A Fully Integrated Microfluidic Device with Immobilized Dyes for Simultaneous Detection of Cell-Free DNA and Histones from Plasma Using Dehydrated Agarose Gates
Bibliographic record
Abstract
Sepsis, a life-threatening condition resulting from a failing host response to infection, causes millions of deaths annually, necessitating rapid and simple prognostic assessments. A variety of genomic and proteomic biomarkers have been developed for sepsis. For example, it has been shown that the level of plasma cell-free DNA (cfDNA) and circulating histones increases considerably during sepsis, and they are linked with sepsis severity and mortality. Developing a diagnostic tool that is capable of assessing such diverse biomarkers is challenging as the detection methodology is quite different for each. Here, a fully integrated microfluidic device capable of detecting a genomic biomarker (cfDNA) and a proteomic biomarker (total circulating histones) using a common detection platform has been demonstrated. The microfluidic device utilizes dehydrated agarose gates loaded with pH-specific agarose to electrophoretically trap cfDNA and histones at their respective isoelectric points. It also incorporates fluorescent dyes within the device, eliminating the need for off-chip sample preparation and allowing the direct testing of plasma samples without the need for labeling DNA and histones with fluorescent dyes beforehand. Xurography, which is a low-cost and rapid method for fabrication of microfluidics, is used in all the fabrication steps. Experimental results demonstrate the effective accumulation and separation of cfDNA and histones in the agarose gates in a total processing time of 20 min, employing 10 and 30 Volts for cfDNA and histone accumulation and detection, respectively. The device can potentially be used to distinguish between the survivors and non-survivors of sepsis. The integration of the detection of both biomarkers into a single device and dye immobilization enhances its clinical utility for rapid point-of-care assessment of sepsis prognosis.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".