AI Article Synopsis

  • One in ten women of reproductive age have PCOS, characterized by subfertility, high LH levels, and potential dysfunction in the kisspeptin neurons that regulate GnRH.
  • Researchers studied the GnRH pulse generator in two mouse models of PCOS: the peripubertal androgen (PPA) model showed fewer synchronized neuron events, while the prenatal androgen (PNA) model revealed variable GnRH activity but cyclical patterns indicating complexity.
  • Findings indicate that in the PNA model, ARN neurons had increased activity during specific stages and less sensitivity to progesterone, highlighting the need to understand GnRH regulation in PCOS-related conditions.

Article Abstract

One in ten women in their reproductive age suffer from polycystic ovary syndrome (PCOS) that, alongside subfertility and hyperandrogenism, typically presents with increased luteinizing hormone (LH) pulsatility. As such, it is suspected that the arcuate kisspeptin (ARN) neurons that represent the GnRH pulse generator are dysfunctional in PCOS. We used here in vivo GCaMP fiber photometry and other approaches to examine the behavior of the GnRH pulse generator in two mouse models of PCOS. We began with the peripubertal androgen (PPA) mouse model of PCOS but found that it had a reduction in the frequency of ARN neuron synchronization events (SEs) that drive LH pulses. Examining the prenatal androgen (PNA) model of PCOS, we observed highly variable patterns of pulse generator activity with no significant differences detected in ARN neuron SEs, pulsatile LH secretion, or serum testosterone, estradiol, and progesterone concentrations. However, a machine learning approach identified that the ARN neurons of acyclic PNA mice continued to exhibit cyclical patterns of activity similar to that of normal mice. The frequency of ARN neuron SEs was significantly increased in algorithm-identified 'diestrous stage' PNA mice compared to controls. In addition, ARN neurons exhibited reduced feedback suppression to progesterone in PNA mice and their gonadotrophs were also less sensitive to GnRH. These observations demonstrate the importance of understanding GnRH pulse generator activity in mouse models of PCOS. The existence of cyclical GnRH pulse generator activity in the acyclic PNA mouse indicates the presence of a complex phenotype with deficits at multiple levels of the hypothalamo-pituitary-gonadal axis.

Download full-text PDF

Source
http://dx.doi.org/10.7554/eLife.97179DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11703500PMC

Publication Analysis

Top Keywords

pulse generator
24
gnrh pulse
20
generator activity
16
mouse models
12
arn neurons
12
arn neuron
12
pna mice
12
activity mouse
8
polycystic ovary
8
ovary syndrome
8

Similar Publications

Background: Use of health applications (apps) to support healthy lifestyles has intensified. Different app features may support effectiveness, including gamification defined as the use of game elements in a non-game situation. Whether health apps with gamification can impact behaviour change and cardiometabolic risk factors remains unknown.

View Article and Find Full Text PDF

Transcatheter Aortic Valve-in-Valve Implantation with Newer Generation Evolut Valve by Size of Failed Bioprosthesis.

Anatol J Cardiol

January 2025

Department of Cardiothoracic Surgery Research, Lankenau Institute for Medical Research, Wynnewood, Pennsylvania, USA ; Department of Cardiothoracic Surgery, Lankenau Heart Institute, Main Line Health Wynnewood, Pennsylvania, USA.

Background: To evaluate the clinical outcomes of valve-in-valve transcatheter aortic valve replacement (ViV TAVR) with newer-generation self-expanding Evolut valves according to the size of the failed surgical bioprosthesis.

Methods: This single-center retrospective study evaluated consecutive patients undergoing ViV TAVR with the Evolut Pro/Pro+/Fx between 2018 and 2022. These patients were compared based on the true internal diameter (ID) of the failed bioprosthesis, specifically ≤19 mm (small group) vs.

View Article and Find Full Text PDF

TiMON: a real-time integrated monitor for improving the placement and wear of emergency tourniquets.

BMC Emerg Med

January 2025

Wellman Center for Photomedicine, Harvard Medical School, Massachusetts General Hospital, CNY149, 13th St, Charlestown, 02129, MA, USA.

Background: The use of emergency tourniquets among military personnel has helped to dramatically reduce battlefield deaths and has recently gained popularity in the civilian sector. Yet, even well-trained individuals can find it difficult to assess proper tourniquet application. Emergency tourniquets are typically deemed sufficiently tightened through cursory visual confirmation or pulse assessment.

View Article and Find Full Text PDF

Deep Learning Analysis of White Matter Hyperintensity and its Association with Comprehensive Vascular Factors in Two Large General Populations.

J Imaging Inform Med

January 2025

Department of Convergence Medicine, Asan Medical Institute of Convergence Science and Technology, Asan Medical Center, University of Ulsan College of Medicine, 88 Olympic-Ro 43-Gil, Seoul, 05505, Republic of Korea.

Although the relationships between basic clinical parameters and white matter hyperintensity (WMH) have been studied, the associations between vascular factors and WMH volume in general populations remain unclear. We investigated the associations between clinical parameters including comprehensive vascular factors and WMH in two large general populations. This retrospective, cross-sectional study involved two populations: individuals who underwent general health examinations at the Asan Medical Center (AMC) and participants from a regional cohort, the Korean Genome and Epidemiology Study (KoGES).

View Article and Find Full Text PDF

We developed a deep learning-based extraction of electrocardiographic (ECG) waves from ballistocardiographic (BCG) signals and explored their use in R-R interval (RRI) estimation. Preprocessed BCG and reference ECG signals were inputted into the bidirectional long short-term memory network to train the model to minimize the loss function of the mean squared error between the predicted ECG (pECG) and genuine ECG signals. Using a dataset acquired with polyvinylidene fluoride and ECG sensors in different recumbent positions from 18 participants, we generated pECG signals from preprocessed BCG signals using the learned model and evaluated the RRI estimation performance by comparing the predicted RRI with the reference RRI obtained from the ECG signal using a leave-one-subject-out cross-validation scheme.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!