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Record W1967463897 · doi:10.2174/092986709787846541

Recovery of Locomotor Function with Combinatory Drug Treatments Designed to Synergistically Activate Specific Neuronal Networks

2009· article· en· W1967463897 on OpenAlexaff
Pierre A. Guertin

Bibliographic record

VenueCurrent Medicinal Chemistry · 2009
Typearticle
Languageen
FieldChemistry
TopicChemical synthesis and alkaloids
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsCentral pattern generatorSerotonergicNeuroscienceSpinal cordDopaminergicDopamineSpinal cord injuryMedicineDrugPharmacologyReceptorSerotoninBiologyInternal medicine

Abstract

fetched live from OpenAlex

Spinal cord injury (SCI) is a devastating condition generally leading to a permanent and irreversible loss of sensory and motor functions. We have identified recently a number of serotonergic, adrenergic and dopaminergic receptor agonists or precursors that can acutely elicit some motor and locomotor-like movements in completely spinal cord-transected (thoracic level) animals. However, only partial central network-activating effects were found with single molecules since none administered separately could elicit weight-bearing and functional stepping movements in Tx animals. In turn, a recent breakthrough revealed that full spinal locomotor network-activating effects may be induced with synergistic drug combinations. Indeed, specific cocktails comprising some of these agonists and precursors were found, indeed, to powerfully generate weight-bearing stepping with plantar foot placement in untrained, non-assisted and non-sensory-stimulated Tx mice. This significant finding provides clear evidence suggesting that combinatorial approaches based on drug-drug synergistic interactions may constitute innovative solutions for the design and development of novel pharmacological therapies in the field of SCI and related neurological disorders.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
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 score0.096
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 integrity0.0000.000
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 head0.010
GPT teacher head0.225
Teacher spread0.215 · 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.

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

Citations23
Published2009
Admission routes1
Has abstractyes

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