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Record W2398895954 · doi:10.1111/rda.12702

Fusion of Boar Sperm with Nanoliposomes Prepared from Synthetic Phospholipids

2016· article· en· W2398895954 on OpenAlexafffund
Vanmathy Kasimanickam, MM Buhr

Bibliographic record

VenueReproduction in Domestic Animals · 2016
Typearticle
Languageen
FieldMedicine
TopicSperm and Testicular Function
Canadian institutionsUniversity of Saskatchewan
FundersOntario Ministry of Agriculture, Food and Rural Affairs
KeywordsSphingomyelinPhosphatidylethanolamineSpermPhosphatidylserineLipid bilayer fusionBOARCapacitationLiposomePhosphatidylcholineMembraneAcrosomeBiologyBiochemistryChemistryCell biologyPhospholipidBotany

Abstract

fetched live from OpenAlex

Liposomes are artificial membrane vesicles that can be used to test and model the functions and interactions of various biological membranes, or as a carrier system to deliver biologically active substances into the cells, or to incorporate lipids into the plasma membrane of target cells to modify membrane structure-function relationships. Sperm plasma membrane undergoes lipid modification during maturation in epididymis and during capacitation in the female reproductive tract to facilitate fertilization. Natural variation in the amounts and composition of lipids in the sperm plasma membrane may also contribute to the species-specific sperm sensitivities to handling and storage conditions. Boar sperm are notoriously susceptible to membrane damage and are resistant to compositional alteration by artificial liposomes. This study used flow cytometry to demonstrate stable incorporation of nanoliposomes prepared from a complex mixture of various phospholipids (phosphatidylcholine : phosphatidylethanolamine : sphingomyelin : phosphatidylserine : phosphatidylinositol) with high fusion efficiency. Over 90% of sperm rapidly took up fluorescently labelled liposomes and retained the lipids for at least 60 min, in a significant time- and concentration-dependent manner. This unique fusion efficacy could be used to alter sperm plasma membrane composition and hence membrane-based functional responses.

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)0.000
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 score0.497
Threshold uncertainty score0.759

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0010.000

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 head0.012
GPT teacher head0.251
Teacher spread0.239 · 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

Citations6
Published2016
Admission routes2
Has abstractyes

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