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Record W1982624510 · doi:10.1089/neu.2006.0079

Protein Biomarkers in Serum of Pediatric Patients with Severe Traumatic Brain Injury Identified by ICAT–LC-MS/MS

2007· article· en· W1982624510 on OpenAlexafffund
Arsalan S. Haqqani, James S. Hutchison, Roxanne Ward, Danica Stanimirovic

Bibliographic record

VenueJournal of Neurotrauma · 2007
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicS100 Proteins and Annexins
Canadian institutionsHospital for Sick ChildrenChildren's Hospital of Eastern OntarioInstitute for Biological Sciences
FundersNational Research Council CanadaCanadian Institute for Theoretical AstrophysicsGenomic Health
KeywordsTraumatic brain injuryMedicineConcussionPoison controlInjury preventionInternal medicineEmergency medicinePsychiatry

Abstract

fetched live from OpenAlex

This report is a feasibility study on the utility of gel-free proteomics in identifying peripheral biomarkers of brain injury. The study was performed in six pediatric patients admitted to the intensive care unit for severe traumatic brain injury (TBI). Serum samples collected at admission (less than 8 h after injury) were used for determining the levels of S100beta by enzyme-linked immunosorbent assay (ELISA) and for proteomics analyses. Serum samples were depleted of high abundant albumin and immunoglobulin, and were compared to a pooled reference from several healthy individuals. After labeling and separation on an ionic column, six different serum fractions were analyzed using Isotope-Coded Affinity Tag (ICAT), followed by tandem mass spectrometry (MS/MS) protein sequencing and identification. Ninety-five unique, differentially expressed proteins were identified, including several with a likely brain origin. Several proteins with pattern similarity to S100beta identified by hierarchical clustering could be considered for evaluation in a larger patient sample as potential peripheral markers of TBI.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.535
Threshold uncertainty score0.630

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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)0.0000.000

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 head0.017
GPT teacher head0.268
Teacher spread0.252 · 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.

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

Citations73
Published2007
Admission routes2
Has abstractyes

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