Use of Photoacoustic Imaging to Study the Effects of Anemia on Placental Oxygen Saturation in Normoxic and Hypoxic Conditions
Bibliographic record
Abstract
Abstract We aimed to evaluate fetal and placental oxygen saturation (sO2) in anemic and non-anemic pregnant rats throughout gestation using photoacoustic imaging (PAI). Female Sprague Dawley rats were fed an iron-restricted or iron-replete diet before and during pregnancy. On gestational days 13, 18, and 21, PAI was coupled with high resolution ultrasound to measure oxygenation of the fetus, whole placenta, mesometrial triangle, as well as the maternal and fetal faces of the placenta. PAI was performed in 3D, which allowed sO2 to be measured within an entire region, as well as in 2D, which enabled SO2 measurements in response to a hypoxic event in real time. Both 3D and 2D PAI were performed at varying levels of FiO2 (fraction of inspired oxygen). Iron restriction caused anemia in dams and fetuses, a reduction in fetal body weight, and an increase in placental weight, but overall had minimal effects on sO2. Reductions in FiO2 caused corresponding reductions in sO2 which correlated to the severity of the hypoxic challenge. Regional differences in sO2 were evident within the placenta, and between the placenta and fetus. In conclusion, PAI enables non-invasive measurement of sO2 both rapidly and with a high degree of sensitivity. The lack of overt changes in SO2 levels between control and anemic fetuses may suggest reduced oxygen extraction and utilization in the latter group, which could be attributed to compensatory changes in growth and developmental trajectories.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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 | 0.001 |
| 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".