Microfluidic Platform for Intracellular Liquid-liquid Phase Separation Studies
Bibliographic record
Abstract
Membraneless organelles (MLOs) formed through liquid-liquid phase separation (LLPS) are becoming increasingly relevant to understand viral-host interactions, neurodegenerative disease, and cancer. The modulation of LLPS involves many parameters and components. To describethesemodulators, typical in vitro studiesrequirelaborious, manual samplepreparation of different concentrations and costly biological reagents. In this thesis, a minimal reagent, microfluidic platform is developed to systematically generate samples of different concentrations and trigger phase separation. The platform’s utility is tested by constructing phase diagrams describing the modulation of LLPS using an aqueous two-phase system (ATPS) and an MLO-based phase separating system. Then, on-chip biophysical characterization typical of in vitro studies is conducted. This platform will be utilized by scientists to study the growing number of MLOs and inform clinical treatments for pathology related to LLPS.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".