Hsp70 in bovine lenses during temperature stress.
Bibliographic record
Abstract
PURPOSE: To determine the effects of heat shock treatment on cold cataract formation in bovine lenses. METHODS: A laser scanning system (ScanTox) was used to analyze the optical quality of bovine lenses during a cooling and warming cycle. Cycloheximide, a compound that prevents new protein synthesis was used to inhibit inducible heat shock protein 70 (Hsp70) production during heat stress. Cycloheximide was also used to verify that the induction of Hsp70 takes place in lenses during heat stress. Western blots determined the relative accumulation of Hsp70 in urea soluble lens fractions. Lenses from animals approximately 2 years of age (n=60) were used in this experiment. RESULTS: The decrease in relative light transmittance during cold cataract varies for each group of lenses, with the greatest decrease appearing in the heat shock group in culture medium and the least in the control groups and the heat shock group with cycloheximide. The primary result is that heat shock lenses were most affected by the cold cataract (57% decrease in intensity of refracted beam), and that this effect is prevented by cycloheximide. Western blot results show an increase of Hsp70 with heat shock in the urea soluble lens fractions. CONCLUSIONS: The results show that heat shock treatment increased both light scattering and the presence of Hsp70. Also cycloheximde prevented both the heat shock effect and the expression of Hsp70 in bovine lenses.
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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 | 0.000 | 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) | 0.002 | 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".