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Record W4362590951 · doi:10.1016/j.heliyon.2023.e15166

Understanding the potential immunogenetic role of TNFα-308 polymorphism in the pathogenesis of recurrent miscarriage

2023· article· en· W4362590951 on OpenAlexfundno aff
Shafat Ali, Sabhiya Majid, Md. Niamat Ali, Mujeeb Zafar Banday, Shahnaz Taing

Bibliographic record

VenueHeliyon · 2023
Typearticle
Languageen
FieldImmunology and Microbiology
TopicReproductive System and Pregnancy
Canadian institutionsnot available
FundersMount Royal University
KeywordsGenotypeSingle-nucleotide polymorphismPathogenesisAlleleSNPPolymorphism (computer science)Molecular biologyProinflammatory cytokineRecurrent miscarriagePromoterTumor necrosis factor alphaBiologyInternal medicineMedicineGeneImmunologyGeneticsInflammationGene expressionMiscarriagePregnancy

Abstract

fetched live from OpenAlex

Background: Recurrent miscarriage (RM) represents the spontaneous termination of two or more successive pregnancies. TNFα is a proinflammatory cytokine that is often considered harmful for embryonic development when expressed beyond normal levels. Aim: The study was conducted to assess the association between TNFα-308 polymorphism and RM pathogenesis. Methods: Samples of blood were obtained from patients and controls through venipuncture. The levels of TNFα in serum were measured by ELISA. TNFα gene promoter-associated single-nucleotide polymorphism was investigated with polymerase chain reaction (PCR) and restriction fragment length polymorphism (RFLP) techniques with precise primers and the restriction endonuclease, NcoI. Results: = 0.0117; p = 0.9942). Conclusion: The serum TNFα levels were significantly higher in the patients than the controls. The genotyping analysis also demonstrated that TNFα-308G/A SNP significantly increases the overall risk of RM, suggesting that the SNP modulates the TNFα gene expression and thereby increases serum TNFα levels that adversely affect the pregnancy outcome.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.660
Threshold uncertainty score0.268

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 head0.040
GPT teacher head0.244
Teacher spread0.205 · 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

Citations1
Published2023
Admission routes1
Has abstractyes

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