Novel blood test to predict neoplastic activity in healthy patients and metastatic recurrence after primary tumor resection
Bibliographic record
Abstract
We reported that single oncosuppressor-mutated (SOM) cells turn malignant when exposed to cancer patients' sera. We tested the possibility to incorporate this discovery into a biological platform able to detect cancer in healthy individuals and to predict metastases after tumor resection. Blood was drawn prior to tumor resection and within a year after surgery. Blood samples from healthy individuals or metastatic patients were used as negative and positive controls, respectively. Patients at risk for cancer were included in the screening cohort. Once treated, cells were injected into nonobese diabetic/severe combined immunodeficiency mice to monitor tumor growth. All samples of sera coming from metastatic patients transformed SOM cells into malignant cells. Four samples from screened patients transformed SOM cells. Further clinical tests done on these patients showed the presence of early cancerous lesions despite normal tumor markers. Based on the xenotransplants size, we were able to predict metastasis in three patients before diagnostic tests confirmed the presence of the metastatic lesions. These data show that this serum-based platform has potentials to be used for cancer screening and for identification of patients at risks to develop metastases regardless of the Tumor Node Metastasis (TNM) stage or tumor markers level.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 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".