In the last two decades new noninvasive mobile electroencephalography (EEG) solutions have been developed to overcome limitations of conventional clinical EEG and to improve monitoring of patients with long‐term conditions. Despite the availability of mobile innovations, their adoption is still very limited. The aim of this study is to review the current state‐of‐the‐art and highlight the main advantages of adopting noninvasive mobile EEG solutions in clinical trials and research studies of people with epilepsy or suspected seizures. Device characteristics are described, and their evaluation is presented. Two authors independently performed a literature review in accordance with Preferred Reporting Items for Systematic Reviews and Meta‐Analyses (PRISMA) guidelines. A combination of different digital libraries was used (Embase, MEDLINE, Global Health, PsycINFO and https://clinicaltrials.gov/). Twenty‐three full‐text, six conference abstracts, and eight webpages were included, where a total of 14 noninvasive mobile solutions were identified. Published studies demonstrated at different levels how EEG recorded via mobile EEG can be used for visual detection of EEG abnormalities and for the application of automatic‐detection algorithms with acceptable specificity and sensitivity. When the quality of the signal was compared with scalp EEG, many similarities were found in the background activities and power spectrum. Several studies indicated that the experience of patients and health care providers using mobile EEG was positive in different settings. Ongoing trials are focused mostly on improving seizure‐detection accuracy and also on testing and assessing feasibility and acceptability of noninvasive devices in the hospital and at home. This review supports the potential clinical value of noninvasive mobile EEG systems and their advantages in terms of time, technical support, cost, usability, and reliability when applied to seizure detection and management. On the other hand, the limitations of the studies confirmed that future research is needed to provide more evidence regarding feasibility and acceptability in different settings, as well as the data quality and detection accuracy of new noninvasive mobile EEG solutions.

Respiratory tract infections account for high morbidity and mortality around the world. Fragile patients are at high risk of developing complications such as pneumonia and may die from it. Limited information is available on the extent of the circulation of respiratory viruses in the hospital setting. Most knowledge relates to influenza viruses (FLU) but several other viruses produce flu-like illness. The study was conducted at the University Hospital Policlinico Tor Vergata, Rome, Italy. Clinical and laboratory data from hospitalized patients with respiratory tract infections during the period October 2016-March 2019 were analysed. The retrospective analysis included 17 viral agents detected by FilmArray test and clinical data from medical records and hospital discharge sheets. Models were adjusted for relevant confounders such as clinical severity and risk of death, socio-demographic characteristics and surgical procedures. From a total of 539 specimens analysed, 180 (33.39%) were positive for one or more respiratory viruses. Among them, 83 (46.1 %) were positive for influenza viruses (FLU), 36 (20%) rhino/enteroviruses (RHV/EV), 17 (9.44%) human coronaviruses (HCOV-229E, -HKU1, -NL63, and -OC43), 17 (9.44%) respiratory syncytial virus, 15 (8.33%) human metapneumovirus (HMPV), 8 (4.44%) parainfluenza viruses (PIV) and 4 (2.22%) adenoviruses (ADV). The distribution of viral agents varied across age groups and month of detection. The positive specimens were from 168 patients [102 M, 66 F; median age (range): 64 years (19−93)]. Overall, 40% of them had a high-grade clinical severity and a 27% risk of death; 27 patients died and 22 of them (81.5%) had received a clinical diagnosis of pneumonia. Respiratory viral infections may have a severe course and a poor prognosis in hospitalized patients, due to underlying comorbidities. Monitoring the circulation of respiratory viruses in hospital settings is important to improve diagnosis, prevention and treatment.

Background: Transcranial magnetic stimulation can be combined with electromyography (TMS-EMG) and electroencephalography (TMS-EEG) to evaluate the excitatory and inhibitory functions of the cerebral cortex in a standardized manner. It has been postulated that schizophrenia is a disorder of functional neural connectivity underpinned by a relative imbalance of excitation and inhibition. The aim of this review was to provide a comprehensive overview of TMS-EMG and TMS-EEG research in schizophrenia, focused on excitation or inhibition, connectivity, motor cortical plasticity and the effect of antipsychotic medications, symptom severity and illness duration on TMS-EMG and TMS-EEG indices. Methods: We searched PsycINFO, Embase and Medline, from database inception to April 2020, for studies that included TMS outcomes in patients with schizophrenia. We used the following combination of search terms: transcranial magnetic stimulation OR tms AND interneurons OR glutamic acid OR gamma aminobutyric acid OR neural inhibition OR pyramidal neurons OR excita* OR inhibit* OR GABA* OR glutam* OR E-I balance OR excitation-inhibition balance AND schizoaffective disorder* OR Schizophrenia OR schizophreni*. Results: TMS-EMG and TMS-EEG measurements revealed deficits in excitation or inhibition, functional connectivity and motor cortical plasticity in patients with schizophrenia. Increased duration of the cortical silent period (a TMS-EMG marker of γ-aminobutyric acid B receptor activity) with clozapine was a relatively consistent finding. Limitations: Most of the studies used patients with chronic schizophrenia and medicated patients, employed cross-sectional group comparisons and had small sample sizes. Conclusion: TMS-EMG and TMS-EEG offer an opportunity to develop a novel and improved understanding of the physiologic processes that underlie schizophrenia and to assess the therapeutic effect of antipsychotic medications. In the future, these techniques may also help predict disease progression and further our understanding of the excitatory/inhibitory balance and its implications for mechanisms that underlie treatment-resistant schizophrenia.

