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Record W4310773420 · doi:10.1213/ane.0000000000006221

The Impact of Inflammation and General Anesthesia on Memory and Executive Function in Mice

2022· article· en· W4310773420 on OpenAlexafffund
Shahin Khodaei, Dian-Shi Wang, Anthony Ariza, Raza M. Syed, Beverley A. Orser

Bibliographic record

VenueAnesthesia & Analgesia · 2022
Typearticle
Languageen
FieldMedicine
TopicIntensive Care Unit Cognitive Disorders
Canadian institutionsHealth Sciences CentreSunnybrook Health Science CentreUniversity of Toronto
FundersCanadian Institutes of Health Research
KeywordsMedicineInflammationFunction (biology)AnesthesiaIntensive care medicineInternal medicine

Abstract

fetched live from OpenAlex

BACKGROUND: Perioperative neurocognitive disorders (PNDs) are complex, multifactorial conditions that are associated with poor long-term outcomes. Inflammation and exposure to general anesthetic drugs are likely contributing factors; however, the relative impact of each factor alone versus the combination of these factors remains poorly understood. The goal of this study was to compare the relative impact of inflammation, general anesthesia, and the combination of both factors on memory and executive function. METHODS: To induce neuroinflammation at the time of exposure to an anesthetic drug, adult male mice were treated with lipopolysaccharide (LPS) or vehicle. One day later, they were anesthetized with etomidate (or vehicle). Levels of proinflammatory cytokines were measured in the hippocampus and cortex 24 hours after LPS treatment. Recognition memory and executive function were assessed starting 24 hours after anesthesia using the novel object recognition assay and the puzzle box, respectively. Data are expressed as mean (or median) differences (95% confidence interval). RESULTS: LPS induced neuroinflammation, as indicated by elevated levels of proinflammatory cytokines, including interleukin-1β (LPS versus control, hippocampus: 3.49 pg/mg [2.06-4.92], P < .001; cortex: 2.60 pg/mg [0.83-4.40], P = .010) and tumor necrosis factor-α (hippocampus: 3.50 pg/mg [0.83-11.82], P = .002; cortex: 2.38 pg/mg [0.44-4.31], P = .021). Recognition memory was impaired in mice treated with LPS, as evinced by a lack of preference for the novel object (novel versus familiar: 1.03 seconds [-1.25 to 3.30], P = .689), but not in mice treated with etomidate alone (novel versus familiar: 2.38 seconds [0.15-4.60], P = .031). Mice cotreated with both LPS and etomidate also exhibited memory deficits (novel versus familiar: 1.40 seconds [-0.83 to 3.62], P = .383). In the puzzle box, mice treated with either LPS or etomidate alone showed no deficits. However, the combination of LPS and etomidate caused deficits in problem-solving tasks (door open task: -0.21 seconds [-0.40 to -0.01], P = .037; plug task: -0.30 seconds [-0.50 to -0.10], P < .001; log values versus control), indicating impaired executive function. CONCLUSIONS: Impairments in recognition memory were driven by inflammation. Deficits in executive function were only observed in mice cotreated with LPS and etomidate. Thus, an interplay between inflammation and etomidate anesthesia led to cognitive deficits that were not observed with either factor alone. These findings suggest that inflammation and anesthetic drugs may interact synergistically, or their combination may unmask covert or latent deficits induced by each factor alone, leading to PNDs.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0020.001

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 head0.009
GPT teacher head0.252
Teacher spread0.243 · 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

Citations9
Published2022
Admission routes2
Has abstractyes

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