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Neutrophil apoptosis in preeclampsia, do steroids confound the relationship?

2004· article· en· W2148199092 on OpenAlexaff
Akiko Fuchisawa, Stephan van Eeden, Laura A. Magee, Beth A. Whalen, Peter C. K. Leung, James A. Russell, Keith R. Walley, Peter von Dadelszen

Bibliographic record

VenueJournal of obstetrics and gynaecology research · 2004
Typearticle
Languageen
FieldMedicine
TopicPregnancy and preeclampsia studies
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsPreeclampsiaMedicineBetamethasoneDexamethasoneApoptosisAnnexinEndocrinologyInternal medicinePregnancyPropidium iodideAndrologyFlow cytometryImmunologyProgrammed cell death

Abstract

fetched live from OpenAlex

AIM: To investigate the influence of maternal corticosteroid administration on neutrophil apoptosis in early onset preeclampsia. METHODS: We investigated five groups: early onset preeclampsia (EOPET, <34 weeks, n = 10); late-onset preeclampsia (LOPET, > or =34 weeks, n = 7); normotensive intrauterine growth restriction (nIUGR, n = 11); normal pregnancy (NPC, n = 22); and non-pregnancy (n = 10). We examined, by flow cytometry, spontaneous neutrophil apoptosis after 18 h culture (hypodiploid DNA, Annexin V binding, propidium iodide [PI] permeability). RESULTS: For the 10 women with EOPET exposed to betamethasone in the previous 48 h, we found that neutrophil apoptosis was not inappropriately inhibited, in contrast to our previous findings in women not thus exposed. Neither LOPET nor nIUGR differed from normal pregnancy. CONCLUSION: Betamethasone alters the rate of spontaneous neutrophil apoptosis in EOPET. The anti-inflammatory influence of betamethasone may explain some of the differences between our previous and present findings with respect to neutrophil apoptosis in EOPET. Corticosteroids ameliorate the course of antenatal and postnatal preeclampsia. These results may reflect the mechanisms that underlie the transient improvements seen with antenatal dexamethasone use.

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)0.002
metaresearch head score (Gemma)0.009
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.031
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.002
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 head0.096
GPT teacher head0.367
Teacher spread0.271 · 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.

Study designObservational
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

Citations8
Published2004
Admission routes1
Has abstractyes

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