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The increasing availability of time series data depicting the evolution of physical system properties has prompted the development of methods focused on extracting insights into the system behavior over time, discerning whether it stems from deterministic or stochastic dynamical systems. Surrogate data testing plays a crucial role in this process by facilitating robust statistical assessments. This ensures that the observed results are not mere occurrences by chance, but genuinely reflect the inherent characteristics of the underlying system. The initial process involves formulating a null hypothesis, which is tested using surrogate data in cases where assumptions about the underlying distributions are absent. A discriminating statistic is then computed for both the original data and each surrogate data set. Significantly deviating values between the original data and the surrogate data ensemble lead to the rejection of the null hypothesis. In this work, we present various surrogate methods designed to assess specific statistical properties in random processes. Specifically, we introduce methods for evaluating the presence of autodependencies and nonlinear dynamics within individual processes, using Information Storage as a discriminating statistic. Additionally, methods are introduced for detecting coupling and nonlinearities in bivariate processes, employing the Mutual Information Rate for this purpose. The surrogate methods introduced are first tested through simulations involving univariate and bivariate processes exhibiting both linear and nonlinear dynamics. Then, they are applied to physiological time series of Heart Period (RR intervals) and respiratory flow (RESP) variability measured during spontaneous and paced breathing. Simulations demonstrated that the proposed methods effectively identify essential dynamical features of stochastic systems. The real data application showed that paced breathing, at low breathing rate, increases the predictability of the individual dynamics of RR and RESP and dampens nonlinearity in their coupled dynamics.

The concept of self-predictability plays a key role for the analysis of the self-driven dynamics of physiological processes displaying richness of oscillatory rhythms. While time domain measures of self-predictability, as well as time-varying and local extensions, have already been proposed and largely applied in different contexts, they still lack a clear spectral description, which would be significantly useful for the interpretation of the frequency-specific content of the investigated processes. Herein, we propose a novel approach to characterize the linear self-predictability (LSP) of Gaussian processes in the frequency domain. The LSP spectral functions are related to the peaks of the power spectral density (PSD) of the investigated process, which is represented as the sum of different oscillatory components with specific frequency through the method of spectral decomposition. Remarkably, each of the LSP profiles is linked to a specific oscillation of the process, and it returns frequency-specific measures when integrated along spectral bands of physiological interest, as well as a time domain self-predictability measure with a clear meaning in the field of information theory, corresponding to the well-known information storage, when integrated along the whole frequency axis. The proposed measure is first illustrated in a theoretical simulation, showing that it clearly reflects the degree and frequency-specific location of predictability patterns of the analyzed process in both time and frequency domains. Then, it is applied to beat-to-beat time series of arterial compliance obtained in young healthy subjects. The results evidence that the spectral decomposition strategy applied to both the PSD and the spectral LSP of compliance identifies physiological responses to postural stress of low and high frequency oscillations of the process which cannot be traced in the time domain only, highlighting the importance of computing frequency-specific measures of self-predictability in any oscillatory physiologic process.

Network Physiology is a rapidly growing field of study that aims to understand how physiological systems interact to maintain health. Within the information theory framework the information storage (IS) allows to measure the regularity and predictability of a dynamic process under stationarity assumption. However, this assumption does not allow to track over time the transient pathways occurring in the dynamical activity of a physiological system. To address this limitation, we propose a time-varying approach based on the recursive least squares algorithm (RLS) for estimating IS at each time instant, in non-stationary conditions. We tested this approach in simulated time-varying dynamics and in the analysis of electroencephalographic (EEG) signals recorded from healthy volunteers and timed with the heartbeat to investigate brain-heart interactions. In simulations, we show that the proposed approach allows to track both abrupt and slow changes in the information stored in a physiological system. These changes are reflected in its evolution and variability over time. The analysis of brain-heart interactions reveals marked differences across the cardiac cycle phases of the variability of the time-varying IS. On the other hand, the average IS values exhibit a weak modulation over parieto-occiptal areas of the scalp. Our study highlights the importance of developing more advanced methods for measuring IS that account for non-stationarity in physiological systems. The proposed time-varying approach based on RLS represents a useful tool for identifying spatio-temporal dynamics within the neurocardiac system and can contribute to the understanding of brain-heart interactions.

