ABSTRACT
Stress is known to be associated with adverse health outcomes. The COVID-19 pandemic and its associated lockdowns are examples of chronic stressors. Lockdown measures inadvertently caused significant psychological distress and became a powerful source of anxiety/stress, sleep disturbances, nutritional changes and weight gain. Stress is known to impact women's health specifically, through hypothalamic-pituitary-gonadal (HPG) axis dysfunction and resultant ovulatory dysfunction. Such dysfunction may manifest in menstrual irregularities and/or infertility due to hypothalamic hypogonadism. Here, we review the key physiological mediators of stress and associated ovulatory dysfunction. The kisspeptinergic system is comprised of sets of neurons located in the hypothalamus, the rostral periventricular region of the third ventricle (RP3V) and the arcuate nucleus (ARC). This system links nutrition, reproductive signals and stress. It plays a key role in the function of the HPG axis. During chronic stress, the kisspeptinergic system affects the HPG axis, GnRH pulsatility, and, therefore, ovulation. Leptin, insulin and corticotrophin-releasing hormone (CRH) are thought to be additional key modulators in the behavioral responses to chronic stress and may contribute to stress-related ovulatory dysfunction. This mini-review also summarizes and appraises the available evidence on the negative impact of chronic stress as a result of the COVID-19 pandemic lockdowns. It proposes physiological mechanisms to explain the observed effects on women's reproductive health and well-being. The review suggests areas for future research.
ABSTRACT
Background Chest radiography may play an important role in triage for coronavirus disease 2019 (COVID-19), particularly in low-resource settings. Purpose To evaluate the performance of an artificial intelligence (AI) system for detection of COVID-19 pneumonia on chest radiographs. Materials and Methods An AI system (CAD4COVID-XRay) was trained on 24 678 chest radiographs, including 1540 used only for validation while training. The test set consisted of a set of continuously acquired chest radiographs (n = 454) obtained in patients suspected of having COVID-19 pneumonia between March 4 and April 6, 2020, at one center (223 patients with positive reverse transcription polymerase chain reaction [RT-PCR] results, 231 with negative RT-PCR results). Radiographs were independently analyzed by six readers and by the AI system. Diagnostic performance was analyzed with the receiver operating characteristic curve. Results For the test set, the mean age of patients was 67 years ± 14.4 (standard deviation) (56% male). With RT-PCR test results as the reference standard, the AI system correctly classified chest radiographs as COVID-19 pneumonia with an area under the receiver operating characteristic curve of 0.81. The system significantly outperformed each reader (P < .001 using the McNemar test) at their highest possible sensitivities. At their lowest sensitivities, only one reader significantly outperformed the AI system (P = .04). Conclusion The performance of an artificial intelligence system in the detection of coronavirus disease 2019 on chest radiographs was comparable with that of six independent readers. © RSNA, 2020.