Conceptual foundations of acetylcarnitine supplementation in neuropsychiatric long COVID syndrome: a narrative review.

Post-acute sequelae of COVID-19 can present as multi-organ pathology, with neuropsychiatric symptoms being the most common symptom complex, characterizing long COVID as a syndrome with a significant disease burden for affected individuals. Several typical symptoms of long COVID, such as fatigue, depressive symptoms and cognitive impairment, are also key features of other psychiatric disorders such as myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) and major depressive disorder (MDD). However, clinically successful treatment strategies are still lacking and are often inspired by treatment options for diseases with similar clinical presentations, such as ME/CFS. Acetylcarnitine, the shortest metabolite of a class of fatty acid metabolites called acylcarnitines and one of the most abundant blood metabolites in humans can be used as a dietary/nutritional supplement with proven clinical efficacy in the treatment of MDD, ME/CFS and other neuropsychiatric disorders. Basic research in recent decades has established acylcarnitines in general, and acetylcarnitine in particular, as important regulators and indicators of mitochondrial function and other physiological processes such as neuroinflammation and energy production pathways. In this review, we will compare the clinical basis of neuropsychiatric long COVID with other fatigue-associated diseases. We will also review common molecular disease mechanisms associated with altered acetylcarnitine metabolism and the potential of acetylcarnitine to interfere with these as a therapeutic agent. Finally, we will review the current evidence for acetylcarnitine as a supplement in the treatment of fatigue-associated diseases and propose future research strategies to investigate the potential of acetylcarnitine as a treatment option for long COVID.

Objective structured clinical examination to teach competency in planetary health care and management - a prospective observational study.

BACKGROUND: Health professionals are increasingly called upon and willing to engage in planetary health care and management. However, so far, this topic is rarely covered in medical curricula. As the need for professional communication is particularly high in this subject area, this study aimed to evaluate whether the objective structured clinical examination (OSCE) could be used as an accompanying teaching tool. METHODS: During the winter semester 2022/2023, 20 third- and fifth-year medical students voluntarily participated in a self-directed online course, three workshops, and a formal eight-station OSCE on planetary health care and management. Each examinee was also charged alternatingly as a shadower with the role of providing feedback. Experienced examiners rated students' performance using a scoring system supported by tablet computers. Examiners and shadowers provided timely feedback on candidates` performance in the OSCE. Immediately after the OSCE, students were asked about their experience using a nine-point Likert-scale survey and a videotaped group interview. Quantitative analysis included the presentation of the proportional distribution of student responses to the survey and of box plots showing percentages of maximum scores for the OSCE performance. The student group interview was analyzed qualitatively. RESULTS: Depending on the sub-theme, 60% -100% of students rated the subject of planetary health as likely to be useful in their professional lives. Similar proportions (57%-100%) were in favour of integrating planetary health into required courses. Students perceived learning success from OSCE experience and feedback as higher compared to that from online courses and workshops. Even shadowers learned from observation and feedback discussions. Examiners assessed students' OSCE performance at a median of 80% (interquartile range: 83%-77%) of the maximum score. CONCLUSIONS: OSCE can be used as an accompanying teaching tool for advanced students on the topic of planetary health care and management. It supports learning outcomes, particularly in terms of communication skills to sensitise and empower dialogue partners, and to initiate adaptation steps at the level of individual patients and local communities.

[Structures, aims and needs of early career scientists at the German Center for Mental Health]. / Strukturen, Ziele und Bedürfnisse der Early Career Scientists am Deutschen Zentrum für Psychische Gesundheit.

BACKGROUND: Early career scientists (ECS) are agents of change and driving forces in the promotion of mental health. The German Center for Mental Health (DZPG) is a powerful initiative to guide and support careers in the field of mental health. OBJECTIVE: The DZPG aims to make investments to educate, engage, excite, and empower ECS in an interdisciplinary and interinstitutional scientific community. STRUCTURES, TOPICS AND INITIATIVES: To achieve this, the ECS Board at the DZPG plays a central role and consists of 18 elected ECS representatives. The ECS culture gives members the right of voice and embraces bottom-to-top ideas and acknowledges autonomy and co-determination. The DZPG academy was developed to facilitate communication and networking and encourage collaboration among ECS members. The DZPG also navigates several key issues, such as equality, diversity, inclusion, family friendliness and work-life balance, which are essential for a functioning research landscape. The DZPG also extends opportunities to ECS to develop skills and competencies that are essential for contemporary ECS. It complements nationwide support for ECS with funding opportunities, mental health support at work, careers advice and guidance activities. Importantly, the ECS Board is committed to patient and public involvement and engagement, scientific communication and knowledge transfer to multiple settings. CONCLUSION: The DZPG will contribute to fostering ECS training programs for student and academic exchanges, collaborative research, and pooling of resources to acquire grants and scholarships. It will also support the establishment of hubs for ECS networks and promote the expansion of international competence of ECS in Germany.

