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Record W4290861146 · doi:10.1038/s41598-022-17503-6

BDNF, proBDNF and IGF-1 serum levels in naïve and medicated subjects with autism

2022· article· en· W4290861146 on OpenAlexaff
María de los Ángeles Robinson‐Agramonte, Bernadeta Michalski, Belkis Vidal-Martínez, Leyanis Ramos Hernández, Mabel Whilby Santiesteban, Margaret Fahnestock

Bibliographic record

VenueScientific Reports · 2022
Typearticle
Languageen
FieldNeuroscience
TopicAutism Spectrum Disorder Research
Canadian institutionsMcMaster University
Fundersnot available
KeywordsAutismMedicineAudiologyPsychologyInternal medicinePsychiatry

Abstract

fetched live from OpenAlex

Brain-derived neurotrophic factor (BDNF) and insulin-like growth factor 1 (IGF-1) promote the development and maintenance of neural circuits. Alterations in these factors might contribute to autism spectrum disorder (ASD). We asked whether serum BDNF, proBDNF, and IGF-1 levels are altered in an ASD population compared to controls. We measured serum BDNF, proBDNF, and IGF-1 immunoreactive protein in boys and girls aged 5-15 years old with mild to moderate ASD and non-autistic controls by ELISA. IGF-1 was increased in ASD serum compared to controls and was correlated with age and with CARS scores. Serum BDNF levels did not differ between groups, however, proBDNF serum levels were decreased in subjects with ASD compared to non-autistic controls. Medicated, but not unmedicated, ASD subjects exhibited lower serum proBDNF levels compared to controls, while neither IGF-1 nor BDNF levels differed between treatment groups. These data support the involvement of proBDNF and IGF-1 in the pathogenesis and treatment of autism.

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)0.001
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 score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
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 head0.030
GPT teacher head0.277
Teacher spread0.247 · 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
Published2022
Admission routes1
Has abstractyes

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