Correlation between sperm parameters and circulating thyroid hormones and testosterone concentrations in Labrador Retriever dog
Bibliographic record
Abstract
Thyroid hormones physiologically regulate the semen quality, by altering testosterone concentrations, and different seminal parameters, as well as sperm motility, viability and semen volume. Serum thyroxine (T4), free thyroxine (fT4), thyroid-stimulating hormone (TSH) and testosterone (T) concentrations were measured in 26 adult Labrador Retriever dogs (n. 20 normospermic and n. 6 azoospermic groups), aged 3 ± 0.5 years old, to determine their specific references, by taking into account the related conventional and kinematic sperm parameters and clinical ultrasound evaluations. The mean values of T4, fT4, TSH and T concentrations, as soon as those of sperm parameters and clinical evaluations of normospermic group were in line with dog's physiological range of literature data. Normospermic group showed significant correlations between T4 and T (r = −0.681; p < .01), sperm progressive motility (%) (r = −0.623; p < .01), and sperm non-progressive motility (%) (r = 0.625; p < .02). The azoospermic group showed higher T4, fT4, TSH and lower T concentrations, compared to normospermic group, and a positive correlation between T4 and T (r = 0.8548; p < .046). The use of breed-specific hormonal ranges and sperm parameters will improve the knowledge of their interaction in Labrador Retriever dogs, adding a new segment of scientific literature.Highlights Thyroxine (T4), free thyroxine (fT4), thyroid stimulating hormone (TSH) and testosterone (T) concentrations were measured in male dogs. Conventional and kinematic sperm parameters were also evaluated. Dogs were divided in 20 normospermic and n. 6 azoospermic Labrador Retrivers. Azoospermic group showed higher values of T4, fT4, TSH and lower values of T compared to normospermic Use of breed-specific hormonal ranges and sperm parameters will improve the knowledge of scientific literature.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| 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 | 0.001 |
| 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 teacher head, 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".