Evidence map›Paper›PMID 21997401›Full record

ReviewJournal of mammary gland biology and neoplasia2011

Gene polymorphisms: the keys for marker assisted selection and unraveling core regulatory pathways for mastitis resistance.

Gina M Pighetti, A A Elliott

Abstract readReview
PubMed Publisher
In one paragraph

Review in Journal of mammary gland biology and neoplasia, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.

0numbers the graph read from it
0cells of the map it votes in
19citing papers in PubMed
3.9field-weighted citation impact, top 8% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

19 citing papers in PubMed, 46 citations in OpenAlex.

  1. Article
  2. Article
  3. Article
  4. Selection for Resilience in Livestock Production Systems.International journal of molecular sciences · 2024
    Review
  5. Article
  6. Article
  7. Article
  8. Review
  9. Animals : an open access journal from MDPI · 2022
    Article
  10. Review
  11. Article
  12. Article
  13. Article
  14. Article
  15. Article
  16. Article
  17. Article
  18. Article
  19. Bovine mastitis: frontiers in immunogenetics.Frontiers in immunology · 2014
    Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors at 2 institutions in 1 country.

Gina M PighettiDepartment of Animal Science, The University of Tennessee, Knoxville, TN 37996, USA. pighetti@utk.edu
A A Elliott
University of Missouri · USUniversity of Tennessee at Knoxville · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

One of the most frequent mammary diseases impacting lactating animals is mastitis, an inflammation of the mammary gland most commonly caused by bacterial infection. The severity of mastitis is greatly influenced by the invading organism and the subsequent immune response which must recognize the foreign organism, recruit immune cells, eliminate the invading pathogen, and resolve the inflammatory response. The speed, strength, and duration of this response and subsequent disease susceptibility are critically tied to the genetic background of an animal. However, the genetic contribution has been difficult to identify due to the complex interactions that must occur for effective disease resistance. Recent studies have utilized polymorphisms to better define the genes and chromosomal regions that contribute to mastitis resistance. This review will examine these studies with primary emphasis in bovine systems, as the most work regarding mastitis has been conducted in this species.

Indexed as

Signal TransductionAnimalsBacterial InfectionsCattleDisease ResistanceFemaleGenesGenetic MarkersMastitis, BovinePolymorphism, GeneticGenetic Markers

Identifiers

PMID21997401
OpenAlexW2001367381

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.