<i>Escherichia coli</i> Heat Shock Protein DnaK: Production and Consequences in Terms of Monitoring Cooking
Bibliographic record
Abstract
Through use of commercially available DnaK proteins and anti-DnaK monoclonal antibodies, a competitive enzyme-linked immunosorbent assay was developed to quantify this heat shock protein in Escherichia coli ATCC 25922 subjected to various heating regimens. For a given process lethality (F(70)(10) of 1, 3, and 5 min), the intracellular concentration of DnaK in E. coli varied with the heating temperature (50 or 55 degrees C). In fact, the highest DnaK concentrations were found after treatments at the lower temperature (50 degrees C) applied for a longer time. Residual DnaK after heating was found to be necessary for cell recovery, and additional DnaK was produced during the recovery process. Overall, higher intracellular concentrations of DnaK tended to enhance cell resistance to a subsequent lethal stress. Indeed, E. coli cells that had undergone a sublethal heat shock (105 min at 55 degrees C, F(70)(10) = 3 min) accompanied by a 12-h recovery (containing 76,786 +/- 25,230 molecules/cell) resisted better than exponentially growing cells (38,500 +/- 6,056 molecules/cell) when later heated to 60 degrees C for 50 min (F(70)(10) = 5 min). Results reported here suggest that using stress protein to determine cell adaptation and survival, rather than cell counts alone, may lead to more efficient heat treatment.
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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) | 0.001 | 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) | 0.000 | 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".