O-055 A large proportion of cycle failure is associated with an excessive inflammation response
Bibliographic record
Abstract
Abstract Our recent studies aims to understand IVF cycle failure using transcriptomic analysis of pooled granulosa cells from one oocyte collection session to characterize the patient response to the treatment and identify the cause of failure. We first performed a genomic analysis to identify the granulosa gene expression profile of women (n = 32) that didn’t get pregnant after an IVF cycle and identify potential failure causes. 165 differentially expressed genes (DEGs) were found between cells from follicles associated with failed and successful IVF cycles. The biological functions significantly affected in the negative patients were mainly related to immune and inflammatory responses. Indeed, many of the affected genes encode pro-inflammatory cytokines (e.g. IL1B and EGR1) or other inflammation-related factors that are transcriptionally active in human granulosa cells. Overexpression of several factors, including some acting upstream from VEGF, also indicates increased permeability and vasodilation. Another mechanism that appears more pronounced in the negative group is the recruitment of immune cells to ovarian tissues. Additionally, to this sustained pro-inflammatory response, the anti-inflammatory mechanisms normally helping restoration of homeostasis seem to be impaired in the negative group supporting the hypothesis that the ovarian environment created by hormonal stimulation is prone to dysregulation. Using these inflammation markers and other genes indicative of follicular status (e.g. growth phase or over-differentiated follicles) we analyzed an enlarged cohort of negative patients (n = 63) by qRT-PCR. A hierarchical cluster analysis showed that the negative patients could cluster into 3 groups. Moreover, these groups mainly differ on the expression of genes indicative of different failure causes. These results highlight the possibility of creating a simple diagnostic tool to identify the probable cause of failure and means to improve success in the next cycle.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".