Quality assurance of late gadolinium enhancement cardiac magnetic resonance images: a deep learning classifier for confidence in the presence or absence of abnormality with potential to prompt real-time image optimization.

BACKGROUND: Late gadolinium enhancement (LGE) of the myocardium has significant diagnostic and prognostic implications, with even small areas of enhancement being important. Distinguishing between definitely normal and definitely abnormal LGE images is usually straightforward, but diagnostic uncertainty arises when reporters are not sure whether the observed LGE is genuine or not. This uncertainty might be resolved by repetition (to remove artifact) or further acquisition of intersecting images, but this must take place before the scan finishes. Real-time quality assurance by humans is a complex task requiring training and experience, so being able to identify which images have an intermediate likelihood of LGE while the scan is ongoing, without the presence of an expert is of high value. This decision-support could prompt immediate image optimization or acquisition of supplementary images to confirm or refute the presence of genuine LGE. This could reduce ambiguity in reports. METHODS: Short-axis, phase-sensitive inversion recovery late gadolinium images were extracted from our clinical cardiac magnetic resonance (CMR) database and shuffled. Two, independent, blinded experts scored each individual slice for "LGE likelihood" on a visual analog scale, from 0 (absolute certainty of no LGE) to 100 (absolute certainty of LGE), with 50 representing clinical equipoise. The scored images were split into two classes-either "high certainty" of whether LGE was present or not, or "low certainty." The dataset was split into training, validation, and test sets (70:15:15). A deep learning binary classifier based on the EfficientNetV2 convolutional neural network architecture was trained to distinguish between these categories. Classifier performance on the test set was evaluated by calculating the accuracy, precision, recall, F1-score, and area under the receiver operating characteristics curve (ROC AUC). Performance was also evaluated on an external test set of images from a different center. RESULTS: One thousand six hundred and forty-five images (from 272 patients) were labeled and split at the patient level into training (1151 images), validation (247 images), and test (247 images) sets for the deep learning binary classifier. Of these, 1208 images were "high certainty" (255 for LGE, 953 for no LGE), and 437 were "low certainty". An external test comprising 247 images from 41 patients from another center was also employed. After 100 epochs, the performance on the internal test set was accuracy = 0.94, recall = 0.80, precision = 0.97, F1-score = 0.87, and ROC AUC = 0.94. The classifier also performed robustly on the external test set (accuracy = 0.91, recall = 0.73, precision = 0.93, F1-score = 0.82, and ROC AUC = 0.91). These results were benchmarked against a reference inter-expert accuracy of 0.86. CONCLUSION: Deep learning shows potential to automate quality control of late gadolinium imaging in CMR. The ability to identify short-axis images with intermediate LGE likelihood in real-time may serve as a useful decision-support tool. This approach has the potential to guide immediate further imaging while the patient is still in the scanner, thereby reducing the frequency of recalls and inconclusive reports due to diagnostic indecision.

Interactions between calcium-induced arrhythmia triggers and the electrophysiological-anatomical substrate underlying the induction of atrial fibrillation.

