Design and Characterization of a Miniaturized Fluorescence Analysis System for Measurement of Cell-Free DNA
Bibliographic record
Abstract
Sepsis is a dysregulated systemic response to infection and is one of the leading causes of in-hospital mortality in Canada. Accurate distinction between survivors and non-survivors of sepsis has recently been demonstrated through quantification of cell-free DNA (cfDNA) concentration in blood. In an analysis of 80 septic patients, non-survivors of sepsis had significantly higher cfDNA concentration levels than that of survivors or healthy patients. Real time separation of cfDNA from contaminants in blood has also been done using a cross channel microfluidic device. Current methods for DNA quantification utilize time consuming and complicated laboratory equipment and therefore are not suitable for bedside real-time testing. Thus a handheld cfDNA fluorescence device coined the Sepsis Check was designed that can perform DNA characterization in a reservoir device and DNA detection in a microfluidic cross channel device. The goal is to use this system along with the cross channel devices to set apart survivors or healthy donors from non-survivors in patients with sepsis. The design consists of a 470𝑛𝑚 light emitting diode (LED) with 170𝑚𝑊 of optical power (LED470L – ThorLabs), an aspherical uncoated lens with a focal length of 15𝑚𝑚 (LA1540-ML – ThorLabs), a 488𝑛𝑚 bandpass filter with a 3𝑛𝑚 full width at half maximum (FWHM) (FL05488-3 – ThorLabs), an aspherical uncoated lens with a focal length of 25𝑚𝑚 (LA1560-ML – ThorLabs), an aspherical uncoated lens with a focal length of 35𝑚𝑚 (LA1027-ML – ThorLabs), a 525𝑛𝑚 longpass filter with an optical density >4.0 (F84744 – Edmund Optics), and a Raspberry Pi Camera V2 (Raspberry Pi Foundation). The Sepsis Check is made to excite the dsDNA specific PicoGreen fluorophore which has a peak absorbance at 502𝑛𝑚 and a peak emission at 523𝑛𝑚. In summary, the Sepsis Check in this thesis is capable of calibrating dsDNA concentration from 1𝜇𝑔/𝑚𝐿 to 10𝜇𝑔/𝑚𝐿 and detect DNA accumulation of 5𝜇𝑔/𝑚𝐿 and 10𝜇𝑔/𝑚𝐿 in the cross channel device. This tool can be a valuable addition to the ICU to rapidly assess the severity of sepsis for informed decision making.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 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".