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Record W4206007832 · doi:10.5455/javar.2021.h555

A combination of taurine and caffeine maintains sperm quality in equine semen during chilled storage

2021· article· en· W4206007832 on OpenAlexaff
Hermelinda Ramirez-Perez, Hilda M. Guerrero‐Netro, Paulina Torres‐Rodríguez, Maricruz Díaz-Durán, Ana Myriam Boeta-Acosta, Mouhamadou Diaw

Bibliographic record

VenueJournal of Advanced Veterinary and Animal Research · 2021
Typearticle
Languageen
FieldMedicine
TopicSperm and Testicular Function
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsExtenderTaurineSemenCaffeineSpermAndrologySemen extenderBiologySemen analysisSperm motilityChemistryEndocrinologyInfertilityMedicineBiochemistry

Abstract

fetched live from OpenAlex

OBJECTIVE: The objective of this study was to evaluate the effects of caffeine and taurine on the motility and viability of chilled equine semen. MATERIALS AND METHODS: A total of 12 ejaculates were collected from three mature stallions with proven fertility during the breeding season. The gel-free spermatic fraction of each ejaculate was divided into two aliquots and diluted with a semen extender (either INRA 96® or BotuSemen Gold®). The aliquots were then split and assigned to one of the six treatment groups: control (no supplement), caffeine (2 and 4 mM), taurine (25 and 50 mM), and a combination of caffeine (2 mM) plus taurine (25 mM). Samples were stored at 4°C and analyzed at different time points (0, 24, 48, 72, and 96 h) to evaluate total (TMOT) and progressive (PMOT) motility and viability by computer-assisted sperm analysis. RESULTS: Regardless of the extender, PMOT and TMOT decreased over time. However, compared with the control, the treatment with 4 mM caffeine significantly mitigated the decrease in PMOT at 72 h. Additionally, semen treated with a combination of caffeine plus taurine maintained a significantly higher PMOT at 96 h, with improved viability at all time points. CONCLUSIONS: The combination of caffeine plus taurine helps maintain chilled equine semen viability and progressive motility up to 96 h independently of the extender used.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.945
Threshold uncertainty score0.269

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.082
GPT teacher head0.421
Teacher spread0.339 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations5
Published2021
Admission routes1
Has abstractyes

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