Background: Despite numerous reports on the alterations of microRNA-1246 (miR-1246) expression level in digestive system cancers, its role in gastrointestinal cancers (GICs) remains unclear. This meta-analysis aimed to assess the diagnostic potential of circulating miR-1246 in GICs.
Methods: Meta-disc version 1.4 and Comprehensive Meta-Analysis (CMA) version 3.7 software were used to calculate pooled sensitivity, specificity, likelihood ratios, diagnostic odds ratio (DOR), area under the curve (AUC), Q*index and summary receiver-operating characteristic (SROC). Subgroup analyses were conducted for cancer type, sample type and geographical region. Publication bias was assessed using Begg's and Egger's tests.
Results: A total of 14 articles involving 18 studies and 1526 participants (972 cases and 554 controls) were included. The diagnostic accuracy of miRNA-1246 in GICs was as follows: pooled sensitivity: 0.81 (95% CI: 0.79 - 0.83), specificity: 0.74 (95% CI: 0.71 - 0.77), PLR: 3.315 (95% CI: 2.33 - 4.72), NLR: 0.221 (95% CI: 0.153 - 0.319), DOR: 16.87 (95% CI: 9.45 - 30.09), AUC: 0.891, and Q*-index: 0.807. No publication bias was found based on Begg's ( = 0.172) and Egger's ( = 0.113) tests.
Conclusion: Circulating miR-1246 shows promise as a non-invasive biomarker for early detection of GICs.
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http://dx.doi.org/10.1080/1354750X.2024.2350714 | DOI Listing |
Metabolomics
January 2025
Owlstone Medical Ltd., Cambridge, UK.
Introduction: Breath Volatile organic compounds (VOCs) are promising biomarkers for clinical purposes due to their unique properties. Translation of VOC biomarkers into the clinic depends on identification and validation: a challenge requiring collaboration, well-established protocols, and cross-comparison of data. Previously, we developed a breath collection and analysis method, resulting in 148 breath-borne VOCs identified.
View Article and Find Full Text PDFTransl Lung Cancer Res
December 2024
School of Medicine, Southeast University, Nanjing, China.
Background: Resistance to chemoimmunotherapy in patients with advanced non-small cell lung cancer (NSCLC) necessitates effective prognostic biomarkers. Although F-fluorodeoxyglucose (FDG) positron emission tomography (PET)/computed tomography (CT) has shown potential for efficacy assessment, it has been mainly evaluated in immuno-monotherapy setting, lacking elaborations in the scenarios of immunotherapy combined with chemotherapy. To tackle this dilemma, we aimed to build a non-invasive PET/CT-based model for stratifying tumor heterogeneity and predicting survival in advanced NSCLC patients undergoing chemoimmunotherapy.
View Article and Find Full Text PDFFront Public Health
January 2025
Engineering Research Center of Photoelectric Detection and Perception Technology, Yunnan Normal University, Kunming, China.
The rising incidence of Alzheimer's disease (AD) poses significant challenges to traditional diagnostic methods, which primarily rely on neuropsychological assessments and brain MRIs. The advent of deep learning in medical diagnosis opens new possibilities for early AD detection. In this study, we introduce retinal vessel segmentation methods based on U-Net ad iterative registration Learning (ReIU), which extract retinal vessel maps from OCT angiography (OCT-A) facilities.
View Article and Find Full Text PDFInt J Nephrol Renovasc Dis
January 2025
Autoimmunity Lab, School of Medical Science, Universidade Estadual de Campinas, Campinas, Brazil.
Approximately one in five patients with systemic lupus erythematosus (SLE) has disease-onset during childhood (cSLE). Lupus nephritis is more common in cSLE than adult-onset SLE and is associated with significant and increased morbidity and mortality. In this article, we review lupus nephritis in cSLE, including pathogenesis, diagnosis, biomarkers, and management through PUBMED search between July and December 2024.
View Article and Find Full Text PDFExpert Syst Appl
October 2024
Department of Cell Systems and Anatomy, University of Texas Health Science Center at San Antonio, TX, United States.
Hepatocellular carcinoma (HCC) remains a global health challenge with high mortality rates, largely due to late diagnosis and suboptimal efficacy of current therapies. With the imperative need for more reliable, non-invasive diagnostic tools and novel therapeutic strategies, this study focuses on the discovery and application of novel genetic biomarkers for HCC using explainable artificial intelligence (XAI). Despite advances in HCC research, current biomarkers like Alpha-fetoprotein (AFP) exhibit limitations in sensitivity and specificity, necessitating a shift towards more precise and reliable markers.
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