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INTRODUCTION: Melatonin, a hormone produced by the pineal gland, regulates the sleep-wake cycle and is effective in restoring biological rhythms. Prolonged-release melatonin (PRM) is designed to mimic the natural physiological pattern of melatonin release. In circadian medicine, PRM can be used to treat sleep and circadian rhythm disorders, as well as numerous organic diseases associated with sleep disorders. EVIDENCE ACQUISITION: This systematic review analyzed 62 studies and adhered to the PRISMA guidelines, examining the effectiveness of PRM in organic pathologies and mental disorders. EVIDENCE SYNTHESIS: The main evidence concerns primary insomnia in subjects over the age of 55, showing significant improvements in sleep quality. In neurodevelopmental disorders, there is evidence of a positive impact on sleep quality and quality of life for patients and their caregivers. PRM shows efficacy in the treatment of sleep disorders in mood disorders, schizophrenia, and neurocognitive disorders, but requires further confirmation. The additional use of PRM is supported for the withdrawal of chronic benzodiazepine therapies. The tolerability and safety of PRM are excellent, with ample evidence supporting the absence of tolerance and dependence. CONCLUSIONS: Overall, PRM in circadian medicine is an effective chronopharmaceutical for restoring the sleep-wake rhythm in patients with insomnia disorder. This efficacy may also extend to sleep disorders associated with mood, neurodevelopmental and neurocognitive disorders, suggesting a further potential role in insomnia associated with various organic diseases.
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Introduction: Conducted under the auspices of the Italian Society of Consultation Liaison Psychiatry (SIPC) the aim of this study was to describe the characteristics of Consultation Liaison Psychiatry (CLP) activity in Italy (SIPC-2-2018) over the past 20 years by comparing with data from the first Italian nation-wide study (SIPC-1-1998). Methods: We collected data on CLP visits of 3,943 patients from 10 Italian hospitals over a period of 1 year. Data were compared with those from the SIPC-1 1998 study (4,183 participants). Patients were assessed with the same ad hoc 60-item Patient Registration Form recording information from five different areas: Sociodemographic, hospitalization-related, consultation-related, interventions and outcome. Results: Compared with participants from the previous study, SIPC-2-2018 participants were significantly older (d = 0.54) and hospitalized for a longer duration (d = 0.20). The current study detected an increase in the proportion of referrals from surgical wards and for individuals affected by onco-hematologic diseases. Depressive disorders still represented the most frequent psychiatric diagnosis, followed by adjustment and stress disorders and delirium/dementia. Also, CLP psychiatrists prescribed more often antidepressants (Φ = 0.13), antipsychotics (Φ = 0.09), mood stabilizers (Φ = 0.24), and less often benzodiazepines (Φ = 0.07). Conclusion: CLP workload has increased considerably in the past 20 years in Italy, with changes in patient demographic and clinical characteristics. A trend toward increase in medication-based patient management was observed. These findings suggest that the psychiatric needs of patients admitted to the general hospital are more frequently addressed by referring physicians, although Italian CLP services still deserve better organization and autonomy.
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A sensorized face mask could be a useful tool in the case of a viral pandemic event, as well as the Covid-19 emergency. In the context of the proposed project "RESPIRE", we have developed a "Smart-Mask" able to collect the signal patterns of body temperature, respiration, and symptoms such as cough, through a set of textile sensors. The signals have been analyzed by Artificial Intelligence algorithms in order to compare them with gold standard measurements, and to recognize the physiological changes associated with a viral infection. This low-cost prototype of a smart face mask is a reliable tool for the estimation of the individual physiological parameters. Moreover, it enables both personal protection and the early and rapid identification and tracking of potentially infected individuals.
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COVID-19 , Masks , Artificial Intelligence , COVID-19/diagnosis , Early Diagnosis , Humans , TextilesABSTRACT
Different forest types based on different tree species composition may have similar spectral signatures if observed with traditional multispectral satellite sensors. Hyperspectral imagery, with a more continuous representation of their spectral behavior may instead be used for their classification. The new hyperspectral Precursore IperSpettrale della Missione Applicativa (PRISMA) sensor, developed by the Italian Space Agency, is able to capture images in a continuum of 240 spectral bands ranging between 400 and 2500 nm, with a spectral resolution smaller than 12 nm. The new sensor can be employed for a large number of remote sensing applications, including forest types discrimination. In this study, we compared the capabilities of the new PRISMA sensor against the well-known Sentinel-2 Multi-Spectral Instrument (MSI) in recognition of different forest types through a pairwise separability analysis carried out in two study areas in Italy, using two different nomenclature systems and four separability metrics. The PRISMA hyperspectral sensor, compared to Sentinel-2 MSI, allowed for a better discrimination in all forest types, increasing the performance when the complexity of the nomenclature system also increased. PRISMA achieved an average improvement of 40% for the discrimination between two forest categories (coniferous vs. broadleaves) and of 102% in the discrimination between five forest types based on main tree species groups.
