MétaCan
Menu
Back to cohort
Record W4385248331 · doi:10.3168/jds.2022-22966

Lactational performance effects of supplemental histidine in dairy cows: A meta-analysis

2023· review· en· W4385248331 on OpenAlexaff
S.E. Räisänen, H. Lapierre, William J. Price, A.N. Hristov

Bibliographic record

VenueJournal of Dairy Science · 2023
Typereview
Languageen
FieldVeterinary
TopicAnimal health and immunology
Canadian institutionsAgriculture and Agri-Food Canada
FundersNational Institute of Food and AgricultureU.S. Department of Agriculture
KeywordsSilageAnimal scienceMeta-analysisLactationConfidence intervalStrictly standardized mean differenceRumenDairy cattleMean differencePooled varianceBiologyBiotechnologyMedicineInternal medicineFood sciencePregnancy

Abstract

fetched live from OpenAlex

The objective of this meta-analysis was to examine the effects of supplemental His on lactational performance, plasma His concentration and efficiency of utilization of digestible His (Eff His ) in dairy cows. The meta-analysis was performed on data from 17 studies published in peer-reviewed journals between 1999 and 2022. Five publications reported data from 2 separate experiments, which were included in the analyses as separate studies, therefore resulting in a total of 22 studies. In 10 studies, His was supplemented as rumen-protected (RP) His; in 1 study, 2 basal diets with different dHis levels were fed; and in the remaining experiments, free His was infused into the abomasum (4 studies), the jugular vein (3 studies) or deleted from a mixture of postruminally infused AA (4 studies). The main forages in the diets were corn silage in 14 and grass silage in 8 studies. If not reported in the publications, the supplies of dietary CP, metabolizable protein (MP), net energy of lactation, and digestible His (dHis) were estimated using NRC (2001). An initial meta-analysis was performed to test the standard mean difference (SMD; raw mean difference of treatment and control means divided by the pooled standard deviation of the means), that is, effect size, and the corresponding 95% confidence interval (CI) in production parameters between His-supplemented groups versus control. Further, regression analyses were also conducted to examine and compare the relationships between several response variables and dHis supply. Across studies, His supplementation increased plasma His concentration (SMD = 1.39; 95% CI: 1.17–1.61), as well as DMI (SMD = 0.240; 95% CI: 0.051–0.429) and milk yield (MY; SMD=0.667; 95% CI: 0.468–0.866), respectively. Further, milk true protein concentration (MTP; SMD=0.236; 95% CI: 0.046–0.425) and milk true protein yield (MTPY; SMD=0.581; 95% CI: 0.387–0.776) were increased by His supplementation. Notably, the increase in MTP concentration and MTPY were 3.9 and 1.3 times greater for studies with MP-deficient (according to NRC 2001) diets compared with studies with MP-adequate diets. The regression analyses revealed that production parameters (DMI, MY, and MTPY) responded in a nonlinear manner to increasing His supply. Further, we detected a difference in the magnitude of change in MTPY and plasma His concentration with the level of His supply and between His supplementation methods, being greater for infused His compared with RPHis. Lastly, a linear and negative relationship between Eff His and the ratio of total digestible His to net energy for lactation supply was observed, indicating an important interaction between dHis and energy supply and Eff His (i.e., utilization of dHis to support protein export). Overall, these analyses confirm His as an important AA in dairy cattle nutrition.

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)0.013
metaresearch head score (Gemma)0.016
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: Meta-analysis
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.013
Threshold uncertainty score0.070

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0130.016
Meta-epidemiology (narrow)0.0030.002
Meta-epidemiology (broad)0.0130.044
Bibliometrics0.0030.004
Science and technology studies0.0010.001
Scholarly communication0.0030.001
Open science0.0020.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.212
GPT teacher head0.442
Teacher spread0.230 · 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 designMeta-analysis
Domainnot available
GenreReview

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

Citations15
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Dairy ScienceSame topicAnimal health and immunologyFrench-language works237,207