Nano biosensor unlocks tumor derived immune signals for the early detection of ovarian cancer
Bibliographic record
Abstract
Ovarian cancer is a critical health issue for women nowadays. Its impact is significant because of its high mortality rate (324,603 worldwide), late-stage diagnosis and poor survival rate. Lack of screening tests, vague symptoms, misdiagnosis, and age factor makes it even more difficult to detect. Neutrophils, a subset of immune cells, undergo tumor-specific changes as ovarian cancer progresses inside ovarian tumour microenvironment. Therefore, monitoring the time-specific activity of neutrophils in circulation has the potential to aid in the diagnosis of ovarian cancer. Most ovarian tumor-specific antigens are unknown, making it difficult to identify neutrophils associated with ovarian tumor. We present ovarian tumor-associated circulating neutrophil cell profiling as a stand-alone cancer diagnostic method using a liquid biopsy. Using a SERS-functionalized nano probe, the metabolic profiles of neutrophils from ovarian tumor interaction are detected. We demonstrate that neutrophils associated with cancer stem cells have a distinct metabolic profile and are useful in the diagnosis of early ovarian cancer. Using 5 μL of peripheral blood and an artificial neural network, the characteristics of neutrophil profiles in patient blood could distinguish cancer cohort from non-cancer (healthy) with a 90 % sensitivity and 100 % specificity. Our results demonstrate the viability of using circulating neutrophils for non-invasive cancer diagnostics.
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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".