Fourier-transform infrared spectroscopy: a pharmacotoxicologic tool for in vivo monitoring radical aggression
Bibliographic record
Abstract
Among the physico-chemical methods that can be used to investigate induced peroxidation in living cells, Fourier transform infrared (FT-IR) spectroscopy appears to be a valuable technique as it is non-destructive and sensitive for monitoring changes in the vibrational spectra of samples. We examined microsomal fractions from rat liver and brain by FT-IR to study the effect of radical aggression induced in vivo by carbon tetrachloride (CCl4). The length of the acyl chains was increased as a consequence of peroxidation induced by the xenobiotic. Moreover, an enhanced level of cholesterol esters and an increase in phospholipids were observed in the liver and the brain, respectively. The conformational structure of the membrane proteins was changed in both the liver and the brain. In the polysaccharide region, we observed an important loss in glucidic structures, such as a decrease in liver glycogen and in some brain glycolipids. These alterations are probably due to the interactions between cells and CCl4and the metabolic changes caused by CCl4. Thus, FT-IR spectroscopy appears to be an useful tool and an accurate means for rapidly investigating the in vivo biochemical alterations induced by CCl4in microsomes, and for correlating them with biochemical and physiological data.Key words: brain, carbon tetrachloride, FT-IR, liver, microsomes.
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 | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".