Search Results (16)

Major depressive disorder (MDD) affects approximately $4.4\%$ of the global population. Its prevalence is increasing among adolescents and has led to the psychosocial condition known as hikikomori. MDD is typically assessed by self-report questionnaires, which, although informative, are subject to evaluator bias and subjectivity. To address these limitations, recent studies have explored machine learning (ML) for automated MDD detection. Among the input data used, speech signals stand out due to their low cost and minimal intrusiveness. However, many speech-based approaches lack integration with cognitive behavioral therapy (CBT) and adherence to evidence-based, patient-centered care—often aiming to replace rather than support clinical monitoring. In this context, we propose ML models to assess MDD in hikikomori patients using speech data from a real-world clinical trial. The trial is conducted in Italy, supervised by physicians, and comprises an eight-session CBT plan that is clinical evidence-based and follows patient-centered practices. Patients’ speech is recorded during therapy, and the Mel-Frequency Cepstral Coefficients (MFCCs) and wav2vec 2.0 embedding are extracted to train the models. The results show that the Multi-Layer Perceptron (MLP) predicted depression outcomes with a Root Mean Squared Error (RMSE) of $0.064$ using only MFCCs from the first session, suggesting that early-session speech may be valuable for outcome prediction. When considering the entire CBT treatment (i.e., all sessions), the MLP achieved an RMSE of $0.063$ using MFCCs and a lower RMSE of $0.057$ with wav2vec 2.0, indicating approximately a $9.5\%$ performance improvement. To aid the interpretability of the treatment outcomes, a binary task was conducted, where Logistic Regression (LR) achieved $70\%$ recall in predicting depression improvement among young adults using wav2vec 2.0. These findings position speech as a valuable predictive tool in clinical informatics, potentially supporting clinicians in anticipating treatment response. Full article

Pain is a complex and burdensome symptom frequently experienced by oncological patients. Despite increased attention from healthcare providers and novel approaches, pain remains undertreated and prevalent in this patient population. Programs of patient education about pain (pain education, PE) have been proposed as a potential method to enhance pain management. However, the effectiveness of such programs and their impact on pain relief have shown variable results across studies. In this narrative review, we analyzed existing systematic reviews and meta-analyses on PE in oncological patients. A comprehensive literature search was conducted using PubMed, Scopus, and the Cochrane Library databases, following established guidelines. Studies meeting the selection criteria were selected and analyzed to evaluate the effectiveness of PE interventions. A total of nine publications, comprising six systematic reviews and three meta-analyses, were included. Across various clinical settings (inpatient and outpatient), the impact of pain education (PE) on pain intensity showed considerable variability: some reviews reported significant reductions, while others observed minimal or no effects. In contrast, PE consistently improved patients’ knowledge about pain and adherence to medication. However, the available evidence did not demonstrate significant improvements in quality of life. The observed heterogeneity in the results of pain relief outcomes could be attributed to the various types of pain analyzed and the diversity of clinical settings evaluated. Furthermore, differences in study designs, comprising the inclusion of non-randomized studies, contributed to the variability in findings. It remains unclear whether the effectiveness of PE is solely attributed to the educational content or if the attention provided to patients during the intervention partly explains the effect. Full article

Sample entropy embeds time series into m-dimensional spaces and estimates entropy based on the distances between points in these spaces. However, when samples can be considered as missing or invalid, defining distance in the embedding space becomes problematic. Preprocessing techniques, such as deletion or interpolation, can be employed as a solution, producing time series without missing or invalid values. While deletion ignores missing values, interpolation replaces them using approximations based on neighboring points. This paper proposes a novel approach for the computation of sample entropy when values are considered as missing or invalid. The proposed algorithm accommodates points in the m-dimensional space and handles them there. A theoretical and experimental comparison of the proposed algorithm with deletion and interpolation demonstrates several advantages over these other two approaches. Notably, the deviation of the expected sample entropy value for the proposed methodology consistently proves to be lowest one. Full article

