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Record W2371793330

EFFECT OF rhIL-18 ON IMMUNOREGULATION OF MICE WITH RADIATION DAMAGE

2010· article· en· W2371793330 on OpenAlexaff
Yu Dong

Bibliographic record

VenueJournal of Preventive Medicine of the Chinese People's Liberation Army · 2010
Typearticle
Languageen
FieldPharmacology, Toxicology and Pharmaceutics
TopicPharmacological Effects of Natural Compounds
Canadian institutionsL'Alliance Boviteq
Fundersnot available
KeywordsCytotoxic T cellSplenocyteImmune systemImmunologyChemistryIrradiationIn vitroMolecular biologyBiologyBiochemistry
DOInot available

Abstract

fetched live from OpenAlex

Objective To study the immunoregulation effect of rhIL-18 on irradiated mice.Methods Thirty-two C57 mice were randomly divided into normal control group,irradiated group,rhIL-18+irradiated group,irradiated +rhIL-18 group.The animals were irradiated with 60Co (γ rays 4.0 Gy)and treated with rhIL-18 alternately.After 2 weeks,the lymphocyte transform ability,NK cells cytotoxic activity,T cells subtype were examined,the levels of IL-2,IFN-γ,GM-CSF,and IL-4 in the supernatant of cultured splenocytes in vitro and content of IgG in serum were also tested.Results rhIL-18 could make T and B lymphocyte transform function of 60Co irradiated mice to recover or even better than normal levels (P0.05).The stimulating index (SI) in irradiated +rhIL-18 group reachad to 2.9 (ConA test) and 6.1 (LPS test).rhIL-18 could enhance cytotoxic activity mediated by NK cells against tumor cells of A375,U937 and KG1 cells.The rate of killing tumor cells by NK cells was 21.8 to 35.6%(P0.05).Moreover,rhIL-18 could up-regulate the number of CD4 T cells to 50 cells/ml and improve splenocytes of irradiated mice to secret cytokines of IL-2,IFN-γ,GM-CSF(P0.05) but not IL-4 and IgG.Conclusion rhIL-18 could improve the immune function of irradiated mice.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.018
GPT teacher head0.399
Teacher spread0.381 · 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 designObservational
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
Published2010
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Preventive Medicine of the Chinese People's Liberation ArmySame topicPharmacological Effects of Natural CompoundsFrench-language works237,207