Robust biomarkers for anti-epileptic drugs (AEDs) activity in the human brain are essential to increase the probability of successful drug development. The frequency analysis of electroencephalographic (EEG) activity, either spontaneous or evoked by transcranial magnetic stimulation (TMS-EEG) can provide cortical readouts for AEDs. However, a systematic evaluation of the effect of AEDs on spontaneous oscillations and TMS-related spectral perturbation (TRSP) has not yet been provided. We studied the effects of Lamotrigine, Levetiracetam, and of a novel potassium channel opener (XEN1101) in two groups of healthy volunteers. Levetiracetam suppressed TRSP theta, alpha and beta power, whereas Lamotrigine decreased delta and theta but increased the alpha power. Finally, XEN1101 decreased TRSP delta, theta, alpha and beta power. Resting-state EEG showed a decrease of theta band power after Lamotrigine intake. Levetiracetam increased theta, beta and gamma power, while XEN1101 produced an increase of delta, theta, beta and gamma power. Spontaneous and TMS-related cortical oscillations represent a powerful tool to characterize the effect of AEDs on in vivo brain activity. Spectral fingerprints of specific AEDs should be further investigated to provide robust and objective biomarkers of biological effect in human clinical trials.

In Italy, the National Plan for the Elimination of Measles and Congenital Rubella 2010–15 suggests offering Measles, Mumps and Rubella (MMR) vaccination to susceptible women who underwent voluntary termination of pregnancy (VTP) In Rome, S. Eugenio Hospital is one of the structures where VTP is practised in an Operative Unit called “Family Planning” The primary goal of this study was to estimate the prevalence of susceptibility to rubella, using IgG and IgM immunoassays, among women accessing VTP and to offering MMR vaccination to susceptible women. Secondarily, this study evaluated acceptance of the vaccination offer From 2013 to 2015, data were collected from 1513 voluntary termination of pregnancy (VTP) cases The results show a significant increase of 5 percent in susceptibility prevalence in the target group from 13.6% in 2013 and 2014 to 18.4% in 2015 The association between rubella susceptibility and age was statistically significant (p<0.01) Throughout the entire period, acceptance of the vaccine proposal was 19% (45/232) among susceptible women; 58% (135/232) refused the vaccine and 23% (52/232) took time to think about it This study shows an increase of 5 percent in the prevalence of rubella susceptibility over two years. This result is worrying, even considering the short span of the data collection The rate of acceptance of vaccination is unsatisfactory considering the possibility of future pregnancies This issue deserves continued action, which, going forward, might transform a “project” into a shared strategy as part of a wider network with the goal of aligning Italy with international recommendations.

