Illuminating Cellular Physiology: Recent Developments
Bibliographic record
Abstract
Bioluminescent methods are gaining more and more attention among scientists due to their sensitivity, selectivity and simplicity; coupled with the fact that the bioluminescence can be monitored both in vitro and in vivo. Since the discovery of bioluminescence in the 19th century, enzymes involved in the bioluminescent process have been isolated and cloned. The bioluminescent reactions in several different organisms have also been fully characterized and used as reporters in a wide variety of biochemical assays. From the 1990s it became clear that bioluminescence can be detected and quantified directly from inside a living cell. This gave rise to numerous possibilities for the in vivo monitoring of intracellular processes non-invasively using bioluminescent molecules as reporters. This review describes recent developments in the area of bioluminescent imaging for cell biology. Newly developed imaging methods allow transcriptional/translational regulation, signal transduction, protein-protein interaction, oncogenic transformation, cell and protein trafficking, and target drug action to be monitored in vivo in real-time with high temporal and spatial resolution; thus providing researchers with priceless information on cellular functions. Advantages and limitations of these novel bioluminescent methods are discussed and possible future developments identified.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.005 |
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".