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Updated: Jan 11, 2026

A Model of Cardiac Remodeling Through Constriction of the Abdominal Aorta in Rats

A Model of Cardiac Remodeling Through Constriction of the Abdominal Aorta in Rats

Published on: December 2, 2016

Cardiac Remodeling in Preeclampsia: A Large Language Model-Assisted Meta-Analysis and Meta-Regression.

Lefteris Teperikidis^1,2,3, Aristi Boulmpou³, Ghadir Amin⁴

¹Synthesa, Inc., New York, NY.

Journal of Cardiovascular Pharmacology

|November 18, 2025

Summary

This summary is machine-generated.

Preeclampsia is linked to subtle heart changes, including reduced ejection fraction and impaired global longitudinal strain, alongside increased left ventricular mass. This study highlights the potential of AI in large-scale medical research synthesis.

Keywords:

artificial intelligence bayesian large language models meta-analysis preeclampsia synthesa

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Studying Left Ventricular Reverse Remodeling by Aortic Debanding in Rodents

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Last Updated: Jan 11, 2026

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A Rat Model of Pressure Overload Induced Moderate Remodeling and Systolic Dysfunction as Opposed to Overt Systolic Heart Failure

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Studying Left Ventricular Reverse Remodeling by Aortic Debanding in Rodents

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Published on: July 14, 2021

Area of Science:

Cardiology
Obstetrics
Medical Informatics

Background:

Preeclampsia, a hypertensive disorder of pregnancy, poses significant maternal morbidity and long-term cardiovascular risks.
The precise nature and consistency of echocardiographic remodeling in preeclampsia remain unclear.
Existing research lacks comprehensive synthesis of left ventricular function and geometry alterations.

Purpose of the Study:

To conduct a mega-meta-analysis of left ventricular function and geometry in preeclampsia.
To define the echocardiographic remodeling phenotype associated with preeclampsia.
To evaluate the utility of large language model (LLM)-based tools in evidence synthesis.

Main Methods:

A PROSPERO-registered systematic review and mega-meta-analysis.
Searched PubMed, Scopus, and Embase without date restrictions.
Utilized an LLM-based suite (Synthesisa AI) for abstract screening, data extraction, risk of bias assessment, and Bayesian code generation, with human validation.

Main Results:

Preeclampsia associated with reduced ejection fraction ( -0.87%) and impaired global longitudinal strain ( -3.08%).
Significantly increased left ventricular mass index (+13.10 g/m 2 ) and relative wall thickness (+0.062) observed.
No significant difference in fractional shortening; BMI, parity, and gestational age were significant moderators.

Conclusions:

Preeclampsia is characterized by a remodeling phenotype of preserved ejection fraction, impaired strain, and hypertrophic adaptation, indicative of subclinical systolic dysfunction.
Demonstrates the transformative potential of LLM-based tools for automated, scalable, and standardized evidence syntheses.
Provides a clearer understanding of cardiovascular adaptations in preeclampsia, informing long-term risk assessment.