Gut microbiome in atypical depression.

BACKGROUND: Recent studies showed that immunometabolic dysregulation is related to unipolar major depressive disorder (MDD) and that it more consistently maps to MDD patients endorsing an atypical symptom profile, characterized by energy-related symptoms including increased appetite, weight gain, and hypersomnia. Despite the documented influence of the microbiome on immune regulation and energy homeostasis, studies have not yet investigated microbiome differences among clinical groups in individuals with MDD. METHODS: Fifteen MDD patients with atypical features according to the Diagnostic and Statistical Manual of Mental Disorders (DSM-5)-5, forty-four MDD patients not fulfilling the DSM-5 criteria for the atypical subtype, and nineteen healthy controls were included in the study. Participants completed detailed clinical assessment and stool samples were collected. Samples were sequenced for the prokaryotic 16S rRNA gene, in the V3-V4 variable regions. Only samples with no antibiotic exposure in the previous 12 months and a minimum of >2000 quality-filtered reads were included in the analyses. RESULTS: There were no statistically significant differences in alpha- and beta-diversity between the MDD groups and healthy controls. However, within the atypical MDD group, there was an increase in the Verrucomicrobiota phylum, with Akkermansia as the predominant bacterial genus. LIMITATIONS: Cross-sectional data, modest sample size, and significantly increased body mass index in the atypical MDD group. CONCLUSIONS: There were no overall differences among the investigated groups. However, differences were found at several taxonomic levels. Studies in larger longitudinal samples with relevant confounders are needed to advance the understanding of the microbial influences on the clinical heterogeneity of depression.

Investigating the structural network underlying brain-immune interactions using combined histopathology and neuroimaging: a critical review for its relevance in acute and long COVID-19.

Current views on immunity support the idea that immunity extends beyond defense functions and is tightly intertwined with several other fields of biology such as virology, microbiology, physiology and ecology. It is also critical for our understanding of autoimmunity and cancer, two topics of great biological relevance and for critical public health considerations such as disease prevention and treatment. Central to this review, the immune system is known to interact intimately with the nervous system and has been recently hypothesized to be involved not only in autonomic and limbic bio-behaviors but also in cognitive function. Herein we review the structural architecture of the brain network involved in immune response. Furthermore, we elaborate upon the implications of inflammatory processes affecting brain-immune interactions as reported recently in pathological conditions due to SARS-Cov-2 virus infection, namely in acute and post-acute COVID-19. Moreover, we discuss how current neuroimaging techniques combined with ad hoc clinical autopsies and histopathological analyses could critically affect the validity of clinical translation in studies of human brain-immune interactions using neuroimaging. Advances in our understanding of brain-immune interactions are expected to translate into novel therapeutic avenues in a vast array of domains including cancer, autoimmune diseases or viral infections such as in acute and post-acute or Long COVID-19.

Multi-site benchmark classification of major depressive disorder using machine learning on cortical and subcortical measures.

Machine learning (ML) techniques have gained popularity in the neuroimaging field due to their potential for classifying neuropsychiatric disorders. However, the diagnostic predictive power of the existing algorithms has been limited by small sample sizes, lack of representativeness, data leakage, and/or overfitting. Here, we overcome these limitations with the largest multi-site sample size to date (N = 5365) to provide a generalizable ML classification benchmark of major depressive disorder (MDD) using shallow linear and non-linear models. Leveraging brain measures from standardized ENIGMA analysis pipelines in FreeSurfer, we were able to classify MDD versus healthy controls (HC) with a balanced accuracy of around 62%. But after harmonizing the data, e.g., using ComBat, the balanced accuracy dropped to approximately 52%. Accuracy results close to random chance levels were also observed in stratified groups according to age of onset, antidepressant use, number of episodes and sex. Future studies incorporating higher dimensional brain imaging/phenotype features, and/or using more advanced machine and deep learning methods may yield more encouraging prospects.