Ultrasound Molecular Imaging of Neovascularization for Evaluation of Endometrial Receptivity Using Magnetic iRGD-Modified Lipid-Polymer Hybrid Microbubbles
Bibliographic record
Abstract
Background: Angiogenesis plays an important role in endometrial receptivity, determining the response of the endometrium to the blastocyst at the early stage of embryo implantation. During the application of assisted reproduction technologies, it is very important to evaluate the status of uterine angiogenesis before deciding on embryo implantation. Targeted microbubbles (MBs)-based ultrasound molecular imaging (UMI) can noninvasively detect the expression status of biomarkers at the molecular level, thereby being a potential diagnosis strategy for various diseases and their therapeutic evaluation. Methods: The iRGD-lipopeptide (DSPE-PEG2000-iRGD) conjugate was prepared with iRGD peptides and DSPE-PEG2000-maleimide through the Michael-type addition reaction. Then, the magnetic iRGD-modified lipid-polymer hybrid MBs (Mag-iLPMs) were prepared with the double-emulsification-solvent-evaporation method. Magnetic targeting of Mag-iLPMs was confirmed under the microscope, followed by a rectangular magnet. Next, the in vitro targeted binding of MBs to murine brain-derived endothelial cells.3 (bEnd.3) and human umbilical vein endothelial cells (HUVEC) were evaluated. The ratio of MBs binding to bEnd.3 and HUVEC at the same field was also compared. For in vivo studies, bolus injections of targeted or control MBs were randomly administrated to non-pregnant or pregnant rats on day 5. Then, the uteri were imaged using a VisualSonics Vevo 2100 ultrasound system (Fujifilm VisualSonics Inc., Ontario, Canada) equipped with a high-frequency transducer. Ultrasonic imaging signals were acquired from Mag-iLPMs, and compared with Mag-LPMs, iLPMs, and LPMs. Results: The Mag-iLPMs showed excellent performance in ultrasound contrast imaging and binding affinity to target cells. Using the magnetic field, 10.5- and 12.47-fold higher binding efficiency to bEnd.3 and HUVEC were achieved compared to non-magnetic iLPMs, respectively. Significantly enhanced UMI signals were also observed in the uteri of rats intravenously injected pregnant rats (6.58-fold higher than rats injected with iLPMs). Conclusion: We provided a powerful ultrasonic molecular functional imaging tool for uterine angiogenesis evaluation before embryonic implantation.
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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) | 0.000 | 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) | 0.001 | 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".