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This paper presents a real-time digital twin (DT) of the power conversion system used in offshore wind applications. The proposed DT is exploited to identify key electrical parameters of both the permanent magnet synchronous generator (PMSG) and the three-phase boost rectifier and has been developed with a Condition Monitoring (CM)-oriented approach. A Gated Recurrent Unit (GRU) neural network is adopted as a real-time digital model (RTDM) to estimate online the PMSG phase resistance and synchronous inductance, as well as the DC-link capacitance at the rectifier output. The network is trained in MATLAB using data generated by a Typhoon HIL 606 emulator, covering both balanced and unbalanced operating conditions and a wide range of parameter variations. The trained GRU is then deployed on the control board and implemented in LabVIEW Real-Time for embedded execution. Experimental tests on a PMSG-based generating unit confirm the effectiveness of the proposed RTDM, achieving low root-mean-square and mean percentage errors in parameter estimation. The results demonstrate that the enhanced neural real-time DT is a promising tool for condition monitoring and predictive maintenance of power conversion systems in offshore wind applications.

Objectives: Among existing biologics used within the field of orthopaedics, bone marrow aspirate concentrate (BMAC) has become a popular option for surgeons in recent years. BMAC has shown to contribute to satisfactory outcomes, including improved wound healing and superior patient-reported outcomes, following various orthopaedic procedures. Several studies have documented the surgical technique for meniscus repair with the use of BMAC, while others have described the effects of the use of BMAC on the quality of meniscal tissue following repair. However, few studies have investigated patient-reported outcomes after meniscus repair with the use of BMAC. Therefore, the purpose of this study was to compare clinical outcomes in patients undergoing arthroscopic meniscus repair with and without the use of BMAC. Methods: A prospective, multi-surgeon cohort study was conducted on all patients undergoing arthroscopic meniscus repair without BMAC (MR) and with BMAC (MR-BMAC) between October 2021 and January 2024. An electronic survey of patient-reported outcome measures (PROMs) was completed by each patient at a minimum of 6 months postoperatively. PROMs included a visual analog scale (VAS) for pain, University of California, Los Angeles (UCLA) activity scale, the Lysholm Knee Scoring Scale (LKSS), the Knee Outcome Score - Sports Activities Scale (KOS-SAS), and the International Knee Documentation Committee (IKDC) Subjective Knee Evaluation Form. Results: Ninety patients were reached for follow-up (46 MR, 44 MR-BMAC). Patients in the MR group were significantly younger than patients in the MR-BMAC group at the time of surgery (32.0 ± 15.6 vs. 41.8 ± 14.8 years old, p=0.003). No differences were found between MR and MR-BMAC groups in terms of sex (51.4% vs. 48.6% female, p=0.34), body mass index (BMI) (26.4 ± 4.4 vs. 27.4 ± 4.7, p=0.46), or time to follow-up (11.5 ± 5.6 vs. 13.1 ± 6.7 months, p=0.24). There were no significant differences between MR and MR-BMAC groups in terms of preoperative PROMs including VAS (4.5 ± 2.3 vs. 5.0 ± 1.8, p=0.34), UCLA (5.2 ± 2.7 vs. 5.0 ± 2.8, p=0.76), LKSS (54.0 ± 23.4 vs. 54.1 ± 24.1, p=0.99), KOS-SAS (37.8 ± 26.0 vs. 33.5 ± 27.0 p=0.54), and IKDC (43.2 ± 18.0 vs. 41.5 ± 18.4, p=0.70). The most commonly performed concomitant procedure in both groups was anterior cruciate ligament (ACL) reconstruction. There were no differences between MR and MR-BMAC groups in terms of the proportion of patients undergoing meniscus repair with all-inside (87% vs. 86%, p=0.82), inside-out (2.2% vs. 0%, p=0.54), outside-in (0% vs. 2.5%, p=0.38) techniques, or meniscus root repairs (11% vs. 13%, p=0.79). There were no differences between MR and MR-BMAC groups in terms of their postoperative PROMs including VAS (2.4 ± 2.2 vs. 2.0 ± 1.7, p=0.27), UCLA (7.8 ± 2.0 vs. 7.7 ± 2.1, p=0.82), LKSS (84.9 ± 14.5 vs. 84.1 ± 16.1, p=0.82), KOS-SAS (76.0 ± 22.8 vs. 75.5 ± 25.4, p=0.93), and IKDC (71.8 ± 20.0 vs. 74.7 ± 18.7, p=0.51) (Table 1). Conclusions: Our study demonstrates comparable postoperative PROMs in patients undergoing arthroscopic meniscus repair with and without the use of BMAC. Larger studies with longer follow-up duration are warranted to further corroborate these findings and better delineate the effects of BMAC on meniscus repair.