Wearable health devices (WHDs) are rapidly gaining ground in the biomedical field due to their ability to monitor the individual physiological state in everyday life scenarios, while providing a comfortable wear experience. This study introduces a novel wearable biomedical device capable of synchronously acquiring electrocardiographic (ECG), photoplethysmographic (PPG), galvanic skin response (GSR) and motion signals. The device has been specifically designed to be worn on a finger, enabling the acquisition of all biosignals directly on the fingertips, offering the significant advantage of being very comfortable and easy to be employed by the users. The simultaneous acquisition of different biosignals allows the extraction of important physiological indices, such as heart rate (HR) and its variability (HRV), pulse arrival time (PAT), GSR level, blood oxygenation level (SpO2), and respiratory rate, as well as motion detection, enabling the assessment of physiological states, together with the detection of potential physical and mental stress conditions. Preliminary measurements have been conducted on healthy subjects using a measurement protocol consisting of resting states (i.e., SUPINE and SIT) alternated with physiological stress conditions (i.e., STAND and WALK). Statistical analyses have been carried out among the distributions of the physiological indices extracted in time, frequency, and information domains, evaluated under different physiological conditions. The results of our analyses demonstrate the capability of the device to detect changes between rest and stress conditions, thereby encouraging its use for assessing individuals’ physiological state. Furthermore, the possibility of performing synchronous acquisitions of PPG and ECG signals has allowed us to compare HRV and pulse rate variability (PRV) indices, so as to corroborate the reliability of PRV analysis under stationary physical conditions. Finally, the study confirms the already known limitations of wearable devices during physical activities, suggesting the use of algorithms for motion artifact correction.

The properties of cardio-respiratory coupling (CRC) are affected by various pathological conditions related to the cardiovascular and/or respiratory systems. In heart failure, one of the most common cardiac pathological conditions, the degree of CRC changes primarily depend on the type of heart-rhythm alterations. In this work, we investigated CRC in heart-failure patients, applying measures from information theory, i.e., Granger Causality (GC), Transfer Entropy (TE) and Cross Entropy (CE), to quantify the directed coupling and causality between cardiac (RR interval) and respiratory (Resp) time series. Patients were divided into three groups depending on their heart rhythm (sinus rhythm and presence of low/high number of ventricular extrasystoles) and were studied also after cardiac resynchronization therapy (CRT), distinguishing responders and non-responders to the therapy. The information-theoretic analysis of bidirectional cardio-respiratory interactions in HF patients revealed the strong effect of nonlinear components in the RR (high number of ventricular extrasystoles) and in the Resp time series (respiratory sinus arrhythmia) as well as in their causal interactions. We showed that GC as a linear model measure is not sensitive to both nonlinear components and only model free measures as TE and CE may quantify them. CRT responders mainly exhibit unchanged asymmetry in the TE values, with statistically significant dominance of the information flow from Resp to RR over the opposite flow from RR to Resp, before and after CRT. In non-responders this asymmetry was statistically significant only after CRT. Our results indicate that the success of CRT is related to corresponding information transfer between the cardiac and respiratory signal quantified at baseline measurements, which could contribute to a better selection of patients for this type of therapy.

Brain plasticity and functional reorganization are mechanisms behind functional motor recovery of patients after an ischemic stroke. The study of resting-state motor network functional connectivity by means of EEG proved to be useful in investigating changes occurring in the information flow and find correlation with motor function recovery. In the literature, most studies applying EEG to post-stroke patients investigated the undirected functional connectivity of interacting brain regions. Quite recently, works started to investigate the directionality of the connections and many approaches or features have been proposed, each of them being more suitable to describe different aspects, e.g., direct or indirect information flow between network nodes, the coupling strength or its characteristic oscillation frequency. Each work chose one specific measure, despite in literature there is not an agreed consensus, and the selection of the most appropriate measure is still an open issue. In an attempt to shed light on this methodological aspect, we propose here to combine the information of direct and indirect coupling provided by two frequency-domain measures based on Granger’s causality, i.e., the directed coherence (DC) and the generalized partial directed coherence (gPDC), to investigate the longitudinal changes of resting-state directed connectivity associated with sensorimotor rhythms α and β, occurring in 18 sub-acute ischemic stroke patients who followed a rehabilitation treatment. Our results showed a relevant role of the information flow through the pre-motor regions in the reorganization of the motor network after the rehabilitation in the sub-acute stage. In particular, DC highlighted an increase in intra-hemispheric coupling strength between pre-motor and primary motor areas, especially in ipsi-lesional hemisphere in both α and β frequency bands, whereas gPDC was more sensitive in the detection of those connection whose variation was mostly represented within the population. A decreased causal flow from contra-lesional premotor cortex towards supplementary motor area was detected in both α and β frequency bands and a significant reinforced inter-hemispheric connection from ipsi to contra-lesional pre-motor cortex was observed in β frequency. Interestingly, the connection from contra towards ipsilesional pre-motor area correlated with upper limb motor recovery in α band. The usage of two different measures of directed connectivity allowed a better comprehension of those coupling changes between brain motor regions, either direct or mediated, which mostly were influenced by the rehabilitation, revealing a particular involvement of the pre-motor areas in the cerebral functional reorganization.

