Proteomic profiling to identify potential biomarkers of alpha-particle radiation exposure in human lung epithelial cells
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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 | 0.001 |
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".