Atrial fibrillation (AF) is the most common cardiac arrhythmia and is sustained by spontaneous focal excitations and re-entry. Spontaneous electrical firing in the pulmonary vein (PV) sleeves is implicated in AF generation. The aim of this simulation study was to identify the mechanisms determining the localisation of AF triggers in the PVs and their contribution to the genesis of AF. A novel biophysical model of the canine atria was used that integrates stochastic, spontaneous subcellular Ca2+ release events (SCRE) with regional electrophysiological heterogeneity in ionic properties and a detailed three-dimensional model of atrial anatomy, microarchitecture and patchy fibrosis. Simulations highlighted the importance of the smaller inward rectifier potassium current (IK1 ) in PV cells compared to the surrounding atria, which enabled SCRE more readily to result in delayed-afterdepolarisations that induced triggered activity. There was a leftward shift in the dependence of the probability of triggered activity on sarcoplasmic reticulum Ca2+ load. This feature was accentuated in 3D tissue compared to single cells (Δ half-maximal [Ca2+ ]SR  = 58 µM vs. 22 µM). In 3D atria incorporating electrical heterogeneity, excitations preferentially emerged from the PV region. These triggered focal excitations resulted in transient re-entry in the left atrium. Addition of fibrotic patches promoted localised emergence of focal excitations and wavebreaks that had a more substantial impact on generating AF-like patterns than the PVs. Thus, a reduced IK1 , less negative resting membrane potential, and fibrosis-induced changes of the electrotonic load all contribute to the emergence of complex excitation patterns from spontaneous focal triggers. KEY POINTS: Focal excitations in the atria are most commonly associated with the pulmonary veins, but the mechanisms for this localisation are yet to be elucidated. We applied a multi-scale computational modelling approach to elucidate the mechanisms underlying such localisations. Myocytes in the pulmonary vein region of the atria have a less negative resting membrane potential and reduced time-independent potassium current; we demonstrate that both of these factors promote triggered activity in single cells and tissues. The less negative resting membrane potential also contributes to heterogeneous inactivation of the fast sodium current, which can enable re-entrant-like excitation patterns to emerge without traditional conduction block.

Aortic stenosis assessment from the 3-chamber cine: Ratio of balanced steady-state-free-precession (bSSFP) blood signal between the aorta and left ventricle predicts severity.

BACKGROUND: Cardiovascular magnetic resonance (CMR) imaging is an important tool for evaluating the severity of aortic stenosis (AS), co-existing aortic disease, and concurrent myocardial abnormalities. Acquiring this additional information requires protocol adaptations and additional scanner time, but is not necessary for the majority of patients who do not have AS. We observed that the relative signal intensity of blood in the ascending aorta on a balanced steady state free precession (bSSFP) 3-chamber cine was often reduced in those with significant aortic stenosis. We investigated whether this effect could be quantified and used to predict AS severity in comparison to existing gold-standard measurements. METHODS: Multi-centre, multi-vendor retrospective analysis of patients with AS undergoing CMR and transthoracic echocardiography (TTE). Blood signal intensity was measured in a ∼1 cm2 region of interest (ROI) in the aorta and left ventricle (LV) in the 3-chamber bSSFP cine. Because signal intensity varied across patients and scanner vendors, a ratio of the mean signal intensity in the aorta ROI to the LV ROI (Ao:LV) was used. This ratio was compared using Pearson correlations against TTE parameters of AS severity: aortic valve peak velocity, mean pressure gradient and the dimensionless index. The study also assessed whether field strength (1.5 T vs. 3 T) and patient characteristics (presence of bicuspid aortic valves (BAV), dilated aortic root and low flow states) altered this signal relationship. RESULTS: 314 patients (median age 69 [IQR 57-77], 64% male) who had undergone both CMR and TTE were studied; 84 had severe AS, 78 had moderate AS, 66 had mild AS and 86 without AS were studied as a comparator group. The median time between CMR and TTE was 12 weeks (IQR 4-26). The Ao:LV ratio at 1.5 T strongly correlated with peak velocity (r = -0.796, p = 0.001), peak gradient (r = -0.772, p = 0.001) and dimensionless index (r = 0.743, p = 0.001). An Ao:LV ratio of < 0.86 was 84% sensitive and 82% specific for detecting AS of any severity and a ratio of 0.58 was 83% sensitive and 92% specific for severe AS. The ability of Ao:LV ratio to predict AS severity remained for patients with bicuspid aortic valves, dilated aortic root or low indexed stroke volume. The relationship between Ao:LV ratio and AS severity was weaker at 3 T. CONCLUSIONS: The Ao:LV ratio, derived from bSSFP 3-chamber cine images, shows a good correlation with existing measures of AS severity. It demonstrates utility at 1.5 T and offers an easily calculable metric that can be used at the time of scanning or automated to identify on an adaptive basis which patients benefit from dedicated imaging to assess which patients should have additional sequences to assess AS.

