Elemental Fingerprinting Combined with Machine Learning Techniques as a Powerful Tool for Geographical Discrimination of Honeys from Nearby Regions.

Discrimination of honey based on geographical origin is a common fraudulent practice and is one of the most investigated topics in honey authentication. This research aims to discriminate honeys according to their geographical origin by combining elemental fingerprinting with machine-learning techniques. In particular, the main objective of this study is to distinguish the origin of unifloral and multifloral honeys produced in neighboring regions, such as Sardinia (Italy) and Spain. The elemental compositions of 247 honeys were determined using Inductively Coupled Plasma Mass Spectrometry (ICP-MS). The origins of honey were differentiated using Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA), and Random Forest (RF). Compared to LDA, RF demonstrated greater stability and better classification performance. The best classification was based on geographical origin, achieving 90% accuracy using Na, Mg, Mn, Sr, Zn, Ce, Nd, Eu, and Tb as predictors.

Prospective clinical validation of the Empatica EmbracePlus wristband as a reflective pulse oximeter.

Introduction: Respiratory diseases such as chronic obstructive pulmonary disease, obstructive sleep apnea syndrome, and COVID-19 may cause a decrease in arterial oxygen saturation (SaO2). The continuous monitoring of oxygen levels may be beneficial for the early detection of hypoxemia and timely intervention. Wearable non-invasive pulse oximetry devices measuring peripheral oxygen saturation (SpO2) have been garnering increasing popularity. However, there is still a strong need for extended and robust clinical validation of such devices, especially to address topical concerns about disparities in performances across racial groups. This prospective clinical validation aimed to assess the accuracy of the reflective pulse oximeter function of the EmbracePlus wristband during a controlled hypoxia study in accordance with the ISO 80601-2-61:2017 standard and the Food & Drug Administration (FDA) guidance. Methods: Healthy adult participants were recruited in a controlled desaturation protocol to reproduce mild, moderate, and severe hypoxic conditions with SaO2 ranging from 100% to 70% (ClinicalTrials.gov registration #NCT04964609). The SpO2 level was estimated with an EmbracePlus device placed on the participant's wrist and the reference SaO2 was obtained from blood samples analyzed with a multiwavelength co-oximeter. Results: The controlled hypoxia study yielded 373 conclusive measurements on 15 subjects, including 30% of participants with dark skin pigmentation (V-VI on the Fitzpatrick scale). The accuracy root mean square (Arms) error was found to be 2.4%, within the 3.5% limit recommended by the FDA. A strong positive correlation between the wristband SpO2 and the reference SaO2 was observed (r = 0.96, P < 0.001), and a good concordance was found with Bland-Altman analysis (bias, 0.05%; standard deviation, 1.66; lower limit, -4.7%; and upper limit, 4.8%). Moreover, acceptable accuracy was observed when stratifying data points by skin pigmentation (Arms 2.2% in Fitzpatrick V-VI, 2.5% in Fitzpatrick I-IV), and sex (Arms 1.9% in females, and 2.9% in males). Discussion: This study demonstrates that the EmbracePlus wristband could be used to assess SpO2 with clinically acceptable accuracy under no-motion and high perfusion conditions for individuals of different ethnicities across the claimed range. This study paves the way for further accuracy evaluations on unhealthy subjects and during prolonged use in ambulatory settings.

How healthcare workers reacted to the different COVID-19 waves: An Italian survey.

INTRODUCTION: The COVID-19 pandemic had a huge impact on radiology departments all over the world, affecting both management and healthcare workers (HCWs). Therefore, it became challenging to guarantee high standards of diagnosis while keeping up with the workload. METHODS: The study was approved by the institutional review board. Its aim was to assess the impact of the COVID-19 pandemic over the radiology departments and HCWs through a survey. The questionnaire was available online from January to March 2022. Twelve areas of interest (sessions) were highlighted in the survey. RESULTS: The number of total responders was 1376 and 73.7% of participants worked in public healthcare facilities. Comparisons between participants working in public versus private healthcare facilities were carried out using chi-square tests and Fisher tests. Within public healthcare workers, 82% affirmed having operating instruction protocols regarding confirmed or suspected COVID-19 patient CT management (p< 0.001). Private healthcare facilities had fewer CT scanners available in general (p< 0.001); in fact, only 18% of them affirmed having two or more CT scanners, and did not have CT scanners dedicated to confirmed or suspected COVID-19 patients (p< 0.001). Finally, public facilities strongly reduced (by 88%) the number of examinations booked during the first wave, compared to private healthcare facilities (p< 0.001). CONCLUSION: This survey showed that public facilities appeared to be better prepared from an organizational point of view than private facilities. Rescheduling the examinations booked during the first COVID-19 wave was challenging and not always possible.

