Zuranolone, a neuroactive drug, used in the treatment of postpartum depression by modulation of GABAA receptors.

BACKGROUND: Postpartum depression [PPD] is a prevalent and debilitating mood disorder that affects mothers in the weeks to months after childbirth. Zuranolone (Zurzuvae) is a novel pharmaceutical agent that was approved by the US FDA on 4 August 2023 for the management of PPD. This review article provides a comprehensive overview of zuranolone, focusing on its dosing, chemistry, mechanism of action, clinical trials, adverse drug reaction, and overall conclusion regarding its utility in the management of PPD. It also discusses the recommended dosing strategies to achieve optimal efficacy while minimizing adverse effects as the dosage regimen of zuranolone is critical for its therapeutic application. Moreover, it gives insights into neurobiological pathways involved in PPD. METHODOLOGY: Data from randomized controlled trials and observational studies was collected to provide a comprehensive understanding of zuranolone in the management and treatment of PPD. CONCLUSION: Zuranolone represents a promising therapeutic option for women suffering from postpartum depression. However, ongoing research and post-marketing surveillance are essential to further elucidate its long-term safety and efficacy. The integration of zuranolone into clinical practice may significantly improve the quality of life for mothers facing the challenges of postpartum depression.

An ensemble learning-based experimental framework for smart landslide detection, monitoring, prediction, and warning in IoT-cloud environment.

Landslides occur every year during the monsoon season in hilly areas. This natural disaster annually leads to several fatalities, injuries, and property destruction. Monitoring landslides and promptly alerting people to looming disasters in light of these injuries and fatalities are crucial. To date, no efficient technique is in practice to predict landslides. The tools that are now available monitor landslides at a very high cost and do not offer early warning or forecasts of soil movement. An innovative, low-cost Internet of Things (IoT)-based system for landslip warning, monitoring, and prediction is the major objective of this research. Its assessment, implementation, and development are described in detail. This study proposes an IoT-based smart landslide detection, warning, prediction, and monitoring system. The pre and post-measures use sensors and other hardware to deal with landslide disasters. It uses real-time environment monitoring (landslide site) for any changes and provides appropriate output by comparing the threshold values. The proposed system is tested on a prototype model, which performed well in our tests. The database was updated 2.5 s after the landslide thanks to a steady Internet connection. In less than 5 s after the event, the Thingspeak channel can display a graphical depiction of the data and its position. Multiple readings showed an 80-85% system accuracy rate. Further, the proposed ensemble learning-based risk prediction model is applied to static and dynamic data to predict the landslide for future reference. The ensemble classifier model has 98.67% recall, 96.56% accuracy, 97.35% F1-value, and 96.07% precision. The alert SMS is also sent to concerned authorities for medical emergency/PWD department/district administration.