Automated screening using microfluidic chip‐based PCR and product detection to assess risk of BK virus‐associated nephropathy in renal transplant recipients
Bibliographic record
Abstract
The cost-effective detection of viral particles in bodily fluids could enable more effective responses to viral outbreaks, whether isolated clinical cases, or influenza epidemics. In renal transplant recipients, complications arising from high levels of BK virus can lead to graft dysfunction, graft loss, and/or reduced patient survival. We describe a microfluidic system for the sensitive analysis of BK virus (viral load) in unprocessed urine samples that are applied directly onto the chip, thus avoiding labor-intensive processing and sources of inter-assay variability. Integration of small volume genetic amplification (PCR) and electrophoretic analysis detects as few as 1-2 viral copies, distinguishes between high, medium and low levels of virus and reliably identifies viral loads requiring clinical intervention. As a first step to wider application in the clinic and in the field, the present work presents an entirely microchip-based system, validated against conventional clinical methods using clinical samples.
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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 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".