A Low-Cost and Portable Smart Instrumentation for Detecting Colorectal Cancer Cells
Bibliographic record
Abstract
Fluorescence imaging is a well-known method for monitoring fluorescence emitted from the subject of interest and provides important insights about cell dynamics and molecules in mammalian cells. Currently, many solutions exist for measuring fluorescence, but the application methods are complex and the costs are high. This paper describes the design and development of a low-cost, smart and portable fluorimeter for the detection of colorectal cancer cell expressing IRFP702. A flashlight is used as a light source, which emits light in the visible range and acts as an excitation source, while a photodiode is used as a detector. It also uses a longpass filter to only allow the wavelength of interest to pass from the cultured cell. It eliminates the need of both the dichroic mirror and excitation filter, which makes the developed device low cost, compact and portable as well as lightweight. The custom-built sample chamber is black in color to minimize interference and is printed with a 3D printer to accommodate the detector circuitry. An established colorectal cancer cell line (human colorectal carcinoma (HCT116)) was cultured in the laboratory environment. A near-infrared fluorescent protein IRFP702 was expressed in the colorectal cancer cells that were used to test the proof-of-concept. The fluorescent cancer cells were first tested with a commercial imaging system (Odyssey® CLx) and then with the developed prototype to validate the result in a preclinical setting. The developed fluorimeter is versatile as it can also be used to detect multiple types of cancer cells by simply replacing the filters based on the fluorophore.
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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".