Imaging in osteogenesis imperfecta: Where we are and where we are going

Osteogenesis imperfecta (OI) is a rare phenotypically and genetically heterogeneous group of inherited skeletal dysplasias. The hallmark features of OI include bone fragility and susceptibility to fractures, bone deformity, and diminished growth, along with a plethora of associated secondary features (both skeletal and extraskeletal). The diagnosis of OI is currently made on clinical grounds and may be confirmed by genetic testing. However, imaging remains pivotal in the evaluation of this disease. The aim of this article is to review the current role played by the various radiologic techniques in the diagnosis and monitoring of OI in the postnatal setting as well as to discuss recent advances and future perspectives in OI imaging. Conventional Radiography and Dual-energy X-ray Absorptiometry (DXA) are currently the two most used imaging modalities in OI. The cardinal radiographic features of OI include generalized osteopenia/osteoporosis, bone deformities, and fractures. DXA is currently the most available technique to assess Bone Mineral Density (BMD), specifically areal BMD (aBMD). However, DXA has important limitations and cannot fully characterize bone fragility in OI based on aBMD. Novel DXA-derived parameters, such as Trabecular Bone Score (TBS), may provide further insight into skeletal changes induced by OI, but evidence is still limited. Techniques like Computed Tomography (CT) and Magnetic Resonance Imaging (MRI) can be useful as problem-solvers or in specific settings, including the evaluation of cranio-cervical abnormalities. Recent evidence supports the use of High-Resolution peripheral Quantitative Computed Tomography (HR-pQCT) as a promising tool to improve the characterization of bone fragility in OI. However, HR-pQCT remains a primarily research technique at present. Quantitative Computed Tomography (QCT) is an alternative to DXA for the determination of BMD at central sites, with distinct advantages but considerably higher radiation exposure. Quantitative Ultrasound (QUS) is a portable, inexpensive, and radiation-free modality that may complement DXA evaluation, providing information on bone quality. However, evidence of usefulness of QUS in OI is poor. Radiofrequency Echographic Multi Spectrometry (REMS) is an emerging non-ionizing imaging method that holds promise for the diagnosis of low BMD and for the prediction of fracture risk, but so far only one published study has investigated its role in OI. To conclude, several different radiologic techniques have proven to be effective in the diagnosis and monitoring of OI, each with their own specificities and peculiarities. Clinicians should be aware of the strategic role of the various modalities in the different phases of the patient care process. In this scenario, the development of international guidelines including recommendations on the role of imaging in the diagnosis and monitoring of OI, accompanied by continuous active research in the field, could significantly improve the standardization of patient care.

Maternal prenatal depression is associated with dysregulation over the first five years of life moderated by child polygenic risk for comorbid psychiatric problems

Dysregulation is a combination of emotion, behavior, and attention problems associated with lifelong psychiatric comorbidity. There is evidence for the stability of dysregulation from childhood to adulthood, which would be more fully characterized by determining the likely stability from infancy to childhood. Early origins of dysregulation can further be validated and contextualized in association with environmental and biological factors, such as prenatal stress and polygenic risk scores (PRS) for overlapping child psychiatric problems. We aimed to determine trajectories of dysregulation from 3 months to 5 years (N = 582) in association with maternal prenatal depression moderated by multiple child PRS (N = 232 pairs with available PRS data) in a prenatal cohort. Mothers reported depression symptoms at 24-26 weeks' gestation and child dysregulation at 3, 6, 18, 36, 48, and 60 months. The PRS were for major depressive disorder, attention deficit hyperactivity disorder, cross disorder, and childhood psychiatric problems. Covariates were biological sex, maternal education, and postnatal depression. Analyses included latent classes and regression. Two dysregulation trajectories emerged: persistently low dysregulation (94%), and increasingly high dysregulation (6%). Stable dysregulation emerged at 18 months. High dysregulation was associated with maternal prenatal depression, moderated by PRS for child comorbid psychiatric problems. Males were at greater risk of high dysregulation.

A Systematic Survey of Data Augmentation of ECG Signals for AI Applications

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.

Recommender system for ablation lines to treat complex atrial tachycardia

BACKGROUND AND OBJECTIVE

Planning the optimal ablation strategy for the treatment of complex atrial tachycardia (CAT) is a time consuming task and is error-prone. Recently, directed network mapping, a technology based on graph theory, proved to efficiently identify CAT based solely on data of clinical interventions. Briefly, a directed network was used to model the atrial electrical propagation and reentrant activities were identified by looking for closed-loop paths in the network. In this study, we propose a recommender system, built as an optimization problem, able to suggest the optimal ablation strategy for the treatment of CAT.

METHODS

The optimization problem modeled the optimal ablation strategy as that one interrupting all reentrant mechanisms while minimizing the ablated atrial surface. The problem was designed on top of directed network mapping. Considering the exponential complexity of finding the optimal solution of the problem, we introduced a heuristic algorithm with polynomial complexity. The proposed algorithm was applied to the data of i) 6 simulated scenarios including both left and right atrial flutter; and ii) 10 subjects that underwent a clinical routine.

RESULTS

The recommender system suggested the optimal strategy in 4 out of 6 simulated scenarios. On clinical data, the recommended ablation lines were found satisfactory on 67% of the cases according to the clinician's opinion, while they were correctly located in 89%. The algorithm made use of only data collected during mapping and was able to process them nearly real-time.

CONCLUSIONS

The first recommender system for the identification of the optimal ablation lines for CAT, based solely on the data collected during the intervention, is presented. The study may open up interesting scenarios for the application of graph theory for the treatment of CAT.

Antarctic permafrost degassing in Taylor Valley by extensive soil gas investigation

Ongoing studies conducted in northern polar regions reveal that permafrost stability plays a key role in the modern carbon cycle as it potentially stores considerable quantities of greenhouse gases. Rapid and recent warming of the Arctic permafrost is resulting in significant greenhouse gas emissions, both from physical and microbial processes. The potential impact of greenhouse gas release from the Antarctic region has not, to date, been investigated. In Antarctica, the McMurdo Dry Valleys comprise 10 % of the ice-free soil surface areas in Antarctica and like the northern polar regions are also warming albeit at a slower rate. The work presented herein examines a comprehensive sample suite of soil gas (e.g., CO₂, CH₄ and He) concentrations and CO₂ flux measurements conducted in Taylor Valley during austral summer 2019/2020. Analytical results reveal the presence of significant concentrations of CO₂, CH₄ and He (up to 3.44 vol%, 18,447 ppmv and 6.49 ppmv, respectively) at the base of the active layer. When compared with the few previously obtained measurements, we observe increased CO₂ flux rates (estimated CO₂ emissions in the study area of 21.6 km² ≈ 15 tons day^-1). We suggest that the gas source is connected with the deep brines migrating from inland (potentially from beneath the Antarctic Ice Sheet) towards the coast beneath the permafrost layer. These data provide a baseline for future investigations aimed at monitoring the changing rate of greenhouse gas emissions from Antarctic permafrost, and the potential origin of gases, as the southern polar region warms.

