Droplet digital microfluidic system for screening filamentous fungi based on enzymatic activity
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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