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Record W2909081237 · doi:10.1080/01616412.2018.1564451

Serum levels of insulin-like growth factor-1 and brain-derived neurotrophic factor as potential recovery biomarkers in stroke

2019· article· en· W2909081237 on OpenAlexafffund
Michael King, Liam Kelly, Elizabeth M. Wallack, S. M. Mahmudul Hasan, Megan C. Kirkland, Marie E. Curtis, Tanaya Chatterjee, Jason McCarthy, Michelle Ploughman

Bibliographic record

VenueNeurological Research · 2019
Typearticle
Languageen
FieldNeuroscience
TopicNerve injury and regeneration
Canadian institutionsMemorial University of Newfoundland
FundersResearch and Development Corporation of Newfoundland and LabradorCanada Research Chairs
KeywordsNeurotrophic factorsInsulin-like growth factorGrowth factorBrain-derived neurotrophic factorNerve growth factorMedicineStroke (engine)Internal medicineEndocrinologyInsulinNeurosciencePsychologyReceptor

Abstract

fetched live from OpenAlex

Objectives: Our objectives were: 1) to determine whether maximal aerobic exercise increased serum neurotrophins in chronic stroke and 2) to determine the factors that predict resting and exercise-dependent levels.Methods: We investigated the potential predictors of resting and exercise-dependent serum insulin-like growth factor-1 and brain-derived neurotrophic factor among 35 chronic stroke patients. Predictors from three domains (demographic, disease burden, and cardiometabolic) were entered into 4 separate stepwise linear regression models with outcome variables: resting insulin-like growth factor, resting brain-derived neurotrophic factor, exercise-dependent change in insulin-like growth factor, and exercise-dependent change brain-derived neurotrophic factor.Results: Insulin-like growth factor decreased after exercise (p = 0.001) while brain-derived neurotrophic factor did not change (p = 0.38). Greater lower extremity impairment predicted higher resting brain-derived neurotrophic factor (p = 0.004, r2 = 0.23). Higher fluid intelligence predicted greater brain-derived neurotrophic factor response to exercise (p = 0.01, r2 = 0.18). There were no significant predictors of resting or percent change insulin-like growth factor-1.Discussion: Biomarkers have the potential to characterize an individual’s potential for recovery from stroke. Neurotrophins such as insulin-like growth factor-1 and brain-derived neurotrophic factor are thought to be important in neurorehabilitation; however, the factors that modulate these biomarkers are not well understood. Resting brain-derived neurotrophic factor and percent change in brain-derived neurotrophic factor were related to physical and cognitive recovery in chronic stroke, albeit weakly. Insulin-like growth factor-1 was not an informative biomarker among chronic stroke patients. The novel finding that fluid intelligence positively correlated with exercise-induced change in brain-derived neurotrophic factor warrants further research.

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)0.002
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
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 head0.098
GPT teacher head0.346
Teacher spread0.248 · 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

Citations35
Published2019
Admission routes2
Has abstractyes

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