Micro-haemodynamics at the maternal–fetal interface: Experimental, theoretical and clinical perspectives
Bibliographic record
Abstract
The placenta is a vital interface between the mother and her developing fetus. Micro-haemodynamics of the placenta, where the particulate nature of blood flow cannot be ignored, mediates the relationship between the organ's structure and its function. However, the placenta's complex architecture and its relation to pregnancy pathologies remain poorly understood. This review covers current challenges in characterising placental micro-haemodynamics. Recent progress in three-dimensional multiscale imaging has stimulated the development of image-based theoretical models, but existing approaches do not fully harness the available data, and new tools are needed for the assimilation of complex imaging datasets. Although the placenta at term is available for in vivo imaging or ex vivo experimentation, insight into placental micro-rheology is limited, necessitating the use of biomimetic models. Microfluidic approaches offer opportunities for well-controlled characterisation of micro-rheology in complex geometries, but challenges remain in the robust fabrication of these systems. Recent advances in high-performance simulations for suspension flows enable parametrisation of key physical processes at the micro-scale. Future progress can be made by optimising computational architecture and integrating micro-haemodynamics with solute transport. Both experimental and computational approaches require translation to the organ scale. New upscaling approaches will need to accommodate non-local interactions in microvascular network flows and address the lack of clear scale separation across the placental architecture. Together, recent advances in cross-disciplinary imaging and modelling over the last ten years have opened a pathway for an in silico human placenta, accelerating the development of precision obstetrics medicine in the next decade.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".