Context: Women worldwide are delaying childbearing, but are they aware of the age-related decline in fertility?
Aims: The aim of this study is to investigate awareness of age-related decline in fertility and oocyte cryopreservation.
Settings And Design: A primary analysis of a cross-sectional electronic survey with a nationally representative sample of nulliparous women aged 25-45 years.
Subjects And Methods: A national online survey performed March 4-March 9, 2016.
Statistical Analysis Used: A linear regression model and ANOVA tests were performed.
Results: A total of 1213 women completed the survey. A significant difference was discovered in fecundity knowledge between women who identified as in a partnership compared to those who did not. Partnered women were more likely to respond "know a lot" about the age-related decline in fertility, whereas unpartnered women were more likely to respond "never heard of it" ( < 0.01). Partnered women are also more likely to respond that they would have made different life choices had they been more knowledgeable about fertility at a younger age ( = 0.01). The majority of the survey population had heard of oocyte cryopreservation but did not know much about it.
Conclusions: Slightly over half of participants had an understanding of the natural age-related decline in fertility. Having a partner significantly increased the likelihood that a woman reported more knowledge about fertility. More effort is necessary to educate all women on assisted reproductive technologies and the natural age-related decline in fertility, specifically single women of childbearing age.
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http://dx.doi.org/10.4103/jhrs.JHRS_158_17 | DOI Listing |
Drug Deliv Transl Res
January 2025
Department of Pharmaceutics, School of Pharmaceutical Education and Research, Jamia Hamdard, New Delhi, 110062, India.
The global prevalence of Parkinson's Disease (PD) is on the rise, driven by an ageing population and ongoing environmental conditions. To gain a better understanding of PD pathogenesis, it is essential to consider its relationship with the ageing process, as ageing stands out as the most significant risk factor for this neurodegenerative condition. PD risk factors encompass genetic predisposition, exposure to environmental toxins, and lifestyle influences, collectively increasing the chance of PD development.
View Article and Find Full Text PDFBMC Pharmacol Toxicol
January 2025
Yantai Affiliated Hospital of Binzhou Medical University, Yantai, Shandong, 264100, PR China.
Background: Alzheimer's disease (AD), a hallmark of age-related cognitive decline, is defined by its unique neuropathology. Metabolic dysregulation, particularly involving glutamine (Gln) metabolism, has emerged as a critical but underexplored aspect of AD pathophysiology, representing a significant gap in our current understanding of the disease.
Methods: To investigate the involvement of GlnMgs in AD, we conducted a comprehensive bioinformatic analysis.
Mol Psychiatry
January 2025
Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, USA.
Age-related dopamine (DA) neuron loss is a primary feature of Parkinson's disease. However, whether similar biological processes occur during healthy aging, but to a lesser degree, remains unclear. We therefore determined whether midbrain DA neurons degenerate during aging in mice and humans.
View Article and Find Full Text PDFNat Rev Drug Discov
January 2025
Division of Medicine, University College London, London, UK.
Immunity declines with age. This results in a higher risk of age-related diseases, diminished ability to respond to new infections and reduced response to vaccines. The causes of this immune dysfunction are cellular senescence, which occurs in both lymphoid and non-lymphoid tissue, and chronic, low-grade inflammation known as 'inflammageing'.
View Article and Find Full Text PDFJ Neurosci Methods
January 2025
School of Electrical and Computer Engineering, Gallogly College of Engineering, University of Oklahoma, Norman, OK 73019, USA.
Background: Recent advances in multimodal signal analysis enable the identification of subtle drug-induced anomalies in sleep that traditional methods often miss.
New Method: We develop and introduce the Dynamic Representation of Multimodal Activity and Markov States (DREAMS) framework, which embeds explainable artificial intelligence (XAI) techniques to model hidden state transitions during sleep using tensorized EEG, EMG, and EOG signals from 22 subjects across three age groups (18-29, 30-49, and 50-66 years). By combining Tucker decomposition with probabilistic Hidden Markov Modeling, we quantified age-specific, temazepam-induced hidden states and significant differences in transition probabilities.
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