Improving the sensitivity of electrochemiluminescence (ECL) systems is highly desired for in vitro ECL diagnosis and bio-detections due to the often-low content of biomarkers in diseases. And dissolved O 2 (DO) as a co-reactant is considered superior to H 2 O 2 in the most commonly used luminol ECL systems due to better stability and low biotoxicity, but it still suffers from low ECL performance due to the low reactivity of DO. In this study, an efficient luminol-DO ECL system was developed through the complexing of Fe, Co dual single-atom catalysts (D-SACs) supported by N-doped graphene with the luminol-capped Ag nanoparticles (AgNPs). Benefiting from the electronic interaction between Fe and Co metal sites in the relevant D-SACs and plasmon enhancement of AgNPs, the performance of the corresponding ECL system could be significantly boosted up to ≈677-fold under optimal testing conditions, comparable to the classic luminol-O 2 system. Furthermore, the developed luminol-DO ECL system was successfully applied for the stable ultrasensitive detection of prostate-specific antigen (PSA) in a wide linear range of 1 fg/mL to 1 μg/mL, with a low limit of detection (0.98 fg/mL).

Exosomes are a class of extracellular vesicles secreted by cells, which can be used as promising noninvasive biomarkers for the early diagnosis and treatment of diseases, especially cancer. However, due to the heterogeneity of exosomes, it remains a grand challenge to distinguish accurately and reliably exosomes from clinical samples. Herein, we achieve accurate fuzzy discrimination of exosomes from human serum samples for accurate diagnosis of breast cancer and cervical cancer through machine learning-based label-free surface-enhanced Raman spectroscopy (SERS), by using "hot spot" rich 3D plasmonic AuNPs nanomembranes as substrates. Due to the existence of some weak distinguishable SERS fingerprint signals and the high sensitivity of the method, the machine learning-based SERS analysis can precisely identify three (normal and cancerous) cell lines, two of which are different types of cancer cells, without specific labeling of biomarkers. The prediction accuracy based on the machine learning algorithm was up to 91.1% for the discrimination of different cell lines (H8, HeLa, and MCF-7 cell)-derived exosomes. Our model trained with SERS spectra of cell-derived exosomes could reach 93.3% prediction accuracy for clinical samples. Furthermore, the action mechanism of the chemotherapeutic process of MCF-7 cells can be revealed by dynamic monitoring of SERS profiling of the exosomes secreted. The method would be useful for noninvasive and accurate diagnosis and postoperative assessment of cancer or other diseases in the future.