Every year cardiovascular diseases kill the highest number of people worldwide. Among these, pathologies characterized by sporadic symptoms, such as atrial fibrillation, are difficult to be detected as state-of-the-art solutions, e.g., 12-leads electrocardiogram (ECG) or Holter devices, often fail to tackle these kinds of pathologies. Many portable devices have already been proposed, both in literature and in the market. Unfortunately, they all miss relevant features: they are either not wearable or wireless and their usage over a long-term period is often unsuitable. In addition, the quality of recordings is another key factor to perform reliable diagnosis. The ECG WATCH is a device designed for targeting all these issues. It is inexpensive, wearable (size of a watch), and can be used without the need for any medical expertise about positioning or usage. It is non-invasive, it records single-lead ECG in just 10 s, anytime, anywhere, without the need to physically travel to hospitals or cardiologists. It can acquire any of the three peripheral leads; results can be shared with physicians by simply tapping a smartphone app. The ECG WATCH quality has been tested on 30 people and has successfully compared with an electrocardiograph and an ECG simulator, both certified. The app embeds an algorithm for automatically detecting atrial fibrillation, which has been successfully tested with an official ECG simulator on different severity of atrial fibrillation. In this sense, the ECG WATCH is a promising device for anytime cardiac health monitoring.

The electrocardiogram (ECG) signal describes the heart’s electrical activity, allowing it to detect several health conditions, including cardiac system abnormalities and dysfunctions. Nowadays, most patient medical records are still paper-based, especially those made in past decades. The importance of collecting digitized ECGs is twofold: firstly, all medical applications can be easily implemented with an engineering approach if the ECGs are treated as signals; secondly, paper ECGs can deteriorate over time, therefore a correct evaluation of the patient’s clinical evolution is not always guaranteed. The goal of this paper is the realization of an automatic conversion algorithm from paper-based ECGs (images) to digital ECG signals. The algorithm involves a digitization process tested on an image set of 16 subjects, also with pathologies. The quantitative analysis of the digitization method is carried out by evaluating the repeatability and reproducibility of the algorithm. The digitization accuracy is evaluated both on the entire signal and on six ECG time parameters (R-R peak distance, QRS complex duration, QT interval, PQ interval, P-wave duration, and heart rate). Results demonstrate the algorithm efficiency has an average Pearson correlation coefficient of 0.94 and measurement errors of the ECG time parameters are always less than 1 mm. Due to the promising experimental results, the algorithm could be embedded into a graphical interface, becoming a measurement and collection tool for cardiologists.

