an accuracy of 93.81%, demonstrating robustness against variations in lighting, shadows, and capture angles, and proving effective in identifying patterns associated with the disease at early stages, enabling rapid and reliable diagnoses. This advancement represents a significant contribution to the automated detection of plant diseases, providing an accessible tool that enhances agricultural productivity and promotes sustainable practices in crop care. Full article

Background and Objectives: Development of acute coronary syndrome (ACS) after aneurysmal subarachnoid hemorrhage (aSAH) strongly affects further neuro-intensive care management. We aimed to analyze the incidence, risk factors and clinical impact of ACS in aSAH patients. Materials and Methods: This retrospective analysis included 855 aSAH cases treated between 01/2003 and 06/2016. The occurrence of ACS during 3 weeks of aSAH was documented. Patients’ demographic, clinical, radiographic and laboratory characteristics at admission were collected as potential ACS predictors. The association between ACS and the aSAH outcome was analyzed as the occurrence of cerebral infarcts in the computed tomography scans and unfavorable outcome (modified Rankin scale > 3) at 6 months after aSAH. Univariable and multivariable analyses were performed. Results: ACS was documented in 28 cases (3.3%) in the final cohort (mean age: 54.9 years; 67.8% females). In the multivariable analysis, there was a significant association between ACS, an unfavorable outcome (adjusted odds ratio [aOR] = 3.43, p = 0.027) and a borderline significance with cerebral infarcts (aOR = 2.5, p = 0.066). The final prediction model for ACS occurrence included five independent predictors (age > 55 years [1 point], serum sodium < 142 mmol/L [3 points], blood sugar ≥ 170 mg/dL [2 points], serum creatine kinase ≥ 255 U/L [3 points] and gamma-glutamyl transferase ≥ 36 U/L [1 point]) and showed high diagnostic accuracy for ACS prediction (AUC = 0.879). Depending on the cumulative score value, the risk of ACS in the cohort varied between 0% (0 points) and 66.7% (10 points). Conclusions: ACS is a rare, but clinically very relevant, complication of aSAH. The development of ACS can reliably be predicted by the presented prediction model, which enables the early identification of aSAH individuals at high risk for ACS. External validation of the prediction model is mandatory. Full article

Medulloblastoma and pilocytic astrocytoma are the two most common pediatric brain tumors with overlapping imaging features. In this proof-of-concept study, we investigated using a deep learning classifier trained on a multicenter data set to differentiate these tumor types. We developed a patch-based 3D-DenseNet classifier, utilizing automated tumor segmentation. Given the heterogeneity of imaging data (and available sequences), we used all individually available preoperative imaging sequences to make the model robust to varying input. We compared the classifier to diagnostic assessments by five readers with varying experience in pediatric brain tumors. Overall, we included 195 preoperative MRIs from children with medulloblastoma (n = 69) or pilocytic astrocytoma (n = 126) across six university hospitals. In the 64-patient test set, the DenseNet classifier achieved a high AUC of 0.986, correctly predicting 62/64 (97%) diagnoses. It misclassified one case of each tumor type. Human reader accuracy ranged from 100% (expert neuroradiologist) to 80% (resident). The classifier performed significantly better than relatively inexperienced readers (p < 0.05) and was on par with pediatric neuro-oncology experts. Our proof-of-concept study demonstrates a deep learning model based on automated tumor segmentation that can reliably preoperatively differentiate between medulloblastoma and pilocytic astrocytoma, even in heterogeneous data. Full article

Aptamers developed using in vitro Systematic Evolution of Ligands by Exponential Enrichment (SELEX) technology are single-stranded nucleic acids 10–100 nucleotides in length. Their targets, often with specificity and high affinity, range from ions and small molecules to proteins and other biological molecules as well as larger systems, including cells, tissues, and animals. Aptamers often rival conventional antibodies with improved performance, due to aptamers’ unique biophysical and biochemical properties, including small size, synthetic accessibility, facile modification, low production cost, and low immunogenicity. Therefore, there is sustained interest in engineering and adapting aptamers for many applications, including diagnostics and therapeutics. Recently, aptamers have shown promise as early diagnostic biomarkers and in precision medicine for neurodegenerative and neurological diseases. Here, we critically review neuro-targeting aptamers and their potential applications in neuroscience research, neuro-diagnostics, and neuro-medicine. We also discuss challenges that must be overcome, including delivery across the blood–brain barrier, increased affinity, and improved in vivo stability and in vivo pharmacokinetic properties. Full article