Neuroimaging profiling identifies distinct brain maturational subtypes of youth with mood and anxiety disorders

Mood and anxiety disorders typically begin in adolescence and have overlapping clinical features but marked inter-individual variation in clinical presentation. The use of multimodal neuroimaging data may offer novel insights into the underlying brain mechanisms. We applied Heterogeneity Through Discriminative Analysis (HYDRA) to measures of regional brain morphometry, neurite density, and intracortical myelination to identify subtypes of youth, aged 9-10 years, with mood and anxiety disorders (N = 1931) compared to typically developing youth (N = 2823). We identified three subtypes that were robust to permutation testing and sample composition. Subtype 1 evidenced a pattern of imbalanced cortical-subcortical maturation compared to the typically developing group, with subcortical regions lagging behind prefrontal cortical thinning and myelination and greater cortical surface expansion globally. Subtype 2 displayed a pattern of delayed cortical maturation indicated by higher cortical thickness and lower cortical surface area expansion and myelination compared to the typically developing group. Subtype 3 showed evidence of atypical brain maturation involving globally lower cortical thickness and surface coupled with higher myelination and neural density. Subtype 1 had superior cognitive function in contrast to the other two subtypes that underperformed compared to the typically developing group. Higher levels of parental psychopathology, family conflict, and social adversity were common to all subtypes, with subtype 3 having the highest burden of adverse exposures. These analyses comprehensively characterize pre-adolescent mood and anxiety disorders, the biopsychosocial context in which they arise, and lay the foundation for the examination of the longitudinal evolution of the subtypes identified as the study sample transitions through adolescence.

Predicting human cardiac QT alterations and pro-arrhythmic effects of compounds with a 3D beating heart-on-chip platform

Determining the potential cardiotoxicity and pro-arrhythmic effects of drug candidates remains one of the most relevant issues in the drug development pipeline (DDP). New methods enabling to perform more representative preclinical in vitro studies by exploiting induced pluripotent stem cell-derived cardiomyocytes (iPSC-CM) are under investigation to increase the translational power of the outcomes. Here we present a pharmacological campaign conducted to evaluate the drug-induced QT alterations and arrhythmic events on uHeart, a 3D miniaturized in vitro model of human myocardium encompassing iPSC-CM and dermal fibroblasts embedded in fibrin. uHeart was mechanically trained resulting in synchronously beating cardiac microtissues in 1 week, characterized by a clear field potential (FP) signal that was recorded by means of an integrated electrical system. A drug screening protocol compliant with the new International Council for Harmonisation of Technical Requirements for Pharmaceuticals for Human Use (ICH) guidelines was established and uHeart was employed for testing the effect of 11 compounds acting on single or multiple cardiac ion channels and well-known to elicit QT prolongation or arrhythmic events in clinics. The alterations of uHeart's electrophysiological parameters such as the beating period, the FP duration, the FP amplitude, and the detection of arrhythmic events prior and after drug administration at incremental doses were effectively analyzed through a custom-developed algorithm. Results demonstrated the ability of uHeart to successfully anticipate clinical outcome and to predict the QT prolongation with a sensitivity of 83.3%, a specificity of 100% and an accuracy of 91.6%. Cardiotoxic concentrations of drugs were notably detected in the range of the clinical highest blood drug concentration (Cmax), qualifying uHeart as a fit-to-purpose preclinical tool for cardiotoxicity studies.

Examining attachment, cortisol secretion, and cognitive neurodevelopment in preschoolers and its predictive value for telomere length at age seven

Background

Secure attachment reflects caregiver-child relationship in which the caregiver is responsive when support and comforting are needed by the child. This pattern of bond has an important buffering role in the response to stress by the reduction of the negative experience and its associated physiological response. Disruption of the physiological stress system is thought to be a central mechanism by which early care impacts children. Early life stress causes cellular and molecular changes in brain regions associated with cognitive functions that are fundamental for early learning.

Methods

The association between attachment, cortisol response before and after the Strange Situation Experiment, and neurodevelopment was examined in a sample of 107 preschoolers at age three. Also, the predictive effect of cortisol reactivity and attachment on telomere length at age seven was investigated in a followed-up sample of 77 children.

Results

Children with insecure attachment had higher cortisol secretion and poorer neurodevelopmental skills at age three. A significant cortisol change was observed across the experiment with non-significant interaction with attachment. The attachment and neurodevelopment association was not mediated by cortisol secretion. Preschoolers' attachment and cortisol did not associate nor interacted to predict telomere length at age seven.

Conclusion

These findings add evidence to the detrimental effects of insecure attachment as an aggravator of the physiological response to stress and poorer neurodevelopment during the preschool period. Although attachment and cortisol were not predictive of telomere length, intervention policies that promote secure attachment are more likely to positively echo on several health domains.

Spatial Correlation Between Myocyte's Repolarization Times and their Alternans Drives T-Wave Alternans on the ECG

OBJECTIVE

T-wave alternans (TWA) manifests as beat-to-beat fluctuations of T-wave morphology on the electrocardiogram (ECG), with physiological bases not fully understood. Using a biophysical model of the ECG, we demonstrate and give explicit relations that TWA depends on the i) spatial covariance between myocytes' repolarization time and alternans; and ii) global alternans (common to every myocyte).

METHODS

We quantified the spatial covariance and global alternans by means of two new metrics, R index and δ, respectively. They were validated on both synthetic and real signals. Computerized simulations were generated using a biophysical model linking the action potentials with the surface ECG. Then, the metrics were computed in STAFF-III dataset, containing ECGs from patients who underwent coronary angioplasty with prolonged balloon inflations, and the time courses of the metrics were analyzed together with TWA measured on the surface ECG.

RESULTS

The metrics properly estimated the spatial covariance and global alternans in the synthetic data. In the STAFF-III dataset, the R index progressively increased from baseline to the fourth minute of inflation (median ∆R=0.81 ms; p 0.05), whereas δ was mostly unaltered during the intervention ( δ=0 ms).

CONCLUSION

We reported, for the first time, that TWA is significantly driven by the myocyte's spatial covariance between their repolarization times and alternans, and not by global alternans, when TWA is generated by regional ischemia.

SIGNIFICANCE

The metrics may reveal new complementary insights into the mechanisms underlying TWA.

Family Pediatrician and Public Health collaboration, an alliance to increase vaccination coverage: an experience with MenB vaccination in Italy

BACKGROUND

Invasive Meningococcal Disease is a severe disease mainly affecting infants and young children. Most infections are caused by serogroups A, B, C, W, X, and Y. In the last 10 years, serogroup B has been the main cause of Invasive Meningococcal Disease in Europe. Recent data resulting from an observational study conducted in Italy show a significant reduction in the number of Invasive Meningococcal Disease cases due to Neisseria meningitidis B after the introduction of vaccine 4CMenB. Thus, the Naples Team of Federation of Italian Primary Care Pediatricians and the Public Health Department started an active collaboration focused on vaccination process management (named "Progetto Via") with the aim of increasing Meningococcal B vaccination coverage.

STUDY DESIGN

Source of data is the regional platform "GE.VA.". Every Primary care Pediatrician uses daily to record vaccination activity. This platform is integrated with data entered by operators of the District/Vaccination Center.

METHODS

Time: January 2019 - December 2019. The Federation of Italian Primary Care Pediatricians/Naples organized a meeting to identify six coordinators. The pediatricians could choose to counsel in their own offices and send children to the vaccination center or to counsel and vaccinate directly in their own clinics.

