healthy baseline

Informative molecules from tumour transrenal

0 (0–0) informative molecules of tumour transrenal reach the detector, against 3,000 needed.

• Detection at the operating tumour fraction of 1%, tumour-informed aggregate readout: 0% (0%–0%).
• Limit of detection at 95%: ∞ (∞–∞).
• Feasibility ratio, achieved over required molecules: 0 (0–0).
• The median draw carries fewer informative molecules than the assay class needs (3,000), so this is an architectural failure: no optimisation of depth fixes it.

From shedding to the detector

This arm carries no tumour transrenal source, so there is nothing to follow from shedding to the detector: every stop would read zero.

Detection probability against tumour fraction

The curve says where the assay starts to work; the band says how sure the priors are of it.

Where the molecules of tumour transrenal go

The largest single loss at the medians is , which removes 0% of what reaches it.

• Extracted DNA yield, all sources: 6.91 (2.33–22.3) ng/mL.
• Median fragment length of the extracted population: 65 (50–80) bp.

What the nuclease and the footprint leave

The population after M3 sits far below the footprint for most of its molecules; the informative population is the tail the assay can still read.

Collected volume against assay footprint

Each cell replays the same draws through one design; the comparison is between designs, not between samplings.

Detection at the operating point: median, 5–95% range, and the median informative molecules. Green clears the target at the 5% quantile, amber clears it only at the median, red has too few molecules for the assay class. The current design is outlined. Every other control applies to every cell.

How this page computes

• The chain M0 to M6 runs in this page in JavaScript, checked against the Python model on fixed draws to a relative 1e-8 on every stage and readout.
• The 512 shipped draws are Python's, seeds 0 to 511, with their post-nuclease states solved at build time. Volume, kit, footprint, chemistry, platform, tumour volume, injury and urine output are downstream or linear, so they recompute in the page.
• Hold, temperature, preservative, pH, filtration and proteinuria change the nuclease input, so the page re-solves the fragmentation master equation for every draw in a pool of workers, by the action of the matrix exponential on each draw's own profiles.
• A prior edit reweights the draws by the ratio of the new density to the old, which is exact but thins the sample; the effective sample size says by how much. Fresh draws sample the edited priors in the page and re-solve.