Quantum Dots (QDs) are small semiconductor nanoparticles (<10 nm) with strong, relatively stable, and tunable luminescent properties, which are increasingly applied in the sensing and detection of various analytes, including metal ions, biomarkers, explosives, proteins, RNA/DNA fragments, pesticides, drugs, and pollutants. In this review, we critically assess recent developments and advancements in luminescent QD-based sensors from an analytical perspective. We collected, tabulated, and analyzed relevant data reported in 124 peer-reviewed articles. The key analytical figures of merit, including the limit of detection (LOD), excitation and emission wavelengths, and size of the particles were extracted, tabulated, and analyzed with graphical representations. We calculated the geometric mean and median LODs from those tabulated publications. We found the following geometric mean LODs: 38 nM for QD-fluorescent-based sensors, 26 nM for QD-phosphorescent-based sensors, and an impressively low 0.109 pM for QD-chemiluminescent-based sensors, which demonstrate by far the best sensitivity in QD-based detection. Moreover, AI-based sensing methods, including the ATTBeadNet model, optimized principal component analysis(OPCA) model, and Support Vector Machine (SVM)-based system, were reviewed as they enhance the analytical performance of the detection. Despite these advances, there are still challenges that include improvements in recovery values, biocompatibility, stability, and overall performance. This review highlights trends to guide the future design of robust, high-performance, QD-based luminescent sensors.

Gliomas are intracranial tumors characterized by limited diagnostics and treatment approaches. Blood-circulating miRNAs represent a regulatory class of molecules that change their expression under pathological conditions and can relatively easily be detected. The present study evaluates the diagnostic potential of blood-circulating miRNAs in glioma. All grades of gliomas are included in the analysis. The articles were retrieved from the PubMed, Web of Science and Scopus databases up to October 2025. The studies were considered to be eligible if they used glioma patients and healthy controls and compared their miRNA levels, indicating sensitivity and specificity values. Risk of bias was assessed using the QUADAS-2 tool. The collected data was pooled by the STATA 19.0 MP bivariate random effects model and indicated heterogeneity using the I² statistic value. To identify possible reasons for heterogeneity, we utilized subgroup analysis and meta-regression. Publication bias was assessed with Deeks’ funnel plot, and the test diagnostic potential was evaluated with Fagan’s nomogram. We analyzed 31 original reports covering 2299 glioma patients and 1719 healthy controls. A meta-analysis on 59 data points extracted from the analyzed papers was conducted. The combined pooled sensitivity was found to be equal to 0.83 (95%CI: 0.80–0.86), the specificity 0.88 (95%CI: 0.85–0.90), the positive likelihood ratio 6.7 (95%CI: 5.4–8.5), the negative likelihood ratio 0.19 (95%CI: 0.16–0.23), and the diagnostic odds ratio 35 (95%CI: 25–50). An SROC analysis revealed an AUC equal to 0.92 (95%CI: 0.90–0.94). The reported diagnostic parameters imply that blood-circulating miRNAs hold the potential to be developed into diagnostic biomarkers for glioma identification. However, the high heterogeneity in the analyzed studies suggests that the results should be considered as exploratory only.