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Record W2989738337 · doi:10.1071/rdv32n2ab138

138 Associations of sperm head morphometrics with quality parameters of frozen-thawed ram semen

2019· article· en· W2989738337 on OpenAlexaff
J. Navaranjan, Joanna Szymanowicz, M. Murawski, T. Schwarz, Pawel M. Bartlewski

Bibliographic record

VenueReproduction Fertility and Development · 2019
Typearticle
Languageen
FieldMedicine
TopicSperm and Testicular Function
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsSpermSemenBiologyAndrologyElectroejaculationExtenderSemen qualitySperm motilitySemen collectionAnatomyArtificial inseminationChemistryBotanyGeneticsPregnancyMedicine

Abstract

fetched live from OpenAlex

Current methods of mammalian semen evaluation focus on determining spermatozoa motility, concentration, mitochondrial status, and nucleus or chromatin structure integrity, quantifying their ability to bind to ova or measuring seminal plasma content of various biochemical markers. However, there is a paucity of studies that address relationships between sperm head morphometry (the external shape and dimensions of the sperm) and fertilising ability. Sperm head morphometrics are influenced by many molecular and biochemical factors such as genetics, DNA or protein condensation, and cell membrane permeability, all of which can affect semen viability. The objective of this experimental work was to determine quantitative correlations between sperm head dimensions and various indices of sperm quality in frozen-thawed ram semen. Ejaculates were collected from 16 clinically healthy rams (4 Polish Lowland (PON), 4 Olkuska, 5 synthetic line BCP (Berrichon du Cher × Charolais × PON/Polish Merino), and 3 synthetic line SCP (Suffolk × Charolais × PON/Polish Merino) aged 4-12 years) into an artificial vagina in the middle portion of the breeding season. Ejaculates from each ram were divided into two equal portions, diluted with a commercial semen extender prepared in deionised water or nanowater (water declusterised using cold plasma treatment) to a final concentration of 400 × 106 spermatozoa mL−1, and frozen in 0.25-mL plastic straws. After 6 months of being cryogenically preserved, semen samples were thawed and used for the preparation of smears stained with eosin or SpermBlue. Images of the samples containing at least 100 spermatozoa were taken under 200× magnification and used for determination of sperm head morphology with the image analytical software Image Pro Plus (Media Cybernetics Inc.). Sperm progressive motility and survival time, as well as extender concentrations of alkaline phosphatase and aspartate aminotransferase, were measured. Finally, 128 BCP ewes were inseminated laparoscopically with the ram semen and fertility parameters were recorded. The present data were analysed using a multivariate analysis of variance in SAS (SAS Institute Inc.) and Spearman correlation tests. There were no significant effects or interactions of breed, staining method, or extender diluent on sperm head dimensions (head length, width, area, perimeter, and roundness). The mean head length was negatively correlated (P < 0.05) with the percentages of spermatozoa with vacuolated, detached, or amorphous heads or small acrosomes; thick and thin midpiece defects, distal droplet, broken tail plus distal droplet, short tail plus distal droplet, and thick midpiece plus proximal droplet; and sperm progressive motility. In addition, sperm head roundness was negatively correlated with the proportion of spermatozoa with coiled tails. There were no correlations of sperm head dimensions with survival time, alkaline phosphatase and aspartate aminotransferase concentrations, or conception and pregnancy rates of artificially inseminated ewes. Sperm length and roundness (but no other measurements) were significantly correlated with segmental sperm defects and motility that may impinge the fertilising ability of frozen-thawed ram semen.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.044
Threshold uncertainty score0.351

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 head0.068
GPT teacher head0.302
Teacher spread0.234 · 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 designObservational
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".

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Citations0
Published2019
Admission routes1
Has abstractyes

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