S-duct flow distortion with non-uniform inlet conditions.

Convoluted aero-engine intakes are often required to enable closer integration between engine and airframe. Although the majority of previous research focused on the distortion of S-duct intakes with undistorted inlet conditions, there is a need to investigate the impact of more challenging inlet conditions at which the intake duct is expected to operate. The impact of inlet vortices and total pressure profiles on the inherent unsteady flow distortion of an S-duct intake was assessed with stereo particle image velocimetry. Inlet vortices disrupted the characteristic flow switching mode but had a modest impact on the peak levels and unsteady fluctuations. Non-uniform inlet total pressure profiles increased the peak swirl intensity and its unsteadiness. The frequency of swirl angle fluctuations was sensitive to the azimuthal orientation of the non-uniform total pressure distribution. The modelling of peak distortion with the extreme value theory revealed that although for some inlet configurations the measured peak swirl intensity was similar, the growth rate of the peak values beyond the experimental observations was substantially different and it was related with the measured flow unsteadiness. This highlights the need of unsteady swirl distortion measurements and the use of statistical models to assess the time-invariant peak distortion levels. Overall, the work shows it is vital to include the effect of the inlet flow conditions as it substantially alters the characteristics of the complex intake flow distortion.

COVID-19 outbreak impact on health professionals: A survey on the Italian radiographer experience.

AIM: To evaluate the impact of the Phase 1 COVID-19 (C19) outbreak on Italian Radiographers. MATERIAL AND METHODS: COVID-19 has spread rapidly worldwide. Many patients underwent radiological examinations, leading to a high risk of infection within the radiology department's staff. Italy was the first-hit European country to face the COVID-19 outbreak and the impact on radiographers was huge. An online survey was disseminated to investigate the involvement and working environment of Italian radiographers during the first outbreak of COVID-19. RESULTS: Of the 840 responders, 65% were men. The majority of the responding Health-care Workers (HCW) was represented by radiographers (96%), from high-prevalence regions (82%; p<.05). Forty-five percent were involved in the activation of the protocol for the management of COVID-19 positive patients, without exhaustive indication for Plain Radiography and Computed Tomography (CT). Only 17% of hospitals counted on available guidelines for serious infections (p<0.05). Diagnostic examinations were mainly performed by a single radiographer (62%). Many professionals (69%) confirmed wearing all indispensable PPE in case of COVID-19 positive patients. CONCLUSION: The primary objective of management strategies should be to redact standardized policies for the safeguarding of patient's health and operator's safety. All front-line workers, including radiographers working in diagnostic services, should be involved in the decision-making process to generate wellness and awareness.

Sleep assessment by means of a wrist actigraphy-based algorithm: agreement with polysomnography in an ambulatory study on older adults.

The purpose of the present work is to examine, on a clinically diverse population of older adults (N = 46) sleeping at home, the performance of two actigraphy-based sleep tracking algorithms (i.e., Actigraphy-based Sleep algorithm, ACT-S1 and Sadeh's algorithm) compared to manually scored electroencephalography-based PSG (PSG-EEG). ACT-S1 allows for a fully automatic identification of sleep period time (SPT) and within the identified sleep period, the sleep-wake classification. SPT detected by ACT-S1 did not differ statistically from using PSG-EEG (bias = -9.98 min; correlation 0.89). In sleep-wake classification on 30-s epochs within the identified sleep period, the new ACT-S1 presented similar or slightly higher accuracy (83-87%), precision (86-89%) and F1 score (90-92%), significantly higher specificity (39-40%), and significantly lower, but still high, sensitivity (96-97%) compared to Sadeh's algorithm, which achieved 99% sensitivity as the only measure better than ACT-S1's. Total sleep times (TST) estimated with ACT-S1 and Sadeh's algorithm were higher, but still highly correlated to PSG-EEG's TST. Sleep quality metrics of sleep period efficiency and wake-after-sleep-onset computed by ACT-S1 were not significantly different from PSG-EEG, while the same sleep quality metrics derived by Sadeh's algorithm differed significantly from PSG-EEG. Agreement between ACT-S1 and PSG-EEG reached was highest when analyzing the subset of subjects with least disrupted sleep (N = 28). These results provide evidence of promising performance of a full-automation of the sleep tracking procedure with ACT-S1 on older adults. Future longitudinal validations across specific medical conditions are needed. The algorithm's performance may further improve with integrating multi-sensor information.

