Mining the malignant ascites proteome for pancreatic cancer biomarkers
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
Pancreatic cancer (PC) is one of the most lethal malignancies and disease-specific biomarkers are desperately needed for better diagnosis, prognosis, monitoring treatment efficacy and for accelerating the development of novel targeted therapeutics. Being an advanced stage manifestation and a proximal fluid in contact with cancer tissues, the ascitic fluid presents itself as a promising rich source of biomarkers. Herein, we present a comprehensive proteomic analysis of pancreatic ascitic fluid. To fractionate the complex ascites proteome, we adopted a multi-dimensional chromatographic approach that included size-exclusion, ion-exchange and lectin-affinity chromatographic techniques. Our detailed proteomic analysis with high-resolution Orbitrap(®) mass spectrometer resulted in the identification of 816 proteins. We followed rigorous filtering criteria that consisted of PC-specific information obtained from three publicly available databases (Oncomine, Protein Atlas and Unigene) to segregate 20 putative biomarker candidates for future validation. Since these proteins are of membranous and extra-cellular origin, most are glycosylated, and many of them are over-expressed in cancer tissues, the probability of these proteins entering the peripheral blood circulation is high. Their validation as serological PC biomarkers in the future is highly warranted.
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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) | 0.000 | 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 it