Publications by authors named "M Gander"

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Prax Kinderpsychol Kinderpsychiatr

November 2024

Article Synopsis
  • - Recent research highlights the importance of attachment-related factors in treating personality disorders, especially in adolescents, indicating an opportunity to evaluate specific therapeutic methods tailored to this age group.
  • - Despite the emergence of new psychotherapeutic approaches for adolescents with personality disorders, there remains a significant gap in studies focusing on attachment characteristics.
  • - The study presents findings on attachment-related aspects in hospitalized adolescents, suggesting the development of a new attachment-based intervention tool aimed at addressing attachment trauma and fostering better emotion regulation skills in social situations.
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The sharp rise in the number of predominantly natal female adolescents experiencing gender dysphoria and seeking treatment in specialized clinics has sparked a contentious and polarized debate among both the scientific community and the public sphere. Few explanations have been offered for these recent developments. One proposal that has generated considerable attention is the notion of "rapid-onset" gender dysphoria, which is assumed to apply to a subset of adolescents and young adults.

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Coffin-Siris syndrome (CSS) is a rare genetic disorder and often co-occurs with attention-deficit hyperactivity disorder (ADHD) and autism spectrum (ASD). The present case study illustrates possible therapeutic interventions of these common psychiatric comorbidities taking into account the family interaction patterns. This can contribute to improve holistic management and overall level of functionality.

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This study examined the severity of unresolved attachment underlying adolescent identity diffusion. Our sample consisted of 180 inpatient adolescents aged 14 to 18 years (77% female, = 15.13, = 1.

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Dose-response curves are key metrics in pharmacology and biology to assess phenotypic or molecular actions of bioactive compounds in a quantitative fashion. Yet, it is often unclear whether or not a measured response significantly differs from a curve without regulation, particularly in high-throughput applications or unstable assays. Treating potency and effect size estimates from random and true curves with the same level of confidence can lead to incorrect hypotheses and issues in training machine learning models.

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