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Record W1997839199 · doi:10.1186/cc9107

Development of Klebsiella pneumoniae B5055-induced mouse model of sepsis-associated brain inflammation in BALB/c mice

2010· article· en· W1997839199 on OpenAlexaff
Vijay Kumar, Atul Sharma

Bibliographic record

VenueCritical Care · 2010
Typearticle
Languageen
FieldNeuroscience
TopicNeuroinflammation and Neurodegeneration Mechanisms
Canadian institutionsCentre Hospitalier Universitaire Sainte-Justine
FundersNational Institutes of HealthFundação de Amparo à Pesquisa e Inovação do Estado de Santa CatarinaNational and Kapodistrian University of AthensNovo NordiskInstitut National de la Santé et de la Recherche MédicaleConselho Nacional de Desenvolvimento Científico e TecnológicoUniversidade de São PauloCoordenação de Aperfeiçoamento de Pessoal de Nível SuperiorFundação de Amparo à Pesquisa do Estado de São PauloDeutsche Forschungsgemeinschaft
KeywordsMedicineKlebsiella pneumoniaeSepsisInflammationBALB/cKlebsiellaImmunologyMicrobiologyEscherichia coliImmune systemBiologyGene

Abstract

fetched live from OpenAlex

Incidence of sepsis is continuously increasing in the developing as well as the developed world. Severe sepsis is associated with the development of multiorgan dysfunction syndrome. In addition to other vital organs (that is, lungs, kidneys, heart, or liver), the brain is one of the severely affected organs in sepsis. Autopsy studies from septic patients reveal various cerebral lesions including ischemia, hemorrhage, microthrombi, microabscesses, multifocal necrotizing leukoencephalopathy, and bacterial invasion of the nervous system. Until now no animal model of sepsis has been developed that in a true sense represents the brain inflammation associated with evolving sepsis originating from Gram-negative bacteria. This study comprises development of a mouse model of sepsis-induced brain inflammation.

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)0.001
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0040.002

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 head0.054
GPT teacher head0.307
Teacher spread0.253 · 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 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

Citations1
Published2010
Admission routes1
Has abstractyes

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