RESULTS

A total of 78 pediatricians took part in the project: 46 did only counseling and 32 did both counseling and vaccination in their medical clinic. Data obtained show an overall average vaccination coverage growth of about 13% in the first 4 months of the survey, and a further growth of about 11% in the following seven months, with a total growth in the entire period of 24%. The pediatricians' counseling is essential to recover non-compliant subjects, considering both the relationship of trust with the families and the visits already scheduled as an ideal moment for vaccinations' status check.

CONCLUSIONS

The project highlights how an effective collaboration between family pediatricians and the Local Health Authority becomes valuable in getting closer to reach the Ministerial goal of 95%. Vaccination coverage increased significantly when family pediatricians supported the activity of vaccine centers in distress in many regional situations. The trust relationship, the hourly availability and the capillary network of family pediatricians' clinics were key elements for the success of this project and were also recognized by parents.

Machine Learning Using a Single-Lead ECG to Identify Patients With Atrial Fibrillation-Induced Heart Failure

Aims

Atrial fibrillation (AF) and heart failure often co-exist. Early identification of AF patients at risk for AF-induced heart failure (AF-HF) is desirable to reduce both morbidity and mortality as well as health care costs. We aimed to leverage the characteristics of beat-to-beat-patterns in AF to prospectively discriminate AF patients with and without AF-HF.

Methods

A dataset of 10,234 5-min length RR-interval time series derived from 26 AF-HF patients and 26 control patients was extracted from single-lead Holter-ECGs. A total of 14 features were extracted, and the most informative features were selected. Then, a decision tree classifier with 5-fold cross-validation was trained, validated, and tested on the dataset randomly split. The derived algorithm was then tested on 2,261 5-min segments from six AF-HF and six control patients and validated for various time segments.

Results

The algorithm based on the spectral entropy of the RR-intervals, the mean value of the relative RR-interval, and the root mean square of successive differences of the relative RR-interval yielded an accuracy of 73.5%, specificity of 91.4%, sensitivity of 64.7%, and PPV of 87.0% to correctly stratify segments to AF-HF. Considering the majority vote of the segments of each patient, 10/12 patients (83.33%) were correctly classified.

Conclusion

Beat-to-beat-analysis using a machine learning classifier identifies patients with AF-induced heart failure with clinically relevant diagnostic properties. Application of this algorithm in routine care may improve early identification of patients at risk for AF-induced cardiomyopathy and improve the yield of targeted clinical follow-up.

International perspective on integrated care models in child and adult mental health

The dearth of child and adolescent mental health services (CAMHS) is a global problem. Integrating CAMHS in primary care has been offered as a solution. We sampled integrated care perspectives from colleagues around the world. Our findings include various models of integrated care namely: the stepped care model in Australia; shared care in the United Kingdom (UK) and Spain; school-based collaborative care in Qatar, Singapore and the state of Texas in the US; collaborative care in Canada, Brazil, US, and Uruguay; coordinated care in the US; and, developing collaborative care models in low-resource settings, like Kenya and Micronesia. These findings provide insights into training initiatives necessary to build CAMHS workforce capacity using integrated care models, each with the ultimate goal of improving access to care. Despite variations and progress in implementing integrated care models internationally, common challenges exist: funding within complex healthcare systems, limited training mechanisms, and geopolitical/policy issues. Supportive healthcare policy, robust training initiatives, ongoing quality improvement and measurement of outcomes across programs would provide data-driven support for the expansion of integrated care and ensure its sustainability.

Hybrid machine learning to localize atrial flutter substrates using the surface 12-lead electrocardiogram

Aims

Atrial flutter (AFlut) is a common re-entrant atrial tachycardia driven by self-sustainable mechanisms that cause excitations to propagate along pathways different from sinus rhythm. Intra-cardiac electrophysiological mapping and catheter ablation are often performed without detailed prior knowledge of the mechanism perpetuating AFlut, likely prolonging the procedure time of these invasive interventions. We sought to discriminate the AFlut location [cavotricuspid isthmus-dependent (CTI), peri-mitral, and other left atrium (LA) AFlut classes] with a machine learning-based algorithm using only the non-invasive signals from the 12-lead electrocardiogram (ECG).

Methods and results

Hybrid 12-lead ECG dataset of 1769 signals was used (1424 in silico ECGs, and 345 clinical ECGs from 115 patients—three different ECG segments over time were extracted from each patient corresponding to single AFlut cycles). Seventy-seven features were extracted. A decision tree classifier with a hold-out classification approach was trained, validated, and tested on the dataset randomly split after selecting the most informative features. The clinical test set comprised 38 patients (114 clinical ECGs). The classifier yielded 76.3% accuracy on the clinical test set with a sensitivity of 89.7%, 75.0%, and 64.1% and a positive predictive value of 71.4%, 75.0%, and 86.2% for CTI, peri-mitral, and other LA class, respectively. Considering majority vote of the three segments taken from each patient, the CTI class was correctly classified at 92%.

Conclusion

Our results show that a machine learning classifier relying only on non-invasive signals can potentially identify the location of AFlut mechanisms. This method could aid in planning and tailoring patient-specific AFlut treatments.

Opening the black box: interpretability of machine learning algorithms in electrocardiography

Recent studies have suggested that cardiac abnormalities can be detected from the electrocardiogram (ECG) using deep machine learning (DL) models. However, most DL algorithms lack interpretability, since they do not provide any justification for their decisions. In this study, we designed two new frameworks to interpret the classification results of DL algorithms trained for 12-lead ECG classification. The frameworks allow us to highlight not only the ECG samples that contributed most to the classification, but also which between the P-wave, QRS complex and T-wave, hereafter simply called 'waves', were the most relevant for the diagnosis. The frameworks were designed to be compatible with any DL model, including the ones already trained. The frameworks were tested on a selected Deep Neural Network, trained on a publicly available dataset, to automatically classify 24 cardiac abnormalities from 12-lead ECG signals. Experimental results showed that the frameworks were able to detect the most relevant ECG waves contributing to the classification. Often the network relied on portions of the ECG which are also considered by cardiologists to detect the same cardiac abnormalities, but this was not always the case. In conclusion, the proposed frameworks may unveil whether the network relies on features which are clinically significant for the detection of cardiac abnormalities from 12-lead ECG signals, thus increasing the trust in the DL models. This article is part of the theme issue 'Advanced computation in cardiovascular physiology: new challenges and opportunities'.

