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Neonatal Programming by Neuroimmune Challenge: Effects on Responses and Tolerance to Septic Doses of Lipopolysaccharide in Adult Male and Female Rats

2010· article· en· W2084428844 on OpenAlexafffund
Sarah J. Spencer, E. J. Field, Quentin J. Pittman

Bibliographic record

VenueJournal of Neuroendocrinology · 2010
Typearticle
Languageen
FieldImmunology and Microbiology
TopicImmune Response and Inflammation
Canadian institutionsInstitute of Infection and Immunity
FundersCanadian Institutes of Health Research
KeywordsLipopolysaccharideImmune systemHypothermiaSepsisInternal medicineEndocrinologyHyperthermiaMedicineImmunology

Abstract

fetched live from OpenAlex

A mild immune challenge experienced during the neonatal period leads to attenuated febrile responses to a similar challenge experienced later in life. However, the immune response to an endotoxin differs depending upon the severity of the challenge and it is not clear whether a neonatal immune challenge will also affect responses to a severe, potentially life-threatening stimulus, such as sepsis. In the present study, we examined the effects of a neonatal immune challenge with lipopolysaccharide (LPS) on adult sickness responses, as well as the development of endotoxin tolerance, to a septic dose (1 or 3 mg/kg) of the same LPS in male and female rats. We demonstrate significant differences, particularly in males, in the fever profiles of neonatally LPS-treated rats compared to neonatally saline-treated controls. Specifically, male rats treated neonatally with LPS have reduced hypothermic and enhanced hyperthermic responses to both septic doses of LPS in adulthood. A somewhat different profile is seen in females, with neonatally LPS-treated females having reduced hypothermia and enhanced hyperthermia compared to controls with 1 mg/kg but no differences with 3 mg/kg LPS. The results obtained demonstrate that alterations in innate immune responses previously reported for low doses of LPS can, for the most part, also be observed after severe immune challenge in later life.

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)0.000
metaresearch head score (Gemma)0.001
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 score0.059
Threshold uncertainty score0.651

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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 integrity0.0000.001
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 head0.008
GPT teacher head0.249
Teacher spread0.242 · 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

Citations27
Published2010
Admission routes2
Has abstractyes

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