Carbon quantum dots (CQDs) exhibit rich photophysical behaviors, including excitation-dependent emission, surface-state variability, and multimodal fluorescence pathways, which complicate accurate, signal interpretation in chemical sensing. Recent advances in machine learning (ML) offer powerful solutions for modeling these complexities and enhancing fluorescence-based detection performance. This review provides a comprehensive analysis of ML-driven methodologies for denoising, spectral decomposition, feature extraction, and high-accuracy classification in CQD fluorescence systems. Mathematical foundations of key ML paradigms are outlined to establish a rigorous framework for signal reconstruction and generalization. Evaluations of recent applications demonstrate how ML enables ultra-low-level analyte detection, interpretable photophysical modeling, and real-time intelligent sensing across chemical and biological environments. Emerging trends—including physics-informed learning, generative data augmentation, autonomous closed-loop sensing, and distributed multimodal architectures—are examined as frontiers poised to redefine CQD fluorescence analytics. Collectively, the integration of ML with CQD photophysics represents a transformative pathway toward robust, adaptive, and next-generation chemical sensing platforms.
B. T. Sayed, Maharshi B Shukla, Sumit Sharma et al.· RSC Advances· 0 citations
The topology of RNA therapeutics is emerging as a critical design dimension in precision oncology. Unlike linear mRNA, circular RNA (circRNA) lacks free ends, conferring exceptional resistance to exonuclease degradation and enabling sustained protein expression for days to weeks. Beyond their use as engineered therapeutics, endogenous circRNAs exhibit cancer-associated expression patterns and persistence in biofluids, supporting complementary roles in tumor biology and as candidate biomarkers for diagnosis and longitudinal disease monitoring. This review argues that circular topology should be viewed as an active pharmacologic variable, not merely a stability enhancement. We dissect recent advances in cap-independent translation initiation, including IRES elements and m6A-driven mechanisms, rolling-circle translation for multi-epitope vaccine design, and programmable stability circuits that integrate tumor-microenvironment cues such as miRNA signatures. Delivery innovations are equally transformative: antibody-guided lipid nanoparticles and engineered extracellular vesicles enable increasingly selective RNA delivery, while local depot formulations and organ-selective systemic routes expand therapeutic reach. Safety considerations are re-evaluated as double-edged tools-innate immunogenicity can serve as a self-adjuvant for cancer vaccines, whereas back-splice-junction neoantigens offer both vaccine opportunities and tolerance risks. Recent advances in scarless circularization, topology-sensitive purification, dsRNA depletion, and lyophilized formulations have begun to address key manufacturing bottlenecks, although clinical-scale recovery and process scalability remain insufficiently characterized. Key applications include circRNA cancer vaccines, transient CAR-T/NK cell engineering, tumor-suppressor replacement, and circRNA-encoded bispecific T-cell engagers. The field now requires real-time pharmacokinetic tracking, reproducible and scalable manufacturing, validated liquid-biopsy assays, and indication-specific regulatory pathways to translate circRNA from bench to bedside.
Amr A. El-Sehrawy, H. Al-Ameer, J. Rizaev et al.· Biotechnology and Bioenginee...· 0 citations