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Record W7133096563

The Persistent Impact of Inflammation and General Anesthesia on Cognition and Inhibitory Neurotransmission

2023· dissertation· W7133096563 on OpenAlexaff
Shahin Khodaei

Bibliographic record

VenueTSpace · 2023
Typedissertation
Language
FieldMedicine
TopicIntensive Care Unit Cognitive Disorders
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsInflammationNeurotransmissionAnestheticPostoperative cognitive dysfunctionInhibitory postsynaptic potentialSevofluraneCognitionIsofluraneHippocampus
DOInot available

Abstract

fetched live from OpenAlex

Perioperative neurocognitive disorders (PNDs), which are characterized by lasting cognitive deficits after general anesthesia and surgery, commonly occur in patients. Growing evidence indicates that inflammation and general anesthetic drugs are two likely contributing factors to the development of PNDs. In animal models, inflammation due to surgery or treatment with the pathogen-associated molecule lipopolysaccharide (LPS) impairs cognition. Similarly, exposure to general anesthetic drugs has been reported to cause subtle, yet persistent, cognitive deficits. At the molecular level, inflammation- and anesthetic-induced cognitive deficits may be due to dysregulated inhibitory neurotransmission. Inflammatory mediators acutely disrupt both synaptic and tonic inhibition in the brain, though it is unknown whether such disruptions are sustained for days. Furthermore, general anesthetic drugs are reported to trigger a lasting increase in tonic inhibitory transmission in the hippocampus. Therefore, inhibitory neurotransmission may represent a point of convergence in the pathophysiology of PNDs.Despite the co-occurrence of inflammation and general anesthesia in the perioperative period, the relative impact of each factor alone, versus their combination, is not well understood. Thus, I first studied the impact of these factors on two cognitive domains: memory and executive function. Using a mouse model of LPS-induced inflammation followed by etomidate anesthesia, I observed memory deficits that were driven by inflammation. However, deficits in executive function were only observed when mice received both LPS and etomidate, suggesting an interplay between inflammation and general anesthesia. I next assessed the impact of these factors on inhibitory neurotransmission in the hippocampus and found that LPS-induced inflammation and sevoflurane anesthesia exert differential effects. LPS did not persistently dysregulate inhibition 1–3 days after treatment, whereas sevoflurane caused a sustained increase in tonic inhibition. Synaptic plasticity remained intact after treatment with the combination of LPS and sevoflurane, despite the increased tonic inhibition. Thus, additional mechanisms likely underlie functional deficits in this model. My findings indicate that an interplay between inflammation and general anesthesia may play a key role in the development of PNDs. While inhibitory neurotransmission may be a viable target for treating anesthetic-induced impairment, complementary strategies to mitigate parallel inflammation-induced disruptions are likely necessary to treat 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)0.000
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 score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0010.000

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 head0.020
GPT teacher head0.330
Teacher spread0.311 · 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
Published2023
Admission routes1
Has abstractyes

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