91 Can propylene glycol modulate insulin and insulin-like growth factor-1 in superovulated dairy heifers?
Bibliographic record
Abstract
The aim of this study was to determine the effect of propylene glycol (PPG) on serum concentrations of insulin and insulin-like growth factor (IGF)-1 in superovulated dairy heifers. We hypothesised that administration of PPG would have a positive effect on superovulation results via increased insulin and IGF-1. A total of 20 clinically healthy Holstein heifers with an average age of 12 months were used for this experiment. Superovulation was performed using a standard protocol. Briefly, each heifer received 3 mg of oestradiol-17ß IM and an intravaginal progesterone-releasing insert (CIDR) containing 1.9 g of progesterone at random stages of the oestrous cycle (designated Day 0). From Day 4 to 8, heifers received a total of 200 mg of NIH-follicle-stimulating hormone-P1 administered intramuscularly through 9 injections of decreasing doses (from 50 to 10 mg) at 12-h intervals. On Day 7, heifers received 2 injections of 500 µg of cloprostenol, a PGF2a analogue, at ~6-h intervals, after which intravaginal inserts were removed. Artificial insemination was performed on Day 10, 12 h after treatment with 100 µg of gonadotropin-releasing hormone IM. Embryos were flushed from the uterus of donor heifers 6 days after AI. The method consisted of simultaneously using 1 catheter per uterine horn. Catheters were maintained in place to perform 2 flushes 1 h apart. A total of 1 L of flushing medium was used, 700 and 300 mL for the first and the second flush, respectively. Embryos were assessed for viability immediately after collection using the IETS classification. Heifers were divided into 2 groups (PPG and control group). From Day 4 to 14 of the superovulation protocol, PPG group received a daily dose of 400 mL of a 66.7% propylene glycol solution, whereas the control group received the same amount of water. Two months later, the same experiment was conducted by inverting the groups. At Day 4 and 14, four blood samples were collected to measure insulin and IGF-1 at 25-min intervals. The first sample (0) was taken before heifers received PPG or water. Insulin was analysed using an ELISA kit following manufacturer’s instructions, whereas IGF-1 was determined using a chemiluminescence immunoassay. Embryo associated data were analysed using t-test. Both IGF-1 and insulin data were analysed using a two-way ANOVA, followed by Bonferroni post-hoc test. Treatment with PPG had no effect on the number of transferable embryos (8 ± 5.1 v. 7 ± 5.5), degenerated embryos (0.5 ± 0.8 v. 1.5 ± 2.4), or unfertilized oocytes (0.3 ± 0.7 v. 0.7 ± 1.2) recovered. There was also no effect of PPG on IGF-1 serum concentrations at the beginning (Day 4) or the end (Day 14) of the treatment regimen. However, PPG treatment enhanced (P = 0.02) serum insulin concentrations 50 min after administration on Day 4 (10.69 v. 6.88 pmol/L), as well as at 25 (19.58 v. 9.64 pmol/L) and 50 min (16.67 v. 8.21 pmol/L) on Day 14. It has been suggested that PPG metabolism may stimulate insulin and IGF-1 secretion, which can promote embryo development. However, in the present study, there was no effect of oral doses of PPG on IGF-1. Although higher serum concentrations of insulin were observed after PPG treatment, there was no effect of PPG treatment on the number of transferable embryos recovered following superovulation.
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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".