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Record W2914121994 · doi:10.3168/jds.2018-14616

Effect of repeated intravenous lipopolysaccharide infusions on systemic inflammatory response and endometrium gene expression in Holstein heifers

2019· article· en· W2914121994 on OpenAlexafffund
Artur Cezar de Carvalho Fernandes, Saeideh Davoodi, Manveen Kaur, D. M. Veira, Lúcio Esmeraldo Honório de Melo, R.L.A. Cerri

Bibliographic record

VenueJournal of Dairy Science · 2019
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicReproductive Physiology in Livestock
Canadian institutionsUniversity of British Columbia
FundersHealth CanadaUniversity of British ColumbiaIchimura Foundation of New TechnologyUniversity of Missouri
KeywordsHaptoglobinProinflammatory cytokineEndometriumLipopolysaccharideOvulationEndocrinologyInternal medicineTumor necrosis factor alphaMedicineWhite blood cellAndrologyArtificial inseminationInseminationInflammationBiologyHormonePregnancy

Abstract

fetched live from OpenAlex

This study aimed to evaluate the effect of repeated intravenous lipopolysaccharide (LPS) infusions in nonlactating heifers on (1) the systemic proinflammatory state as measured by biomarkers in blood and plasma, and (2) endometrial gene expression of candidate transcripts on d 15 of gestation. Our hypothesis was that target transcripts related to a major functional group would be negatively modified in the preimplantation endometrium by the LPS treatments. In the first experiment (n = 13), a systemic proinflammatory state [defined as increased plasma concentrations of tumor necrosis factor (TNF)-α and haptoglobin for 2 wk] was established using 2 different sequential LPS infusion protocols. In the second experiment, heifers (n = 22; 11 mo of age) had their time of ovulation synchronized by a modified Ovsynch protocol and were enrolled in 1 of 2 treatments: control (CON; n = 11), which received sterile saline solution i.v., and LPS treatment (LPS; n = 11), submitted to repeated i.v. LPS injections (0.10, 0.25, 0.50, 0.75, 1.00, and 1.25 µg/kg) starting 2 d after artificial insemination (AI; d 0) and then every other day until d 15 after AI. At each LPS injection, rectal temperatures were measured hourly for 6 h. Blood samples were collected from d -1 to d 13 for analyses of progesterone, TNF-α, and haptoglobin in plasma, along with white blood cell (WBC) count and differential analysis. On d 15, endometrium tissue biopsies were taken and kept at -80°C until quantitative real-time PCR analysis of 30 target transcripts related to the immune system, adhesion molecules, and endometrium receptivity. Data were checked for normality and analyzed by repeated-measures ANOVA using PROC UNIVARIATE and PROC MIXED of SAS (SAS Institute Inc., Cary, NC). After each LPS injection, temperature was greater in the first 4 h in the LPS group compared with CON. Both TNF-α and haptoglobin increased in the LPS treatment with a significant treatment by day interaction. Total leukocyte count did not differ between treatments, but the differential count increased for neutrophils, band cells, and monocytes, and decreased for lymphocytes and eosinophils in LPS compared with CON. Progesterone concentrations in plasma did not differ between treatments during the experimental period. Out of 30 target genes analyzed, 3 transcripts were differentially expressed: indoleamine 2,3-dioxygenase (IDO; fold-change = 0.48) and pentraxin-3 (PTX3; fold-change = 0.38) were downregulated, whereas myxovirus-resistance protein (MX1; fold-change = 2.85) was upregulated in the LPS group. Sequential LPS injections were able to induce a prolonged systemic proinflammatory state, but effects on gene expression were limited to transcripts associated with the immune system. These results suggest that a mechanism for subfertility is linked to a proinflammatory state in dairy heifers.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.008
GPT teacher head0.235
Teacher spread0.227 · 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 source (direct Gemma or distilled Codex), 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

Citations13
Published2019
Admission routes2
Has abstractyes

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