Relationship Between Deceleration Morphology and Phase Rectified Signal Averaging-Based Parameters During Labor

During labor, uterine contractions trigger the response of the autonomic nervous system (ANS) of the fetus, producing sawtooth-like decelerations in the fetal heart rate (FHR) series. Under chronic hypoxia, ANS is known to regulate FHR differently with respect to healthy fetuses. In this study, we hypothesized that such different ANS regulation might also lead to a change in the FHR deceleration morphology. The hypothesis was tested in an animal model comprising nine normoxic and five chronically hypoxic fetuses that underwent a protocol of umbilical cord occlusions (UCOs). Deceleration morphologies in the fetal inter-beat time interval (FRR) series were modeled using a trapezoid with four parameters, i.e., baseline b, deceleration depth a, UCO response time τ u and recovery time τ r . Comparing normoxic and hypoxic sheep, we found a clear difference for τ u (24.8±9.4 vs. 39.8±9.7 s; p < 0.05), a (268.1±109.5 vs. 373.0±46.0 ms; p < 0.1) and Δτ = τ u - τ r (13.2±6.9 vs. 23.9±7.5 s; p < 0.05). Therefore, the animal model supported the hypothesis that hypoxic fetuses have a longer response time τ u and larger asymmetry Δτ as a response to UCOs. Assessing these morphological parameters during labor is challenging due to non-stationarity, phase desynchronization and noise. For this reason, in the second part of the study, we quantified whether acceleration capacity (AC), deceleration capacity (DC), and deceleration reserve (DR), computed through Phase-Rectified Signal Averaging (PRSA, known to be robust to noise), were correlated with the morphological parameters. DC, AC and DR were correlated with τ u , τ r and Δτ for a wide range of the PRSA parameter T (Pearson's correlation ρ > 0.8, p < 0.05). In conclusion, deceleration morphologies have been found to differ between normoxic and hypoxic sheep fetuses during UCOs. The same difference can be assessed through PRSA based parameters, further motivating future investigations on the translational potential of this methodology on human data.

Directed Network Mapping Approach to Rotor Localization in Atrial Fibrillation Simulation

Catheter ablation for atrial fibrillation (AF) is one of the most commonly performed electrophysiology procedures. Despite significant advances in our understanding of AF mechanisms in the last years, ablation outcomes remain suboptimal for many patients, particularly those with persistent or long-standing AF. A possible reason is that ablation techniques mainly focus on anatomic, rather than patient-specific functional targets for ablation. The identification of such ablation targets remains challenging. The purpose of this study is to investigate a novel approach based on directed networks, which allow the automatic detection of important arrhythmia mechanisms, that can be convenient for guiding the ablation strategy. The networks are generated by processing unipolar electrograms (EGMs) collected by the catheters positioned at the different regions of the atria. Network vertices represent the locations of the recordings and edges are determined using cross-covariance time-delay estimation method. The algorithm identifies rotational activity, spreading from vertex to vertex creating a cycle. This work is a simulation study and it uses a highly detailed computational 3D model of human atria in which sustained rotor activation of the atria was achieved. Virtual electrodes were placed on the endocardial surface, and EGMs were calculated at each of these electrodes. The propagation of the electric wave fronts in the atrial myocardium during AF is very complex, so in order to properly capture wave propagation patterns, we split EGMs into multiple short time frames. Then, a specific network for each of these time frames was generated, and the cycles repeating in consecutive networks point us to the stable rotor's location. The respective atrial voltage map served as reference. By detecting a cycle between the same 3 nodes in 19 out of 58 networks, where 10 of these networks were in consecutive time frames, a stable rotor was successfully located.

Atrial Flutter Mechanism Detection Using Directed Network Mapping

Atrial flutter (AFL) is a common atrial arrhythmia typically characterized by electrical activity propagating around specific anatomical regions. It is usually treated with catheter ablation. However, the identification of rotational activities is not straightforward, and requires an intense effort during the first phase of the electrophysiological (EP) study, i.e., the mapping phase, in which an anatomical 3D model is built and electrograms (EGMs) are recorded. In this study, we modeled the electrical propagation pattern of AFL (measured during mapping) using network theory (NT), a well-known field of research from the computer science domain. The main advantage of NT is the large number of available algorithms that can efficiently analyze the network. Using directed network mapping, we employed a cycle-finding algorithm to detect all cycles in the network, resembling the main propagation pattern of AFL. The method was tested on two subjects in sinus rhythm, six in an experimental model of in-silico simulations, and 10 subjects diagnosed with AFL who underwent a catheter ablation. The algorithm correctly detected the electrical propagation of both sinus rhythm cases and in-silico simulations. Regarding the AFL cases, arrhythmia mechanisms were either totally or partially identified in most of the cases (8 out of 10), i.e., cycles around the mitral valve, tricuspid valve and figure-of-eight reentries. The other two cases presented a poor mapping quality or a major complexity related to previous ablations, large areas of fibrotic tissue, etc. Directed network mapping represents an innovative tool that showed promising results in identifying AFL mechanisms in an automatic fashion. Further investigations are needed to assess the reliability of the method in different clinical scenarios.

A Two-Steps-Ahead Estimator for Bubble Entropy

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 Collapse

Key Words

• bubble entropy
• entropy
• limited dependence on parameters

Collapse

MESH Headings Collapse

Grants Collapse

Affiliation(s)

George Manis Department of Computer Science and Engineering, University of Ioannina, 45500 Ioannina, Greece
Matteo Bodini Dipartimento di Informatica, Università degli Studi di Milano, 20133 Milan, Italy; (M.B.); (M.W.R.)
Massimo W. Rivolta Dipartimento di Informatica, Università degli Studi di Milano, 20133 Milan, Italy; (M.B.); (M.W.R.)
Roberto Sassi Dipartimento di Informatica, Università degli Studi di Milano, 20133 Milan, Italy; (M.B.); (M.W.R.)

Collapse

LIVES for families psychological first aid training programme to address COVID-19 psychological distress: a mixed methods acceptability and feasibility protocol

INTRODUCTION

Best practice approaches for addressing COVID-19-related psychological distress among young people (<25 years) and their families remain unclear. Psychological first aid (PFA) is promoted by public health authorities to provide psychological support in the context of extreme events; however, there is limited evidence for its effectiveness. As a prerequisite to conducting a randomised controlled trial to examine programme effectiveness, this project is evaluating the acceptability and feasibility of implementing and evaluating a PFA training programme ('LIVES for Families') for mental health (MH) practitioners to improve their ability to recognise and respond to COVID-19-related psychological distress among their clients.

METHODS AND ANALYSIS

We are using a triangulation mixed methods research design; complementary strands of quantitative and qualitative data are being collected in parallel and will be merged at the interpretation phase of the project. The quantitative strand uses a repeated measures design; a consecutive sample of MH practitioners (n=80) providing MH support to young people or their families are being recruited to participate in the LIVES for Families PFA training programme and complete quantitative measures at baseline (pretraining), 2-week and 6-month follow-up time points. The qualitative strand uses fundamental description and semistructured interviews with a subset of practitioners (n=30), as well as managers of MH agencies (n=20). A mixed methods joint display and associated narrative will generate a comprehensive understanding regarding acceptability and feasibility.

ETHICS AND DISSEMINATION

The Hamilton Integrated Research Ethics Board approved the study (project number: 11295). Results will be shared broadly with the policy and practice community through publications, presentations and public webinars. As a brief, evidence-informed intervention, the LIVES for Families PFA training programme is suitable in its mode of delivery across care settings. The outcomes of this study could have international implications for mitigating the MH impacts of viral pandemics.

Machine learning enables noninvasive prediction of atrial fibrillation driver location and acute pulmonary vein ablation success using the 12-lead ECG

BACKGROUND

Atrial fibrillation (AF) is the most common supraventricular arrhythmia, characterized by disorganized atrial electrical activity, maintained by localized arrhythmogenic atrial drivers. Pulmonary vein isolation (PVI) allows to exclude PV-related drivers. However, PVI is less effective in patients with additional extra-PV arrhythmogenic drivers.

