Bridging endometrial receptivity and implantation: network of hormones, cytokines, and growth factors
Bibliographic record
Abstract
The prerequisite of successful implantation depends on achieving the appropriate embryo development to the blastocyst stage and at the same time the development of an endometrium that is receptive to the embryo. Implantation is a very intricate process, which is controlled by a number of complex molecules like hormones, cytokines, and growth factors and their cross talk. A network of these molecules plays a crucial role in preparing receptive endometrium and blastocyst. Furthermore, timely regulation of the expression of embryonic and maternal endometrial growth factors and cytokines plays a major role in determining the fate of embryo. Most of the existing data comes from animal studies due to ethical issues. In this study, we comprehend the data from both animal models and humans for better understanding of implantation and positive outcomes of pregnancy. The purpose of this review is to describe the potential roles of embryonic and uterine factors in implantation process such as prostaglandins, cyclooxygenases, leukemia inhibitory factor, interleukin (IL) 6, IL11, transforming growth factor-β, IGF, activins, NODAL, epidermal growth factor (EGF), and heparin binding-EGF. Understanding the function of these players will help us to address the reasons of implantation failure and infertility.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".