Smart and low-cost fluorometer for identifying breast cancer malignancy based on lipid droplets accumulation
Bibliographic record
Abstract
The most common cause of breast cancer-related death is tumor recurrence. To develop more effective treatments, the identification of cancer cell specific malignancy indicators is therefore critical. Lipid droplets are known as an emerging hallmark in aggressive breast tumors. A common technique that can be used for observing molecules in cancer microenvironment is fluorescence microscopy. We describe the design, development and applicability of a smart fluorometer to detect lipid droplet accumulation based on the emitted fluorescence signals from highly malignant (MDA-MB-231) and mildly malignant (MCF7) breast cancer cell lines, that are stained with BODIPY dye. This device uses a visible-range light source as an excitation source and a spectral sensor as the detector. A commercial imaging system was used to examine the fluorescent cancer cell lines before being validated in a preclinical setting with the developed prototype. The outcomes indicate that this low-cost fluorometer can effectively detect the alterations levels of lipid droplets and hence distinguish between "moderately malignant" and "highly malignant" cancer cells. In comparison to prior research that used fluorescence spectroscopy techniques to detect cancer biomarkers, this study revealed enhanced capability in classifying mildly and highly malignant cancer cell lines.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".