Bibliographic record
Abstract
The field of drug discovery has been the primary driver behind the development of quantitative high throughput mass spectrometry (HTMS) over the past several decades. Only in the past few years has the throughput of mass spectrometry-based assays risen to a point to match the daily throughputs of optical plate reader technology by achieving sampling rates of one sample per second (1 Hz). It is the purpose of this manuscript to attempt to explain how this came about, not through an exhaustive review of the literature but rather by defining what the trends in the field of HTMS were over the past 40 years using specific examples to illustrate its evolution. This perspective attests to the importance of addressing as many of the bottlenecks in the overall HTMS workflow as possible so a wide variety of technologies are shown to be contributors to the solution. This includes the development of MALDI (matrix-assisted laser desorption ionization) and, in particular, atmospheric pressure ionization (API) that exhibits broad compound coverage, can be adapted to high inlet fluid flow rates, and is reliable and robust under high sample loads. High speed and parallelized chromatographic systems have played a major role and ion mobility has entered the HTMS scene as a faster alternative to some of the functions high performance liquid chromatography (HPLC) provides. Improvements to the sensitivity of mass spectrometers over time have been key to enabling HTMS systems by reducing and sometimes eliminating sample preparation requirements as a consequence of the reduction in sample volumes required to achieve biologically relevant quantitation limits. And finally, the development of a variety of low volume, contact-free fluid dispensers and means to transport and ionize these samples in the ion source indicate that routine throughputs an order of magnitude faster than one sample per second are within reach in the not-too-distant future. The historical relationship and interdependence of these various technologies as components of HTMS systems are described.
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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 | 0.028 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".