Kenbun Sone

Background: Enhanced AT ₁ R (angiotensin II type 1 receptor) signaling contributes to preeclampsia development; however, angiotensin receptor blockers are contraindicated during pregnancy because of associated fetal toxicity. We aimed to determine whether chemical modification of an angiotensin receptor blocker restricts placental transfer while preserving maternal pharmacological activity. Methods: Polyethylene glycol‐modified olmesartan derivatives were synthesized to increase molecular size and hydrophilicity and evaluated in vitro for AT ₁ R inhibitory activity. Placental transfer was assessed using an angiotensin II‐induced pregnant mouse model and an ex vivo human placental perfusion model to identify a derivative with reduced placental permeability. The selected compound was evaluated in vivo for pharmacological activity and fetal toxicity in pregnant mice. Results: A series of polyethylene glycol‐modified olmesartan derivatives maintained pharmacologically relevant AT ₁ R inhibitory activity in vitro. Evaluation in angiotensin II‐treated pregnant mice revealed compound #6, a polyethylene glycol‐modified dimer containing an ionizable tertiary amine, which exhibited reduced placental transfer (∼7% relative to olmesartan). Consistently, compound #6 exhibited minimal transplacental transfer in the human placental perfusion model, comparable to insulin, a negative control. Regarding fetal toxicity, olmesartan induced neonatal renal developmental toxicity at doses as low as one tenth of its minimum effective dose, whereas compound #6 exhibited clear renal toxicity only above its effective dose, indicating a discernible safety margin. Conclusions: Restricting placental transfer through rational chemical modification enables decoupling of maternal efficacy from fetal toxicity of angiotensin receptor blockers. Although further optimization is required, these findings provide proof of concept that AT ₁ R inhibition may represent a therapeutic strategy for preeclampsia during pregnancy.

Background This study aimed to develop and evaluate a deep learning-based surgical navigation system capable of recognizing the ureter, uterine artery, and bladder–uterine dissection plane during minimally invasive gynecologic surgery. Methods An artificial intelligence (AI) model was developed at the University of Tokyo Hospital using videos of prior surgeries. Surgical videos of 27 laparoscopic or robot-assisted total hysterectomies were used to create training and validation datasets, with an additional set of cases serving as an independent test set. Key frames were manually annotated to train segmentation models for the ureter and uterine artery. A separate model visualized loose connective tissue fibers (LCTF) to aid in recognizing the bladder–uterine peritoneal dissection plane. Quantitative performance was assessed using standard segmentation metrics, and a qualitative evaluation was conducted by nine gynecologic surgeons using predefined scoring criteria. Results The segmentation models achieved moderate quantitative performance, with Dice similarity coefficients of approximately 0.51 for the ureter and 0.45 for the uterine artery. In contrast, qualitative evaluation demonstrated favorable clinical interpretability. The mean recognition scores assigned by nine expert surgeons were 4.12 for the ureter and 3.45 for the uterine artery on a five-point scale, indicating that most structures were recognized clearly with only minor misrecognition. For bladder dissection, visualization of connective tissue fibers enabled identification of the correct dissection plane in the majority of evaluated frames; more than 70–80% of connective tissue was recognizable in most frames, and substantial misrecognition was uncommon. Conclusion This study demonstrates that a deep learning-based system can recognize three key elements of a total hysterectomy: the ureter, the uterine artery, and the bladder–uterine dissection plane. Despite modest quantitative metrics, qualitative assessments indicated strong clinical utility. These findings establish a foundation for an integrated AI-assisted surgical navigation platform to enhance the safety and standardization of minimally invasive gynecologic surgery.

Supplementary Table S9. Gene set enrichment analysis statistics for representative pathways.

Supplementary Table S2. Significance thresholds used for GSEA and ssGSEA analyses.

Supplementary Figure S7. Gene expression profiles across different RNA clusters in The Cancer Genome Atlas Program dataset.

Supplementary Table S1. List of samples analyzed in this study.

Supplementary Table S5. Detailed evaluation of discordance among TP53 mutation status, p53 immunohistochemistry, and CN-based molecular classification in non-POLE/non-MSI cases.

Supplementary Figure S10. Prognostic impact of transcriptomic clusters in TP53-mutated, high-grade endometrial carcinoma.

Exome sequencing revealed genomic landscape across high-grade endometrial cancers. A, Bar plots showing the number of mutations and signature proportions. The top shows the number of mutations per megabase. The middle shows the relative contribution of different mutational signatures. The bottom two show the length of genomic regions with CN loss or gain and the relative contribution of different CN signatures. B, Summary of genetic and clinical features for each sample. Each column corresponds to a sample. Each row corresponds to a different genetic or clinical feature or gene mutation status. C, Bar plots showing the frequency of specific gene mutations by subtype. The left shows the frequency of mutations in various genes by histologic classification. The right shows mutation frequency by molecular subtype. APOBEC, apolipoprotein B mRNA-editing enzyme, catalytic polypeptide; clear-S, clear cell carcinoma mixed with serous carcinoma; CS, carcinosarcoma; EM, endometrioid; G1/G2, grades 1–2 endometrioid; MSS, microsatellite stable; VUS, variant of unknown significance.

Supplementary Figure S4. Microsatellite instability score and mutational signatures for each sample across different molecular subtypes.