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ObjectivesMethodsResultsConclusionsClinical ImplicationsAs transgender individuals age, they are at risk for neurocognitive disorders which pose not only medical but also bioethical questions. We present a case study of a transgender older adult with dementia who experienced changes in gender identity and explore the bioethical implications of identity over time, including end-of-life care.We reviewed clinical notes and relevant medical history to describe the transition and detransition process and examined ethical frameworks related to autonomy, psychological continuity, and transgender care.The individual transitioned as a transgender woman in mid-life but detransitioned shortly before being diagnosed with dementia. This case highlighted conflicts between precedent autonomy and current gender identity in the context of neurocognitive decline and end-of-life care.The case underscores the complexity of managing gender identity in transgender older adults with dementia, emphasizing the need for personalized and ethically sound care plans.Clinicians should be vigilant about the impact of neurocognitive disorders on gender identity, balancing respect for patients’ prior decisions with their current values, and develop personalized end-of-life care plans that honor the evolving identities and preferences of transgender individuals with dementia. [ABSTRACT FROM AUTHOR]

Background: Bipolar disorder (BD) involves significant mood and energy shifts reflected in speech patterns. Detecting these patterns is crucial for diagnosis and monitoring, currently assessed subjectively. Advances in natural language processing offer opportunities to objectively analyze them. Aims: To (i) correlate speech features with manic-depressive symptom severity in BD, (ii) develop predictive models for diagnostic and treatment outcomes, and (iii) determine the most relevant speech features and tasks for these analyses. Methods: This naturalistic, observational study involved longitudinal audio recordings of BD patients at euthymia, during acute manic/depressive phases, and after-response. Patients participated in clinical evaluations, cognitive tasks, standard text readings, and storytelling. After automatic diarization and transcription, speech features, including acoustics, content, formal aspects, and emotionality, will be extracted. Statistical analyses will (i) correlate speech features with clinical scales, (ii) use lasso logistic regression to develop predictive models, and (iii) identify relevant speech features. Results: Audio recordings from 76 patients (24 manic, 21 depressed, 31 euthymic) were collected. The mean age was 46.0 ± 14.4 years, with 63.2% female. The mean YMRS score for manic patients was 22.9 ± 7.1, reducing to 5.3 ± 5.3 post-response. Depressed patients had a mean HDRS-17 score of 17.1 ± 4.4, decreasing to 3.3 ± 2.8 post-response. Euthymic patients had mean YMRS and HDRS-17 scores of 0.97 ± 1.4 and 3.9 ± 2.9, respectively. Following data pre-processing, including noise reduction and feature extraction, comprehensive statistical analyses will be conducted to explore correlations and develop predictive models. Conclusions: Automated speech analysis in BD could provide objective markers for psychopathological alterations, improving diagnosis, monitoring, and response prediction. This technology could identify subtle alterations, signaling early signs of relapse. Establishing standardized protocols is crucial for creating a global speech cohort, fostering collaboration, and advancing BD understanding. [ABSTRACT FROM AUTHOR]