Information theory-based direct causality measure to assess cardiac fibrillation dynamics.

Understanding the mechanism sustaining cardiac fibrillation can facilitate the personalization of treatment. Granger causality analysis can be used to determine the existence of a hierarchical fibrillation mechanism that is more amenable to ablation treatment in cardiac time-series data. Conventional Granger causality based on linear predictability may fail if the assumption is not met or given sparsely sampled, high-dimensional data. More recently developed information theory-based causality measures could potentially provide a more accurate estimate of the nonlinear coupling. However, despite their successful application to linear and nonlinear physical systems, their use is not known in the clinical field. Partial mutual information from mixed embedding (PMIME) was implemented to identify the direct coupling of cardiac electrophysiology signals. We show that PMIME requires less data and is more robust to extrinsic confounding factors. The algorithms were then extended for efficient characterization of fibrillation organization and hierarchy using clinical high-dimensional data. We show that PMIME network measures correlate well with the spatio-temporal organization of fibrillation and demonstrated that hierarchical type of fibrillation and drivers could be identified in a subset of ventricular fibrillation patients, such that regions of high hierarchy are associated with high dominant frequency.

Efficient labelling for efficient deep learning: the benefit of a multiple-image-ranking method to generate high volume training data applied to ventricular slice level classification in cardiac MRI.

Background: Getting the most value from expert clinicians' limited labelling time is a major challenge for artificial intelligence (AI) development in clinical imaging. We present a novel method for ground-truth labelling of cardiac magnetic resonance imaging (CMR) image data by leveraging multiple clinician experts ranking multiple images on a single ordinal axis, rather than manual labelling of one image at a time. We apply this strategy to train a deep learning (DL) model to classify the anatomical position of CMR images. This allows the automated removal of slices that do not contain the left ventricular (LV) myocardium. Methods: Anonymised LV short-axis slices from 300 random scans (3,552 individual images) were extracted. Each image's anatomical position relative to the LV was labelled using two different strategies performed for 5 hours each: (I) 'one-image-at-a-time': each image labelled according to its position: 'too basal', 'LV', or 'too apical' individually by one of three experts; and (II) 'multiple-image-ranking': three independent experts ordered slices according to their relative position from 'most-basal' to 'most apical' in batches of eight until each image had been viewed at least 3 times. Two convolutional neural networks were trained for a three-way classification task (each model using data from one labelling strategy). The models' performance was evaluated by accuracy, F1-score, and area under the receiver operating characteristics curve (ROC AUC). Results: After excluding images with artefact, 3,323 images were labelled by both strategies. The model trained using labels from the 'multiple-image-ranking strategy' performed better than the model using the 'one-image-at-a-time' labelling strategy (accuracy 86% vs. 72%, P=0.02; F1-score 0.86 vs. 0.75; ROC AUC 0.95 vs. 0.86). For expert clinicians performing this task manually the intra-observer variability was low (Cohen's κ=0.90), but the inter-observer variability was higher (Cohen's κ=0.77). Conclusions: We present proof of concept that, given the same clinician labelling effort, comparing multiple images side-by-side using a 'multiple-image-ranking' strategy achieves ground truth labels for DL more accurately than by classifying images individually. We demonstrate a potential clinical application: the automatic removal of unrequired CMR images. This leads to increased efficiency by focussing human and machine attention on images which are needed to answer clinical questions.

Deep Learning Segmentation of the Right Ventricle in Cardiac MRI: The M&Ms Challenge.

