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Record W6891903561 · doi:10.48448/bd8p-rs45

Neuroprosthetic baroreflex controls haemodynamics after spinal cord injury

2021· other· en· W6891903561 on OpenAlexaff

Bibliographic record

VenueUnderline Science Inc. · 2021
Typeother
Languageen
Field
Topic
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsHemodynamicsBaroreflexSpinal cord injuryContext (archaeology)Spinal cordEfferentStimulation

Abstract

fetched live from OpenAlex

Spinal cord injury (SCI) induces hemodynamic instability that threatens survival, impairs neurological recovery, increases cardiovascular disease risk, and reduces quality of life. Hemodynamic instability in this context is due to the interruption of supraspinal efferent commands to sympathetic circuits located in the spinal cord, which prevents the natural baroreflex from controlling these circuits to adjust peripheral vascular resistance. We previously showed that epidural electrical stimulation (EES) of the spinal cord can compensate for interrupted supraspinal commands to motor circuits below injury, which restored walking after paralysis. Here, we leveraged these concepts to develop EES protocols that restored hemodynamic stability after SCI. We established a novel preclinical model that enabled us to dissect the topology and dynamics of the sympathetic circuits, and understand how EES can engage these circuits. We incorporated these spatial and temporal features into stimulation protocols to conceive a clinical-grade biomimetic hemodynamic regulator operating in closed-loop. This neuroprosthetic baroreflex controlled hemodynamics for extended periods of time in rodents, non-human primates, and humans, both after acute and chronic SCI. We will now conduct clinical trials to turn the neuroprosthetic baroreflex into a commonly available therapy for people with SCI.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.640
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.003
Science and technology studies0.0000.004
Scholarly communication0.0010.000
Open science0.0020.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0050.004

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 head0.024
GPT teacher head0.336
Teacher spread0.312 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreOther

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
Published2021
Admission routes1
Has abstractyes

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