Cellular transfection using rapid decrease in hydrostatic pressure
Bibliographic record
Abstract
Of all methods exercised in modern molecular biology, modification of cellular properties through the introduction or removal of nucleic acids is one of the most fundamental. As such, several methods have arisen to promote this process; these include the condensation of nucleic acids with calcium, polyethylenimine or modified lipids, electroporation, viral production, biolistics, and microinjection. An ideal transfection method would be (1) low cost, (2) exhibit high levels of biological safety, (3) offer improved efficacy over existing methods, (4) lack requirements for ongoing consumables, (5) work efficiently at any scale, (6) work efficiently on cells that are difficult to transfect by other methods, and (7) be capable of utilizing the widest array of existing genetic resources to facilitate its utility in research, biotechnical and clinical settings. To address such issues, we describe here Pressure-jump-poration (PJP), a method using rapid depressurization to transfect even difficult to modify primary cell types such as embryonic stem cells. The results demonstrate that PJP can be used to introduce an array of genetic modifiers in a safe, sterile manner. Finally, PJP-induced transfection in primary versus transformed cells reveals a surprising dichotomy between these classes which may provide further insight into the process of cellular transformation.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".