Droplet digital microfluidic system for screening filamentous fungi based on enzymatic activity
Bibliographic record
Abstract
Fungal cell-wall-degrading enzymes have great utility in the agricultural and food industries. These cell-wall-degrading enzymes are known to have functions that can help defend against pathogenic organisms. The existing methods used to discover these enzymes are not well adapted to fungi culture and morphology, which prevents the proper evaluation of these enzymes. We report the first droplet-based microfluidic method capable of long-term incubation and low-voltage conditions to sort filamentous fungi inside nanoliter-sized droplets. The new method was characterized and validated in solid-phase media based on colloidal chitin such that the incubation of single spores in droplets was possible over multiple days (2-4 days) and could be sorted without droplet breakage. With long-term culture, we examined the activity of cell-wall-degrading enzymes produced by fungi during solid-state droplet fermentation using three highly sensitive fluorescein-based substrates. We also used the low-voltage droplet sorter to select clones with highly active cell-wall-degrading enzymes, such as chitinases, β-glucanases, and β-N-acetylgalactosaminidases, from a filamentous fungi droplet library that had been incubated for >4 days. The new system is portable, affordable for any laboratory, and user-friendly compared to classical droplet-based microfluidic systems. We propose that this system will be useful for the growing number of scientists interested in fungal microbiology who are seeking high-throughput methods to incubate and sort a large library of fungal cells.
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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) | 0.000 | 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".