COVID-19 is an infectious disease that has greatly affected worldwide healthcare systems, due to the high number of cases and deaths. As COVID-19 patients may develop cardiac comorbidities that can be potentially fatal, electrocardiographic monitoring can be crucial. This work aims to identify electrocardiographic and vectorcardiographic patterns that may be related to mortality in COVID-19, with the application of the Advanced Repeated Structuring and Learning Procedure (AdvRS&LP). The procedure was applied to data from the “automatic computation of cardiovascular arrhythmic risk from electrocardiographic data of COVID-19 patients” (COVIDSQUARED) project to obtain neural networks (NNs) that, through 254 electrocardiographic and vectorcardiographic features, could discriminate between COVID-19 survivors and deaths. The NNs were validated by a five-fold cross-validation procedure and assessed in terms of the area under the curve (AUC) of the receiver operating characteristic. The features’ contribution to the classification was evaluated through the Local-Interpretable Model-Agnostic Explanations (LIME) algorithm. The obtained NNs properly discriminated between COVID-19 survivors and deaths (AUC = 84.31 ± 2.58% on hold-out testing datasets); the classification was mainly affected by the electrocardiographic-interval-related features, thus suggesting that changes in the duration of cardiac electrical activity might be related to mortality in COVID-19 cases. Full article

Many popular entropy definitions for signals, including approximate and sample entropy, are based on the idea of embedding the time series into an m-dimensional space, aiming to detect complex, deeper and more informative relationships among samples. However, for both approximate and sample entropy, the high computational cost is a severe limitation. Especially when large amounts of data are processed, or when parameter tuning is employed premising a large number of executions, the necessity of fast computation algorithms becomes urgent. In the past, our research team proposed fast algorithms for sample, approximate and bubble entropy. In the general case, the bucket-assisted algorithm was the one presenting the lowest execution times. In this paper, we exploit the opportunities given by the multithreading technology to further reduce the computation time. Without special requirements in hardware, since today even our cost-effective home computers support multithreading, the computation of entropy definitions can be significantly accelerated. The aim of this paper is threefold: (a) to extend the bucket-assisted algorithm for multithreaded processors, (b) to present updated execution times for the bucket-assisted algorithm since the achievements in hardware and compiler technology affect both execution times and gain, and (c) to provide a Python library which wraps fast C implementations capable of running in parallel on multithreaded processors. Full article

AI techniques have recently been put under the spotlight for analyzing electrocardiograms (ECGs). However, the performance of AI-based models relies on the accumulation of large-scale labeled datasets, which is challenging. To increase the performance of AI-based models, data augmentation (DA) strategies have been developed recently. The study presented a comprehensive systematic literature review of DA for ECG signals. We conducted a systematic search and categorized the selected documents by AI application, number of leads involved, DA method, classifier, performance improvements after DA, and datasets employed. With such information, this study provided a better understanding of the potential of ECG augmentation in enhancing the performance of AI-based ECG applications. This study adhered to the rigorous PRISMA guidelines for systematic reviews. To ensure comprehensive coverage, publications between 2013 and 2023 were searched across multiple databases, including IEEE Explore, PubMed, and Web of Science. The records were meticulously reviewed to determine their relevance to the study’s objective, and those that met the inclusion criteria were selected for further analysis. Consequently, 119 papers were deemed relevant for further review. Overall, this study shed light on the potential of DA to advance the field of ECG diagnosis and monitoring. Full article

The beetles of the subtribe Oedionychina (Chrysomelidae, Alticinae) are the only ones that have the atypical giant and achiasmatic sex chromosomes, which are substantially larger than the autosomes. Previous cytogenetic analyses suggest a large accumulation of repetitive DNA in the sex chromosomes. In this study, we examined the similarity of X and Y chromosomes in four Omophoita species and compared genomic differentiation to better understand the evolutionary process and the giant sex chromosomes origin. Intraspecific genomic comparation using male and female genomes of O. octoguttata and interespecific analyses using genomic DNA of O. octoguttata, O. sexnotata, O. magniguttis, and O. personata were performed. In addition, whole chromosome painting (WCP) experiments were performed with X and Y chromosome probes of O. octogutatta. CGH analysis revealed great genomic similarity between the sexes and a sex-specific region on the Y chromosome, and interspecific analysis revealed a genomic divergence between species. In contrast, WCP results revealed that the sex chromosomes of O. octoguttata have high intra- and interspecific similarity with the studied species. Our data support a common origin under the canonical evolution of the sex chromosomes in this group, as they have high genomic similarity between them. Full article

