Real‐time 2<scp>D</scp> visualization of metabolic activities in zebrafish embryos using a microfluidic technology
Bibliographic record
Abstract
Non-invasive and real-time visualization of metabolic activities in living small model organisms such as embryos and larvae of zebrafish has not yet been attempted largely due to profound analytical limitations of existing technologies. Historically, our capacity to examine oxygen gradients surrounding eggs and embryos has been severely limited, so much so that to date, most of the articles characterizing in situ oxygen gradients have described predominantly mathematical simulations. These drawbacks can, however, be experimentally addressed by an emerging field of microfluidic Lab-on-a-Chip (LOC) technologies combined with sophisticated optoelectronic sensors. In this work, we outline a proof-of-concept approach utilizing microfluidic living embryo array system to enable in situ Fluorescence Ratiometric Imaging (FRIM) on developing zebrafish embryos. The FRIM is an innovative method for kinetic quantification of the temporal patterns of aqueous oxygen gradients at a very fine scale based on signals coming from an optical sensor referred to as a sensor foil. We envisage that future integration of microfluidic chip-based technologies with FRIM represents a noteworthy direction to miniaturize and revolutionize research on metabolism and physiology in vivo.
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.
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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".