Hybrid Stenting of Pulmonary Venous Baffle Stenosis: Subxiphoid Approach in Transposition of the Great Arteries.

A 31-year-old woman with transposition of the great arteries status post-Senning operation presents with severe pulmonary venous baffle obstruction. Both standards of care (percutaneous stenting or open repair) were deemed suboptimal and/or high risk. A multidisciplinary, hybrid approach via subxiphoid incision, guided by 3-dimensional modeling, provided a lower risk and minimally invasive intervention.

Cracking the Chronic Pain code: A scoping review of Artificial Intelligence in Chronic Pain research.

OBJECTIVE: The aim of this review is to identify gaps and provide a direction for future research in the utilization of Artificial Intelligence (AI) in chronic pain (CP) management. METHODS: A comprehensive literature search was conducted using various databases, including Ovid MEDLINE, Web of Science Core Collection, IEEE Xplore, and ACM Digital Library. The search was limited to studies on AI in CP research, focusing on diagnosis, prognosis, clinical decision support, self-management, and rehabilitation. The studies were evaluated based on predefined inclusion criteria, including the reporting quality of AI algorithms used. RESULTS: After the screening process, 60 studies were reviewed, highlighting AI's effectiveness in diagnosing and classifying CP while revealing gaps in the attention given to treatment and rehabilitation. It was found that the most commonly used algorithms in CP research were support vector machines, logistic regression and random forest classifiers. The review also pointed out that attention to CP mechanisms is negligible despite being the most effective way to treat CP. CONCLUSION: The review concludes that to achieve more effective outcomes in CP management, future research should prioritize identifying CP mechanisms, CP management, and rehabilitation while leveraging a wider range of algorithms and architectures. SIGNIFICANCE: This review highlights the potential of AI in improving the management of CP, which is a significant personal and economic burden affecting more than 30% of the world's population. The identified gaps and future research directions provide valuable insights to researchers and practitioners in the field, with the potential to improve healthcare utilization.

Intradiscal vacuum phenomenon matches lumbar spine degeneration patterns in an ageing population.

PURPOSE: Intra-Discal Vacuum phenomenon (IDVP) is well-recognised, yet poorly visualised and poorly understood radiological finding in disc degeneration, particularly with regard to its role in spinal alignment. CT analysis of the lumbar spine in an aging population aims to identify patterns associated with IDVP including lumbopelvic morphology and associated spinal diagnoses. METHODS: An analysis was performed of an over-60s population sample of 2020 unrelated abdominal CT scans, without acute spinal presentations. Spinal analysis included sagittal lumbopelvic reconstructions to assess for IDVP and pelvic incidence (PI). Subjects with degenerative pathologies, including previous vertebral fractures, auto-fusion, transitional vertebrae, and listhesis, were also selected out and analysed separately. RESULTS: The prevalence of lumbar spine IDVP was 50.3% (955/1898) and increased with age (125 exclusions). This increased in severity towards the lumbosacral junction (L1L2 8.3%, L2L3 10.9%, L3L4 11.5%, L4L5 23.9%, and L5S1 46.3%). A lower PI yielded a higher incidence of IDVP, particularly at L5S1 (p < 0.01). A total of 292 patients had IDVP with additional degenerative pathologies, which were more likely to occur at the level of isthmic spondylolisthesis, adjacent to a previous fracture or suprajacent to a lumbosacral transitional vertebra (p < 0.05). CONCLUSIONS: This study identified the prevalence and severity of IDVP in an aging population. Sagittal patterns that influence the pattern of IVDP, such as pelvic incidence and degenerative pathologies, provide novel insights into the function of aging spines.

Nearshore wave buoy data from southeastern Australia for coastal research and management.

Wind wave observations in shallow coastal waters are essential for calibrating, validating, and improving numerical wave models to predict sediment transport, shoreline change, and coastal hazards such as beach erosion and oceanic inundation. Although ocean buoys and satellites provide near-global coverage of deep-water wave conditions, shallow-water wave observations remain sparse and often inaccessible. Nearshore wave conditions may vary considerably alongshore due to coastline orientation and shape, bathymetry and islands. We present a growing dataset of in-situ wave buoy observations from shallow waters (<35 m) in southeast Australia that comprises over 7,000 days of measurements at 20 locations. The moored buoys measured wave conditions continuously for several months to multiple years, capturing ambient and storm conditions in diverse settings, including coastal hazard risk sites. The dataset includes tabulated time series of spectral and time-domain parameters describing wave height, period and direction at half-hourly temporal resolution. Buoy displacement and wave spectra data are also available for advanced applications. Summary plots and tables describing wave conditions measured at each location are provided.