The cleaning of textile artefacts and in particular historical tapestries is generally carried out using standard methods. Different cleaning procedures, including a new method based on a hydro-aspiration mechanism, recently developed by restorers with the aim of improving the efficiency of the cleaning system, were applied to a historical tapestry belonging to the lower edge of one of the tapestries of the “Ulysses Stories” series exhibited at the Quirinale Palace (Rome). The tapestry was made of wool and silk and has precious decorations made of metal yarns, which are particularly fragile. The new cleaning system was compared with the traditional methods commonly used by restorers for tapestry cleaning. For this purpose, the quantity and chemical composition of the particles removed and collected on quartz fibre filters by applying the different cleaning systems, were estimated by means of analytical techniques, such as IC (Ion Chromatography) for the quantification of the ionic species collected into the rinsing water, the TOT (Thermal Optical Transmittance) method for the quantification of the carbonaceous particles and SEM-EDX (Scanning Electron Microscopy coupled to Energy Dispersive X-ray Spectroscopy) for yarn morphological characterization and elemental analysis of the deposited particles. The objective of this study is to identify the correct cleaning method to apply to the polymaterial tapestry and, in particular, to the gilded silver and gold metallic yarns, whose conservation state requires the preservation of the “self-protection” patina necessary for the future exhibition inside the Quirinale Palace. The new hydro-aspiration method was found to be more efficient in removing dirt and preserving the structure of the metallic threads being in this way less invasive in detaching the fragile surface patina and at the same time more effective in removing dirt. Full article

Progressive pseudorheumatoid dysplasia (PPRD) is an autosomal recessive inherited skeletal dysplasia characterized by progressive non-inflammatory arthropathy affecting primarily the articular cartilage. Currently, little is known about the functional musculoskeletal aspects of these patients. In particular, an abnormal gait pattern has been described, without a clear hypothesis of the underlying causes in terms of muscular activity. This study presents the case of two siblings, 4 and 9 years old, a boy and a girl, respectively, suffering from PPRD at different stages of the disease. In addition to the clinical assessment, an instrumental gait analysis was performed. Swelling of the interphalangeal finger joints and fatigue were present in both cases. Gait abnormalities consisted of a relevant reduction in the ankle plantarflexion in the terminal phase of the gait cycle, associated with reduced gastrocnemius EMG activity and increased activity of the tibialis anterior, resulting in overloading at the initial peak of ground reaction forces. Gait anomalies observed were similar in both siblings with PPRD, although at different ages, and confirm walking patterns previously described in the literature. The calf muscle strength deficit and reduced activity during the stance phase of gait present in these two siblings indicate the typical absence of the propulsive phase. A stomping gait pattern, with the foot striking the ground hard on each step, was originally described. Further neurophysiological investigations are required to determine the origin of muscle weakness. Full article

CeO₂ nanoparticles were coated with polydopamine (PDA) by dopamine polymerization in water dispersions of CeO₂ and characterized by Infrared and Near Edge X-ray Absorption Fine Structure spectroscopy, Transmission Electron Microscopy, Thermogravimetric analysis and X-ray diffraction. The resulting materials (PDAx@CeO₂, with x = PDA wt% = 10, 25, 50) were employed as fillers of composite proton exchange membranes with Aquivion 830 as ionomer, to reduce the ionomer chemical degradation due to hydroxyl and hydroperoxyl radicals. Membranes, loaded with 3 and 5 wt% PDAx@CeO₂, were prepared by solution casting and characterized by conductivity measurements at 80 and 110 °C, with relative humidity ranging from 50 to 90%, by accelerated ex situ degradation tests with the Fenton reagent, as well as by in situ open circuit voltage stress tests. In comparison with bare CeO₂, the PDA coated filler mitigates the conductivity drop occurring at increasing CeO₂ loading especially at 110 °C and 50% relative humidity but does not alter the radical scavenger efficiency of bare CeO₂ for loadings up to 4 wt%. Fluoride emission rate data arising from the composite membrane degradation are in agreement with the corresponding changes in membrane mass and conductivity. Full article

Aims: Bubble entropy ($bEn$) is an entropy metric with a limited dependence on parameters. $bEn$ does not directly quantify the conditional entropy of the series, but it assesses the change in entropy of the ordering of portions of its samples of length m, when adding an extra element. The analytical formulation of $bEn$ for autoregressive (AR) processes shows that, for this class of processes, the relation between the first autocorrelation coefficient and $bEn$ changes for odd and even values of m. While this is not an issue, per se, it triggered ideas for further investigation. Methods: Using theoretical considerations on the expected values for AR processes, we examined a two-steps-ahead estimator of $bEn$, which considered the cost of ordering two additional samples. We first compared it with the original $bEn$ estimator on a simulated series. Then, we tested it on real heart rate variability (HRV) data. Results: The experiments showed that both examined alternatives showed comparable discriminating power. However, for values of $10<m<20$, where the statistical significance of the method was increased and improved as m increased, the two-steps-ahead estimator presented slightly higher statistical significance and more regular behavior, even if the dependence on parameter m was still minimal. We also investigated a new normalization factor for $bEn$, which ensures that $bEn$$=1$ when white Gaussian noise (WGN) is given as the input. Conclusions: The research improved our understanding of bubble entropy, in particular in the context of HRV analysis, and we investigated interesting details regarding the definition of the estimator. Full article

