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Record W2947314253 · doi:10.1111/bjh.15996

The effects of hydroxycarbamide on the plasma proteome of children with sickle cell anaemia

2019· article· en· W2947314253 on OpenAlexaff
John Brewin, Sanjay Tewari, Stephan Menzel, Fenella J. Kirkham, Baba Inusa, George Renney, Malcolm Ward, David C. Rees

Bibliographic record

VenueBritish Journal of Haematology · 2019
Typearticle
Languageen
FieldMedicine
TopicHemoglobinopathies and Related Disorders
Canadian institutionsSt. Thomas Hospital
FundersKing's College LondonStroke Association
KeywordsHaptoglobinBlood proteinsProtein SMedicineInternal medicineEndocrinologyImmunologyProtein C

Abstract

fetched live from OpenAlex

We investigated changes in the plasma proteome of children with sickle cell anaemia (SCA) associated with hydroxycarbamide (HC) use, to further characterize the actions of HC. Fifty-one children with SCA consented to take part in this study. Eighteen were taking HC at a median dose of 22 mg/kg, and 33 were not on HC. Plasma was analysed using an unbiased proteomic approach and a panel of 92 neurological biomarkers. HC was associated with increased haemoglobin (Hb) (89·8 vs. 81·4 g/l, P = 0·007) and HbF (6·7 vs. 15·3%, P < 0·001). Seventeen proteins were decreased on HC compared to controls by a factor of <0·77, and six proteins showed >1·3 increased concentration. HC use was associated with reduced haemolysis (lower α, β, δ globin chains, haptoglobin-related protein, complement C9; higher haemopexin), reduced inflammation (lower α-1-acid glycoprotein, CD5 antigen-like protein, ceruloplasmin, factor XII, immunoglobulins, cysteine-rich secretory protein 3, vitamin D-binding protein) and decreased activation of coagulation (lower factor XII, carboxypeptidase B2, platelet basic protein). There was a significant correlation between the increase in HbF% on HC and haemopexin levels (r = 0·603, P = 0·023). This study demonstrated three ways in which HC may be beneficial in SCA, and identified novel proteins that may be useful to monitor therapeutic response.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.441
Threshold uncertainty score0.233

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.002
GPT teacher head0.184
Teacher spread0.182 · 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

Citations11
Published2019
Admission routes1
Has abstractyes

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