Fluorescence hyperspectral imaging for live monitoring of multiple spheroids in microfluidic chips
Bibliographic record
Abstract
Tumor spheroids represent a realistic 3D in vitro cancer model because they provide a missing link between monolayer cell culture and live tissues. While microfluidic chips can easily form and assay thousands of spheroids simultaneously, few commercial instruments are available to analyze this massive amount of data. Available techniques to measure spheroid response to external stimuli, such as confocal imaging and flow cytometry, are either not appropriate for 3D cultures, or destructive. We designed a wide-field hyperspectral imaging system to analyze multiple spheroids trapped in a microfluidic chip in a single acquisition. The system and its fluorescence quantification algorithm were assessed using liquid phantoms mimicking spheroid optical properties. Spectral unmixing was tested on three overlapping spectral entities. Hyperspectral images of co-culture spheroids expressing two fluorophores were compared with confocal microscopy and spheroid growth was measured over time. The system can spectrally analyze multiple fluorescent markers simultaneously and allows multiple time-points assays, providing a fast and versatile solution for analyzing lab on a chip devices.
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 | 0.001 | 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".