Neuromonitoring is a critical tool for emergency rooms and intensive care units to promptly identify and treat brain injuries. The case report of a patient with status epilepticus necessitating orotracheal intubation and intravenous lorazepam administration is presented. A pattern of epileptiform activity was detected in the left temporal region, and intravenous Acyclovir was administered based on the diagnostic hypothesis of herpetic meningoencephalitis. The neurointensivist opted for multimodal non-invasive bedside neuromonitoring due to the complexity of the patient’s condition. A Brain4care (B4C) non-invasive intracranial compliance monitor was utilized alongside the assessment of an optic nerve sheath diameter (ONSD) and transcranial Doppler (TCD). Based on the collected data, a diagnosis of intracranial hypertension (ICH) was made and a treatment plan was developed. After the neurosurgery team’s evaluation, a stereotaxic biopsy of the temporal lesion revealed a grade 2 diffuse astrocytoma, and an urgent total resection was performed. Research suggests that monitoring patients in a dedicated neurologic intensive care unit (Neuro ICU) can lead to improved outcomes and shorter hospital stays. In addition to being useful for patients with a primary brain injury, neuromonitoring may also be advantageous for those at risk of cerebral hemodynamic impairment. Lastly, it is essential to note that neuromonitoring technologies are non-invasive, less expensive, safe, and bedside-accessible approaches with significant diagnostic and monitoring potential for patients at risk of brain abnormalities. Multimodal neuromonitoring is a vital tool in critical care units for the identification and management of acute brain trauma as well as for patients at risk of cerebral hemodynamic impairment. Full article

Traumatic brain injury (TBI) affects millions of people of all ages around the globe. TBI is notoriously hard to diagnose at the point of care, resulting in incorrect patient management, avoidable death and disability, long-term neurodegenerative complications, and increased costs. It is vital to develop timely, alternative diagnostics for TBI to assist triage and clinical decision-making, complementary to current techniques such as neuroimaging and cognitive assessment. These could deliver rapid, quantitative TBI detection, by obtaining information on biochemical changes from patient’s biofluids. If available, this would reduce mis-triage, save healthcare providers costs (both over- and under-triage are expensive) and improve outcomes by guiding early management. Herein, we utilize Raman spectroscopy-based detection to profile a panel of 18 raw (human, animal, and synthetically derived) TBI-indicative biomarkers (N-acetyl-aspartic acid (NAA), Ganglioside, Glutathione (GSH), Neuron Specific Enolase (NSE), Glial Fibrillary Acidic Protein (GFAP), Ubiquitin C-terminal Hydrolase L1 (UCHL1), Cholesterol, D-Serine, Sphingomyelin, Sulfatides, Cardiolipin, Interleukin-6 (IL-6), S100B, Galactocerebroside, Beta-D-(+)-Glucose, Myo-Inositol, Interleukin-18 (IL-18), Neurofilament Light Chain (NFL)) and their aqueous solution. The subsequently derived unique spectral reference library, exploiting four excitation lasers of 514, 633, 785, and 830 nm, will aid the development of rapid, non-destructive, and label-free spectroscopy-based neuro-diagnostic technologies. These biomolecules, released during cellular damage, provide additional means of diagnosing TBI and assessing the severity of injury. The spectroscopic temporal profiles of the studied biofluid neuro-markers are classed according to their acute, sub-acute, and chronic temporal injury phases and we have further generated detailed peak assignment tables for each brain-specific biomolecule within each injury phase. The intensity ratios of significant peaks, yielding the combined unique spectroscopic barcode for each brain-injury marker, are compared to assess variance between lasers, with the smallest variance found for UCHL1 (σ² = 0.000164) and the highest for sulfatide (σ² = 0.158). Overall, this work paves the way for defining and setting the most appropriate diagnostic time window for detection following brain injury. Further rapid and specific detection of these biomarkers, from easily accessible biofluids, would not only enable the triage of TBI, predict outcomes, indicate the progress of recovery, and save healthcare providers costs, but also cement the potential of Raman-based spectroscopy as a powerful tool for neurodiagnostics. Full article

