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Record W4411014697 · doi:10.1016/j.xagr.2025.100533

Prospective determination of heat shock protein serum levels in Saskatchewan women during pregnancy

2025· article· en· W4411014697 on OpenAlexaffabout
Ewa Miśkiewicz, Jocelyne Martel, Daniel J. MacPhee

Bibliographic record

VenueAJOG Global Reports · 2025
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicHeat shock proteins research
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsPregnancyShock (circulatory)Heat shock proteinProspective cohort studyObstetricsMedicineAndrologyChemistryInternal medicineBiologyBiochemistry

Abstract

fetched live from OpenAlex

ABSTRACT Background The stress proteins Heat shock protein (HSP) 27, HSP70, and αB-crystallin have all been detected in human serum under normal and disease conditions. HSP70 serum levels are known to increase in women with advancing gestation, but HSP27, HSP70, and αB-crystallin have never been assessed prospectively over pregnancy and at labour. Objective The purpose of this study was to determine the serum levels of HSP27, HSP70, and αB-crystallin in pregnant women over pregnancy to assess whether the levels increase significantly over time and particularly at term and/or labour. Study Design We conducted a prospective cohort pilot study of serum levels of HSP27, HSP70, and αB-crystallin in Saskatoon-area women with singleton pregnancies using enzyme-linked immunosorbent assays (ELISA). Serum samples were collected from each patient at three periods: <14 weeks, 22-33 weeks at glucose screening, and at term non-labour/active labour. ELISA data were subjected to D’Agostino and Pearson tests for normality. If data were not normally distributed, Friedman tests followed by Dunn’s multiple comparisons tests were conducted. If data were normally distributed, they were subjected to repeated measures one-way analysis of variance followed by Tukey’s multiple comparisons tests. A Spearman correlation analysis of HSP levels with increasing gestational age was also conducted. All data were assessed and graphed using GraphPad Prism software. Results HSP27 levels were significantly elevated at term/labour compared to the two earlier timepoints assessed (Dunn’s p<0.0001 vs <14 weeks and p=0.0014 vs 22-33 weeks). HSP70 serum levels were also significantly elevated at term/labour compared to earlier timepoints (Dunn’s p=0.0005 vs <14 weeks and p=0.0179 vs 22-33 weeks). In contrast, αB-crystallin levels were not significantly different over gestation. When the serum levels of the HSPs over pregnancy were separately examined in the prospective cohorts who were sampled at term or at active spontaneous or induced labour, HSP27 levels were still significantly elevated at term (Tukey’s p=0.003 vs <14 weeks and p=0.031 vs 22-33 weeks) or at labour (Tukey’s p<0.0001 vs <14 weeks and 22-33 weeks). Of note, HSP27 levels at term were significantly elevated over levels at labour (3.949 ± 0.622 vs 2.139 ± 0.241, respectively; Welch’s p=0.0211). HSP70 levels were only significantly elevated at term (Dunn’s p=0.029) or at labour (Tukey’s p=0.015) compared to <14 weeks. αB-crystallin levels were not significantly different over gestation in these two cohorts. Overall, there was a significant positive correlation between gestational age and HSP27 or HSP70 serum concentrations regardless of whether we examined term/labour as a group or in separate cohorts of patients who were sampled at term or labour. Conclusion Serum levels of HSP27 and HSP70 in pregnant women at term and/or labour were significantly elevated relative to earlier periods of pregnancy and they positively correlated with gestational age. However, HSP27 levels were also significantly elevated at term compared to labour thereby identifying this small HSP as a candidate biomarker of impending labour. Such a biomarker could be valuable for screening term pregnant women in rural settings who are distant from urban delivery centres to minimize travel, economic costs, and maternal stress.

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)0.000
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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.065
Threshold uncertainty score0.650

Codex and Gemma teacher scores by category

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)0.0000.000

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 head0.008
GPT teacher head0.287
Teacher spread0.280 · 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".

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Citations0
Published2025
Admission routes2
Has abstractyes

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