Heart Rate Variability (HRV) and Blood Pressure Variability (BPV) are widely employed tools for characterizing the complex behavior of cardiovascular dynamics. Usually, HRV and BPV analyses are carried out through short-term (ST) measurements, which exploit ~five-minute-long recordings. Recent research efforts are focused on reducing the time series length, assessing whether and to what extent Ultra-Short-Term (UST) analysis is capable of extracting information about cardiovascular variability from very short recordings. In this work, we compare ST and UST measures computed on electrocardiographic R-R intervals and systolic arterial pressure time series obtained at rest and during both postural and mental stress. Standard time–domain indices are computed, together with entropy-based measures able to assess the regularity and complexity of cardiovascular dynamics, on time series lasting down to 60 samples, employing either a faster linear parametric estimator or a more reliable but time-consuming model-free method based on nearest neighbor estimates. Our results are evidence that shorter time series down to 120 samples still exhibit an acceptable agreement with the ST reference and can also be exploited to discriminate between stress and rest. Moreover, despite neglecting nonlinearities inherent to short-term cardiovascular dynamics, the faster linear estimator is still capable of detecting differences among the conditions, thus resulting in its suitability to be implemented on wearable devices.

Abstract Physical Restraint (PR) is a coercive procedure used in emergency psychiatric care to ensure safety in life-threatening situations. Because of its traumatic nature, studies emphasize the importance of considering the patient’s subjective experience. We pursued this aim by overcoming classic qualitative approaches and innovatively applying a multilayered semiautomated language analysis to a corpus of narratives about PR collected from 99 individuals across seven mental health services in Italy. Compared to a reference corpus, PR narratives were characterized by reduced fluency and lexical density, yet a greater use of emotional and cognitive terms, verbs, and first-person singular pronouns. Sadness was the most represented emotion, followed by anger and fear. One-third of the PR narratives contained at least one metaphor, with Animals and War/Prison as the most distinctive source domains. The quality and length of the PR experience impacted both the structure and the sentiment of the narratives. Findings confirm the distressful nature of PR but also point to the use of various linguistic mechanisms which might serve as an early adaptive response toward healing from the traumatic experience. Overall, the study highlights the importance of Natural Language Processing as an unobtrusive window into subjective experience, offering insights for therapeutic choices.

Aim: Since SARS-CoV-2 infection rapidly spread around the world, Italy has quickly become one of the most affected countries. Healthcare systems introduced strict infection control measures to ensure optimal care, especially in frail groups such as cancer patients (pts). This study investigated the efficacy of SARS-CoV-2 pre-procedure screening and whether COVID-19 influenced timely diagnosis and therapy. Methods: Data of oncological procedures of pts with confirmed or suspected cancer diagnosis, treated at Oncology Department or coming from Emergency Department of San Luigi Gonzaga Hospital between June 2020 and March 2021 were retrospectively collected. A nasopharyngeal swab (NPS) was performed in outpatients 24/48 h before procedures. Inpatients were tested by NPS before and after hospitalization. Results: Two hundred and twenty-one pts were included in this analysis. Median age was 73 years, males were 58%. Eastern Cooperative Oncology Group (ECOG) Performance Status was 0 or 1 in 88% of pts. The most frequent cancer type was lung cancer (57%). Stages IV were 77%. Two hundred and forty-three scheduled procedures were performed with diagnostic (n: 142; 58%), therapeutic (n: 55; 23%), and palliative (n: 46; 19%) intent. One hundred and four and 139 procedures were performed in out- and in-pts, respectively. Of the 234 NPS performed, 10 (4%) were positive. Two pts were infected during hospitalization, 8 in community. Most of them were asymptomatic, while only 2 had mild symptoms. Eight procedures (3%) were postponed, 1 cancelled, while 2 were performed in positive pts. Median time to resolution of the infection was 17 days (11–36). Median delay in the procedures was 25 days (14–55). Five pts started systemic treatment, after a median time of 37.5 days (13–57). Conclusions: SARS-CoV-2 infection led to the postponement of a small, but not negligible percentage of oncological procedures. However, the low infection rate observed suggests that structured screening for COVID-19 is critical for the best management of scheduled procedures during pandemic.

