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Record W4225813429 · doi:10.3389/fimmu.2022.816619

Consequences of Viral Infection and Cytokine Production During Pregnancy on Brain Development in Offspring

2022· review· en· W4225813429 on OpenAlex

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 Immunology · 2022
Typereview
Languageen
FieldMedicine
TopicCOVID-19 Impact on Reproduction
Canadian institutionsAlberta Children's HospitalUniversity of Calgary
FundersAgencia Nacional de Investigación y DesarrolloInternational Brain Research Organization
KeywordsOffspringPregnancyNeuroinflammationFetusAutismImmunologyImmune systemNeural stem cellSchizophrenia (object-oriented programming)BiologyNeuroscienceMedicineStem cellInflammationPsychiatry

Abstract

fetched live from OpenAlex

Infections during pregnancy can seriously damage fetal neurodevelopment by aberrantly activating the maternal immune system, directly impacting fetal neural cells. Increasing evidence suggests that these adverse impacts involve alterations in neural stem cell biology with long-term consequences for offspring, including neurodevelopmental disorders such as autism spectrum disorder, schizophrenia, and cognitive impairment. Here we review how maternal infection with viruses such as Influenza A, Cytomegalovirus, and Zika during pregnancy can affect the brain development of offspring by promoting the release of maternal pro-inflammatory cytokines, triggering neuroinflammation of the fetal brain, and/or directly infecting fetal neural cells. In addition, we review insights into how these infections impact human brain development from studies with animal models and brain organoids. Finally, we discuss how maternal infection with SARS-CoV-2 may have consequences for neurodevelopment of the offspring.

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)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.975
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
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 head0.038
GPT teacher head0.320
Teacher spread0.282 · 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