[Update to the position paper of the Italian Society of Interventional Cardiology (SICI-GISE) on institutional and operator requirements for transcatheter aortic valve implantation (TAVI). Chapter 2: Technical nursing care in TAVI]. / Update del documento di posizione della Società Italiana di Cardiologia Interventistica (SICI-GISE) sui requisiti minimi per ospedali ed operatori che eseguono procedure di impianto transcatetere di protesi valvolare aortica (TAVI). Capitolo 2: La realtà tecnico-infermieristica nella TAVI.

Calcific degenerative aortic stenosis is the most frequent valve disease in the western population. Transcatheter aortic valve implantation procedures are significantly increasing, as they now represent the first choice in inoperable patients and have been shown to be non-inferior to cardiac surgery in patients at high and intermediate surgical risk. In this scenario, it is necessary to define and standardize the technical nursing care to guarantee patient safety and improve quality of care.The purpose of this document is to propose, on the basis on currently available literature, a model for the development of assistance based on shared objectives and clinical competence.

Multicenter clinical assessment of improved wearable multimodal convulsive seizure detectors.

OBJECTIVE: New devices are needed for monitoring seizures, especially those associated with sudden unexpected death in epilepsy (SUDEP). They must be unobtrusive and automated, and provide false alarm rates (FARs) bearable in everyday life. This study quantifies the performance of new multimodal wrist-worn convulsive seizure detectors. METHODS: Hand-annotated video-electroencephalographic seizure events were collected from 69 patients at six clinical sites. Three different wristbands were used to record electrodermal activity (EDA) and accelerometer (ACM) signals, obtaining 5,928 h of data, including 55 convulsive epileptic seizures (six focal tonic-clonic seizures and 49 focal to bilateral tonic-clonic seizures) from 22 patients. Recordings were analyzed offline to train and test two new machine learning classifiers and a published classifier based on EDA and ACM. Moreover, wristband data were analyzed to estimate seizure-motion duration and autonomic responses. RESULTS: The two novel classifiers consistently outperformed the previous detector. The most efficient (Classifier III) yielded sensitivity of 94.55%, and an FAR of 0.2 events/day. No nocturnal seizures were missed. Most patients had <1 false alarm every 4 days, with an FAR below their seizure frequency. When increasing the sensitivity to 100% (no missed seizures), the FAR is up to 13 times lower than with the previous detector. Furthermore, all detections occurred before the seizure ended, providing reasonable latency (median = 29.3 s, range = 14.8-151 s). Automatically estimated seizure durations were correlated with true durations, enabling reliable annotations. Finally, EDA measurements confirmed the presence of postictal autonomic dysfunction, exhibiting a significant rise in 73% of the convulsive seizures. SIGNIFICANCE: The proposed multimodal wrist-worn convulsive seizure detectors provide seizure counts that are more accurate than previous automated detectors and typical patient self-reports, while maintaining a tolerable FAR for ambulatory monitoring. Furthermore, the multimodal system provides an objective description of motor behavior and autonomic dysfunction, aimed at enriching seizure characterization, with potential utility for SUDEP warning.

Are Complexity Metrics Reliable in Assessing HRV Control in Obese Patients During Sleep?

