Insights into Biochemical Alteration in Cancer‐Associated Fibroblasts by using Novel Correlative Spectroscopy
Bibliographic record
Abstract
Abstract The microenvironment of a tumor changes chemically and morphologically during cancer progression. Cancer‐stimulated fibroblasts promote tumor growth, however, the mechanism of the transition to a cancer‐stimulated fibroblast remains elusive. Here, the multi‐modal spectroscopic methods Fourier transform infrared imaging (FTIRI), X‐ray absorption spectroscopy (XAS) and X‐ray fluorescence imaging (XFI) are used to characterize molecular and atomic alterations that occur in cancer‐stimulated fibroblasts. In addition to chemical changes in lipids (olefinic and acyl chain) and protein aggregation observed with FTIRI, a new infrared biomarker for oxidative stress in stimulated fibroblasts is reported. Oxidative stress is observed to cause lipid peroxidation, which leads to the appearance of a new band at 1721 cm−1, assigned to 4‐hydroxynonenal. Complementary to FTIRI, XFI is well suited to determining atom concentrations and XAS can reveal the speciation of individual elements. XFI reveals increased concentrations of P, S, K, Ca within stimulated fibroblasts. Furthermore, XAS studies reveal alterations in the speciation of S and Ca in stimulated fibroblasts, which might provide insight into the mechanisms of cancer progression. Using XFI, not only is the concentration change of individual elements observed, but also the subcellular localization. This study demonstrates the wealth of biochemical information provided by a multi‐modal imaging approach and highlights new avenues for future research into the microenvironment of breast tumors.
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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 | 0.000 |
| 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".