In recent years, several deep learning models have been proposed to accurately quantify and diagnose cardiac pathologies. These automated tools heavily rely on the accurate segmentation of cardiac structures in MRI images. However, segmentation of the right ventricle is challenging due to its highly complex shape and ill-defined borders. Hence, there is a need for new methods to handle such structure's geometrical and textural complexities, notably in the presence of pathologies such as Dilated Right Ventricle, Tricuspid Regurgitation, Arrhythmogenesis, Tetralogy of Fallot, and Inter-atrial Communication. The last MICCAI challenge on right ventricle segmentation was held in 2012 and included only 48 cases from a single clinical center. As part of the 12th Workshop on Statistical Atlases and Computational Models of the Heart (STACOM 2021), the M&Ms-2 challenge was organized to promote the interest of the research community around right ventricle segmentation in multi-disease, multi-view, and multi-center cardiac MRI. Three hundred sixty CMR cases, including short-axis and long-axis 4-chamber views, were collected from three Spanish hospitals using nine different scanners from three different vendors, and included a diverse set of right and left ventricle pathologies. The solutions provided by the participants show that nnU-Net achieved the best results overall. However, multi-view approaches were able to capture additional information, highlighting the need to integrate multiple cardiac diseases, views, scanners, and acquisition protocols to produce reliable automatic cardiac segmentation algorithms.

A device for assessing microbial activity under ambient hydrostatic pressure: The in situ microbial incubator (ISMI).

Microbes in the dark ocean are exposed to hydrostatic pressure increasing with depth. Activity rate measurements and biomass production of dark ocean microbes are, however, almost exclusively performed under atmospheric pressure conditions due to technical constraints of sampling equipment maintaining in situ pressure conditions. To evaluate the microbial activity under in situ hydrostatic pressure, we designed and thoroughly tested an in situ microbial incubator (ISMI). The ISMI allows autonomously collecting and incubating seawater at depth, injection of substrate and fixation of the samples after a preprogramed incubation time. The performance of the ISMI was tested in a high-pressure tank and in several field campaigns under ambient hydrostatic pressure by measuring prokaryotic bulk 3H-leucine incorporation rates. Overall, prokaryotic leucine incorporation rates were lower at in situ pressure conditions than under to depressurized conditions reaching only about 50% of the heterotrophic microbial activity measured under depressurized conditions in bathypelagic waters in the North Atlantic Ocean off the northwestern Iberian Peninsula. Our results show that the ISMI is a valuable tool to reliably determine the metabolic activity of deep-sea microbes at in situ hydrostatic pressure conditions. Hence, we advocate that deep-sea biogeochemical and microbial rate measurements should be performed under in situ pressure conditions to obtain a more realistic view on deep-sea biotic processes.

Translation, Cross-Cultural Adaptation, and Validation of the Galician Version of the Nurse Prescribing Self-Efficacy Scale.

Low self-efficacy has been identified as one of the factors that could hinder the prescribing competence of nurses. No valid and reliable existing instruments assess Galician nurses' confidence to prescribe. The aim of this study was to determine the reliability and validity of the Galician version of the Nurse Prescribing Self-Efficacy Scale (G-NP-SES, 19 items). The study was conducted in two phases: the translation and adaptation to the Galician version of the NP-SES, and the assessment of its psychometric properties. G-NP-SES was sent to nurses working in primary healthcare centers in Galicia (Spain) between March and June of 2022. Its content, construct and discriminant validity, and internal consistency reliability were examined. A total of 193 people participated in the study. As the original scale, G-NP-SES also had three dimensions (% of cumulative variance = 80.82%). It showed good internal consistency (Cronbach's alpha coefficient (α) = 0.90, with each factor ranging from 0.86 to 0.89), high content validity (scale's content validity index = 0.92, with item-content validity index ranged from 0.87 to 1), and good discriminant validity. G-NP-SES is an instrument with good psychometric properties which could be used to accurately assess Galician nurses' self-efficacy to prescribe and consequently to improve their job performance.

Deep learning methods for automatic evaluation of delayed enhancement-MRI. The results of the EMIDEC challenge.