OBJECTIVES

To discriminate whether AF drivers are located near the PVs vs extra-PV regions using the noninvasive 12-lead electrocardiogram (ECG) in a computational and clinical framework, and to computationally predict the acute success of PVI in these cohorts of data.

METHODS

AF drivers were induced in 2 computerized atrial models and combined with 8 torso models, resulting in 1128 12-lead ECGs (80 ECGs with AF drivers located in the PVs and 1048 in extra-PV areas). A total of 103 features were extracted from the signals. Binary decision tree classifier was trained on the simulated data and evaluated using hold-out cross-validation. The PVs were subsequently isolated in the models to assess PVI success. Finally, the classifier was tested on a clinical dataset (46 patients: 23 PV-dependent AF and 23 with additional extra-PV sources).

RESULTS

The classifier yielded 82.6% specificity and 73.9% sensitivity for detecting PV drivers on the clinical data. Consistency analysis on the 46 patients resulted in 93.5% results match. Applying PVI on the simulated AF cases terminated AF in 100% of the cases in the PV class.

CONCLUSION

Machine learning-based classification of 12-lead-ECG allows discrimination between patients with PV drivers vs those with extra-PV drivers of AF. The novel algorithm may aid to identify patients with high acute success rates to PVI.

The 360° Performance System in Team Sports: Is It Time to Design a "Personalized Jacket" for Team Sports Players?

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.

Histopatological effects of bleaching and disease on the coral Siderastrea stellata from coastal reefs of Brazil

ABSTRACT Histological analysis of bleached samples of Siderastrea stellata Verrill, 1868, with white plague disease and with alteration in the color pattern, revealed drastic tissue and cellular disturbances, such as lysis of the external epithelium, hyperplasia of gastrodermis, apoptosis of epithelial cells and zooxanthellae, and degradation of mesenteric filaments and reproductive cells. Fungal hyphae, diatoms, and cyanobacteria were present in diseased samples and colonies with alteration in the color pattern. Furthermore, bleached and diseased samples showed significant reductions in the number of zooxanthellae per 100 µm2 of gastrodermis and significant reductions in the diameters and volumes of stage V oocytes. We found that bleaching events, diseases, and colonies with alteration in the color pattern promoted degradation of reproductive cells, resulting in the consequent interruption of the reproduction of the coral S. stellata, which is one of the most common Brazilian coral species. The implications of these indirect effects of bleaching, disease, and changes in coral color patterns in the population dynamics of Brazilian reefs are discussed.

Non-Invasive Identification of Atrial Fibrillation Driver Location Using the 12-lead ECG: Pulmonary Vein Rotors vs. other Locations

Atrial fibrillation (AF) is an irregular heart rhythm due to disorganized atrial electrical activity, often sustained by rotational drivers called rotors. In the present work, we sought to characterize and discriminate whether simulated single stable rotors are located in the pulmonary veins (PVs) or not, only by using non-invasive signals (i.e., the 12-lead ECG). Several features have been extracted from the signals, such as Hjort descriptors, recurrence quantification analysis (RQA), and principal component analysis. All the extracted features have shown significant discriminatory power, with particular emphasis to the RQA parameters. A decision tree classifier achieved 98.48% accuracy, 83.33% sensitivity, and 100% specificity on simulated data.Clinical Relevance-This study might guide ablation procedures, suggesting doctors to proceed directly in some patients with a pulmonary veins isolation, and avoiding the prior use of an invasive atrial mapping system.

236Automatic classification of 20 different types of atrial tachycardia using 12-lead ECG signals

Funding Acknowledgements

Supported by the European Union"s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No.766082 (MY-ATRIA)

Background

 Atrial Flutter (AFl) as a common reentrant atrial tachycardia is driven by self-sustainable mechanisms that cause excitation to propagate along pathways different from sinus rhythm. Intracardiac electrophysiological mapping and catheter ablation is often performed without prior knowledge of the mechanism perpetuating AFl in a given patient, likely prolonging the procedure time of these invasive interventions. We investigated the feasibility of automatically identifying 20 different AFl types based on the non-invasive 12-lead electrocardiogram (ECG) using machine learning. 

Methods

 Electrophysiological fast marching computer simulations of 20 different atrial tachycardia scenarios (micro-/macro-reentry, scar-related/anatomical/functional, figure-of-eight, focal, different locations) were performed and propagated to the standard 12-lead ECG based on the Courtemanche atrial action potential model. The virtual study population comprised combinations of 8 different anatomical bi-atrial models with 2 orientational variants each and 8 different torso models yielding a total of 2512 ECGs. From each ECG, we extracted 114 features from different domains (e.g., time, frequency, entropy, wavelet, non-linear recurrence analysis). The dataset was randomly split into 1256 training samples, 628 validation samples and 628 test samples while maintaining a balanced AFl type distribution. A radial basis neural network (RBNN) was trained as a classifier after selection of the most informative features. 

Results

The RBNN yielded a test set accuracy of 90% regarding the identification of the AFl mechanism using 10 features (from different domains). The most discriminative single feature was the cycle length that alone led to a test set accuracy of 74%, while the remaining feature set without cycle length (9 features) reduced the test set accuracy to 33%. The machine learning approach generalized well regarding unseen torso geometries (90% accuracy if training was performed on only 7 torso models) but rather poor regarding atrial anatomies (23% if the atrial anatomical model was not seen during training) indicating that more than the currently used 8 atrial models should be included during training to cover the relevant anatomical variability. 

Conclusions

Our results show that a machine learning classifier can potentially identify a high number of different AFl types using the 12-lead ECG. This non-invasive method can aid in planning and tailoring AFl treatment for patients. Application to clinical data is necessary as a next step to pave the way for clinical translation.

Abstract Figure.

Assessment of spatial heterogeneity of ventricular repolarization after multi-channel blocker drugs in healthy subjects

BACKGROUND AND OBJECTIVES

In contrast to potassium channel blockers, drugs affecting multiple channels seem to reduce torsadogenic risks. However, their effect on spatial heterogeneity of ventricular repolarization (SHVR) is still matter of investigation. Aim of this work is to assess the effect of four drugs blocking the human ether-à-go-go-related gene (hERG) potassium channel, alone or in combination with other ionic channel blocks, on SHVR, as estimated by the V-index on short triplicate 10 s ECG.

METHODS

The V-index is an estimate of the standard deviation of the repolarization times of the myocytes across the entire myocardium, obtained from multi-lead surface electrocardiograms. Twenty-two healthy subjects received a pure hERG potassium channel blocker (dofetilide) and 3 other drugs with additional varying degrees of sodium and calcium (L-type) channel block (quinidine, ranolazine, and verapamil), as well as placebo. A one-way repeated-measures Friedman test was performed to compare the V-index over time.

RESULTS

Computer simulations and Bland-Altman analysis supported the reliability of the estimates of V-index on triplicate 10 s ECG. Ranolazine, verapamil and placebo did not affect the V-index. On the contrary, after quinidine and dofetilide administration, an increase of V-index from predose to its peak value was observed (ΔΔV-index values were 19 ms and 27 ms, respectively, p < 0.05).