Short QT syndrome (SQTS) is an inherited cardiac ion-channel disease related to an increased risk of sudden cardiac death (SCD) in young and otherwise healthy individuals. SCD is often the first clinical presentation in patients with SQTS. However, arrhythmia risk stratification is presently unsatisfactory in asymptomatic patients. In this context, artificial intelligence-based electrocardiogram (ECG) analysis has never been applied to refine risk stratification in patients with SQTS. The purpose of this study was to analyze ECGs from SQTS patients with the aid of different AI algorithms to evaluate their ability to discriminate between subjects with and without documented life-threatening arrhythmic events. The study group included 104 SQTS patients, 37 of whom had a documented major arrhythmic event at presentation and/or during follow-up. Thirteen ECG features were measured independently by three expert cardiologists; then, the dataset was randomly divided into three subsets (training, validation, and testing). Five shallow neural networks were trained, validated, and tested to predict subject-specific class (non-event/event) using different subsets of ECG features. Additionally, several deep learning and machine learning algorithms, such as Vision Transformer, Swin Transformer, MobileNetV3, EfficientNetV2, ConvNextTiny, Capsule Networks, and logistic regression were trained, validated, and tested directly on the scanned ECG images, without any manual feature extraction. Furthermore, a shallow neural network, a 1-D transformer classifier, and a 1-D CNN were trained, validated, and tested on ECG signals extracted from the aforementioned scanned images. Classification metrics were evaluated by means of sensitivity, specificity, positive and negative predictive values, accuracy, and area under the curve. Results prove that artificial intelligence can help clinicians in better stratifying risk of arrhythmia in patients with SQTS. In particular, shallow neural networks’ processing features showed the best performance in identifying patients that will not suffer from a potentially lethal event. This could pave the way for refined ECG-based risk stratification in this group of patients, potentially helping in saving the lives of young and otherwise healthy individuals.

Brugada syndrome (BrS) is an inherited electrical cardiac disorder that is associated with a higher risk of ventricular fibrillation (VF) and sudden cardiac death (SCD) in patients without structural heart disease. The diagnosis is based on the documentation of the typical pattern in the electrocardiogram (ECG) characterized by a J-point elevation of ≥2 mm, coved-type ST-segment elevation, and negative T wave in one or more right precordial leads, called type 1 Brugada ECG. Risk stratification is particularly difficult in asymptomatic cases. Patients who have experienced documented VF are generally recommended to receive an implantable cardioverter defibrillator to lower the likelihood of sudden death due to recurrent episodes. However, for asymptomatic individuals, the most appropriate course of action remains uncertain. Accurate risk prediction is critical to avoiding premature deaths and unnecessary treatments. Due to the challenges associated with experimental research on human cardiac tissue, alternative techniques such as computational modeling and deep learning-based artificial intelligence (AI) are becoming increasingly important. This study introduces a vision transformer (ViT) model that leverages 12-lead ECG images to predict potentially fatal arrhythmic events in BrS patients. This dataset includes a total of 278 ECGs, belonging to 210 patients which have been diagnosed with Brugada syndrome, and it is split into two classes: event and no event. The event class contains 94 ECGs of patients with documented ventricular tachycardia, ventricular fibrillation, or sudden cardiac death, while the no event class is composed of 184 ECGs used as the control group. At first, the ViT is trained on a balanced dataset, achieving satisfactory results (89% accuracy, 94% specificity, 84% sensitivity, and 89% F1-score). Then, the discarded no event ECGs are attached to additional 30 event ECGs, extracted by a 24 h recording of a singular individual, composing a new test set. Finally, the use of an optimized classification threshold improves the predictions on an unbalanced set of data (74% accuracy, 95% negative predictive value, and 90% sensitivity), suggesting that the ECG signal can reveal key information for the risk stratification of patients with Brugada syndrome.

Canine adenovirus type 1 (CAdV-1) is the causative agent of a systemic and potentially fatal viral disease of domestic and wild canids. In Italy, CAdV-1 infection has also been occasionally described in dogs, but information on the epidemiology and its genomic features is still limited. A study was conducted on 291 dogs suspected of infectious gastrointestinal disease. Samples collected from dogs in southern Italy between 2017 and 2020 were analyzed. Virological and histopathological assays were carried out. The presence of CAdVs and other canine viral enteropathogens was investigated, and sequence and phylogenetic analyses were performed. CAdV-1 was detected in six (2.1%) dead stray dogs alone or in mixed infections with other viruses. Gross lesions and histopathological findings referred to CAdV infection were observed, also involving the central nervous system tissues. All inoculated samples were successfully isolated. Sequence analysis evidenced divergences with the circulating strains previously described in Italy and a closer relation with older CAdV-1 strains collected from other countries, suggesting a genetic heterogeneity of CAdV-1 in Italy. The evidence of the circulation of CAdV-1 and its genomic features allows us to have more in-depth knowledge of the epidemiology and evolution of the CAdV-1 genomic variants.

