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Dysphoricmilk ejection reflex (D-MER) is characterized by a sudden onset of profoundly negative emotions that are temporally linked to milk letdown when breastfeeding or pumping breast milk. These affective experiences have no psychological precipitants and only last for minutes. D-MER is relatively underappreciated in the physician-oriented medical literature, although there are important clinical and public health consequences when a nursing parent experiences unwanted, negative emotions paired with breastfeeding. D-MER can undermine the parent's confidence and may affect bonding with their infant; it may also contribute to weaning earlier than planned. It is especially important for psychiatrists and other health care professionals who may be called upon to evaluate postpartum patients or nursing parents to be knowledgeable about D-MER. This perspective article aims to provide information about D-MER to the psychiatric and broader medical communities, and to underscore the need for additional research on this topic. Drawing on a literature search, the article first traces historical recognition of D-MER as a clinical entity, then summarizes the clinical characteristics of D-MER and recommends an assessment and management approach that emphasizes psychoeducation. The article also discusses factors that have contributed to the underappreciation of this condition, outlines gaps in our understanding, and suggests next steps for epidemiological and clinical research. D-MER is likely a neuroendocrine condition with psychiatric manifestations. Given that it is episodic but with predictable timing, this condition presents a unique opportunity for scientific investigation. [ABSTRACT FROM AUTHOR]

Loss of the sense of smell--anosmia--can be devastating. Without smell, patients describe feelings of emptiness, isolation, and emotional distress. Given that anosmia is a common first symptom of COVID-19 and [...]

Obesity is a problem that affects the general health and wellness of many people around the world. The paper seeks to provide an automated individualised dietary and exercise suggestions based on an individual’s obesity class. The system classifies the user’s level of obesity using Artificial Neural Networks (ANN), Support Vector Machines (SVM), and random forest machine learning algorithms. To provide appropriate food and activity procedures in accordance with the classification of obesity, we use machine learning techniques like decision trees and recommendation algorithms.