Background: Epstein-Barr virus capsid antigen immunoglobulin A (EBV VCA-IgA) exerts an important role in the diagnosis of nasopharyngeal carcinoma (NPC). This meta-analysis aimed to evaluate the pooled diagnostic performance of VCA-IgA for NPC.
Methods: Literature fulfilling the criteria was searched in PubMed and Embase databases. The quality of the studies was assessed in terms of the Quality Assessment of Diagnosis Accuracy Studies (QUADAS) criteria. The pooled diagnostic parameters were generated using a bivariate meta-analysis model. Statistical analysis was performed based on the platforms of Meta-Disc 1.4 and Stata 12.0 software. The trim and fill adjustment method was applied to further assess the possible effects of publication bias.
Result: Twenty one studies comprising 2986 NPC patients and 3507 controls were included in this meta-analysis. The overall pooled sensitivity and specificity of serum VCA-IgA for NPC were 0.83 (95%CI: 0.82 - 0.84) and 0.88 (95% CI: 0.87 - 0.89), respectively, accompanied by a pooled diagnostic odds ratio (DOR) of 49.87 and area under curve (AUC) of 0.9390. Moreover, our stratified analyses suggested that combinations of multiple EBV antigens (sensitivity, specificity, DOR, and AUC of 0.93, 0.95, 331.8, and 0.9850, respectively) yielded higher accuracy than single VCA-IgA test (sensitivity, specificity, DOR and AUC of 0.83, 0.88, 49.87, and 0.9393, respectively). Additionally, the immunoenzyme assay (IEA) seemed to be a better alternative for the analysis of serum VCA-IgA level, with a sensitivity of 0.92, specificity of 0.94, and AUC of 0.9644.
Conclusions: Serum VCA-IgA hallmarks promising accuracy in the management of NPC and that parallel tests of multiple EBV antigens may be more suitable for NPC serodiagnosis than single VCA-IgA assay. .151122)
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http://dx.doi.org/10.7754/clin.lab.2015.151122 | DOI Listing |
Alzheimers Dement
December 2024
King George's Medical University, Lucknow, Uttar Pradesh, India.
Background: Neuropeptides are crucial proteins in the central nervous system, which significantly influence neurophysiological processes. This analysis explores cerebrospinal fluid alterations in Alzheimer's disease, offering insights to better understand the condition and explore novel diagnostic and therapeutic avenues.
Method: We systematically searched MEDLINE (PubMed), EMBASE, Cochrane, and Scopus using specific search strategies.
Background: The development of reliable blood biomarkers for neurodegenerative diseases (NDDs) has been hindered by the lack of tools with sufficient sensitivity to detect low concentrations of brain-derived proteins in plasma or serum in a highly multiplexed manner. NULISA™ (NUcleic acid-Linked Immuno-Sandwich Assay) has emerged as a promising solution, with attomolar sensitivity and capable of high multiplexing in a fully automated system. In this study, we introduce NULISA CNS Disease Panel 120, a 120-plex NULISAseq assay for profiling key hallmarks of NDDs in both blood and cerebrospinal fluid (CSF).
View Article and Find Full Text PDFAlzheimers Dement
December 2024
Thai Red Cross Emerging Infectious Diseases Health Science Centre, King Chulalongkorn Memorial Hospital, Bangkok, Thailand.
Background: The validation of blood-based biomarkers presents a promising role in Alzheimer's disease (AD) diagnosis owing to their accessibility and diminished invasiveness. However, despite awareness of confounding factors like kidney function, inaccuracies persist in AD diagnosis using plasma p-tau. Notably, diverse conditions that modify blood-brain barrier (BBB) permeability have been linked to high plasma p-tau levels, irrespective of AD pathophysiology.
View Article and Find Full Text PDFAlzheimers Dement
December 2024
School of Psychological Sciences and Turner Institute for Brain and Mental Health, Monash University, Monash, VIC, Australia.
Background: Diagnostic and prognostic decisions about Alzheimer's disease (AD) are more accurate when based on large data sets. We developed and validated a machine learning (ML) data harmonization tool for aggregation of prospective data from neuropsychological tests applied to study AD. The online ML-combine application (OML-combine app) allows researchers to utilize the ML-harmonization method for harmonization of their own data with that from other large available data bases (e.
View Article and Find Full Text PDFBackground: Predicting brain age from neuroimaging data is an emerging field. The age gap (AG), the difference between chronological age (CA) and brain age (BA), is crucial for indicating individual neuroanatomical aging. Previous deep learning models faced challenges in generalizability and neuroanatomical interpretability.
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