ReviewMethods in molecular biology (Clifton, N.J.)2012
Rodent models of Helicobacter infection, inflammation, and disease.
Review in Methods in molecular biology (Clifton, N.J.), 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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.
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.
Who cites it
16 citing papers in PubMed, 40 citations in OpenAlex.
- Article
- A critical review onFrontiers in cellular and infection microbiology · 2025Review
- Remodeling of the gastric environment inmSystems · 2024Article
- Solid Lipid Nanoparticles Delivering a DNA Vaccine EncodingInternational journal of molecular sciences · 2024Article
- The cytosolic DNA sensor AIM2 promotes Helicobacter-induced gastric pathology via the inflammasome.Immunology and cell biology · 2023Article
- Animal Models andJournal of clinical medicine · 2022Review
- Revisiting the Mongolian Gerbil Model for Hepatitis E Virus by Reverse Genetics.Microbiology spectrum · 2022Article
- Article
- Microbiome Signatures in a Fast- and Slow-Progressing Gastric Cancer Murine Model and Their Contribution to Gastric Carcinogenesis.Microorganisms · 2021Article
- Regulation of Gastric Lgr5+ve Cell Homeostasis by Bone Morphogenetic Protein (BMP) Signaling and Inflammatory Stimuli.Cellular and molecular gastroenterology and hepatology · 2018Article
- Article
- Animal models to study acute and chronic intestinal inflammation in mammals.Gut pathogens · 2015Review
- Use of larvae of the wax moth Galleria mellonella as an in vivo model to study the virulence of Helicobacter pylori.BMC microbiology · 2014Article
- Article
- Usefulness of a Helicobacter pylori stool antigen test for diagnosing H. pylori infected C57BL/6 mice.Laboratory animal research · 2013Article
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors at 2 institutions in 1 country.
Funding
Abstract
Animal models are essential for in vivo analysis of Helicobacter-related diseases. Transgenic mice and Mongolian gerbil models have been the corner stone of present research focusing on both bacterial virulence factors and host response to infection. Establishing a reproducible rodent model of persistent Helicobacter pylori infection that resembles the H. pylori-associated gastritis observed in humans was a considerable challenge until Lee et al. (Gastroenterology 112:1386-1397, 1997) successfully adapted a clinical Cag A- and Vac A-expressing strain for the mouse stomach. This so-called SS1 (Sydney) strain has since been extensively used for H. pylori research; other rodent-adapted Helicobacter strains have subsequently been developed and utilized in wild-type and genetically engineered rodent models. These bacteria include both H. pylori and the larger but related species H. felis (originally isolated from cats). In this chapter we focus mainly on these two Helicobacter strains and review the rodent models that have been employed to investigate how Helicobacter species induce gastric inflammation and disease.
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Registered trials
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.