Generic serial and parallel on‐line direct‐injection using turbulent flow liquid chromatography/tandem mass spectrometry
Why this work is in the frame
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Bibliographic record
Abstract
The development of turbulent flow chromatography (TFC) has enabled considerable growth in the utility of on-line direct-injection technologies. TFC has now become established in a large number of varied analytical environments, particularly drug discovery/pharmacokinetics, metabolite profiling, combinatorial library purification, pre-clinical and clinical GLP applications. The utility of turbulent flow technology for in-house pre-clinical and clinical quantitative applications has necessitated extensive valve-cleaning procedures, and consequently lengthy cycle-times, to effectively remove the system carry-over. In-house requirements for assay validation require carry-over less than 20% of the lowest level of quantification (LLOQ), corresponding to 0.02% carry-over for a linear calibration range incorporating 3 orders. A generic turbulent flow chromatography protocol has been developed for drug discovery that incorporates polymeric turbulent flow extraction (cyclone) with C18-based reverse-phase chromatography. Further, multiple wash steps are incorporated within the methodology to meet in-house requirements for carry-over. Selection of novel switching-valve materials based on polyarylethyl ketone (PAEK) and Hastelloy/Valcon E autosampler injection hardware has enabled us to significantly impact the cycle-time required to reduce carry-over. Consequently, optimal usage of switching valves has enabled parallel operation for a generic on-line direct-injection methodology to successfully reduce the total cycle-time. Overall reductions from 4 min per sample to 90 s per sample are shown with comparable data quality using a proprietary target molecule from 0.1-100 ng/mL. This paper describes the hardware configuration and methodologies utilized to perform generic serial and parallel on-line direct-injection using a Turboflow HTLC 2300 system.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| 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) | 0.003 | 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