A key factor for assessing the state of the heart after myocardial infarction (MI) is to measure whether the myocardium segment is viable after reperfusion or revascularization therapy. Delayed enhancement-MRI or DE-MRI, which is performed 10 min after injection of the contrast agent, provides high contrast between viable and nonviable myocardium and is therefore a method of choice to evaluate the extent of MI. To automatically assess myocardial status, the results of the EMIDEC challenge that focused on this task are presented in this paper. The challenge's main objectives were twofold. First, to evaluate if deep learning methods can distinguish between non-infarct and pathological exams, i.e. exams with or without hyperenhanced area. Second, to automatically calculate the extent of myocardial infarction. The publicly available database consists of 150 exams divided into 50 cases without any hyperenhanced area after injection of a contrast agent and 100 cases with myocardial infarction (and then with a hyperenhanced area on DE-MRI), whatever their inclusion in the cardiac emergency department. Along with MRI, clinical characteristics are also provided. The obtained results issued from several works show that the automatic classification of an exam is a reachable task (the best method providing an accuracy of 0.92), and the automatic segmentation of the myocardium is possible. However, the segmentation of the diseased area needs to be improved, mainly due to the small size of these areas and the lack of contrast with the surrounding structures.

Secondary Immunosuppression in Pediatric Kidney Transplant Recipients.

OBJECTIVES: Currentimmunosuppressive treatments for kidney transplant recipients have improved graft viability at the expense of impaired immune surveillance. The tools for monitoring immune status in pediatric kidney transplant recipients have not been widely investigated. Better knowledge could help recognize over immunosuppression and allow implementation of individualized preventive strategies. MATERIALS AND METHODS: This retrospective and observational study included 28 pediatric kidney transplant recipients treated at a tertiary hospital. We measured peripheral blood lymphocyte subpopulations, immunoglobulins, immunosuppressivedrug levels, and viral loads. Reference analytical values for different age ranges were used to determine immune status. We recorded overall hospitalizations due to opportunistic infections and positive viral loads posttransplant. RESULTS: We found hypogammaglobulinemia and lymphopenia in 19% and 41% of the patients, respectively. Peripheral blood lymphocyte subpopulations were below normal limits in one-third of the sample. These parameters were not related to the current number or plasma levels of immunosuppressive drugs. During follow-up, cytomegalovirus, Epstein-Barr virus, and BK virus viremias were detected in 60.7% of the patients. Admissions due to opportunistic infections happened in 57.1%, mainly related to severe viral disease (30%) or gastrointestinal infections (26.7%). Most occurred in younger transplant recipients and during the first 2 years posttransplant (73.3%). We found no significant relation between peripheral blood lymphocyte subpopulations and hospital admissions for opportunistic infections or positive viral loads during follow-up. CONCLUSIONS: Recurrent hospitalizations for opportunistic infections and analytical disorders in the immune system suggested that secondary immunosuppression in pediatric kidney transplant recipients was frequent. Immunosuppression was not directly related to plasma drug levels or the number of immunosuppressive drugs. Thus, immune monitoring might be helpful in combination with immunosuppressant levels to assess immunosuppression status and to establish individualized preventive measures.

LA-Net: A Multi-Task Deep Network for the Segmentation of the Left Atrium.

Although atrial fibrillation (AF) is the most common sustained atrial arrhythmia, treatment success for this condition remains suboptimal. Information from magnetic resonance imaging (MRI) has the potential to improve treatment efficacy, but there are currently few automatic tools for the segmentation of the atria in MR images. In the study, we propose a LA-Net, a multi-task network optimised to simultaneously generate left atrial segmentation and edge masks from MRI. LA-Net includes cross attention modules (CAMs) and enhanced decoder modules (EDMs) to purposefully select the most meaningful edge information for segmentation and smoothly incorporate it into segmentation masks at multiple-scales. We evaluate the performance of LA-Net on two MR sequences: late gadolinium enhanced (LGE) atrial MRI and atrial short axis balanced steady state free precession (bSSFP) MRI. LA-Net gives Hausdorff distances of 12.43 mm and Dice scores of 0.92 on the LGE (STACOM 2018) dataset and Hausdorff distances of 17.41 mm and Dice scores of 0.90 on the bSSFP (in-house) dataset without any post-processing, surpassing previously proposed segmentation networks, including U-Net and SEGANet. Our method allows automatic extraction of information about the LA from MR images, which can play an important role in the management of AF patients.

