Voyage inside the cell: Microsystems and nanoengineering for intracellular measurement and manipulation
Bibliographic record
Abstract
Abstract Properties of organelles and intracellular structures play important roles in regulating cellular functions, such as gene expression, cell motility and metabolism. The ability to directly interrogate intracellular structures inside a single cell for measurement and manipulation has significant implications in the understanding of subcellular and suborganelle activities, diagnosing diseases, and potentially developing new therapeutic approaches. In the past few decades, a number of technologies have been developed to study single-cell properties. However, methods of measuring intracellular properties and manipulating subcellular structures have been largely underexplored. Due to the even smaller size of intracellular targets and lower signal-to-noise ratio than that in whole-cell studies, the development of tools for intracellular measurement and manipulation is challenging. This paper reviews emerging microsystems and nanoengineered technologies for sensing and quantitative measurement of intracellular properties and for manipulating structures inside a single cell. Recent progress and limitations of these new technologies as well as new discoveries and prospects are discussed.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".