Zsuzsanna Zsengeller | Preeclampsia | Research Excellence Award

Assist. Prof. Dr. Zsuzsanna Zsengeller | Preeclampsia | Research Excellence Award

Harvard Medical School | United States

Assist. Prof. Dr. Zsuzsanna K. Zsengellér is a distinguished researcher at Harvard Medical School, recognized for her contributions to molecular medicine, mitochondrial biology, and translational research in liver and kidney diseases as well as pregnancy-related disorders such as preeclampsia. With 88 publications cited over 6,100 times, she has established a strong international presence through collaborations with more than 400 co-authors, advancing understanding of oxidative stress, organ-specific injury, and therapeutic interventions. Her work spans high-impact journals, including Science, Journal of Medicinal Chemistry, and Kidney International, reflecting a commitment to developing innovative approaches for disease mitigation and treatment. Through pioneering studies on hydrazone-based therapeutics, multi-organ disease models, and mitochondrial resilience, her research has significantly influenced both clinical and experimental strategies, bridging fundamental science and translational medicine for global health impact

Citation Metrics (Scopus)

6,108
4,000
2,000
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Citations

6,108

Documents

88

h-index

42

Citations

Documents

h-index

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Top 5 Featured Publications

Pietro Pasquini | Gynaecology | Innovative Research Award

Dr. Pietro Pasquini |  Gynaecology | Innovative Research Award 

Doctor | University of Bologna | Italy

Dr. Pietro Pasquini is an Obstetrics and Gynecology Resident at the University of Bologna, specializing in Gynecologic Oncology and Robotic Surgery through the ORSI Academy, Belgium, where he also serves as a Clinical Fellow and Robotic Surgery trainer at AZORG Hospital, Aalst. He was a member of the first European team to perform telesurgery, reflecting his pioneering contributions to robotic gynecologic surgery. His medical training includes a residency at IRCCS Azienda Ospedaliero-Universitaria di Bologna, Italy, clinical fellowships in Tanzania (Consolata Hospital, Ob/Gyn and General Surgery) and Belgium (UZ Brussels, Oncological, Abdominal, Thoracic, and Cardiac Surgery), a Master’s degree in Medicine and Surgery from Alma Mater Studiorum – Università di Bologna, and an extensive role as an Anatomy Dissection Tutor at DIBINEM, Bologna. Pasquini has actively participated in professional development programs, including Erasmus+ internships, IFAB’s Young Talent program, and international anatomy conferences. He holds certifications in multiple robotic platforms (DaVinci® X, Xi, Toumai®, HugoTM RAS, Hinotori®), gynecological ultrasound (IOTA), BLSD, and ECDL. He has presented at major conferences such as the Society of European Robotic Gynaecological Surgery and Society of Robotic Surgery. Pasquini has authored key publications on telesurgery and gynecologic oncology, including Pioneering telesurgery in gynecology: the first European case of total hysterectomy (J Robot Surg. 2025;19:460), and Effect of Diaphragmatic Resection Versus Stripping in Advanced Ovarian Cancer (Ann Surg Oncol. 2025). His h-index is 2 with 11 citations across 4 Scopus-listed documents. He actively leads ongoing research in AI-assisted surgical planning, robotic hysterectomy, peritoneal cancer index detection, and surgical simulation standardization, demonstrating expertise in robotic surgery, patient care, database management, and public speaking.

Profiles: Scopus | ORCID

Featured Publications

1. Pazzaglia, E., Pasquini, P., Jamaer, E., Traen, K., Despierre, E., & Mottrie, A. (2025). Pioneering telesurgery in gynecology: The first European case of total hysterectomy. Journal of Robotic Surgery.

2. Mezzapesa, F., Di Costanzo, S., Coadă, C. A., Bernante, P., Balsamo, F., Garelli, S., Genovesi, L., Pasquini, P., Lambertini, A., Caramelli, F., et al. (2024). Combined robotic surgery for concomitant treatment of endometrial cancer and obesity. Surgical Endoscopy.

3. Santoro, M., Zybin, V., Coada, C. A., Mantovani, G., Paolani, G., Di Stanislao, M., Modolon, C., Di Costanzo, S., Lebovici, A., Ravegnini, G., et al. (2024). Machine learning applied to pre-operative computed-tomography-based radiomic features can accurately differentiate uterine leiomyoma from leiomyosarcoma: A pilot study. Cancers.