CONCLUSIONS

High torsadogenic drugs (dofetilide and quinidine) affected significantly the SHVR, as quantified by the V-index. The metric has therefore a potential in assessing drug arrhythmogenicity.

Workload Monitoring in Top-level Soccer Players During Congested Fixture Periods

This study assessed the internal and external workload of starters and non-starters in a professional top-level soccer team during a congested fixture period. Twenty Serie A soccer players were monitored in this study during two mesocycles of 21 days each. Starters and non-starters were divided based on the match time played in each mesocycle. The following metrics were recorded: exposure time, total distance, relative total distance, high-speed running distance over 20 km·h^-1, very high-speed running distance over 25 km·h^-1, individual very high-speed distance over 80% of maximum peak speed, and rating of perceived exertion. Differences between starters and non-starters were found for: exposure time (effect size=large to very large), rating of perceived exertion (large to very large), total distance (large to very large), and individual very high-speed distance over 80% of maximum peak speed (moderate to large). Furthermore, differences for relative total distance, high-speed running distance over 20 km·h^-1 and very high-speed running distance over 25 km·h^-1 were small to moderate, but not significant. This study reports that during congested fixture periods, starters had higher exposure time, rating of perceived exertion, total distance, and individual very high-speed distance over 80% of maximum peak speed than non-starters.

Strategies and Solutions for Team Sports Athletes in Isolation due to COVID-19

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.

Refined Ventricular Activity Cancellation in Electrograms During Atrial Fibrillation by Combining Average Beat Subtraction and Interpolation

Many techniques have been developed to cancel the ventricular interference in atrial electrograms (AEG) during atrial fibrillation. In particular, average beat subtraction (ABS) and interpolation are among those mostly adopted. However, ABS usually leaves high power residues and discontinuity at the borders, whereas interpolation totally substitutes the residual activity with a forecasting that might fail at the center of the cancellation segment. In this study, we proposed a new algorithm to refine the ventricular estimate provided by ABS, in such a way that the residual activity should likely be distributed as the local atrial activity. Briefly, the local atrial activity is first modeled with an autoregressive (AR) process, then the estimate is refined by maximizing the log likelihood of the atrial residual activity according to the fitted AR model. We tested the new algorithm on both synthetic and real AEGs, and compared the performance with other four algorithms (two variants of ABS, interpolation and zero substitution). On synthetic data, our algorithm outperformed all the others in terms of average root mean square error (0.043 vs 0.046 for interpolation; p <; 0.05). On real data, our methodology outperformed two variants of ABS (p <; 0.05) and performed similarly to interpolation when considering the high power residues left (both <; 5%), and the log likelihood with the fitted AR model.

Quantification of Spatial Heterogeneity of Ventricular Repolarization During Early-Stage Cardiac Ischemia Induced by Coronary Angioplasty

Coronary angioplasty (CA) is a surgical procedure meant to break the plaque and restore the blood flow in obstructed coronary arteries. It is based on inserting an inflatable balloon with a catheter in the clogged artery. When the balloon inflation is prolonged, it also provides an excellent model to investigate the electrophysiological changes due to early ischemia. In this work, we tested whether early cardiac ischemia induced by prolonged balloon inflations might lead to changes in spatial heterogeneity of ventricular repolarization (SHVR), as measured by the V-index on the 12-lead ECG. The metric was recently shown to significantly improve the ECG sensitivity for the diagnosis of non-ST elevation myocardial infarction, in patients presenting to the emergency department. The analysis was retrospectively performed on the data of 104 patients who underwent prolonged CA (STAFF III dataset). The V-index was estimated before, during and post-occlusion (limiting the analysis to the first inflation). Successively, it was quantified on short 90 s overlapping windows, during occlusion, to assess the time evolution of SHVR. V-index values estimated during occlusion were significantly larger (median: 6.2 ms, p <; 0.05) than baseline room values. Also, pre- and post-occlusion values did not differ (p > 0.05), suggesting a complete recovery after CA. SHVR progressively increased during the occlusion with respect to baseline (median reaching 55.6 ms vs 34.2 ms). In conclusion, the V-index detected changes in SHVR due to early-stage cardiac ischemia.

Mixotrophic cultivation of Spirulina platensis in dairy wastewater: Effects on the production of biomass, biochemical composition and antioxidant capacity

Mixotrophic cultivation of microalgae provides a very promising alternative for producing carbohydrate-rich biomass to convert into bioethanol and value-added biocompounds, such as vitamins, pigments, proteins, lipids and antioxidant compounds. Spirulina platensis may present high yields of biomass and carbohydrates when it is grown under mixotrophic conditions using cheese whey. However, there are no previous studies evaluating the influence of this culture system on the profile of fatty acids or antioxidant compounds of this species, which are extremely important for food and pharmaceutical applications and would add value to the cultivation process. S. platensis presented higher specific growth rates, biomass productivity and carbohydrate content under mixotrophic conditions; however, the antioxidant capacity and the protein and lipid content were lower than that of the autotrophic culture. The maximum biomass yield was 2.98 ±0.07 g/L in growth medium with 5.0% whey. The phenolic compound concentration was the same for the biomass obtained under autotrophic and mixotrophic conditions with 2.5% and 5.0% whey. The phenolic compound concentrations showed no significant differences except for that in the growth medium with 10.0% whey, which presented an average value of 22.37±0.14 mg gallic acid/g. Mixotrophic cultivation of S. platensis using whey can be considered a viable alternative to reduce the costs of producing S. platensis biomass and carbohydrates, shorten cultivation time and produce carbohydrates, as it does not require adding expensive chemical nutrients to the growth medium and also takes advantage of cheese whey, an adverse dairy industry byproduct.

Automated cortical thickness and skewness feature selection in bipolar disorder using a semi-supervised learning method

BACKGROUND

Bipolar disorder (BD) broadly affects brain structure, in particular areas involved in emotion processing and cognition. In the last years, the psychiatric field's interest in machine learning approaches has been steadily growing, thanks to the potentiality of automatically discriminating patients from healthy controls.

METHODS

In this work, we employed cortical thickness of 58 regions of interest obtained from magnetic resonance imaging scans of 41 BD patients and 34 healthy controls, to automatically identify the regions which are mostly involved with the disease. We used a semi-supervised method, addressing the criticisms on supervised methods, related to the fact that the diagnosis is not unaffected by uncertainty.

RESULTS

Our results confirm findings in previous studies, with a classification accuracy of about 75% when mean thickness and skewness of up to five regions are considered. We obtained that the parietal lobe and some areas in the temporal sulcus were the regions which were the most involved with BD.

LIMITATIONS

The major limitation of our work is the limited size or our dataset, but in line with other recent machine learning works in the field. Moreover, we considered chronic patients, whose brain characteristics may thus be affected.

CONCLUSIONS

The automatic selection of the brain regions most involved in BD may be of great importance when dealing with the pathogenesis of the disorder. Our method selected regions which are known to be involved with BD, indicating that damage to the identified areas can be considered as a marker of disease.

Theoretical Value of Deceleration Capacity Points to Deceleration Reserve of Fetal Heart Rate

OBJECTIVE

The interpretation of Average Acceleration and Deceleration Capacities (AC/DC), computed through Phase-Rectified Signal Averaging (PRSA), in intrapartum fetal heart rate (FHR) monitoring is still matter of investigation. We aimed to elucidate some behaviors of AC/DC.

