BD is associated with dysfunction of HPA axis activity, with important pathophysiological implications. Targeting HPA axis dysfunctions might be a novel strategy to improve the outcomes of BD.

Clozapine, a dibenzodiazepine developed in 1961, is a multireceptorial atypical antipsychotic approved for the treatment of resistant schizophrenia. Since its introduction, it has remained the drug of choice in treatment-resistant schizophrenia, despite a wide range of adverse effects, as it is a very effective drug in everyday clinical practice. However, clozapine is not considered as a top-of-the-line treatment because it may often be difficult for some patients to tolerate as some adverse effects can be particularly bothersome (i.e. sedation, weight gain, sialorrhea etc.) and it has some other potentially dangerous and life-threatening side effects (i.e. myocarditis, seizures, agranulocytosis or granulocytopenia, gastrointestinal hypomotility etc.). As poor treatment adherence in patients with resistant schizophrenia may increase the risk of a psychotic relapse, which may further lead to impaired social and cognitive functioning, psychiatric hospitalizations and increased treatment costs, clozapine adverse effects are a common reason for discontinuing this medication. Therefore, every effort should be made to monitor and minimize these adverse effects in order to improve their early detection and management. The aim of this paper is to briefly summarize and provide an update on major clozapine adverse effects, especially focusing on those that are severe and potentially life threatening, even if most of the latter are relatively uncommon.

Despite the continuous advancement in neurosciences as well as in the knowledge of human behaviors pathophysiology, currently suicide represents a puzzling challenge. The World Health Organization (WHO) has established that one million people die by suicide every year, with the impressive daily rate of a suicide every 40 s. The weightiest concern about suicidal behavior is how difficult it is for healthcare professionals to predict. However, recent evidence in genomic studies has pointed out the essential role that genetics could play in influencing person’s suicide risk. Combining genomic and clinical risk assessment approaches, some studies have identified a number of biomarkers for suicidal ideation, which are involved in neural connectivity, neural activity, mood, as well as in immune and inflammatory response, such as the mammalian target of rapamycin (mTOR) signaling. This interesting discovery provides the neurobiological bases for the use of drugs that impact these specific signaling pathways in the treatment of suicidality, such as ketamine. Ketamine, an N-methyl-d-aspartate glutamate (NMDA) antagonist agent, has recently hit the headlines because of its rapid antidepressant and concurrent anti-suicidal action. Here we review the preclinical and clinical evidence that lay the foundations of the efficacy of ketamine in the treatment of suicidal ideation in mood disorders, thereby also approaching the essential question of the understanding of neurobiological processes of suicide and the potential therapeutics.

It is well known that alexithymic individuals may show significantly higher levels of anxiety, depression, and psychological suffering than non-alexithymics. There is an increasing evidence that alexithymia may be considered a risk factor for suicide, even simply increasing the risk of development of depressive symptoms or per se. Therefore, the purpose of this narrative mini-review was to elucidate a possible relationship between alexithymia and suicide risk. The majority of reviewed studies pointed out a relationship between alexithymia and an increased suicide risk. In several studies, this relationship was mediated by depressive symptoms. In conclusion, the importance of alexithymia screening in everyday clinical practice and the evaluation of clinical correlates of alexithymic traits should be integral parts of all disease management programs and, especially, of suicide prevention plans and interventions. However, limitations of studies are discussed and must be considered.

Perinatal period may pose a great challenge for the clinical management and treatment of psychiatric disorders in women. In fact, several mental illnesses can arise during pregnancy and/or following childbirth. Suicide has been considered a relatively rare event during the perinatal period. However, in some mental disorders (i.e., postpartum depression, bipolar disorder, postpartum psychosis, etc.) have been reported a higher risk of suicidal ideation, suicide attempt, or suicide. Therefore, a complete screening of mothers’ mental health should also take into account thoughts of suicide and thoughts about harming infants as well. Clinicians should carefully monitor and early identify related clinical manifestations, potential risk factors, and alarm symptoms related to suicide. The present paper aims at providing a focused review about epidemiological data, risk factors, and an overview about the main clinical correlates associated with the suicidal behavior during the pregnancy and postpartum period. Practical recommendations have been provided as well.

A critical issue in the treatment with atypical APs is represented by their metabolic side effect profile (e.g. weight gain, lipid and glycaemic imbalance, risk of diabetes mellitus and diabetic ketoacidosis) which may limit their use in particular clinical samples. Electrolyte imbalance, ECG abnormalities and cardiovascular adverse effects may recommend a careful baseline and periodic assessments.

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Personalized medicine (PM) aims to establish a new approach in clinical decision-making, based upon a patient's individual profile in order to tailor treatment to each patient's characteristics. Although this has become a focus of the discussion also in the psychiatric field, with evidence of its high potential coming from several proof-of-concept studies, nearly no tools have been developed by now that are ready to be applied in clinical practice. In this paper, we discuss recent technological advances that can make a shift toward a clinical application of the PM paradigm. We focus specifically on those technologies that allow both the collection of massive as much as real-time data, i.e., electronic medical records and smart wearable devices, and to achieve relevant predictions using these data, i.e. the application of machine learning techniques.