Obesity is associated with cardiovascular mortality. Linear methods, including time domain and frequency domain analysis, are normally applied on the heart rate variability (HRV) signal to investigate autonomic cardiovascular control, whose imbalance might promote cardiovascular disease in these patients. However, given the cardiac activity non-linearities, non-linear methods might provide better insight. HRV complexity was hereby analyzed during wakefulness and different sleep stages in healthy and obese subjects. Given the short duration of each sleep stage, complexity measures, normally extracted from long-period signals, needed be calculated on short-term signals. Sample entropy, Lempel-Ziv complexity and detrended fluctuation analysis were evaluated and results showed no significant differences among the values calculated over ten-minute signals and longer durations, confirming the reliability of such analysis when performed on short-term signals. Complexity parameters were extracted from ten-minute signal portions selected during wakefulness and different sleep stages on HRV signals obtained from eighteen obese patients and twenty controls. The obese group presented significantly reduced complexity during light and deep sleep, suggesting a deficiency in the control mechanisms integration during these sleep stages. To our knowledge, this study reports for the first time on how the HRV complexity changes in obesity during wakefulness and sleep. Further investigation is needed to quantify altered HRV impact on cardiovascular mortality in obesity.

Effects of the series length on Lempel-Ziv Complexity during sleep.

Lempel-Ziv Complexity (LZC) has been demonstrated to be a powerful complexity measure in several biomedical applications. During sleep, it is still not clear how many samples are required to ensure robustness of its estimate when computed on beat-to-beat interval series (RR). The aims of this study were: i) evaluation of the number of necessary samples in different sleep stages for a reliable estimation of LZC; ii) evaluation of the LZC when considering inter-subject variability; and iii) comparison between LZC and Sample Entropy (SampEn). Both synthetic and real data were employed. In particular, synthetic RR signals were generated by means of AR models fitted on real data. The minimum number of samples required by LZC for having no changes in its average value, for both NREM and REM sleep periods, was 10(4) (p<;0.01) when using a binary quantization. However, LZC can be computed with N >1000 when a tolerance of 5% is considered satisfying. The influence of the inter-subject variability on the LZC was first assessed on model generated data confirming what found (>10(4); p<;0.01) for both NREM and REM stage. However, on real data, without differentiate between sleep stages, the minimum number of samples required was 1.8×10(4). The linear correlation between LZC and SampEn was computed on a synthetic dataset. We obtained a correlation higher than 0.75 (p<;0.01) when considering sleep stages separately, and higher than 0.90 (p<;0.01) when stages were not differentiated. Summarizing, we suggest to use LZC with the binary quantization and at least 1000 samples when a variation smaller than 5% is considered satisfying, or at least 10(4) for maximal accuracy. The use of more than 2 levels of quantization is not recommended.

Decision tree for smart feature extraction from sleep HR in bipolar patients.

The aim of this work is the creation of a completely automatic method for the extraction of informative parameters from peripheral signals recorded through a sensorized T-shirt. The acquired data belong to patients affected from bipolar disorder, and consist of RR series, body movements and activity type. The extracted features, i.e. linear and non-linear HRV parameters in the time domain, HRV parameters in the frequency domain, and parameters indicative of the sleep quality, profile and fragmentation, are of interest for the automatic classification of the clinical mood state. The analysis of this dataset, which is to be performed online and automatically, must address the problems related to the clinical protocol, which also includes a segment of recording in which the patient is awake, and to the nature of the device, which can be sensitive to movements and misplacement. Thus, the decision tree implemented in this study performs the detection and isolation of the sleep period, the elimination of corrupted recording segments and the checking of the minimum requirements of the signals for every parameter to be calculated.

Optimization of time-variant autoregressive models for tracking REM-non REM transitions during sleep.

The aim of this study was the optimization of Time-Variant Autoregressive Models (TVAM) for tracking REM-non REM transitions during sleep, through the analysis of spectral indexes extracted from tachograms. A first improvement of TVAM was achieved by choosing the best typology of forgetting factor in the analysis of a tachogram obtained during a sitting-to-standing test; then, a method for improving robustness of AR recursive identification with respect to outliers was selected by analyzing a tachogram with an ectopic beat. A variable forgetting factor according to the Fortescue method and a specific condition on the prediction error for recursive AR identification gave the best performances. The optimized TVAM was then employed in the analysis of tachograms derived from ECGs recorded during a whole night, through a sensorized T-shirt, from 9 healthy subjects. The spectral indexes (power of tachogram in the LF and HF bands, LF/HF ratio and the absolute value of the spectrum pole in the HF band) were computed from the estimated AR parameters on a beat-to-beat basis. A two groups T-test aimed at comparing values assumed by each spectral index in REM and non-REM sleep epochs was performed. Significant statistical differences (p-value < 0.05) were found in three of the four spectral indexes computed. In conclusion, the combination of the Fortescue variant and of the robustness method based on the prediction error in the TVAM seems to be helpful in the differentiation between REM and non-REM sleep stages.

