Quantum cytosensor for early detection of cancer
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
Abstract Current use of graphene quantum dot for cancer detection is highly impeded by the low Raman cross‐section and used as a carrier for plasmonic materials. Hence, there is a need for a sensor an efficient Raman cross‐section, with maintaining the cellular homeostasis to attain accurate detection. Here, we report a SERS‐activated graphene oxide (GO) quantum Cytosensor for whole cell cancer detection capable for the early detection of cancer down to a single cellular level. On approaching quantum scale, we achieved SERS activation of GO quantum dots by introducing the functionality of quantum confinement. By altering the density of functional groups on the surface, we facilitated accelerated self‐cellular uptake resulting in increased SERS sensitivity. Here, we demonstrate cancer detection by two approaches: biomarker detection in external environment and intracellular detection using three cell lines. The quantum Cytosensor advances cancer detection to the molecular level by sensing the complex biomolecular processes transpiring intracellularly to detect and differentiate cancerous attributes of a cell. We observed a 3000‐, 2500‐ and 3500‐fold increase in enhancement of DNA, RNA and protein, respectively. Discernment of SERS spectral signatures was obtained by employing machine learning techniques which primarily identified the differences between cancer cells and normal cells. The classification and clustering techniques provided a high diagnostic sensitivity and specificity of 84.83% with unparalleled accuracy of 92.3%. Detection of cancer down to a single cellular level using quantum Cytosensor via SERS integrated with machine learning provides a new stepping stone towards adoption of SERS‐based early detection of cancer.
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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) | 0.001 | 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