MétaCan
Menu
Back to cohort
Record W3196814851 · doi:10.3389/froh.2021.735634

Role of Maternal Infections and Inflammatory Responses on Craniofacial Development

2021· review· en· W3196814851 on OpenAlex
Anjali Y. Bhagirath, Manoj Reddy Medapati, Vivianne Cruz de Jesus, Sneha Yadav, Martha Hinton, Shyamala Dakshinamurti, Devi Atukorallaya

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueFrontiers in Oral Health · 2021
Typereview
Languageen
FieldImmunology and Microbiology
TopicReproductive System and Pregnancy
Canadian institutionsUniversity of ManitobaChildren's Hospital Research Institute of Manitoba
Fundersnot available
KeywordsFetusCraniofacialEpigeneticsPregnancyImmunologyBiologyInflammationBioinformaticsGenetics

Abstract

fetched live from OpenAlex

Pregnancy is a tightly regulated immunological state. Mild environmental perturbations can affect the developing fetus significantly. Infections can elicit severe immunological cascades in the mother's body as well as the developing fetus. Maternal infections and resulting inflammatory responses can mediate epigenetic changes in the fetal genome, depending on the developmental stage. The craniofacial development begins at the early stages of embryogenesis. In this review, we will discuss the immunology of pregnancy and its responsive mechanisms on maternal infections. Further, we will also discuss the epigenetic effects of pathogens, their metabolites and resulting inflammatory responses on the fetus with a special focus on craniofacial development. Understanding the pathophysiological mechanisms of infections and dysregulated inflammatory responses during prenatal development could provide better insights into the origins of craniofacial birth defects.

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.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.978
Threshold uncertainty score0.851

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 integrity0.0000.000
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 head0.032
GPT teacher head0.325
Teacher spread0.293 · 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