Elite performance in team sports attracts the attention of the general public. In particular, the best players became incredibly skilled and physically powerful, which is a fact that potentiates the delivery of a product that is considered attractive, exciting, and competitive. Not surprisingly, this is a very valuable product from an economic and social standpoint; thus, all sports professionals are extremely interested in developing new procedures to improve their sports performance. Furthermore, the great interests of the various stakeholders (owners, chief executive officers (CEOs), agents, fans, media, coaches, players, families, and friends) are one of the main reasons for this development under the sports science umbrella and the accompanying sports industry. All their personal performances should be coordinated and put into practice by the sports team. In this scientific and applied study, we primarily dealt with the individual treatment of players in order to improve their personal performance and, consequently, the team’s sporting performance. Full article

In December of 2019, there was an outbreak of a severe acute respiratory syndrome caused by the Coronavirus 2 (SARS-CoV-2 or COVID-19) in China. The virus rapidly spread into the whole World causing an unprecedented pandemic and forcing governments to impose a global quarantine, entering an extreme unknown situation. The organizational consequences of quarantine/isolation are: absence of organized training and competition, lack of communication among athletes and coaches, inability to move freely, lack of adequate sunlight exposure, inappropriate training conditions. Based on the current scientific, we strongly recommend encouraging the athlete to reset their mindset to understand quarantine as an opportunity for development, organizing appropriate guidance, educating and encourage athletes to apply appropriate preventive behavior and hygiene measures to promote immunity and ensuring good living isolation conditions. The athlete’s living space should be equipped with cardio and resistance training equipment (portable bicycle or rowing ergometer). Some forms of body mass resistance circuit-based training could promote aerobic adaptation. Sports skills training should be organized based on the athlete’s needs. Personalized conditioning training should be carried out with emphasis on neuromuscular performance. Athletes should also be educated about nutrition (Vitamin D and proteins) and hydration. Strategies should be developed to control body composition. Mental fatigue should be anticipated and mental controlled. Adequate methods of recovery should be provided. Daily monitoring should be established. This is an ideal situation in which to rethink personal life, understanding the situation, that can be promoted in these difficult times that affect practically the whole world. Full article

Wearable sensors play a significant role for monitoring the functional ability of the elderly and in general, promoting active ageing. One of the relevant variables to be tracked is the number of stair steps (single stair steps) performed daily, which is more challenging than counting flight of stairs and detecting stair climbing. In this study, we proposed a minimal complexity algorithm composed of a hierarchical classifier and a linear model to estimate the number of stair steps performed during everyday activities. The algorithm was calibrated on accelerometer and barometer recordings measured using a sensor platform worn at the wrist from 20 healthy subjects. It was then tested on 10 older people, specifically enrolled for the study. The algorithm was then compared with other three state-of-the-art methods, which used the accelerometer, the barometer or both. The experiments showed the good performance of our algorithm (stair step counting error: 13.8%), comparable with the best state-of-the-art (p > 0.05), but using a lower computational load and model complexity. Finally, the algorithm was successfully implemented in a low-power smartwatch prototype with a memory footprint of about 4 kB. Full article

Sample Entropy is the most popular definition of entropy and is widely used as a measure of the regularity/complexity of a time series. On the other hand, it is a computationally expensive method which may require a large amount of time when used in long series or with a large number of signals. The computationally intensive part is the similarity check between points in m dimensional space. In this paper, we propose new algorithms or extend already proposed ones, aiming to compute Sample Entropy quickly. All algorithms return exactly the same value for Sample Entropy, and no approximation techniques are used. We compare and evaluate them using cardiac inter-beat (RR) time series. We investigate three algorithms. The first one is an extension of the $kd$ -trees algorithm, customized for Sample Entropy. The second one is an extension of an algorithm initially proposed for Approximate Entropy, again customized for Sample Entropy, but also improved to present even faster results. The last one is a completely new algorithm, presenting the fastest execution times for specific values of m, r, time series length, and signal characteristics. These algorithms are compared with the straightforward implementation, directly resulting from the definition of Sample Entropy, in order to give a clear image of the speedups achieved. All algorithms assume the classical approach to the metric, in which the maximum norm is used. The key idea of the two last suggested algorithms is to avoid unnecessary comparisons by detecting them early. We use the term unnecessary to refer to those comparisons for which we know a priori that they will fail at the similarity check. The number of avoided comparisons is proved to be very large, resulting in an analogous large reduction of execution time, making them the fastest algorithms available today for the computation of Sample Entropy. Full article