Introduction Healthcare-associated infections (HAIs) and antimicrobial resistance (AMR) are major public health threats in upper- and lower-middle-income countries. Electronic health records (EHRs) are an invaluable source of data for achieving different goals, including the early detection of HAIs and AMR clusters within healthcare settings; evaluation of attributable incidence, mortality, and disability-adjusted life years (DALYs); and implementation of governance policies. In Italy, the burden of HAIs is estimated to be 702.53 DALYs per 100,000 population, which has the same magnitude as the burden of ischemic heart disease. However, data in EHRs are usually not homogeneous, not properly linked and engineered, or not easily compared with other data. Moreover, without a proper epidemiological approach, the relevant information may not be detected. In this retrospective observational study, we established and engineered a new management system on the basis of the integration of microbiology laboratory data from the university hospital “Policlinico Tor Vergata” (PTV) in Italy with hospital discharge forms (HDFs) and clinical record data. All data are currently available in separate EHRs. We propose an original approach for monitoring alert microorganisms and for consequently estimating HAIs for the entire period of 2018. Methods Data extraction was performed by analyzing HDFs in the databases of the Hospital Information System. Data were compiled using the AREAS-ADT information system and ICD-9-CM codes. Quantitative and qualitative variables and diagnostic-related groups were produced by processing the resulting integrated databases. The results of research requests for HAI microorganisms and AMR profiles sent by the departments of PTV from 01/01/2018 to 31/12/2018 and the date of collection were extracted from the database of the Complex Operational Unit of Microbiology and then integrated. Results We were able to provide a complete and richly detailed profile of the estimated HAIs and to correlate them with the information contained in the HDFs and those available from the microbiology laboratory. We also identified the infection profile of the investigated hospital and estimated the distribution of coinfections by two or more microorganisms of concern. Our data were consistent with those in the literature, particularly the increase in mortality, length of stay, and risk of death associated with infections with Staphylococcus spp, Pseudomonas aeruginosa, Klebsiella pneumoniae, Clostridioides difficile, Candida spp., and Acinetobacter baumannii. Even though less than 10% of the detected HAIs showed at least one infection caused by an antimicrobial resistant bacterium, the contribution of AMR to the overall risk of increased mortality was extremely high. Conclusions The increasing availability of health data stored in EHRs represents a unique opportunity for the accurate identification of any factor that contributes to the diffusion of HAIs and AMR and for the prompt implementation of effective corrective measures. That said, artificial intelligence might be the future of health data analysis because it may allow for the early identification of patients who are more exposed to the risk of HAIs and for a more efficient monitoring of HAI sources and outbreaks. However, challenges concerning codification, integration, and standardization of health data recording and analysis still need to be addressed.

The burden, microbial etiology and clinical impact of hospital-acquired respiratory infections (HARIs) were determined at an Italian teaching hospital over a 12-month period. For this purpose, overall ordinary hospitalizations ≥ 2 days of subjects over 18 years old with discharge from 1 January 2018 to 31 December 2018 were examined by cross-referencing demographic and clinical data from hospital discharge forms with microbiological data from the computer system of the Microbiology Unit. We identified 329 individuals with HARIs (96 females and 233 males; median age 70 years, range 18–93), who represented ¼ of the total hospital-acquired infections (HAIs) in the period. The inpatient setting was medical and surgical in similar proportions (169 vs. 160, respectively) and the mean hospital stay was 38.9 ± 33.6 days. One hundred and forty patients (42.6 % of the total sample) were suffering from one or more chronic diseases. A total of 581 microorganisms (82 antibiotic-resistant and 499 non-resistant) were detected in HARI patients. The most common isolated species were Staphylococcus aureus (16.7%), Klebsiella pneumoniae (13.3%), Pseudomonas spp. (12.6%) and Acinetobacter baumannii (10.5%), followed by Enterobacter spp. (5.3%), Escherichia coli (5.2%) and Enterococcus spp. (4.8%). One hundred and sixty-seven individuals (49.0% of the total) had polymicrobial infections. One hundred thirty-one patients (39.8% of the total) underwent endotracheal intubation and mechanical ventilation and 62.6% of them died, compared to 17.7% of the non-intubated patients. Multivariable analysis confirmed a positive correlation between death and increased age (p = 0.05), surgical MDC (p = 0.007), number of microorganisms over the sample mean (p = 0.001), the presence of chronic diseases (p = 0.046), and intubation and mechanical ventilation (p < 0.0001). A positive correlation between intubation and antibiotic-resistant organisms (p = 0.003) was also found. HARIs are still a major public health problem and require constant surveillance due to their severe clinical outcome.

The frequency analysis of electroencephalographic (EEG) activity, either spontaneous or evoked by transcranial magnetic stimulation (TMS-EEG), is a powerful tool to investigate changes in brain activity and excitability following the administration of antiepileptic drugs (AEDs). However, a systematic evaluation of the effect of AEDs on spontaneous and TMS-induced brain oscillations has not yet been provided. We studied the effects of lamotrigine, levetiracetam, and of a novel potassium channel opener (XEN1101) on TMS-induced and spontaneous brain oscillations in a group of healthy volunteers. Levetiracetam suppressed TMS-induced theta, alpha and beta power, whereas lamotrigine increased TMS-induced alpha power. XEN1101 decreased TMS-induced delta, theta and beta power. Resting-state EEG showed a decrease of theta band power after lamotrigine intake. Levetiracetam increased theta, beta and gamma power, while XEN1101 produced an increase of delta, theta, beta and gamma power. Different AEDs induce specific patterns of power changes in spontaneous and TMS-induced brain oscillations. Spontaneous and TMS-induced cortical oscillations represent a powerful tool to characterize the effect of AEDs on in vivo brain activity. Spectral fingerprints of specific AEDs should be further investigated to provide robust and objective biomarkers of biological effect in human clinical trials.