Variations of Physiological Parameters and HSP70 and HSP90 Polymorphisms in Water Buffaloes in Taiwan During Cool and Warm Season
Bibliographic record
Abstract
Background: This study examined the physiological parameters of water buffaloes in Taiwan in the cool (February) and warm (August) seasons of 2020 and 2021. Methods: Data was collected for a study in February, August 2020, and 2021. The ambitious temperature, humidity, water buffaloes’ rectal temperature (RT), and respiratory rate (RR) were recorded. The plasma expression levels of heat-shock protein (HSP)70 and HSP90 were examined using an ELISA kit. Furthermore, the HSP70 and HSP90 fragment genetic sequence variations were analyzed using the PCR method and MEGA6 software. Results: The results revealed that in the warm season, the rectal temperature (RT), respiratory rate (RR), and heat tolerance coefficient (HTC) were significantly higher compared to the cool season (all P < 0.05). Additionally, the temperature-humidity index (THI) had moderate to high correlations with RT (0.518), RR (0.744), and HTC (0.757). Plasma HSP70 expression levels were higher in the warm season than in the cool season (P < 0.05). The genetic sequences of HSP70 and HSP90 fragments were compared, and five single-nucleotide variation (SNV) sites were identified. However, each genotype showed no significant physiological difference between the cool and warm seasons. Conclusion: Temperature and humidity changes in Taiwan had a significant correlation with the physical condition of water buffaloes. This information can be valuable in improving the living conditions of these animals, leading to better animal welfare. Additionally, the HSP70 and HSP90 gene variations in water buffaloes in Taiwan could be used as a reference for future research on breeding and identifying molecular markers.
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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) | 0.001 | 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".