Modeling time-varying brain networks with a self-tuning optimized Kalman filter.

Brain networks are complex dynamical systems in which directed interactions between different areas evolve at the sub-second scale of sensory, cognitive and motor processes. Due to the highly non-stationary nature of neural signals and their unknown noise components, however, modeling dynamic brain networks has remained one of the major challenges in contemporary neuroscience. Here, we present a new algorithm based on an innovative formulation of the Kalman filter that is optimized for tracking rapidly evolving patterns of directed functional connectivity under unknown noise conditions. The Self-Tuning Optimized Kalman filter (STOK) is a novel adaptive filter that embeds a self-tuning memory decay and a recursive regularization to guarantee high network tracking accuracy, temporal precision and robustness to noise. To validate the proposed algorithm, we performed an extensive comparison against the classical Kalman filter, in both realistic surrogate networks and real electroencephalography (EEG) data. In both simulations and real data, we show that the STOK filter estimates time-frequency patterns of directed connectivity with significantly superior performance. The advantages of the STOK filter were even clearer in real EEG data, where the algorithm recovered latent structures of dynamic connectivity from epicranial EEG recordings in rats and human visual evoked potentials, in excellent agreement with known physiology. These results establish the STOK filter as a powerful tool for modeling dynamic network structures in biological systems, with the potential to yield new insights into the rapid evolution of network states from which brain functions emerge.

[Fragility in a Public Health perspective: principles and tools for a "life course" approach prevention- oriented]. / La Fragilità in una prospettiva di Sanità Pubblica: principi e strumenti per un approccio "life course" orientato alla prevenzione.

The Covid-19 pandemic significantly increased the workload for the Italian Health Service. There is few information in the literature on the pediatric population and on the management of pediatric hospitals. The aim of this article is to describe the management of healthcare services during Covid-19 emergency in Regina Margherita Children's Hospital. The Regina Margherita Children's Hospital is specialized in the prevention, diagnosis and treatment of pediatric diseases. About 1000 health worker work in this Hospital and 278 hospitalization places are available.

Epidemiology of odontogenic sinusitis: an old, underestimated disease, even today. A narrative literature review.

Odontogenic sinusitis is an inflammatory condition of the paranasal sinuses resulting from dental pathology. The aim of this study is to provide an overview of the current literature on the dimensions of the phenomenon, quality of life, economic considerations, and approaches to odontogenic sinusitis. A narrative review was conducted following the methodology proposed by Green et al. (2006). There appears to have been an increase in the incidence over the last decade. Nowadays, evidence in the literature reports that 10-12% up to 40% of all sinusitis cases are associated with odontogenic infections. The iatrogenia was by far the leading cause of odontogenic sinusitis (55.97%) while the first and second molars were the most affected teeth with an incidence of 35.6% and 22%. If not properly diagnosed and treated, these infections may lead to a rapid spread, giving rise to potentially life-threatening complications with a significant general health-related Quality of Life detriment. The proper management of patients in a pre-implant logical setting leads to substantial savings, ranging from €38 million to €152 million, for the Italian National Health Service. Odontogenic sinusitis management should involve shared decisionmaking between the otolaryngologist, dental provider, and patient, where the benefits and risks of dental treatment and endoscopic sinus surgery are discussed.

Estimating EEG Source Dipole Orientation Based on Singular-value Decomposition for Connectivity Analysis.

In the last decade, the use of high-density electrode arrays for EEG recordings combined with the improvements of source reconstruction algorithms has allowed the investigation of brain networks dynamics at a sub-second scale. One powerful tool for investigating large-scale functional brain networks with EEG is time-varying effective connectivity applied to source signals obtained from electric source imaging. Due to computational and interpretation limitations, the brain is usually parcelled into a limited number of regions of interests (ROIs) before computing EEG connectivity. One specific need and still open problem is how to represent the time- and frequency-content carried by hundreds of dipoles with diverging orientation in each ROI with one unique representative time-series. The main aim of this paper is to provide a method to compute a signal that explains most of the variability of the data contained in each ROI before computing, for instance, time-varying connectivity. As the representative time-series for a ROI, we propose to use the first singular vector computed by a singular-value decomposition of all dipoles belonging to the same ROI. We applied this method to two real datasets (visual evoked potentials and epileptic spikes) and evaluated the time-course and the frequency content of the obtained signals. For each ROI, both the time-course and the frequency content of the proposed method reflected the expected time-course and the scalp-EEG frequency content, representing most of the variability of the sources (~ 80%) and improving connectivity results in comparison to other procedures used so far. We also confirm these results in a simulated dataset with a known ground truth.

