Nanoparticle sample preparation and mass spectrometry for rapid diagnosis of microbial infections
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
In vitro diagnostics encompasses a wide range of medical devices and assays, which aim to provide reliable and accurate diagnosis of disease. This can be achieved by detecting a target, for example, a protein biomarker or a pathogen bacterium, and/or host factors such as cytokines induced in an inflammatory response. Detection involves an assay to capture target molecules and distinguish them from other substances in an ex vivo sample matrix. Selective capturing can be achieved using affinity probes, such as antibodies or small molecules, often coupled to a label, for example, an enzyme or a particle, to facilitate detection in complex matrixes (Figure 1). Today, the combination of nanoparticle approaches for sample preparation/concentration, with high information content, rapid analysis by mass spectrometry, is changing the way we detect and identify pathogenic bacteria in the diagnosis of microbial infection.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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