P0069MICRO RNA 155, 210, 494, 29B AND 34A EXPRESSION PROFILE IN PREECLAMPSIA AND NORMAL PREGNANCIES
Bibliographic record
Abstract
Abstract Background and Aims Preeclampsia (PE) is a complex disorder that is characterized by hypertension and proteinuria after the 20th week of pregnancy, and it causes most neonatal morbidity and perinatal mortality. Despite many efforts, the knowledge acquired regarding its pathogenesis and pathophysiology does not allow us to treat it efficiently. It is not possible to arrest its progressive nature, and the available therapies are limited to symptomatic treatment. MicroRNAs (miRNAs) are small non-coding RNAs of approximately 19–23 nucleotides that can bind to the 3’ untranslated region of target mRNAs resulting in the degradation and translation inhibition of the mRNA, thereby regulating gene expression at the post-transcriptional level. Many studies have confirmed deregulated miRNA in pregnant patients with PE, and the function and mechanism of these differentially expressed miRNA are gradually being revealed. Method Sample collection Among the patients in the maternity ward, Department of Obstetrics and Gynecology. pregnant women who carried fetuses from 26 to 40 weeks of gestation were selected for study. Plasma samples were obtained from a total of 90 women in two groups; 48 being preeclamptic and 42 being healthy pregnancies, forming the control group. None of these subjects had undergone an invasive procedure. A volume of 5 mL maternal venous blood was drawn and collected in an ethylenediamine tetraacetic acid (EDTA) tube. The blood samples were centrifuged initially at 1200 g for 10 min and a second time at 10,000 g for 10 min, and 500 mL from the supernatant layer were used. The supernatant layer of the plasma was taken to Department of Molecular Biology and Genetics, Molecular Diagnosis Laboratory for storage at -80°C until the time of study. Total RNA extraction Total RNA was isolated from plasma using Trizol (Invitrogen, Carlsbad, CA) following the manufacturer protocol. Total RNA was analyzed using the commercially available Bioanalyzer Agilent RNA 6000 picoassay. MicroRNA isolation from maternal plasma MicroRNAs were obtained with the High-Specificity miRNA QRT-PCR detection kit (Stratagene-an Agilent Technologies Company, USA-Canada) according to manufacturer recommendations. Micro RNAs were subjected to a polyadenylation reaction as previously described. Next, using an oligo dT primer harboring a consensus sequence, reverse transcription was performed using an Affinity Script RT/RNase Block Enzyme mixture (Stratagene). miRNA was anayzed using the Bioanalyzer 2100- Agilent Small RNA assay. QRT-PCR platform The cDNA was amplified by real-time PCR. The real-time PCR analysis was performed on a Stratagene Mx3005P. This reaction contained a microRNA-specific forward primer, a TaqMan probe complementary to the 3ꞌ of the specific microRNA sequence, as well as part of the polyA adaptor sequence, and a universal reverse primer complementary to the consensus 3ꞌ sequence of the oligodT tail. Results were recorded in clinic sheets and were analyzed using SPss version 22. Results G1 consist of 48 and G2 42 pts. There were no differences in two group in respect to age (mean age was 28± 9.5 and 27± 5.23 in G1 and G2 respectively). Micro RNA155, 210 and 494 were significantly higher in plasma of G1 (Pv 0.001, 0.0001, 0.005 respectively). Micro RNA 29b was significantly lower in G1 (Pv 003). And there was no difference between two groups in respect to Micro RNA 34a (Pv 0.09). And also PE patients with higher plasma creatinine have more plasma level of Micro RNA155 and 210. There was a significant correlation between Micro RNA 494 and anemia in PE patients Conclusion In this study we showed that Micro RNA155, 210 and 494 increased and Micro RNA 29b decreased in PE and after clarifying by further studies may use as a predictor in clinical practice.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".