Development of a low-cost and portable smart fluorometer for detecting breast cancer cells
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Instruments that allow the detection of fluorescence signal are invaluable tools for biomedical and clinical researchers. The technique is widely used in cell biology to microscopically detect target proteins of interest in mammalian cells. Importantly, fluorescence microscopy finds major applications in cancer biology where cancer cells are chemically labelled for detection. However, conventional fluorescence detection instruments such as fluorescence imaging microscopes are expensive, not portable and entail potentially high maintenance costs. Here we describe the design, development and applicability of a low-cost and portable fluorometer for the detection of fluorescence signal emitted from a model breast cancer cell line, engineered to stably express the green fluorescent protein (GFP). This device utilizes a flashlight which works in the visible range as an excitation source and a photodiode as the detector. It also utilizes an emission filter to mainly allow the fluorescence signal to reach the detector while eliminating the use of an excitation filter and dichroic mirror, hence, making the device compact, low-cost, portable and lightweight. The custom-built sample chamber is fabricated with a 3D printer to house the detector circuitry. We demonstrate that the developed fluorometer is able to distinguish between the cancer cell expressing GFP and the control cell. The fluorometer we developed exhibits immense potential for future applicability in the selective detection of fluorescently-labelled breast cancer cells.
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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 it