Sci‐AM Fri ‐ 02: Probing radiation induced cell damage with 31P NMR spectroscopy
Bibliographic record
Abstract
Nuclear magnetic resonance has the ability to detect changes in tumour, and cell metabolism induced by radiation therapy. This study examined the feasibility of using NMR spectroscopy to quantify metabolic change in response to radiation dose. The metabolites of MCF‐7 cancer cells were extracted using a freeze‐thaw procedure developed for this study, and analyzed by NMR spectroscopy. A reproducibility study was performed consisting of 28 non‐irradiated samples processed from 10 different batch experiments. Peak height ratios relative to inorganic phosphate (Pi) were determined for all phosphorous metabolites present. The intra‐batch standard deviation in peak height ratios was determined to be 1.8%, 7.47%, 3.42%, 2.40%, 2.74%, and 3.21% for phosphoethanolamine, phosphocholine, glycerophosphoethanolamine, glycerophosphocholine, and two unidentified groups of peaks in the high‐energy phosphate range, respectively. In addition, the spectral position (ppm) of Pi was investigated, and found to have intra‐batch standard deviation of 0.014 ppm. The biological response delay time of 48 hrs was determined to yield the greatest metabolic change. Three batch experiments with cells in growth medium where irradiated to 6 Gy, 12 Gy and 0 Gy (control) with a 6 MeV electron linear accelerator beam. Although the sensitivity varied between batches, quantitative analysis of the change (relative to the control) in peak height ratios of the metabolites and Pi spectral shift suggests a significant correlation between dose and metabolic response.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".