Pilot randomized experimental study evaluating isopropyl alcohol and ultraviolet-C radiation in the disinfection of healthcare workers' smartphones.

BACKGROUND: Smartphones in medical settings pose infection risks due to harbouring pathogenic bacteria. AIM: This pilot study assessed the effectiveness duration of sanitization methods, focusing on 70% isopropyl alcohol wipes and ultraviolet-C (UVC) boxes, aiming to obtain preliminary data on the reduction in total bacterial load 3 h post-sanitization. METHODS: A randomized monocentric trial with two intervention arms (wipes and UVC boxes) was designed. As participants, healthcare workers from three wards at Fondazione Policlinico Universitario 'A. Gemelli' IRCCS Hospital were recruited, stratified by ward, and block randomized within each ward to control confounders. FINDINGS: Seventy-one healthcare workers, mostly nurses (62%) were included in the study. Initial bacterial load reduction was significant with both disinfection techniques, but after 3 h both methods showed increased bacterial levels, with wipes displaying potentially higher residual efficacy (P=0.056). To adequately size a trial (89% power, significance level 0.05) for assessing the residual efficacy of alcohol-impregnated wipes compared with UVC boxes at 3 h post-sanitization, 503 professionals per group were required. CONCLUSION: This study highlights the necessity for guidelines on hospital smartphone sanitization and educational initiatives for healthcare workers and patients. Further studies, adequately sized, are necessary to determine optimal sanitization intervals and assess pathogen transmission risks.

Serial dependence and representational momentum in single-trial perceptual decisions.

The human brain has evolved to predict and anticipate environmental events from their temporal dynamics. Predictions can bias perception toward the recent past, particularly when the environment contains no foreseeable changes, but can also push perception toward future states of sensory input, like when anticipating the trajectory of moving objects. Here, we show that perceptual decisions are simultaneously influenced by both past and future states of sensory signals. Using an orientation adjustment task, we demonstrate that single-trial errors are displaced toward previous features of behaviorally relevant stimuli and, at the same time, toward future states of dynamic sensory signals. These opposing tendencies, consistent with decisional serial dependence and representational momentum, involve different types of processing: serial dependence occurs beyond objecthood whereas representational momentum requires the representation of a single object with coherent dynamics in time and space. The coexistence of these two phenomena supports the independent binding of stimuli and decisions over time.

Burden of COVID-19: Disability-Adjusted Life Years (DALYs) across 16 European countries.

OBJECTIVE: The aim of this study is to measure and compare the burden of disease of COVID-19 pandemic in 16 EU/EEA countries through the estimation of Disability-Adjusted Life Years (DALYs) over a long period of time. MATERIALS AND METHODS: The observational study was based on data from ECDC and WHO databases collected from 27 January 2020 to 15 November 2020. In addition to the absolute number of DALYs, a weekly trend of DALYs/100,000 inhabitants was computed for each country to assess the evolution of the pandemic burden over time. A cluster analysis and Kolmogorov-Smirnov (KS) test were performed to allow for a country-to-country comparison. RESULTS: The total DALYs amount to 4,354 per 100.000 inhabitants. YLLs were accountable for 98% of total DALYs.  Italy, Czechia and Sweden had the highest values of DALYs/100,000 while Finland, Estonia and Slovakia had the lowest. The latter three countries differed significantly from the others - in terms of DALYs trend over time - as shown by KS test. The cluster analysis allowed for the identification of three clusters of countries sharing similar trends of DALYs during the assessed period of time. These results show that notable differences were observed among different countries, with most of the disease burden attributable to YLLs. CONCLUSIONS: DALYs have proven to be an effective measure of the burden of disease. Public health and policy actions, as well as demographic, epidemiological and cultural features of each country, may be responsible for the wide variations in the health impact that were observed among the countries analyzed.