Prediction of emergency department revisits among child and youth mental health outpatients using deep learning techniques.

BACKGROUND: The proportion of Canadian youth seeking mental health support from an emergency department (ED) has risen in recent years. As EDs typically address urgent mental health crises, revisiting an ED may represent unmet mental health needs. Accurate ED revisit prediction could aid early intervention and ensure efficient healthcare resource allocation. We examine the potential increased accuracy and performance of graph neural network (GNN) machine learning models compared to recurrent neural network (RNN), and baseline conventional machine learning and regression models for predicting ED revisit in electronic health record (EHR) data. METHODS: This study used EHR data for children and youth aged 4-17 seeking services at McMaster Children's Hospital's Child and Youth Mental Health Program outpatient service to develop and evaluate GNN and RNN models to predict whether a child/youth with an ED visit had an ED revisit within 30 days. GNN and RNN models were developed and compared against conventional baseline models. Model performance for GNN, RNN, XGBoost, decision tree and logistic regression models was evaluated using F1 scores. RESULTS: The GNN model outperformed the RNN model by an F1-score increase of 0.0511 and the best performing conventional machine learning model by an F1-score increase of 0.0470. Precision, recall, receiver operating characteristic (ROC) curves, and positive and negative predictive values showed that the GNN model performed the best, and the RNN model performed similarly to the XGBoost model. Performance increases were most noticeable for recall and negative predictive value than for precision and positive predictive value. CONCLUSIONS: This study demonstrates the improved accuracy and potential utility of GNN models in predicting ED revisits among children and youth, although model performance may not be sufficient for clinical implementation. Given the improvements in recall and negative predictive value, GNN models should be further explored to develop algorithms that can inform clinical decision-making in ways that facilitate targeted interventions, optimize resource allocation, and improve outcomes for children and youth.

Natural History of IntraDiscal Vacuum Phenomenon and its role in Advanced Disc Degeneration.

STUDY DESIGN: Observational serial CT analysis of the lumbar spine in a normal-aging population. OBJECTIVE: Assess the natural history of IntraDiscal Vacuum Phenomenon and its role in disc degeneration. Summary of Background Data: The natural history of disc degeneration is well described but our understanding of the end stage of pathogenesis remains incomplete. MRI loses accuracy with advanced degeneration, becoming hyporesonant and indistinct. Cadaveric specimens display adaptive changes in the disc with loss of the hydrostatic capacity of the nucleus, increased intra-discal clefts and end-plate impermeability. IDVP is associated with advanced disc degeneration and CT is the optimal modality to visualise this, yet these insights remain unreported. METHODS: Subjects only included historic CT abdomen scans of those over 60 years of age without acute or relevant spinal pathology, with a diagnosis of at least one level with IDVP on the original CT scan and all of whom had a similar scan >7 years later. A history of clinically significant back pain was also recorded. RESULTS: CT scans included 360 levels in 29 males and 31 females (mean 68.9 years), displaying 82 levels of IDVP, with a second scan included after a mean of 10.3 years, Most levels displayed the same level of severity (persisted, 45) compared to where some progressed (26), regressed (8) and fused (3) (P<0.01). There was also an increased incidence, 37/60 (62%) of developing IDVP at another level. Disc heights were reduced with increased severity of IDVP. A record of back pain was evident in 31/60 subjects, which was not significantly worse in those with worsening severity or additional level involvement over the study period. CONCLUSION: As disc degeneration advances, the associated IDVP persists in most cases, displaying a plateauing of severity over long periods, but rarely with progression to autofusion.

Integrating intention-based systems in human-robot interaction: a scoping review of sensors, algorithms, and trust.