BackgroundThis study aimed at standardizing and providing preliminary evidence on the clinical usability of the Italian telephone-based Verbal Fluency Battery (t-VFB), which includes phonemic (t-PVF), semantic (t-SVF) and alternate (t-AVF) verbal fluency tasks.MethodsThree-hundred and thirty-five Italian healthy participants (HPs; 140 males; age range = 18–96 years; education range = 4–23 years) and 27 individuals with neurodegenerative or cerebrovascular diseases were administered the t-VFB. Switch number and cluster size were computed via latent semantic analyses. HPs underwent the telephone-based Mental State Examination (MMSE) and Backward Digit Span (BDS). Construct validity, factorial structure, internal consistency, test-retest and inter-rater reliability and equivalence with the in-person Verbal Fluency tasks were assessed. Norms were derived via Equivalent Scores. Diagnostic accuracy against clinical populations was assessed.ResultsThe majority of t-VFB scores correlated among each other and with the BDS, but not with the MMSE. Switch number correlated with t-PVF, t-SVF, t-AVF scores, whilst cluster size with the t-SVF and t-AVF scores only. The t-VFB was underpinned by a mono-component structure and was internally consistent (Cronbach’s α = 0.91). Test-retest (ICC = 0.69–0.95) and inter-rater reliability (ICC = 0.98–1) were optimal. Each t-VFB test was statistically equivalent to its in-person version (equivalence bounds yielding a p < 0.05). Education predicted all t-VFB scores, whereas age t-SVF and t-AVF scores and sex only some t-SVF scores. Diagnostic accuracy against clinical samples was optimal (AUC = 0.81–0.86).DiscussionThe t-VFB is a valid, reliable and normed telephone-based assessment tool for language and executive functioning, equivalent to the in-person version; results show promising evidence of its diagnostic accuracy in neurological populations.

The ability of a virus to spread between individuals, its replication capacity and the clinical course of the infection are macroscopic consequences of a multifaceted molecular interaction of viral components with the host cell. The heavy impact of COVID-19 on the world population, economics and sanitary systems calls for therapeutic and prophylactic solutions that require a deep characterization of the interactions occurring between virus and host cells. Unveiling how SARS-CoV-2 engages with host factors throughout its life cycle is therefore fundamental to understand the pathogenic mechanisms underlying the viral infection and to design antiviral therapies and prophylactic strategies. Two years into the SARS-CoV-2 pandemic, this review provides an overview of the interplay between SARS-CoV-2 and the host cell, with focus on the machinery and compartments pivotal for virus replication and the antiviral cellular response. Starting with the interaction with the cell surface, following the virus replicative cycle through the characterization of the entry pathways, the survival and replication in the cytoplasm, to the mechanisms of egress from the infected cell, this review unravels the complex network of interactions between SARS-CoV-2 and the host cell, highlighting the knowledge that has the potential to set the basis for the development of innovative antiviral strategies.

The article is divided into several sections, focusing on extracellular vesicles’ (EVs) nature, features, commonly employed methodologies and strategies for their isolation/preparation, and their characterization/visualization. This work aims to give an overview of advances in EVs’ extensive nanomedical-drug delivery applications. Furthermore, considerations for EVs translation to clinical application are summarized here, before focusing the review on a special kind of extracellular vesicles, the ones derived from red blood cells (RBCEVs). Generally, employing EVs as drug carriers means managing entities with advantageous properties over synthetic vehicles or nanoparticles. Besides the fact that certain EVs also reveal intrinsic therapeutic characteristics, in regenerative medicine, EVs nanosize, lipidomic and proteomic profiles enable them to pass biologic barriers and display cell/tissue tropisms; indeed, EVs engineering can further optimize their organ targeting. In the second part of the review, we focus our attention on RBCEVs. First, we describe the biogenesis and composition of those naturally produced by red blood cells (RBCs) under physiological and pathological conditions. Afterwards, we discuss the current procedures to isolate and/or produce RBCEVs in the lab and to load a specific cargo for therapeutic exploitation. Finally, we disclose the most recent applications of RBCEVs at the in vitro and preclinical research level and their potential industrial exploitation. In conclusion, RBCEVs can be, in the near future, a very promising and versatile platform for several clinical applications and pharmaceutical exploitations.