Development of a checklist as self-assessment tool to evaluate the reprocessing of endoscopic instruments in an Italian teaching hospital.

OBJECTIVE: The present study aims to develop a checklist, as a self-assessment tool, for evaluating all the items involved in the endoscope reprocessing that could be useful for the improvement and/or development of a safety endoscope reprocessing system. MATERIALS AND METHODS: A three-step modified Delphi method, with an embedded qualitative component, was adopted to develop the checklist. According to it, corrective actions were performed before its further re-administration. Contextually, the microbiological surveillance of the endoscopes and of the wash disinfector machine was carried out. RESULTS: Five areas were included in the checklist. After the 1st checklist application, only one of three wards reached the excellent scores in all the items. The other two wards showed an improvement in the Traceability and Endoscope Reprocessing areas after corrective actions. The McNemar's test reported significant difference in the proportion of satisfactory results before and after the 1st and 2nd checklist application. The microbiological surveillance, conducted after the 1st administration, showed unsatisfactory results for the 2 bronchoscopes available in the Intensive Care Unit and for 2 automated endoscope reprocessors. The analysis performed after the 2nd administration showed good results. CONCLUSIONS: The periodic administration of the checklist is functional for a self-assessment of quality reprocessing procedures carried out in the large endoscopic services and in the wards occasionally providing those services, according to the good practice guidelines and for any corrective actions to increase the safety.

Gemelli decision tree Algorithm to Predict the need for home monitoring or hospitalization of confirmed and unconfirmed COVID-19 patients (GAP-Covid19): preliminary results from a retrospective cohort study.

OBJECTIVE: To develop a deep learning-based decision tree for the primary care setting, to stratify adult patients with confirmed and unconfirmed coronavirus disease 2019 (COVID-19), and to predict the need for hospitalization or home monitoring. PATIENTS AND METHODS: We performed a retrospective cohort study on data from patients admitted to a COVID hospital in Rome, Italy, between 5 March 2020 and 5 June 2020. A confirmed case was defined as a patient with a positive nasopharyngeal RT-PCR test result, while an unconfirmed case had negative results on repeated swabs. Patients' medical history and clinical, laboratory and radiological findings were collected, and the dataset was used to train a predictive model for COVID-19 severity. RESULTS: Data of 198 patients were included in the study. Twenty-eight (14.14%) had mild disease, 62 (31.31%) had moderate disease, 64 (32.32%) had severe disease, and 44 (22.22%) had critical disease. The G2 value assessed the contribution of each collected value to decision tree building. On this basis, SpO2 (%) with a cut point at 92 was chosen for the optimal first split. Therefore, the decision tree was built using values maximizing G2 and LogWorth. After the tree was built, the correspondence between inputs and outcomes was validated. CONCLUSIONS: We developed a machine learning-based tool that is easy to understand and apply. It provides good discrimination in stratifying confirmed and unconfirmed COVID-19 patients with different prognoses in every context. Our tool might allow general practitioners visiting patients at home to decide whether the patient needs to be hospitalized.

Time-varying effective EEG source connectivity: the optimization of model parameters.

Adaptive estimation methods based on general Kalman filter are powerful tools to investigate brain networks dynamics given the non-stationary nature of neural signals. These methods rely on two parameters, the model order p and adaptation constant c, which determine the resolution and smoothness of the time-varying multivariate autoregressive estimates. A sub-optimal filtering may present consistent biases in the frequency domain and temporal distortions, leading to fallacious interpretations. Thus, the performance of these methods heavily depends on the accurate choice of these two parameters in the filter design. In this work, we sought to define an objective criterion for the optimal choice of these parameters. Since residual- and information-based criteria are not guaranteed to reach an absolute minimum, we propose to study the partial derivatives of these functions to guide the choice of p and c. To validate the performance of our method, we used a dataset of human visual evoked potentials during face perception where the generation and propagation of information in the brain is well understood and a set of simulated data where the ground truth is available.