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Record W2098488099 · doi:10.1210/en.2014-1779

Fetal Exposure to Sertraline Hydrochloride Impairs Pancreatic β-Cell Development

2015· article· en· W2098488099 on OpenAlexafffund
Nicole E. De Long, Marie Gutgesell, Jim Petrik, Alison C. Holloway

Bibliographic record

VenueEndocrinology · 2015
Typearticle
Languageen
FieldMedicine
TopicPancreatic function and diabetes
Canadian institutionsUniversity of GuelphMcMaster University
FundersCanadian Institutes of Health ResearchMcMaster University
KeywordsEndocrinologyInternal medicineFetusSertralineMedicineHydrochloridePregnancyChemistryBiologyBiochemistryAntidepressant

Abstract

fetched live from OpenAlex

Ten percent to 15% of women take selective serotonin reuptake inhibitor (SSRI) antidepressants during pregnancy. Offspring exposed to SSRIs are more likely to have low birth weight; this is associated with an increased risk of development of diabetes in adulthood in part due to altered pancreatic development. The effects of perinatal exposure to SSRIs on pancreatic development are unknown. Therefore, the objective of this study was to determine the effect of fetal exposure to sertraline hydrochloride on pregnancy outcomes and pancreatic development. Wistar rats were given vehicle (n = 5) or sertraline hydrochloride (10 mg/kg/d; n = 8) via daily subcutaneous injection from the confirmation of mating until parturition. Results from this animal model demonstrated that offspring born to sertraline-exposed dams have no changes in birth weight but had a reduction in pancreatic β-cell area. The altered pancreatic islet development was a result of altered gene expression regulating islet development and survival. Therefore, fetal exposure to sertraline reduces β-cell capacity at birth, raising concerns regarding the long-term metabolic sequelae of such exposures.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.028
GPT teacher head0.266
Teacher spread0.238 · 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 source (direct Gemma or distilled Codex), 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

Citations5
Published2015
Admission routes2
Has abstractyes

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