Lab-on-chips for manipulation of small-scale organisms to facilitate imaging of neurons and organs
Bibliographic record
Abstract
Caenorhabditis elegans (C. elegans) and Drosophila melanogaster (D. melanogaster) are widely-used model organisms for neurological and cardiac studies due to their simple neuronal (302 neurons in C. elegans) and cardiac (simple tubular organ in D. melanogaster) systems. However, their small sizes and continuous mobility impede their precise and timely manipulation, hence, limiting the assays that can be done using conventional manual methods. This has resulted in a need for technologies that allow multidirectional manipulation of model organisms to enable studies on target neurons and organs throughout the body. By integration of rotatable glass capillaries with pneumatic suction into microfluidic devices, we propose novel Lab-on-Chips for multi-directional manipulation and imaging of small organisms. These hybrid Lab-on-Chips can facilitate the processes of animal handling and stimuli control, using modules for single-organism selection, orientation, imaging and chemical stimulation. We show the applications of these hybrid microdevices in manipulating C. elegans for neuronal imaging (neuron-level assay) or D. melanogaster for heart screening (organ level assay). These devices can enhance the throughput of biological assays on whole-organisms and find their applications in drug discovery and toxicology.
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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 | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".