Background and aim: Parkinson’s disease (PD), a neuro-degenerative disorder, is often detected by the onset of its motor symptoms such as rest tremor. Unfortunately, motor symptoms appear only when approximately 40–60% of the dopaminergic neurons in the substantia nigra are lost. In most cases, by the time PD is clinically diagnosed, the disease may already have started 4 to 6 years beforehand. There is therefore a need for developing a test for detecting PD before the onset of motor symptoms. This phase of PD is referred to as Presymptomatic PD (PPD). The motor symptoms of Parkinson’s Disease are manifestations of instability in the sensorimotor system that develops gradually due to the neurodegenerative process. In this paper, based on the above insight, we propose a new method that can potentially be used to detect the degradation of motor control stability, which can be employed for the detection of PPD. Methods: The proposed method tracks the tendency of a feedback control system to transition to an unstable state and uses a machine learning algorithm for its robust detection. This method is explored using a simple simulation example consisting of a simple pendulum with a proportional-integral-derivative (PID) controller as a conceptual representation for both healthy and PPD individuals with a noise variance of 0.01 and a noise variance of 0.1. The present study adopts a longitudinal design to evaluate the effectiveness of the proposed methodology. Specifically, the performance of the proposed approach, with specific choices of features, is compared to that of the Support Vector Machine (SVM) algorithm for machine learning under conditions of incremental delay-induced instability. This comparison is made with results obtained using the Longitudinal Support Vector Machine (LSVM) algorithm for machine learning, which is better suited for longitudinal studies. Results: The results of SVM with one choice of features are comparable with the results of LSVM for a noise variance of 0.01. These results are almost unaffected by a noise variance of 0.1. All of the methods showed a high sensitivity above 96% and specificity above 98% on a training data set. In addition, they perform very well with the validation synthetic data set with sensitivity above 95% and specificity above 98%. These results are robust to further increases in noise variance representing the large variances expected in patient populations. Conclusions: The proposed method is evaluated on a synthetic data set, and the machine learning results show a promise and potential for use for detecting PPD through an early diagnostic device. In addition, an example task with physiological measurement that can potentially be used as a clinical movement control test along with representative data from both healthy individuals and PD patients is also presented, demonstrating the feasibility of performing a longitudinal study to validate and test the robustness of the proposed method. Full article

CDSSs are being continuously developed and integrated into routine clinical practice as they assist clinicians and radiologists in dealing with an enormous amount of medical data, reduce clinical errors, and improve diagnostic capabilities. They assist detection, classification, and grading of brain tumours as well as alert physicians of treatment change plans. The aim of this systematic review is to identify various CDSSs that are used in brain tumour diagnosis and prognosis and rely on data captured by any imaging modality. Based on the 2020 preferred reporting items for systematic reviews and meta-analyses (PRISMA) protocol, the literature search was conducted in PubMed and Engineering Village Compendex databases. Different types of CDSSs identified through this review include Curiam BT, FASMA, MIROR, HealthAgents, and INTERPRET, among others. This review also examines various CDSS tool types, system features, techniques, accuracy, and outcomes, to provide the latest evidence available in the field of neuro-oncology. An overview of such CDSSs used to support clinical decision-making in the management and treatment of brain tumours, along with their benefits, challenges, and future perspectives has been provided. Although a CDSS improves diagnostic capabilities and healthcare delivery, there is lack of specific evidence to support these claims. The absence of empirical data slows down both user acceptance and evaluation of the actual impact of CDSS on brain tumour management. Instead of emphasizing the advantages of implementing CDSS, it is important to address its potential drawbacks and ethical implications. By doing so, it can promote the responsible use of CDSS and facilitate its faster adoption in clinical settings. Full article