METHODS

We derived the theoretical value of PRSA for stationary stochastic Gaussian processes and proved that for these time series AC and DC are necessarily identical in absolute value. The difference between DC and AC, termed Deceleration Reserve (DR), was introduced to detect signal's asymmetric trends. DR was tested on FHR signals from: near-term pregnant sheep model of labor consisting of chronically hypoxic and normoxic fetuses with both groups developing acidemia due to umbilical cord occlusions (UCO); and the CTU-UHB dataset containing fetal CTG recordings collected during labor of newborns that resulted acidotic and non-acidotic, respectively. DR was compared with AC and DC in terms of discriminatory power (AUC), between the groups, after correcting for signal power or deceleration area, respectively.

RESULTS

DR displayed higher discriminatory power on the animal model during severe acidemia, with respect to AC/DC ( ) but also distinguished correctly all chronically hypoxic from normoxic fetuses at baseline prior to UCO. DR also outperformed AC/DC on the CTU-UHB dataset in distinguishing acidemic fetuses at birth (AUC: 0.65).

CONCLUSION

Theoretical results motivated the introduction of DR, that proved to be superior than AC/DC for risk stratification during labor.

SIGNIFICANCE

DR, measured during labor, might permit to distinguish acidemic fetuses due to their different autonomic regulation, paving the way for new monitoring strategies.

Trends and Predictors of Repeat Mental Health Visits to a Pediatric Emergency Department in Hamilton, Ontario

OBJECTIVE

From 2007-2017, pediatric emergency department (ED) visits for mental health concerns increased by 66% in Canada, with repeat visits accounting for a significant proportion of all visits. Our objective was to examine patient and visit characteristics associated with repeat visits to a tertiary care pediatric ED for mental health concerns.

METHOD

Data were obtained from the administrative records of McMaster Children's Hospital ED for mental health-related visits from February 2013-December 2017. Data on 9,018 ED visits made by 4,976 unique patients were included in this study. Logistic regression analysis was used to examine characteristics associated with repeat visit within six months of index presentation.

RESULTS

22% (n=1,088) of individuals returned to the ED for a mental health concern within six months following their index visit. A repeat visit within six months was associated with female sex (OR=1.19, p=0.019), age of 14-17 years (OR=1.42, p=0.016), receiving a risk assessment by the emergency psychiatry team (OR=1.63, p<0.001) and having an inpatient psychiatric admission (OR=1.67, p<0.001) at the index visit. Receiving anxiety-related discharge diagnoses at an index visit reduced the odds of a repeat visit within 6 months (OR=0.76, p=0.035), while receiving depression-related discharge diagnoses increased the odds of a repeat visit, but only for females (OR=1.3, p=0.011 vs. OR=0.93, p=0.589 for males).

CONCLUSIONS

We found that approximately one in five patients presenting to the ED for a mental health concern have a repeat visit within six months, consistent with previous studies. This study provides support for previously identified risk factors for repeat visits and offers information on interactions between patient sex and diagnosis.

Evaluation of the Tinetti score and fall risk assessment via accelerometry-based movement analysis

Gait and balance disorders are among the main predisposing factors of falls in elderly. Clinical scales are widely employed to assess the risk of falling, but they require trained personnel. We investigate the use of objective measures obtained from a wearable accelerometer to evaluate the fall risk, determined by the Tinetti clinical scale. Seventy-nine patients and eleven volunteers were enrolled in two rehabilitation centers and underwent a full Tinetti test, while wearing a triaxial accelerometer at the chest. Tinetti scores were assessed by expert physicians and those subjects with a score ≤18 were considered at high risk. First, we analyzed 21 accelerometer features by means of statistical tests and correlation analysis. Second, one regression and one classification problem were designed and solved using a linear model (LM) and an artificial neural network (ANN) to predict the Tinetti outcome. Pearson's correlation between the Tinetti score and a subset of 9 features (mainly related with standing and walking) was 0.71. The misclassification error of high risk patient was 0.21 and 0.11, for LM and ANN, respectively. The work might foster the development of a new generation of applications meant to monitor the time evolution of the fall risk using low cost devices at home.

Effectiveness of nutrient remotion and cell physiology of Amphora sp. cultured in shrimp farm effluents

Shrimp farm effluents are one of the principal causes of eutrophication in coastal environments. Integrated processes of bioremediation involving the culturing of purifying organisms have been suggested, but very few studies have focused on microalgae. For that purpose evaluated the growth potential of Amphora sp. in the residual waters of shrimp farm activity fulfilled on the Paraíba State, Brazil. The experiments were performed using Conway medium as the control and wastewaters from shrimp farm at 100% concentrations. Amphora sp. demonstrated good growth in the shrimp farm effluents under test conditions, although less than that observed in the control medium. This diatom was found to removed 73.357 and 72.572% of PO₄^- and NO₃^-, respectively, demonstrating a high mitigation potential for this type of effluent. Comparisons of the physiological responses measured by flow cytometry demonstrated higher cell densities of Amphora sp. in the control medium, but a higher lipid content was observed in Amphora cultured in shrimp farm effluents.

Parametric estimation of sample entropy for physical activity recognition

Insufficient amount of physical activity, and hence storage of calories may lead depression, obesity, cardiovascular diseases, and diabetes. The amount of consumed calorie depends on the type of activity. The recognition of physical activity is very important to estimate the amount of calories spent by a subject every day. There are some research works already published in the literature for activity recognition through accelerometers (body worn sensors). The accuracy of any recognition system depends on the robustness of selected features and classifiers. The typical features reported for most physical activities recognitions are autoregressive coefficients (ARcoeffs), signal magnitude area (SMA), tilt angle (TA), and standard deviation (STD). In this study, we have studied the feasibility of using single value of sample entropy estimated parametrically (SETH) of an AR model instead of ARcoeffs. After feasibility study, we also compared the recognition accuracies between two popular classifiers ı.e. artificial neural network (ANN) and support vector machines (SVM). The recognition accuracies using linear structure (where all types of activities are classified using a single classifier) and hierarchical structure (where activities are first divided into static and dynamic events, and then activities of each event are classified in the second stage). The study showed that the use of SETH provides similar recognition accuracy (69.82%) as provided by ARcoeffs (67.67%) using ANN. The linear structure of SVM performs better (average accuracy of SVM: 98.22%) than linear ANN (average accuracy with ANN: 94.78%). The use of hierarchical structure of ANN increases the average recognition accuracy of static activities to about 100%. However, no significant changes are observed using hierarchical SVM than the linear one.

