Tuberculous meningitis (TBM) is the most common and severe form of central nervous system tuberculosis. Due to the non-specific clinical presentation and lack of efficient diagnosis methods, it is difficult to discriminate TBM from other frequent types of meningitis, especially viral meningitis (VM). In order to identify the potential biomarkers for discriminating TBM and VM and to reveal the different pathophysiological processes between TBM and VM, a genome-wide miRNA screening of PBMCs from TBM, VM, and healthy controls (HCs) using microarray assay was performed (12 samples). Twenty-eight differentially expressed miRNAs were identified between TBM and VM, and 11 differentially expressed miRNAs were identified between TBM and HCs. The 6 overlapping miRNAs detected in both TBM vs. VM and TBM vs. HCs were verified by qPCR analysis and showed a 100% consistent expression patterns with that in microarray test. Statistically significant differences of 4 miRNAs (miR-126-3p, miR-130a-3p, miR-151a-3p, and miR-199a-5p) were further confirmed in TBM compared with VM and HCs in independent PBMCs sample set ( = 96, < 0.01). Three of which were also showed significantly different between TBM and VM in CSF samples ( = 70, < 0.05). The receiver operating characteristic curve (ROC) analysis showed that the area under the ROC curve (AUC) of these 4 miRNAs in PBMCs were more than 0.70 in discriminating TBM from VM. Combination of these 4 miRNAs could achieve better discriminative capacity [AUC = 0.893 (0.788-0.957)], with a sensitivity of 90.6% (75.0-98.0%), and a specificity of 86.7% (69.3-96.2%). Additional validation was performed to evaluate the diagnostic panel in another independent sample set ( = 49), which yielded a sensitivity of 81.8% (9/11), and specificity of 90.0% (9/10) in distinguishing TBM and VM, and a sensitivity of 81.8% (9/11), and a specificity of 84.6% (11/13) in discriminating TBM from other non-TBM patients. This study uncovered the miRNA profiles of TBM and VM patients, which can facilitate better understanding of the pathogenesis involved in these two diseases and identified 4 novel miRNAs in distinguishing TBM and VM.
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http://dx.doi.org/10.3389/fcimb.2019.00323 | DOI Listing |
J Clin Tuberc Other Mycobact Dis
February 2025
Department of Neurology, First Affiliated Hospital of Guangxi Medical University, China.
Background: Patients with tuberculous meningitis (TBM) are at high risk of ischemic stroke, and stroke is a poor prognosticator of TBM. However, reports regarding the predictors of stroke in TBM patients are scanty. The aim of this study was to investigate the clinical characteristics and predictors of tuberculous meningitis-related ischemic stroke (TBMRIS).
View Article and Find Full Text PDFAnimals (Basel)
December 2024
Division of Convergence on Marine Science, Korea Maritime and Ocean University, Busan 49112, Republic of Korea.
A two-way {dietary substitution source [chicken by-product meal (CBM), meat meal (MM), and tuna by-product meal (TBM)] × substitution level (25% and 50%)} ANOVA experimental design was adopted. The control (Con) diet included 55% FM. In the Con diet, 25% and 50% of FM were substituted with CBM, MM, and TBM, with the inclusion of 22% JMM exhibiting strong attractiveness for rockfish, named CBM25J, CBM50J, MM25J, MM50J, TBM25J, and TBM50J diets, respectively.
View Article and Find Full Text PDFEur J Case Rep Intern Med
December 2024
Internal Medicine, Dubai Health, Dubai, United Arab Emirates.
Background: Hemophagocytic lymphohistiocytosis (HLH), is characterized by systemic uncontrolled inflammation resulting from immune dysregulation secondary to various triggers, including genetics, infections, autoimmune diseases, and malignancies. Macrophage activation syndrome (MAS) is an immune dysregulation phenomenon, in which an underlying rheumatological disease is present. We report a rare, interesting case of a middle-aged female, with a systemic lupus erythematosus (SLE) flare complicated by macrophage activation syndrome (MAS), in which tuberculous meningitis (TBM) was the identified trigger.
View Article and Find Full Text PDFBMC Med Inform Decis Mak
January 2025
Department of Electrical Engineering, ESAT-STADIUS, KU Leuven, Kasteelpark Arenberg 10, B-3001 Leuven, Belgium.
Background: Waste and fraud are important problems for health insurers to deal with. With the advent of big data, these insurers are looking more and more towards data mining and machine learning methods to help in detecting waste and fraud. However, labeled data is costly and difficult to acquire as it requires expert investigators and known care providers with atypical behavior.
View Article and Find Full Text PDFHead Neck
January 2025
Institute of Head and Neck Studies and Education (InHANSE), Department of Cancer and Genomics, University of Birmingham, UK.
Background: The aim of this clinical survey was to assess variations in head and neck squamous cell carcinoma from an unknown primary (HNSCCUP) diagnostic practices across international centers.
Methods: Clinical practice survey of experts nominated by Head and Neck Cancer International Group (HNCIG) and International Federation of Head and Neck Oncologic Societies (IFHNOS).
Results: Responses were received from 48/49 (97.
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