The increasing adoption of robot systems in industrial settings and teaming with humans have led to a growing interest in human-robot interaction (HRI) research. While many robots use sensors to avoid harming humans, they cannot elaborate on human actions or intentions, making them passive reactors rather than interactive collaborators. Intention-based systems can determine human motives and predict future movements, but their closer interaction with humans raises concerns about trust. This scoping review provides an overview of sensors, algorithms, and examines the trust aspect of intention-based systems in HRI scenarios. We searched MEDLINE, Embase, and IEEE Xplore databases to identify studies related to the forementioned topics of intention-based systems in HRI. Results from each study were summarized and categorized according to different intention types, representing various designs. The literature shows a range of sensors and algorithms used to identify intentions, each with their own advantages and disadvantages in different scenarios. However, trust of intention-based systems is not well studied. Although some research in AI and robotics can be applied to intention-based systems, their unique characteristics warrant further study to maximize collaboration performance. This review highlights the need for more research on the trust aspects of intention-based systems to better understand and optimize their role in human-robot interactions, at the same time establishes a foundation for future research in sensor and algorithm designs for intention-based systems.

Ultra-brief training in cognitive reappraisal or mindfulness reduces anxiety and improves motor performance efficiency under stress.

BACKGROUND AND OBJECTIVES: We examined the effects of ultra-brief training in mindfulness and cognitive reappraisal on affective response and performance under stress. We hypothesized that one or both types of training would decrease affective responding and improve performance, and that these effects might be moderated by acute stress induction. DESIGN: We manipulated training (mindfulness, cognitive reappraisal, control) between subjects and level of stress (low, high) within subjects in a 3 × 2 mixed factorial design. Method: Participants (N = 112, ages 18-35) completed two sessions on different days. In each session, they received mindfulness or cognitive reappraisal training or listened to a control script prior to a low- or high-stress simulated hostage situation. We measured motor performance efficiency (proportion of shots that hit hostile and hostage targets), affective responding (self-reported anxiety, salivary cortisol and alpha amylase, and autonomic physiology), and physical activity. RESULTS: Compared to control instructions, ultra-brief training in cognitive reappraisal or mindfulness reduced subjective anxiety and increased performance efficiency. There were few effects of training on other measures. CONCLUSION: Ultra-brief training in cognitive reappraisal or mindfulness prior to a stressful task may be both helpful and harmful; effects are preliminary and subject to boundary conditions.

COVID-19 international experience in paediatric patients with congenital heart disease.

OBJECTIVE: As COVID-19 continues to affect the global population, it is crucial to study the impact of the disease in vulnerable populations. This study of a diverse, international cohort aims to provide timely, experiential data on the course of disease in paediatric patients with congenital heart disease (CHD). METHODS: Data were collected by capitalising on two pre-existing CHD registries, the International Quality Improvement Collaborative for Congenital Heart Disease: Improving Care in Low- and Middle-Income Countries and the Congenital Cardiac Catheterization Project on Outcomes. 35 participating sites reported data for all patients under 18 years of age with diagnosed CHD and known COVID-19 illness during 2020 identified at their institution. Patients were classified as low, moderate or high risk for moderate or severe COVID-19 illness based on patient anatomy, physiology and genetic syndrome using current published guidelines. Association of risk factors with hospitalisation and intensive care unit (ICU) level care were assessed. RESULTS: The study included 339 COVID-19 cases in paediatric patients with CHD from 35 sites worldwide. Of these cases, 84 patients (25%) required hospitalisation, and 40 (12%) required ICU care. Age <1 year, recent cardiac intervention, anatomical complexity, clinical cardiac status and overall risk were all significantly associated with need for hospitalisation and ICU admission. A multivariable model for ICU admission including clinical cardiac status and recent cardiac intervention produced a c-statistic of 0.86. CONCLUSIONS: These observational data suggest risk factors for hospitalisation related to COVID-19 in paediatric CHD include age, lower functional cardiac status and recent cardiac interventions. There is a need for further data to identify factors relevant to the care of patients with CHD who contract COVID-19 illness.

Crew Autonomy During Simulated Medical Event Management on Long Duration Space Exploration Missions.

