Ultra-Micro-Scale-Fractionation (UMSF) as a Powerful Tool for Bioactive Molecules Discovery
Why this work is in the frame
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Bibliographic record
Abstract
Herein is detailed the development and validation of an ultra-micro-scale-fractionation (UMSF) technique for the discovery of plant-based, bioactive molecules, coupling the advantages of ultra-performance liquid chromatography mass spectrometry (UPLC-MS) separations with microtiter plate-based bioassay screens. This novel one-step approach simultaneously uses UPLC to collect chemical profile information, while performing high-resolution fractionation, greatly improving workflow compared to methods relying on high-performance liquid chromatography (HPLC), solid phase extraction or flash systems for chromatographic separations. Using the UMSF technique, researchers are able to utilize smaller quantities of starting materials, reduce solvent consumption during fractionation, reduce laborious solvent dry down times, replace costly single-use solid-phase-extraction cartridges with reusable analytical-sale UPLC columns, reduce fractionation times to less than 10 min, while simultaneously generating chemical profile data of active fractions and enjoying superior chromatographic resolution. Using this technique, individual bioactive components can be readily purified, identified, and bioassayed in one step from crude extracts, thereby eliminating ambiguous synergistic effects often reported in plant-based natural products research. A successful case-study is presented illustrating the versatility of this technique in identifying lupulone as the principal cytotoxic component from hops (Humulus lupulus L.), using a brine shrimp (Artemia franciscana) model. These results confirm and expand upon previous cell-based bioassay studies using a more complex, multicellular organism, and add to our understanding of structure-function activity relationships for secondary metabolites in hops and the Cannabaceae plant family.
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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) | 0.000 | 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