Advancements in Nanotags for Enhanced Mass Spectrometric Biosensors: Toward Next Generation Bioassay and Cytometry
Bibliographic record
Abstract
Molecular spectroscopic bioassay and flow cytometry are mainstream methods for disease biomarker analysis, which gained great successes in the past. However, as rapid progresses of omics research, conventional spectroscopic methods often confront two thorny challenges. First, the molecular spectroscopic tags are often subject to spectral overlapping interference for complex multitarget analysis. Second, current bioanalytical strategies are constantly challenged by inadequate analytical sensitivity. Mass spectrometry, characterized by its high-throughput sampling method, inherent abundance of detection channels, diverse detection strategies, and high-resolution linear spectra, has been extensively utilized in biosensing and emerged as a potent tool for omics analysis. In this context, mass nanotags are considered beneficial tags to realize multiplex and sensitive analysis by mass spectrometric bioassay and mass cytometry. Nanoparticles are capable of integrating multiple mass labels in a single tag, which in turn results in high signal intensities in mass spectrometric analysis. Herein, this review summarizes strategies for the synthesis, design, and application of mass nanotags, providing a comprehensive overview of research on such mass spectrometric tags. In addition, this review describes the challenges and cutting-edge research results on the use of nanotags for mass spectrometry biosensing, providing insights into how mass nanotags could be more broadly applied to complex and challenging analytical tasks.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".