OBJECTIVE: Our primary aim was to investigate crew performance during medical emergencies with and without ground-support from a flight surgeon located at mission control. BACKGROUND: There are gaps in knowledge regarding the potential for unanticipated in-flight medical events to affect crew health and capacity, and potentially compromise mission success. Additionally, ground support may be impaired or periodically absent during long duration missions. METHOD: We reviewed video recordings of 16 three-person flight crews each managing four unique medical events in a fully immersive spacecraft simulator. Crews were randomized to two conditions: with and without telemedical flight surgeon (FS) support. We assessed differences in technical performance, behavioral skills, and cognitive load between groups. RESULTS: Crews with FS support performed better clinically, were rated higher on technical skills, and completed more clinical tasks from the medical checklists than crews without FS support. Crews with FS support also had better behavioral/non-technical skills (information exchange) and reported significantly lower cognitive demand during the medical event scenarios on the NASA-TLX scale, particularly in mental demand and temporal demand. There was no significant difference between groups in time to treat or in objective measures of cognitive demand derived from heart rate variability and electroencephalography. CONCLUSION: Medical checklists are necessary but not sufficient to support high levels of autonomous crew performance in the absence of real-time flight surgeon support. APPLICATION: Potential applications of this research include developing ground-based and in-flight training countermeasures; informing policy regarding autonomous spaceflight, and design of autonomous clinical decision support systems.

Data-driven approach to quantify trust in medical devices using Bayesian networks.

Bayesian networks are increasingly used to quantify the uncertainty of subjective and stochastic concepts such as trust. In this article, we propose a data-driven approach to estimate Bayesian parameters in the domain of wearable medical devices. Our approach extracts the probability of a trust factor being in a specific state directly from the devices (e.g. sensor quality). The strength of the relationship between related factors is defined by expert knowledge and incorporated into the model. We use propagation rules from requirements engineering to estimate how much each trust factor contributes to the related intermediate nodes in the network and ultimately compute the trust score. The trust score is a relative measure of trustworthiness when different devices are evaluated in the same test conditions and using the same Bayesian structure. To evaluate our approach, we developed Bayesian networks for the trust quantification of similar wearable devices from two manufacturers under identical test conditions and noise levels. The results demonstrated the learnability and generalizability of our approach.

Evaluation methodology for deep learning imputation models.

There is growing interest in imputing missing data in tabular datasets using deep learning. Existing deep learning-based imputation models have been commonly evaluated using root mean square error (RMSE) as the predictive accuracy metric. In this article, we investigate the limitations of assessing deep learning-based imputation models by conducting a comparative analysis between RMSE and alternative metrics in the statistical literature including qualitative, predictive accuracy, statistical distance, and descriptive statistics. We design a new aggregated metric, called reconstruction loss (RL), to evaluate deep learning-based imputation models. We also develop and evaluate a novel imputation evaluation methodology based on RL. To minimize model and dataset biases, we use a regression imputation model and two different deep learning imputation models: denoising autoencoders and generative adversarial nets. We also use two tabular datasets from different industry sectors: health care and financial. Our results show that the proposed methodology is effective in evaluating multiple properties of the deep learning-based imputation model's reconstruction performance.

Abundance and seasonality of phoronid larvae in coastal temperate waters: More abundant than previously thought?

In zooplankton surveys, many smaller taxa or species considered less important are often overlooked. One such example is the actinotrocha larvae of phoronid worms that are rarely quantified in zooplankton samples yet may play important roles in marine food webs. To gain a better understanding of phoronid ecology in coastal waters, we retrospectively analysed 145 plankton samples collected from two coastal sites in Ireland (Lough Hyne and Bantry Bay). Samples were collected using plankton nets from depths of 20 and 40 m. Phoronids were present in 37.7% and 38.2% of samples, with mean abundances of 0.3 ± 0.5 ind. m-3 and 1.2 ± 2.8 ind. m-3, respectively, and were identified as Phoronis muelleri and Phoronis hippocrepia. Phoronids were present consistently each year from April to October at Lough Hyne and from February to October at Bantry Bay. Comparisons with other taxa in Lough Hyne show that abundances are similar to those of fish larvae (1.1 ± 1.8 ind. m-3) and echinoderm larvae (2.3 ± 4.4 ind. m-3). Examination of these samples from Irish waters suggests that phoronids are more abundant in temperate waters than previously reported. Supplementary Information: The online version contains supplementary material available at 10.1007/s10452-022-09982-6.

FcMBL magnetic bead-based MALDI-TOF MS rapidly identifies paediatric blood stream infections from positive blood cultures.

