Association of Tumor Growth Factor-β and Interferon-γ Serum Levels With Insulin Resistance in Normal Pregnancy
Bibliographic record
Abstract
Pregnancy is related to change in glucose metabolism and insulin production. The aim of our study was to determine the association of serum IFN-γ and TGF- β levels with insulin resistance during normal pregnancy. This cross sectional study was carried out on 97 healthy pregnant (in different trimesters) and 28 healthy non-pregnant women. Serum TGF-β and IFN- γ level were measured by ELISA method. Pregnant women had high level TGF-β and low level IFN-γ as compared non-pregnant women. Maternal serum TGF-β concentration significantly increased in third trimester as compared first and second trimester of pregnancy. Maternal serum IFN-γ concentration significantly decreased in third trimester as compared first and second trimester of pregnancy. Pregnant women exhibited higher score of HOMA IR as compared non-pregnant women. There were association between gestational age with body mass index (r=0.28, P=0.005), TGF-β (r=0.45, P<0.001) and IFN-γ (r=-0.50, P<0.001). There was significant association between Insulin resistance and TGF-β (r=0.17, p=0.05). Our findings suggest that changes in maternal cytokine level in healthy pregnant women were anti-inflammatory. Furthermore, Tumor Growth Factor-β appears has a role in induction insulin resistance in healthy pregnant women. However, further studies needed to evaluate role of different cytokines on insulin resistance in normal pregnancy.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".