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Do psychedelics affect sexual functioning postacutely? Anecdotal and qualitative evidence suggests they do, but this has never been formally tested. While sexual functioning and satisfaction are generally regarded as an important aspect of human wellbeing, sexual dysfunction is a common symptom of mental health disorders. It is also a common side effect of selective serotonin reuptake inhibitors (SSRIs), a first line treatment for depression. The aim of the present paper was to investigate the post-acute effects of psychedelics on self-reported sexual functioning, combining data from two independent studies, one large and naturalistic and the other a smaller but controlled clinical trial. Naturalistic use of psychedelics was associated with improvements in several facets of sexual functioning and satisfaction, including improved pleasure and communication during sex, satisfaction with ones partner and physical appearance. Convergent results were found in a controlled trial of psilocybin therapy versus an SSRI, escitalopram, for depression. In this trial, patients treated with psilocybin reported positive changes in sexual functioning after treatment, while patients treated with escitalopram did not. Despite focusing on different populations and settings, this is the first research study to quantitively investigate the effects of psychedelics on sexual functioning. Results imply a potential positive effect on post-acute sexual functioning and highlight the need for more research on this.

The emergence of long COVID during the ongoing COVID-19 pandemic has presented considerable challenges for healthcare professionals and researchers. The task of identifying relevant literature is particularly daunting due to the rapidly evolving scientific landscape, inconsistent definitions, and a lack of standardized nomenclature. This paper proposes a novel solution to this challenge by employing machine learning techniques to classify long COVID literature. However, the scarcity of annotated data for machine learning poses a significant obstacle. To overcome this, we introduce a strategy called medical paraphrasing, which diversifies the training data while maintaining the original content. Additionally, we propose a Data-Reweighting-Based Multi-Level Optimization Framework for Domain Adaptive Paraphrasing, supported by a Meta-Weight-Network (MWN). This innovative approach incorporates feedback from the downstream text classification model to influence the training of the paraphrasing model. During the training process, the framework assigns higher weights to the training examples that contribute more effectively to the downstream task of long COVID text classification. Our findings demonstrate that this method substantially improves the accuracy and efficiency of long COVID literature classification, offering a valuable tool for physicians and researchers navigating this complex and ever-evolving field.