Characterization of microfluidic clamps for immobilizing and imaging of <i>Drosophila melanogaster</i> larva's central nervous system
Why this work is in the frame
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Bibliographic record
Abstract
Drosophila melanogaster is a well-established model organism to understand biological processes and study human diseases at the molecular-genetic level. The central nervous system (CNS) of Drosophila larvae is widely used as a model to study neuron development and network formation. This has been achieved by using various genetic manipulation tools such as microinjection to knock down certain genes or over-express proteins for visualizing the cellular activities. However, visualization of an intact-live neuronal response in larva's Central Nervous System (CNS) is challenging due to robust digging/burrowing behaviour that impedes neuroimaging. To address this problem, dissection is used to isolate and immobilize the CNS from the rest of the body. In order to obtain a true physiological response from the Drosophila CNS, it is important to avoid dissection, while the larva should be kept immobilized. In this paper, a series of microfluidic clamps were investigated for intact immobilization of the larva. As a result, an optimized structure for rapid mechanical immobilization of Drosophila larvae for CNS imaging was determined. The clamping and immobilization processes were characterized by imaging and movement measurement of the CNS through the expression of genetically encoded Calcium sensor GCaMP5 in all sensory and cholinergic interneurons. The optimal structure that included two 3D constrictions inside a narrowed channel considerably reduced the internal CNS capsule movements. It restricts the CNS movement to 10% of the motion from a glued larva and allows motion of only 10 ± 30 μm over 350 s immobilization which was sufficient for CNS imaging. These larva-on-a-chip platforms can be useful for studying CNS responses to sensory cues such as sound, light, chemosensory, tactile, and electric/magnetic fields.
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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 | 0.001 |
| 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