Design of an adaptable sensing platform for metabolomic sensing
Bibliographic record
Abstract
Colorectal cancer is among the top contributors to cancer death, but that is only due to the lack of efficient screening methods. Specific metabolites in urine have been identified as a factor in cancer detection, but a laboratory NMR device is required to identify these metabolites; however, the cost of these devices starts at 25 thousand dollars and requires trained professionals to operate. To increase the accessibility of colorectal cancer screening, a solution was proposed that the absorbance of metabolite-specific assays is to be measured using a portable and inexpensive metabolic biosensor. The first step was to create low-cost quantitative color-based metabolite assays that use redox-sensitive dye or colorimetric reactions that change color intensity when reacting with specific concentrations of these metabolites in urine. To complete the experiments, a device that utilizes the TCS34725 color sensor was created. The sensor is placed 1cm above a microfluidic cartridge with a channel that can hold 100microliter of liquid, and a diffused LED light source is placed 1cm under the cartridge to provide light to the sample liquid in the cartridge channel. All of this is then placed inside of a black box environment to complete the setup. A laboratory microplate reader is used for reference on the quantitative color-based Creatinine assay, and a linear trend was observed with an R2 of 0.999. The sensor device observed a polynomial curve due to light saturation at higher concentrations, but an R2 of 0.992 was obtained. In conclusion, the device can distinguish between the different concentrations of metabolites within samples, thus creating the possibility of an affordable and easy-to-use biosensor platform that can benefit the population within rural and inaccessible communities. In the Future, this portable metabolic biosensor system has the potential to be modified such that screening for other diseases can be done.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".