Rapid identification of potentially life-threatening blood stream infections (BSI) improves clinical outcomes, yet conventional blood culture (BC) identification methods require ~24-72 hours of liquid culture, plus 24-48 hours to generate single colonies on solid media suitable for identification by mass spectrometry (MS). Newer rapid centrifugation techniques, such as the Bruker MBT-Sepsityper® IVD, replace culturing on solid media and expedite the diagnosis of BCs but frequently demonstrate reduced sensitivity for identifying clinically significant Gram-positive bacterial or fungal infections. This study introduces a protocol that utilises the broad-range binding properties of an engineered version of mannose-binding lectin linked to the Fc portion of immunoglobulin (FcMBL) to capture and enrich pathogens combined with matrix-assisted laser desorption-ionisation time-of-flight (MALDI-TOF) MS for enhanced infection identification in BCs. The FcMBL method identified 94.1% (64 of 68) of clinical BCs processed, with a high sensitivity for both Gram-negative and Gram-positive bacteria (94.7 and 93.2%, respectively). The FcMBL method identified more patient positive BCs than the Sepsityper® (25 of 25 vs 17 of 25), notably with 100% (3/3) sensitivity for clinical candidemia, compared to only 33% (1/3) for the Sepsityper®. Additionally, during inoculation experiments, the FcMBL method demonstrated a greater sensitivity, identifying 100% (24/24) of candida to genus level and 9/24 (37.5%) top species level compared to 70.8% (17/24) to genus and 6/24 to species (25%) using the Sepsityper®. This study demonstrates that capture and enrichment of samples using magnetic FcMBL-conjugated beads is superior to rapid centrifugation methods for identification of BCs by MALDI-TOF MS. Deploying the FcMBL method therefore offers potential clinical benefits in sensitivity and reduced turnaround times for BC diagnosis compared to the standard Sepsityper® kit, especially for fungal diagnosis.

First record of biofluorescence in lumpfish (Cyclopterus lumpus), a commercially farmed cleaner fish.

This study is the first known observation of biofluorescence in the lumpfish (Cyclopterus lumpus). Individual lumpfish were illuminated with blue excitation lighting for photography with both hyperspectral and filtered multispectral cameras. All photographed juvenile lumpfish (n = 11) exhibited green biofluorescence. Light emissions were characterised with two peaks observed at 545 and 613 nm, with the greatest intensity along the tubercles of the high crest and the three longitudinal ridges. Further research on the dynamics of biofluorescence through the lifecycle of this species is required.

Evidence of a range expansion in sunfish from 47 years of coastal sightings.

Almost nothing is known about the historical abundance of the ocean sunfish. Yet as an ecologically and functionally important taxa, understanding changes in abundance may be a useful indicator of how our seas are responding to anthropogenic changes including overfishing and climate change. Within this context, sightings from a coastal bird observatory (51.26 ∘ N, 9.30 ∘ W) over a 47 year period (from April to October 1971-2017) provided the first long-term index of sunfish abundance. Using a general linear mixed effect model with a hurdle to deal with imperfect detectability and to model trends, a higher probability of detecting sunfish was found in the 1990s and 2000s. Continuous Plankton Recorder (CPR) phytoplankton color indices and the annual mean position of the 13  ∘ C sea surface isotherm were significantly correlated with the probability of detecting sunfish. An increase in siphonophore abundance (as measured by the CPR) was also documented. However, this increase occurred 10-15 years after the sunfish increase and was not significantly correlated with sunfish abundance. Our results suggest that the observed increase in sunfish sightings is evidence of a range expansion because it was significantly correlated with the mean position of the 13 ∘ C isotherm which moved northwards by over 200 km. Furthermore, the observed increase in sunfish occured  10 years before sunfish sightings are documented in Icelandic and Norwegian waters, and was concurrent with well-known range expansions for other fish species during the 1990s. This study demonstrates how sustained citizen science projects can provide unique insights on the historical abundance of this enigmatic species. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s00227-021-04005-8.

A Radiology-focused Review of Predictive Uncertainty for AI Interpretability in Computer-assisted Segmentation.

The recent advances and availability of computer hardware, software tools, and massive digital data archives have enabled the rapid development of artificial intelligence (AI) applications. Concerns over whether AI tools can "communicate" decisions to radiologists and primary care physicians is of particular importance because automated clinical decisions can substantially impact patient outcome. A challenge facing the clinical implementation of AI stems from the potential lack of trust clinicians have in these predictive models. This review will expand on the existing literature on interpretability methods for deep learning and review the state-of-the-art methods for predictive uncertainty estimation for computer-assisted segmentation tasks. Last, we discuss how uncertainty can improve predictive performance and model interpretability and can act as a tool to help foster trust. Keywords: Segmentation, Quantification, Ethics, Bayesian Network (BN) © RSNA, 2021.