We provide new data on the presence of helminth parasites in 64 individual loggerhead sea turtles Caretta caretta stranded along the coasts of Sicily and the northwest Adriatic Sea between June 2014 and August 2016. The necropsy examination revealed 31 individuals (48.4%) positive for endoparasites, showing a greater prevalence of trematodes than nematodes. In particular, seven species and a single genus of Trematoda (Hapalotrema) and a single species and genus of Nematoda (Kathlania) were identified. Among the Digenea flukes the species with the highest prevalence of infection were Rhytidodes gelatinosus (34.6%) and Hapalotrema sp. (33.3%), while among the Nematoda they were Kathlania sp. (33.3%) and Sulcascaris sulcata (33.3%). Analysis of variance (ANOVA) was applied among the recovery sites of the stranded loggerhead sea turtles and prevalence of endoparasites was used to highlight any relationship between the parasites and the origin of the hosts. ANOVA showed significant differences (p < 0.001) among the data used.

Canine parvovirus type 2 (CPV-2) represents a major viral threat to dogs. Considering the potential effects of pets on antimicrobial resistance, information on the CPV and associated bacterial co-infections is limited. The aim of this study was to analyze the antimicrobial susceptibility and multidrug-resistance profiles of bacterial species from tissue samples of dogs with canine parvovirus infection. A set of PCR assays and sequence analyses was used for the detection and the molecular characterization of the CPV strains and other enteric viruses. Bacterial isolation, the determination of antimicrobial susceptibility via the disk diffusion method, and the determination of the minimum inhibitory concentration were performed. The detection of β-lactamase genes and toxin genes for specific bacteria was also carried out. CPV infection was confirmed in 23 dogs. Forty-three bacterial strains were isolated and all showed phenotypic resistance. Seventeen multidrug-resistant bacteria and bacteria with high resistance to third- and fourth-generation cephalosporins and metronidazole were detected. Almost 50% of the isolated Enterobacteriaceae were positive for at least one β-lactamase gene, with the majority carrying more genes as well. The evidence for multi-resistant bacteria with the potential for intra- or cross-species transmission should be further considered in a One Health approach.

Continuous vital signal monitoring is becoming more relevant in preventing diseases that afflict a large part of the world’s population; for this reason, healthcare equipment should be easy to wear and simple to use. Non-intrusive and non-invasive detection methods are a basic requirement for wearable medical devices, especially when these are used in sports applications or by the elderly for self-monitoring. Arterial blood pressure (ABP) is an essential physiological parameter for health monitoring. Most blood pressure measurement devices determine the systolic and diastolic arterial blood pressure through the inflation and the deflation of a cuff. This technique is uncomfortable for the user and may result in anxiety, and consequently affect the blood pressure and its measurement. The purpose of this paper is the continuous measurement of the ABP through a cuffless, non-intrusive approach. The approach of this paper is based on deep learning techniques where several neural networks are used to infer ABP, starting from photoplethysmogram (PPG) and electrocardiogram (ECG) signals. The ABP was predicted first by utilizing only PPG and then by using both PPG and ECG. Convolutional neural networks (ResNet and WaveNet) and recurrent neural networks (LSTM) were compared and analyzed for the regression task. Results show that the use of the ECG has resulted in improved performance for every proposed configuration. The best performing configuration was obtained with a ResNet followed by three LSTM layers: this led to a mean absolute error (MAE) of 4.118 mmHg on and 2.228 mmHg on systolic and diastolic blood pressures, respectively. The results comply with the American National Standards of the Association for the Advancement of Medical Instrumentation. ECG, PPG, and ABP measurements were extracted from the MIMIC database, which contains clinical signal data reflecting real measurements. The results were validated on a custom dataset created at Neuronica Lab, Politecnico di Torino.