Ultrasensitive assays for proteins
Bibliographic record
Abstract
Proteins are essential components of organisms and are involved in a wide range of biological functions. There are increasing demands for ultra-sensitive protein detection, because many important protein biomarkers are present at ultra-low levels, especially during the early stages of disease. Measuring proteins at low levels is also crucial for investigations of the protein synthesis and functions in biological systems. In this review, we summarize the recent developments of novel technology enabling ultrasensitive protein detection. We focus on two groups of techniques that involve either polymerase amplification of affinity DNA probes or signal amplification by the use of nano-/micro-materials. The polymerase-based amplification of affinity DNA probes indirectly improves the sensitivity of protein detection by increasing the number of detection molecules. The use of nano-/micro-materials conjugated to affinity probes enhances the measurement signals by using the unique electrical, optical, and catalytic properties of these novel materials. This review describes the basic principles, performances, applications, merits, and limitations of these techniques.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.008 |
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".