Neurological Disease Biomarkers

A special issue of Diagnostics (ISSN 2075-4418). This special issue belongs to the section "Pathology and Molecular Diagnostics".

Deadline for manuscript submissions: closed (31 October 2024) | Viewed by 4072

Special Issue Editor


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Guest Editor
Department of Neurosurgery, School of Medicine, Evangelismos Hospital, National and Kapodistrian University of Athens, 10676 Athens, Greece
Interests: neurological disease biomarkers (CNS tumors, hydrocephalus, chronic subdural hematoma, traumatic brain injury); neuroprotection and functional recovery following brain injury; circadian rhythms and sleep disorders; neuroepidemiology (stroke, CNS tumors, epilepsy, traumatic brain injury)
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Special Issue Information

Dear Colleagues, 

Over the past few decades, the advances achieved in the discovery and implementation of neurological disease biomarkers have revolutionized research, transformed clinical trial design, and allowed for improved patient management.

In this Special Issue, we aim to provide a platform for communication on the progress of biomarker identification and utilization in neurological diseases. The welcomed topics include but are not limited to biomarkers in aging-associated neurodegenerative disorders, multiple sclerosis, traumatic brain injury, peripheral neuropathies, and COVID-19-associated neurological damage.

Dr. Theodosis Kalamatianos
Guest Editor

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Keywords

  • biomarkers
  • neurological diseases
  • multiple sclerosis
  • traumatic brain injury
  • peripheral neuropathies
  • COVID-19 neurological-associated damage

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Published Papers (2 papers)

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Research

11 pages, 1116 KiB  
Article
Choroid Plexus Volume Change—A Candidate for a New Radiological Marker of MS Progression
by Anna Jankowska, Kamil Chwojnicki, Małgorzata Grzywińska, Piotr Trzonkowski and Edyta Szurowska
Diagnostics 2023, 13(16), 2668; https://doi.org/10.3390/diagnostics13162668 - 14 Aug 2023
Cited by 9 | Viewed by 1524
Abstract
(1) Background: Multiple sclerosis (MS) is an auto-immune, chronic, neuroinflammatory, demyelinating disease that affects mainly young patients. This progressive inflammatory process causes the chronic loss of brain tissue and results in a deterioration in quality of life. To monitor neuroinflammatory process activity and [...] Read more.
(1) Background: Multiple sclerosis (MS) is an auto-immune, chronic, neuroinflammatory, demyelinating disease that affects mainly young patients. This progressive inflammatory process causes the chronic loss of brain tissue and results in a deterioration in quality of life. To monitor neuroinflammatory process activity and predict the further development of disease, it is necessary to find a suitable biomarker that could easily be used. In this research, we verify the usability of choroid plexus (CP) volume, a new MS biomarker, in the monitoring of the progression of multiple sclerosis disease. (2) Methods: A single-center, prospective study with three groups of patients was conducted based on the following groups: MS patients who received experimental cellular therapy (Treg), treatment-naïve MS patients and healthy controls. (3) Results: This study concludes that there is a correlation between the CPV/TIV (choroid plexus/total intracranial volume) ratio and the progress of multiple sclerosis disease—patients with MS (MS + Treg) had larger volumes of choroid plexuses. CPV/TIV ratios in MS groups were constantly and significantly growing. In the Treg group, patients with relapses had larger plexuses in comparison to the group with no relapses of MS. A similar correlation was observed for the GD+ group (patients with postcontrast enhancing plaques) compared against the non-GD group (patients without postcontrast enhancing plaques). (4) Conclusion: Choroid plexus volume, due to its immunological function, correlates with the inflammatory process in the central nervous system. We consider it to become a valuable radiological biomarker of MS activity. Full article
(This article belongs to the Special Issue Neurological Disease Biomarkers)
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11 pages, 2193 KiB  
Article
Training and External Validation of a Predict Nomogram for Type 2 Diabetic Peripheral Neuropathy
by Yongsheng Li, Yongnan Li, Ning Deng, Haonan Shi, Siqingaowa Caika and Gan Sen
Diagnostics 2023, 13(7), 1265; https://doi.org/10.3390/diagnostics13071265 - 27 Mar 2023
Cited by 1 | Viewed by 1634
Abstract
Background: Diabetic peripheral neuropathy (DPN) is a critical clinical disease with high disability and mortality rates. Early identification and treatment of DPN is critical. Our aim was to train and externally validate a prediction nomogram for early prediction of DPN. Methods: 3012 patients [...] Read more.
Background: Diabetic peripheral neuropathy (DPN) is a critical clinical disease with high disability and mortality rates. Early identification and treatment of DPN is critical. Our aim was to train and externally validate a prediction nomogram for early prediction of DPN. Methods: 3012 patients with T2DM were retrospectively studied. These patients were hospitalized between 1 January 2017 and 31 December 2020 in the First Affiliated Hospital of Xinjiang Medical University in Xinjiang, China. A total of 901 patients with T2DM from the Suzhou BenQ Hospital in Jiangsu, China who were hospitalized between 1 January 2019 and 31 December 2020 were considered for external validation. The least absolute shrinkage and selection operator (LASSO) and multivariate logistic regression were performed to identify independent predictors and establish a nomogram to predict the occurrence of DPN. The performance of the nomogram was evaluated using a receiver operating characteristic curve (ROC), a calibration curve, and a decision curve analysis (DCA). Findings: Age, 25-hydroxyvitamin D3 [25(OH)D3], Duration of T2DM, high-density lipoprotein (HDL), hemoglobin A1c (HbA1c), and fasting blood glucose (FBG) were used to establish a nomogram model for predicting the risk of DPN. In the training and validation cohorts, the areas under the curve of the nomogram constructed from the above six factors were 0.8256 (95% CI: 0.8104–0.8408) and 0.8608 (95% CI: 0.8376–0.8840), respectively. The nomogram demonstrated excellent performance in the calibration curve and DCA. Interpretation: This study has developed and externally validated a nomogram model which exhibits good predictive ability in assessing DPN risk among the type 2 diabetes population. It provided clinicians with an accurate and effective tool for the early prediction and timely management of DPN. Full article
(This article belongs to the Special Issue Neurological Disease Biomarkers)
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