Clozapine (CLZ) is the drug of choice for the treatment of resistant schizophrenia; however, its suitable use is limited by the complex adverse effects’ profile. The best-described adverse effects in the literature are represented by agranulocytosis, myocarditis, sedation, weight gain, hypotension, and drooling; nevertheless, there are other known adverse effects that psychiatrists should readily recognize and manage. This review covers the “rare” and “very rare” known adverse effects of CLZ, which have been accurately described in literature. An extensive search on the basis of predefined criteria was made using CLZ and its combination with adverse effects as keywords in electronic databases. Data show the association between the use of CLZ and uncommon adverse effects, including ischemic colitis, paralytic ileus, hematemesis, gastroesophageal reflux disease, priapism, urinary incontinence, pityriasis rosea, intertriginous erythema, pulmonary thromboembolism, pseudo-pheochromocytoma, periorbital edema, and parotitis, which are influenced by other variables including age, early diagnosis, and previous/current pharmacological therapies. Some of these adverse effects, although unpredictable, are often manageable if promptly recognized and treated. Others are serious and potentially life-threatening. However, an adequate knowledge of the drug, clinical vigilance, and rapid intervention can drastically reduce the morbidity and mortality related to CLZ treatment.

Background Diagnosis of schizophrenia (SCZ) is made exclusively clinically, since specific biomarkers that can predict the disease accurately remain unknown. Machine learning (ML) represents a promising approach that could support clinicians in the diagnosis of mental disorders. Objectives A systematic review, according to the PRISMA statement, was conducted to evaluate its accuracy to distinguish SCZ patients from healthy controls. Methods We systematically searched PubMed, Embase, MEDLINE, PsychINFO and the Cochrane Library through December 2018 using generic terms for ML techniques and SCZ without language or time restriction. Thirty-five studies were included in this review: eight of them used structural neuroimaging, twenty-six used functional neuroimaging and one both, with a minimum accuracy >60% (most of them 75–90%). Sensitivity, Specificity and accuracy were extracted from each publication or obtained directly from authors. Results Support vector machine, the most frequent technique, if associated with other ML techniques achieved accuracy close to 100%. The prefrontal and temporal cortices appeared to be the most useful brain regions for the diagnosis of SCZ. ML analysis can efficiently detect significantly altered brain connectivity in patients with SCZ (eg, default mode network, visual network, sensorimotor network, frontoparietal network and salience network). Conclusion The greater accuracy demonstrated by these predictive models and the new models resulting from the integration of multiple ML techniques will be increasingly decisive for early diagnosis and evaluation of the treatment response and to establish the prognosis of patients with SCZ. To achieve a real benefit for patients, the future challenge is to reach an accurate diagnosis not only through clinical evaluation but also with the aid of ML algorithms.

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Background Several devices have been proposed to assess arterial stiffness in clinical daily use over the past few years, by estimating aortic pulse wave velocity (PWV) from a single measurement of brachial oscillometric blood pressure, using patented algorithms. It is uncertain if these systems are able to provide additional elements, beyond the contribution carried by age and blood pressure levels, in the definition of early vascular damage expressed by the stiffening of the arterial wall. Methods and Results The aim of our study was to compare the estimated algorithm‐based PWV values, provided by the Mobil‐O‐Graph system, with the standard noninvasive assessment of aortic PWV in patients with Marfan syndrome (ie, in subjects characterized by premature aortic stiffening and low blood pressure values). Aortic stiffness was simultaneously evaluated by carotid‐femoral PWV with a validated arterial tonometer and estimated with an arm cuff–based ambulatory blood pressure monitoring Mobil‐O‐Graph device on 103 patients with Marfan syndrome (50 men; mean± SD age, 38±15 years). Aortic PWV, estimated by the Mobil‐O‐Graph, was significantly ( P <0.0001) lower (mean± SD, 6.1±1.3 m/s) than carotid‐femoral PWV provided by arterial tonometry (mean± SD , 8.8±3.1 m/s). The average of differences between PWV values provided by the 2 methods (±1.96×SD) was −2.7±5.7 m/s. Conclusions The Mobil‐O‐Graph provides PWV values related to an ideal subject for a given age and blood pressure, but it is not able to evaluate early vascular aging expressed by high PWV in the individual patient. This is well shown in patients with Marfan syndrome.

Marfan Syndrome (MFS) is a rare connective tissue disorder, resulting from mutations in the fibrillin-1 gene, characterized by pathologic phenotypes in multiple organs, the most detrimental of which affects the thoracic aorta. Indeed, thoracic aortic aneurysms (TAA), leading to acute dissection and rupture, are today the major cause of morbidity and mortality in adult MFS patients. Therefore, there is a compelling need for novel therapeutic strategies to delay TAA progression and counteract aortic dissection occurrence. Unfortunately, the wide phenotypic variability of MFS patients, together with the lack of a complete genotype-phenotype correlation, have represented until now a barrier hampering the conduction of translational studies aimed to predict disease prognosis and drug discovery. In this review, we will illustrate available therapeutic strategies to improve the health of MFS patients. Starting from gold standard surgical overtures and the description of the main pharmacological approaches, we will comprehensively review the state-of-the-art of in vivo MFS models and discuss recent clinical pharmacogenetic results. Finally, we will focus on induced pluripotent stem cells (iPSC) as a technology that, if integrated with preclinical research and pharmacogenetics, could contribute in determining the best therapeutic approach for each MFS patient on the base of individual differences. Finally, we will suggest the integration of preclinical studies, pharmacogenetics and iPSC technology as the most likely strategy to help solve the composite puzzle of precise medicine in this condition.