Novel Passive Clearing Methods for the Rapid Production of Optical Transparency in Whole CNS Tissue
Bibliographic record
Abstract
Since the development of CLARITY, a bioelectrochemical clearing technique that allows for three-dimensional phenotype mapping within transparent tissues, a multitude of novel clearing methodologies including CUBIC (clear, unobstructed brain imaging cocktails and computational analysis), SWITCH (system-wide control of interaction time and kinetics of chemicals), MAP (magnified analysis of the proteome), and PACT (passive clarity technique), have been established to further expand the existing toolkit for the microscopic analysis of biological tissues. The present study aims to improve upon and optimize the original PACT procedure for an array of intact rodent tissues, including the whole central nervous system (CNS), kidneys, spleen, and whole mouse embryos. Termed psPACT (process-separate PACT) and mPACT (modified PACT), these novel techniques provide highly efficacious means of mapping cell circuitry and visualizing subcellular structures in intact normal and pathological tissues. In the following protocol, we provide a detailed, step-by-step outline on how to achieve maximal tissue clearance with minimal invasion of their structural integrity via psPACT and mPACT.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| 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".