Proteome-on-a-chip: Mirage, or on the horizon?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Proteomics has emerged as the next great scientific challenge in the post-genome era. But even the most basic form of proteomics, proteome profiling, i.e., identifying all of the proteins expressed in a given sample, has proven to be a demanding task. The proteome presents unique analytical challenges, including significant molecular diversity, an extremely wide concentration range, and a tendency to adsorb to solid surfaces. Microfluidics has been touted as being a useful tool for developing new methods to solve complex analytical challenges, and, as such, seems a natural fit for application to proteome profiling. In this review, we summarize the recent progress in the field of microfluidics in four key areas related to this application: chemical processing, sample preconcentration and cleanup, chemical separations, and interfaces with mass spectrometry. We identify the bright spots and challenges for the marriage of microfluidics and proteomics, and speculate on the outlook for progress.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.002 |
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 it