High sensitivity fluorescence detection using smart phone cameras
Bibliographic record
Abstract
A low cost and highly sensitive fluorescence detection system using smart phone cameras was developed for biological and biochemical detection experiments. The system was designed for standard 96-well plates, which are typically used in life-sciences laboratories. Traditional fluorescence detection is done using specialized optics, filters and ultra-sensitive detectors such as photo-multipliers, which makes the system expensive. In our method, the 96-well plate is imaged using a smart phone camera inside a light tight enclosure. No specialized filters are used for the imaging. The image is then analysed using an algorithm, developed by us, that separates out the Red-Green-Blue (RGB) component. The Green component is then further processed to extract the fluorescence intensity. The developed hardware system and the algorithm were tested using two types of samples, fluorescein and Green Fluorescent Protein (GFP) incorporated yeast cells, prepared in varied concentrations. The performance of the developed system was compared with measurements taken using a PerkinElmer VICTOR™ X5, 2030 Multilabel Reader. Our system is capable of reliably detecting 1 nM concentrations of fluorescein. We believe the system can be improved further to detect even lower concentrations.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".