Neurocritical care significantly impacts outcomes after moderate-to-severe acquired brain injury, but it is rarely applied in preclinical studies. We created a comprehensive neurointensive care unit (neuroICU) for use in swine to account for the influence of neurocritical care, collect clinically relevant monitoring data, and create a paradigm that is capable of validating therapeutics/diagnostics in the unique neurocritical care space. Our multidisciplinary team of neuroscientists, neurointensivists, and veterinarians adapted/optimized the clinical neuroICU (e.g., multimodal neuromonitoring) and critical care pathways (e.g., managing cerebral perfusion pressure with sedation, ventilation, and hypertonic saline) for use in swine. Moreover, this neurocritical care paradigm enabled the first demonstration of an extended preclinical study period for moderate-to-severe traumatic brain injury with coma beyond 8 h. There are many similarities with humans that make swine an ideal model species for brain injury studies, including a large brain mass, gyrencephalic cortex, high white matter volume, and topography of basal cisterns, amongst other critical factors. Here we describe the neurocritical care techniques we developed and the medical management of swine following subarachnoid hemorrhage and traumatic brain injury with coma. Incorporating neurocritical care in swine studies will reduce the translational gap for therapeutics and diagnostics specifically tailored for moderate-to-severe acquired brain injury. Full article

The pathogenesis of chronic pain entails a series of complex interactions among the nervous, immune, and endocrine systems. Defined as pain lasting or recurring for more than 3 months, chronic pain is becoming increasingly more prevalent among the US adult population. Pro-inflammatory cytokines from persistent low-grade inflammation not only contribute to the development of chronic pain conditions, but also regulate various aspects of the tryptophan metabolism, especially that of the kynurenine pathway (KP). An elevated level of pro-inflammatory cytokines exerts similar regulatory effects on the hypothalamic–pituitary–adrenal (HPA) axis, an intricate system of neuro–endocrine–immune pathways and a major mechanism of the stress response. As the HPA axis counters inflammation through the secretion of endogenous cortisol, we review the role of cortisol along with that of exogenous glucocorticoids in patients with chronic pain conditions. Considering that different metabolites produced along the KP exhibit neuroprotective, neurotoxic, and pronociceptive properties, we also summarize evidence rendering them as reliable biomarkers in this patient population. While more in vivo studies are needed, we conclude that the interaction between glucocorticoid hormones and the KP poses an attractive venue of diagnostic and therapeutic potential in patients with chronic pain. Full article

Introduction: The utility of a clinical tool lies in its clinical performance evaluation and describes the relevance and usefulness of that tool in a medical setting. The utility of urodynamic and video-urodynamic studies in the management of specific urodynamic profiles in the diagnosis, treatment, and prognostic approach in neuro-urological patients is the focus of the current review. Methods: For this narrative review, a PubMed^® search was performed by cross-referencing the keywords “urodynamics”, “neurogenic bladder”, “utility”, “clinical utility” and “clinical performance” with various terms related to the management of neurogenic lower urinary tract dysfunction. Clinical practice guidelines and landmark reviews from the most renowned experts in the field were also used. Analysis: Assessment of the utility of urodynamic study was performed during the diagnostic, therapeutic and prognostic steps of the neuro-urological patients’ management. We focused on its clinical performance in the identification and evaluation of several unfavorable events, such as neurogenic detrusor overactivity, detrusor-sphincter dyssynergia, elevated detrusor leak point pressure and the presence of vesico-ureteral reflux, which may be indicators for a higher risk for the development of urological comorbidities. Conclusion: Despite the paucity of existing literature assessing the utility of urodynamic study—specifically video-urodynamic study—in neuro-urological patients, it does remain the gold standard to assess lower urinary tract function precisely in this patient category. With regard to its utility, it is associated with high clinical performance at every step of management. The feedback on possible unfavorable events allows for prognostic assessment and may lead us to question current recommendations. Full article