MétaCan
Menu
Back to cohort

Susceptibility of PIR-B deficient mice to Salmonella infection (101.1)

2007· article· en· W4313429244 on OpenAlexaff
Ikuko Torii, Satoshi Oka, Muneki Hotomi, William H. Benjamin, John F. Kearney, Toshiyuki Takai, David E. Briles, Hiromi Kubagawa

Bibliographic record

VenueThe Journal of Immunology · 2007
Typearticle
Languageen
FieldImmunology and Microbiology
TopicInflammation biomarkers and pathways
Canadian institutionsInstitute of Aging
Fundersnot available
KeywordsSpleenBiologyImmunologyMicrobiologySepsisMyeloidEx vivoIn vivo

Abstract

fetched live from OpenAlex

Abstract Paired Ig-like receptors of activating (PIR-A) and inhibitory (PIR-B) isoforms are expressed by many blood cell types including B cells, dendritic cells, monocyte/macrophages, granulocytes, mast cells and megakaryocyte/platelets. To determine the role of PIR in infection, various doses of attenuated S. typhimurium (WB335) were inoculated i.v. into PIR-B−/− or wild-type mice. When given high doses of bacteria (>105 CFU), both types of mice died within 3 – 5 days post-infection. However, when given small doses (103– 104 CFU), all PIR-B−/− mice died within 1 – 3 weeks post-infection, whereas many control mice survived. The bacterial loads in liver, spleen and lung tissues during the 1st week of infection were much higher in PIR-B−/− mice than in control mice. PIR-B−/−, but not control, mice developed sepsis around day 7 post-infection. There were no significant differences in serum levels of cytokines (TNFα, IL1), liver enzymes (ALT, AST, ALK) and BUN or in splenic lymphoid and myeloid subsets between PIR-B−/− and control mice. Ingestion of bacteria by phagocytic cells ex vivo was also the same; however PIR-B−/− phagocytic cells exhibited tighter adherence to plates and released more nitrite than control phagocytic cells. These results suggest that PIR-B plays a role in regulating phagocytic cell responses against bacterial infection. (Supported by NIH/NIAID grant AI042127)

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)0.003
metaresearch head score (Gemma)0.000
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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.261
Threshold uncertainty score0.297

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
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)0.0000.000

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 head0.014
GPT teacher head0.245
Teacher spread0.231 · 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

Citations0
Published2007
Admission routes1
Has abstractyes

Explore more

Same venueThe Journal of ImmunologySame topicInflammation biomarkers and pathwaysFrench-language works237,207