Immuno-metabolic depression: from concept to implementation.

Major depressive disorder is a common, disabling mental disorder characterized by extensive etiological and phenotypic heterogeneity. This heterogeneity makes treatment approaches imprecise and often ineffective. Insight into the underlying biological mechanisms underpinning depression and its subtypes may enable more personalized treatments.

Use of psychedelic treatments in psychiatric clinical practice: an EPA policy paper.

Background: Recent years show an exponential increased interest ("renaissance") in the use of psychedelics for the treatment of mental disorders and broader. Some of these treatments, such as psilocybin for depression, are in the process of formal regulation by regulatory bodies in the US (FDA) and Europe (EMA), and as such on the brink of real-world implementation. In the slipstream of these developments increasing commercial initiatives are taking shape.

Systematic review: REM sleep, dysphoric dreams and nightmares as transdiagnostic features of psychiatric disorders with emotion dysregulation - Clinical implications.

Background: Fragmented rapid eye movement (REM) sleep disrupts the overnight resolution of emotional distress, a process crucial for emotion regulation. Emotion dysregulation, which is common across psychiatric disorders, is often associated with sleep disturbances. This systematic review explores how REM sleep and nightmares affect emotion processing and regulation in individuals with psychiatric disorders where emotion dysregulation is a key concern, suggesting novel sleep-related treatment pathways.

Immune-based Machine learning Prediction of Diagnosis and Illness State in schizophrenia and bipolar Disorder: How data bias and overfitting were avoided.

In a letter critiquing our manuscript, Takefuji highlights general pitfalls in machine learning, without directly engaging with our study. The comments provide generic advice rather than a specific critique of our methods or findings. Despite raising important topics, the concerns reflect standard risks in machine learning, which we were aware of and explicitly addressed in our analyses.