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Record W3043021018 · doi:10.31640/jvd.5-6.2019(6)

PREDICTIVE GENETIC SIGNS OF INTRAUTERINE GROWTH RETARDATION SYNDROM IN NEWBORNS

2019· article· en· W3043021018 on OpenAlexaff
Z. R. Kocherga, Tetiana Savrun

Bibliographic record

VenueLikarska sprava · 2019
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGlutathione Transferases and Polymorphisms
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsBiologyDetoxicationGeneticsGeneGlutathioneGenetic analysisEnzymeBiochemistry

Abstract

fetched live from OpenAlex

The influence of genetically determined factors of mutation genetic pressing leads to genetic instability increasing or reducing genom’s sensitivenes to further mutant action, which is of major importance in developing the syndrome of intrauterine growth retardation (IUGR) in newborns. The main markers of genetic instability are the changes in morphological cell characteristics (cytogenetic, cytological, cytodensimetric), polymorphism of genes of xenobiotics detoxication GSTM1 and GSTТ1, changes in enzyme activity of glutathione acid and oxidation protein modification. In order to define the leading factors of destruction of genetic status of newborns with IUGR syndrome a discriminant and correlation analysis was conducted, which determined the links between the genes of xenobiotics detoxication GSTM1 and GSTТ1, by the indices of morphological functional genome state, enzymes activity of glutathione system and oxidation protein modifications and development of IUGR syndrome. The model of the statistic analysis of cytogenic, molecular genetic and biochemical characteristics enables to obtain an objective characteristic of the state of inheritance apparatus of newborns with IGR in comparison with healthy newborns.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.314
Threshold uncertainty score0.541

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)0.0000.000

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 head0.004
GPT teacher head0.202
Teacher spread0.198 · 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 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

Citations0
Published2019
Admission routes1
Has abstractyes

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