Calorimetric and spectroscopic detection of the interaction between a diazo dye and human serum albumin
Bibliographic record
Abstract
Dye effluents are one of the main causes of water pollution. Azo dyes, the most widely applied colorants, are particularly difficult to degrade. Exposure of such dyes to the aquatic environment is hazardous to human health and biota due to their intrinsic harmful mutagenic and carcinogenic properties. Congo Red (CR) is an anionic and synthetic diazo dye, which is recalcitrant to the biodegradative process and metabolizes to produce a potential carcinogen. Research on the interaction of this toxic dye with serum albumin, as a transport protein, is of paramount significance because the physiological and toxicological behaviours of the dye in vivo are associated with its interactive characteristics with the proteins. In this regard, a detailed binding profile of CR with human serum albumin (HSA) was studied using isothermal titration calorimetry (ITC) along with various spectroscopic and microscopic methods. The thermodynamic results from ITC indicated that the CR-HSA non-covalent interaction occured primarily due to favorable entropy and unfavorable enthalpy with a Ka of 106 M-1 at lower concentrations and 105 M-1 at higher concentrations. Steady-state fluorescence data revealed that the intrinsic fluorescence of HSA was quenched in the presence of CR via the static quenching mechanism. Using Förster's non-radioactive energy transfer theory (FRET), the specific binding distance r (2.73 nm) between the donor (Trp-214 from HSA) and the acceptor (CR) was calculated. Our preliminary results indicated that CR had a high affinity to HSA, which can have significant implications in the distribution and elimination of this toxic dye upon exposure.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".