Abstract Background Adenocarcinomas of the oral cavity are rare neoplasms, and only four cases of primary colonic adenocarcinoma of the tongue have ever been described in literature. Very few information about chemotherapy sensitiveness of this type of neoplasia is available, with only one regimen that showed some activity in a metastatic patient. Case presentation We describe the case of a patient bearing a metastatic colonic adenocarcinoma of the tongue submitted to a first-line chemotherapy with oxaliplatin, 5-fluorouracil and folinic acid (FOLFOX regimen). After chemotherapy the patient obtained the complete disappearance of the primitive neoplasia located in the body of the tongue, and a tumor size reduction > 50% of liver and lung metastases. Conclusions This case demonstrated the activity of the combination of oxaliplatin and 5-fluorouracil in this very rare neoplasia. The FOLFOX regimen might be considered either in advanced and especially in the neoadjuvant setting, when the reduction of the primary tumor is highly needed.

Background: Extracorporeal membrane oxygenation (ECMO) is a complex life-saving support for acute cardio-respiratory failure, unresponsive to medical treatment. Emergency events on ECMO are rare but require immediate and proficient management. Multidisciplinary ECMO team members need to acquire and maintain over time cognitive, technical and behavioral skills, to safely face life-threatening clinical scenarios.Methods: A multistep educational program was delivered in a 4-year period to 32 ECMO team members, based on guidelines from the Extracorporeal Life Support Organization. A first traditional module was provided through didactic lectures, hands-on water drills, and laboratory animal training. The second phase consisted of a multi-edition high-fidelity simulation-based training on a modified neonatal mannequin (SimNewB®). In each session, participants were called to face, in small groups, ten critical scenarios, followed by debriefing time. Trainees underwent a pre-test for baseline competency assessment. Once completed the full training program, a post-test was administered. Pre- and post-test scores were compared. Trainees rated the educational program through survey questionnaires.Results: 28 trainees (87.5%) completed the full educational program. ECMO staff skills improved from a median pre-test score of 7.5/18 (IQR = 6–11) to 14/18 (IQR = 14–16) at post-test (P < 0.001, Wilcoxon rank test). All trainees highly rated the educational program and its impact on their practice. They reported high-fidelity simulations to be beneficial to novice learners as it increased self-confidence in ECMO-emergencies (according to 100% of surveyed), theoretical knowledge (61.5%) and team-work/communicative skills (58%).Conclusions: The multistep ECMO team training increased staff' knowledge, technical skills, teamwork, and self-confidence, allowing the successful development of a neonatal respiratory ECMO program. Conventional training was perceived as relevant in the early phase of the program development, while the active learning emerged to be more beneficial to master ECMO knowledge, specific skills, and team performance.

The project "Milano Archeologia" concerning the ancient remains of the city of Milan in Roman age was based on specific methods and tools, such as the collection of data through the previous archive research, and the feedback of information through inspections and then direct contact with monuments. A very interesting and significant case is the funerary complex area of “San Vittore al Corpo”, in which are also preserved the remains of an impressive Imperial Mausoleum. The curtain, that defines the space of the cemetery from the fourth Century AD, is kept in part below two modern buildings, while the remains of the Mausoleum are located below the present church of St. Victor. The analysis of the data collected allowed us to identify similarities and differences between the structures still visible, for example in the techniques adopted for the elevations or foundations, and it has provided the knowledge necessary to assume concrete solutions to protect and enhance this heritage.

Prognosis and therapeutic management of dogs with cutaneous mast cell tumors (MCTs) depend on clinical stage and histological grade. However, the prognostic value of this latter is still questionable. In the present study, MCT transcriptome was analyzed to identify a set of candidate genes potentially useful for predicting the biological behavior of MCTs. Fifty-one canine MCT biopsies were analyzed. Isolated and purified total RNAs were individually hybridized to the Agilent Canine V2 4x44k DNA microarray. The comparison of reference differentiated and undifferentiated MCT transcriptome revealed a total of 597 differentially expressed genes (147 down-regulated and 450 up-regulated). The functional analysis of this set of genes provided evidence that they were mainly involved in cell cycle, DNA replication, p53 signaling pathway, nucleotide excision repair and pyrimidine metabolism. Class prediction analysis identified 13 transcripts providing the greatest accuracy of class prediction and divided samples into two categories (differentiated and undifferentiated), harboring a different prognosis. The Principal Component Analysis of all samples, made by using the selected 13 markers, confirmed MCT classification. The first three components accounted for 99.924% of the total variance. This molecular classification significantly correlated with survival time (p = 0.0026). Furthermore, among all marker genes, a significant association was found between mRNA expression and MCT-related mortality for FOXM1, GSN, FEN1 and KPNA2 (p