Proteomic profiling to identify potential biomarkers of alpha-particle radiation exposure in human lung epithelial cells
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
Of the radiation types, alpha-(α) particles are of particular interest as they are an environmental concern, predominately due to inhalation of radon and its daughter progeny. Furthermore, α-particle emitters like Americium-241, Plutonium-238 and Polonium-210 have been identified as probable isotopes to be used in radiological dispersal devices. Thus, the identification of potential biomarkers to α-particle radiation exposure would be useful for the development of field deployable bioassays which could be used for human risk assessment and public health protection. Human lung cells were exposed to α-particle radiation and assessed for modulations in protein expression using two-dimensional gel electrophoresis (2D-GE). Concurrently, cell culture supernatants were analyzed for cytokine secretion using a multiplex-27 bead array assay. Cell culture supernatants assessed for cytokine secretion expressed 8 statistically significant cytokines following α-particle exposure, among which VEGF was confirmed to be dose-responsive and not modulated in X-irradiated cells. Analysis of whole cell lysates using 2-D gel electrophoresis showed 15 upregulated and 1 downregulated protein spot, of which 4 were identified by mass spectrometry. These data suggest that α-particle exposure results in the alterations in expression-levels of specific proteins which may be potential biomarkers used further for the development of fast and reliable bioassays.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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