Clinical state assessment in bipolar patients by means of HRV features obtained with a sensorized T-shirt.

The aim of this study is to identify parameters extracted from the Heart Rate Variability (HRV) signal that correlate to the clinical state in patients affected by bipolar disorder. 25 ECG and activity recordings from 12 patients were obtained by means of a sensorized T-shirt and the clinical state of the subjects was assessed by a psychiatrist. Features in the time and frequency domain were extracted from each signal. HRV features were also used to automatically compute the sleep profile of each subject by means of an Artificial Neural Network, trained on a control group of healthy subjects. From the hypnograms, sleep-specific parameters were computed. All the parameters were compared with those computed on the control group, in order to highlight significant differences in their values during different stages of the pathology. The analysis was performed by grouping the subjects first on the basis of the depression-mania level and then on the basis of the anxiety level.

Study of Heart Rate Variability in Bipolar Disorder: Linear and Non-Linear Parameters during Sleep.

The aim of the study is to define physiological parameters and vital signs that may be related to the mood and mental status in patients affected by bipolar disorder. In particular we explored the autonomic nervous system through the analysis of the heart rate variability. Many different parameters, in the time and in the frequency domain, linear and non-linear were evaluated during the sleep in a group of normal subject and in one patient in four different conditions. The recording of the signals was performed through a wearable sensorized T-shirt. Heart rate variability (HRV) signal and movement analysis allowed also obtaining sleep staging and the estimation of REM sleep percentage over the total sleep time. A group of eight normal females constituted the control group, on which normality ranges were estimated. The pathologic subject was recorded during four different nights, at time intervals of at least 1 week, and during different phases of the disturbance. Some of the examined parameters (MEANNN, SDNN, RMSSD) confirmed reduced HRV in depression and bipolar disorder. REM sleep percentage was found to be increased. Lempel-Ziv complexity and sample entropy, on the other hand, seem to correlate with the depression level. Even if the number of examined subjects is still small, and the results need further validation, the proposed methodology and the calculated parameters seem promising tools for the monitoring of mood changes in psychiatric disorders.

Automatic sleep staging based on ballistocardiographic signals recorded through bed sensors.

This study presents different methods for automatic sleep classification based on heart rate variability (HRV), respiration and movement signals recorded through bed sensors. Two methods for feature extraction have been implemented: time variant-autoregressive model (TVAM) and wavelet discrete transform (WDT); the obtained features are fed into two classifiers: Quadratic (QD) and Linear (LD) discriminant for staging sleep in REM, nonREM and WAKE periods. The performances of all the possible combinations of feature extractors and classifiers are compared in terms of accuracy and kappa index, using clinical polysomographyc evaluation as golden standard. 17 recordings from healthy subjects, including also polisomnography, were used to train and test the algorithms. When automatic classification is compared. QD-TVAM algorithm achieved a total accuracy of 76.81 ± 7.51 % and kappa index of 0.55 ± 0.10, while LD-WDT achieved a total accuracy of 79 ± 10% and kappa index of 0.51 ± 0.17. The results suggest that a good sleep evaluation can be achieved through non-conventional recording systems that could be used outside sleep centers.

Evaluation of the sleep quality based on bed sensor signals: Time-variant analysis.

Automatic detection of the sleep macrostructure (Wake, NREM -non Rapid Eye Movement- and REM -Rapid Eye Movement-) based on bed sensor signals is presented. This study assesses the feasibility of different methodologies to evaluate the sleep quality out of sleep centers. The study compares a) the features extracted from time-variant autoregressive modeling (TVAM) and Wavelet Decomposition (WD) and b) the performance of K-Nearest Neighbor (KNN) and Feed Forward Neural Networks (FFNN) classifiers. In the current analysis, 17 full polysomnography recordings from healthy subjects were used. The best agreement for Wake-NREM-REM with respect to the gold standard was 71.95 ± 7.47% of accuracy and 0.42 ± 0.10 of kappa index for TVAM-LD while WD-FFNN shows 67.17 ± 11.88% of accuracy and 0.39 ± 0.13 of kappa index. The results suggest that the sleep quality assessment out of sleep centers could be possible and as consequence more people could be beneficiated.