Long COVID refers to the lingering symptoms which persist or appear after the acute illness. The dominant long COVID symptoms in the two years since the pandemic began (2020–2021) have been depression, anxiety, fatigue, concentration and cognitive impairments with few reports of psychosis. Whether other symptoms will appear later on is not yet known. For example, dopamine-dependent movement disorders generally take many years before first symptoms are seen. Post-stroke depression and anxiety may explain many of the early long COVID cases. Hemorrhagic, hypoxic and inflammatory damages of the central nervous system, unresolved systematic inflammation, metabolic impairment, cerebral vascular accidents such as stroke, hypoxia from pulmonary damages and fibrotic changes are among the major causes of long COVID. Glucose metabolic and hypoxic brain issues likely predispose subjects with pre-existing diabetes, cardiovascular or lung problems to long COVID as well. Preliminary data suggest that psychotropic medications may not be a danger but could instead be beneficial in combating COVID-19 infection. The same is true for diabetes medications such as metformin. Thus, a focus on sigma-1 receptor ligands and glucose metabolism is expected to be useful for new drug development as well as the repurposing of current drugs. The reported protective effects of psychotropics and antihistamines against COVID-19, the earlier reports of reduced number of sigma-1 receptors in post-mortem schizophrenic brains, with many antidepressant and antipsychotic drugs being antihistamines with significant affinity for the sigma-1 receptor, support the role of sigma and histamine receptors in neuroinflammation and viral infections. Literature and data in all these areas are accumulating at a fast rate. We reviewed and discussed the relevant and important literature.

Chronic pain is a debilitating medical condition affecting a significant percentage of the population worldwide. Considerable evidence suggests that pain is an independent risk factor for suicide and inadequately managing pain has been identified as a risk for suicidal behaviour. Additionally, medications used to treat pain may also contribute to suicidal behaviour. Extensive research on pain highlights deficiencies in the clinical management on pain with more gaps in care when patients have pain in combination with mental illness and suicidal behaviour. Providing trainees additional knowledge and equipping them with relevant tools to screen and manage chronic pain efficiently is a potential strategy to mitigate suicide risk. Also, trainees need to be educated on how to screen for suicidality in individuals with pain and apply suicide prevention interventions. This paper will emphasise the necessity to improve education about pain, its close relationship with suicide and effective suicide screening as well as management strategies for medical providers. With additional research, it is the hope that novel treatment modalities will be developed to treat pain to improve the quality of life of individuals suffering from this condition and to decrease suicide risk in this patient population.

The COVID-19 pandemic is believed to have a major negative impact on global mental health due to the viral disease itself as well as the associated lockdowns, social distancing, isolation, fear, and increased uncertainty. Individuals with preexisting mental illness are likely to be particularly vulnerable to these conditions and may develop outright ‘COVID-19-related psychopathology’. Here, we trained a machine learning model on structured and natural text data from electronic health records to identify COVID-19 pandemic-related psychopathology among patients receiving care in the Psychiatric Services of the Central Denmark Region. Subsequently, applying this model, we found that pandemic-related psychopathology covaries with the pandemic pressure over time. These findings may aid psychiatric services in their planning during the ongoing and future pandemics. Furthermore, the results are a testament to the potential of applying machine learning to data from electronic health records.