Bubble Entropy: An Entropy Almost Free of Parameters

Objective: A critical point in any definition of entropy is the selection of the parameters employed to obtain an estimate in practice. We propose a new definition of entropy aiming to reduce the significance of this selection. Methods: We call the new definition Bubble Entropy. Bubble Entropy is based on permutation entropy, where the vectors in the embedding space are ranked. We use the bubble sort algorithm for the ordering procedure and count instead the number of swaps performed for each vector. Doing so, we create a more coarse-grained distribution and then compute the entropy of this distribution. Results: Experimental results with both real and synthetic HRV signals showed that bubble entropy presents remarkable stability and exhibits increased descriptive and discriminating power compared to all other definitions, including the most popular ones. Conclusion: The definition proposed is almost free of parameters. The most common ones are the scale factor r and the embedding dimension m . In our definition, the scale factor is totally eliminated and the importance of m is significantly reduced. The proposed method presents increased stability and discriminating power. Significance: After the extensive use of some entropy measures in physiological signals, typical values for their parameters have been suggested, or at least, widely used. However, the parameters are still there, application and dataset dependent, influencing the computed value and affecting the descriptive power. Reducing their significance or eliminating them alleviates the problem, decoupling the method from the data and the application, and eliminating subjective factors.Objective: A critical point in any definition of entropy is the selection of the parameters employed to obtain an estimate in practice. We propose a new definition of entropy aiming to reduce the significance of this selection. Methods: We call the new definition Bubble Entropy. Bubble Entropy is based on permutation entropy, where the vectors in the embedding space are ranked. We use the bubble sort algorithm for the ordering procedure and count instead the number of swaps performed for each vector. Doing so, we create a more coarse-grained distribution and then compute the entropy of this distribution. Results: Experimental results with both real and synthetic HRV signals showed that bubble entropy presents remarkable stability and exhibits increased descriptive and discriminating power compared to all other definitions, including the most popular ones. Conclusion: The definition proposed is almost free of parameters. The most common ones are the scale factor r and the embedding dimension m . In our definition, the scale factor is totally eliminated and the importance of m is significantly reduced. The proposed method presents increased stability and discriminating power. Significance: After the extensive use of some entropy measures in physiological signals, typical values for their parameters have been suggested, or at least, widely used. However, the parameters are still there, application and dataset dependent, influencing the computed value and affecting the descriptive power. Reducing their significance or eliminating them alleviates the problem, decoupling the method from the data and the application, and eliminating subjective factors.

Low Computational Cost for Sample Entropy

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.

Linear-Sigmoidal modelling of accelerometer features and Tinetti score for automatic fall risk assessment

Falling in elderly is a worldwide major problem and it can lead to severe injuries or death. Despite the effort made to ensure home environments safe and foster healthy lifestyles, it is still necessary to provide methodologies that can be used at home for detect risk factors associated with falls. In this study, we proposed a new simple non-linear model, i.e., Linear-Sigmoidal model (LS), easy to fit and simple to interpret, used to model accelerometer features and outcome of the clinical scale Tinetti (clinical scale for fall risk prediction). Also, subjects with a score ≤ 18 were considered as high risk of falling. One-hundred-twelve subjects underwent to a Tinetti test while wearing a 3D axis accelerometer at the chest, and the Tinetti score used as gold standard. Ninety subjects were used as training set and twenty-two ones were employed to test the model. The same sets were used to assess the performance of the standard linear regression (LR). Seven accelerometer features and the body mass index were used in the model regression. LS resulted better than LR in terms of model agreement (R²: 0.76 vs 0.72) and classification accuracy (0.91 vs 0.86) on the test set.

Microcystins production and antibacterial activity of cyanobacterial strains of Synechocystis, Synechococcus and Romeria isolated from water and coral reef organisms of Brazilian coast

Cyanobacteria are widely distributed in terrestrial, freshwater and marine environments, and over the past decades have been recognized as a powerful source of bioactive compounds. In this study, some cyanobacterial strains were isolated from samples of seawater, brackish water and tissue of reef benthic invertebrates (zoanthid Protopalythoa variabilis, the sponges Cynachrella sp. and Haliclona sp., the coral Siderastrea stellata, and ascidians), collected at the states of Paraíba and Rio Grande do Norte (Northeast of Brazil), during the period between July 2010 and February 2014. After standard isolation methods, the cultivation of the strains was carried out in acclimatized culture chamber (25 °C) under constant aeration, for 15 days at 12-hour photoperiod, using Conway and BG11 media made with filtered seawater. After ethanolic and methanolic extracts, the strains were analysed for the microcystin production by the ELISA technique and for the antibacterial activity against Staphylococcus aureus and Pseudomonas aeruginosa by the agar well diffusion method. The detection of the mcyB gene, one of the genes related to the microcystin synthesis, was done by the Polymerase Chain Reaction (PCR) technique. The majority of the eighteen cyanobacterial strains belonged to Synechococcaceae Family. The genera of Synechocystis, Synechococcus and Romeria were represented by ten, six and two strains, respectively. The production of microcystins was observed in five strains belonging to the genus Synechocystis. The presence of mcyB gene was detected in 12 strains of cyanobacteria: Synechocystis (three strains), Synechococcus (six strains) and Romeria (two strains). Only one strain (Synechocystis aquatilis) showed both the microcystin production and the mcyB gene presence. The antibacterial activity was observed for one strain of Romeria gracilis, one strain of Synechocystis aquatilis and two strains of Synechococcus sp. The ethanolic extracts of R. gracilis strain and two Synechococcus spp. strains inhibited the growth of P. aeruginosa. Among methanolic extracts of cyanobacteria, only one strain of S. aquatilis showed activity against S. aureus, and one R. gracilis strain against P. aeruginosa. Some cyanobacterial strains studied were positive for the microcystin production and antibacterial activity against pathogenic bacteria S. aureus and P. aeuruginosa, and may be further explored for additional biotechnological applications.

Effects of cyanobacteria Synechocystis spp. in the host-parasite model Crassostrea gasar-Perkinsus marinus

Perkinsosis is a disease caused by protozoan parasites from the Perkinsus genus. In Brazil, two species, P. beihaiensis and P. marinus, are frequently found infecting native oysters (Crassostrea gasar and C. rhizophorae) from cultured and wild populations in several states of the Northeast region. The impacts of this disease in bivalves from Brazil, as well as the interactions with environmental factors, are poorly studied. In the present work, we evaluated the in vitro effects of the cyanobacteria Synechocystis spp. on trophozoites of P. marinus and haemocytes of C. gasar. Four cyanobacteria strains isolated from the Northeast Brazilian coast were used as whole cultures (WCs) and extracellular products (ECPs). Trophozoites of P. marinus were exposed for short (4h) and long (48h and 7days, the latter only for ECPs) periods, while haemocytes were exposed for a short period (4h). Cellular and immune parameters, i.e. cell viability, cell count, reactive oxygen species production (ROS) and phagocytosis of inert (latex beads) and biological particles (zymosan and trophozoites of P. marinus) were measured by flow cytometry. The viability of P. marinus trophozoites was improved in response to WCs of Synechocystis spp., which could be a beneficial effect of the cyanobacteria providing nutrients and reducing reactive oxygen species. Long-term exposure of trophozoites to ECPs of cyanobacteria did not modify in vitro cell proliferation nor viability. In contrast, C. gasar haemocytes showed a reduction in cell viability when exposed to WCs, but not to ECPs. However, ROS production was not altered. Haemocyte ability to engulf latex particles was reduced when exposed mainly to ECPs of cyanobacteria; while neither the WCs nor the ECPs modified phagocytosis of the biological particles, zymosan and P. marinus. Our results suggest a negative effect of cyanobacteria from the Synechocystis genus on host immune cells, in contrast to a more beneficial effect on the parasite cell, which could together disrupt the balance of the host-parasite interaction and make oysters more susceptible to P. marinus as well as opportunistic infections.