Empirical leucine-to-carbon conversion factors in north-eastern Atlantic waters (50-2000 m) shaped by bacterial community composition and optical signature of DOM.

Microbial heterotrophic activity is a major process regulating the flux of dissolved organic matter (DOM) in the ocean, while the characteristics of this DOM strongly influence its microbial utilization and fate in the ocean. In order to broaden the vertical resolution of leucine-to-carbon conversion factors (CFs), needed for converting substrate incorporation into biomass production by heterotrophic bacteria, 20 dilution experiments were performed in the North Atlantic Ocean. We found a depth-stratification in empirical CFs values from epipelagic to bathypelagic waters (4.00 ± 1.09 to 0.10 ± 0.00 kg C mol Leu-1). Our results demonstrated that the customarily used theoretical CF of 1.55 kg C mol Leu-1 in oceanic samples can lead to an underestimation of prokaryotic heterotrophic production in epi- and mesopelagic waters, while it can overestimate it in the bathypelagic ocean. Pearson correlations showed that CFs were related not only to hydrographic variables such as temperature, but also to specific phylogenetic groups and DOM quality and quantity indices. Furthermore, a multiple linear regression model predicting CFs from relatively simple hydrographic and optical spectroscopic measurements was attempted. Taken together, our results suggest that differences in CFs throughout the water column are significantly connected to DOM, and also reflect differences linked to specific prokaryotic groups.

Calidad de los partes de lesiones por violencia de género en la provincia de Pontevedra / Injury report quality in cases of gender-based violence in the province of Pontevedra

Introducción: La violencia de género es un problema de salud con repercusiones jurídicas. Los/las profesionales sanitarios/as deben comunicarlo a la autoridad judicial a través del parte de lesiones. El objetivo de este estudio ha sido analizar la calidad de los partes de lesiones elaborados en casos de violencia de género, valorando si se siguen las recomendaciones de los protocolos de actuación sanitaria. Material y métodos: Se realizó un estudio retrospectivo en una muestra de 474 documentos emitidos en casos con sospecha fundada de violencia de género. Se elaboró y se aplicó una escala para valorar su calidad. Resultados: La mayoría de los documentos tenían una calidad media. En el 90% de los casos se reflejaron datos referidos al personal facultativo, a la mujer y a la primera atención sanitaria. Asimismo, la especificación de violencia de género y el tipo de violencia se cumplimentaron en el 80%. Las deficiencias más extendidas estaban relacionadas con los antecedentes de maltrato, que solo se incluyeron en el 15%, y la referencia a si la paciente acude sola o acompañada, en el 9%. Además, el 72% de los documentos presentaban abreviaturas. Los mejor elaborados fueron los partes de lesiones procedentes de atención primaria. Conclusiones: La calidad de los partes de lesiones tiene relación con la estructura de los documentos y su procedencia, poniendo de manifiesto la importancia de contar con un documento estándar, así como la necesidad de mejorar la formación y sensibilización de los/las profesionales sanitarios/as. (AU)

Introduction: Gender violence is a health problem with legal consequences. Health professionals must notify these cases to the judicial authority through injury reports. The aim of this study was to analyse the quality of injury reports in gender-based violence cases, assessing whether the recommendations of health protocols are being followed. Material and methods: A retrospective study in a sample of 474 documents issued in cases with suspicion of gender-based violence was carried out. A scale was developed and applied to assess their quality. Results: Most of the mandatory reporting documents were of medium quality. In 90% of cases data referring to medical staff, women and primary health care were reflected. Likewise, gender violence and the type of violence were specified in 80%. The most widespread deficiencies related to the history of abuse, which were only incorporated in 15% and reference to whether the patient came alone or accompanied in 9%. In addition, more than 72% of documents had abbreviations. The best injury reports were made in primary care facilities. Conclusions: The quality of injury reports is related to the structure of documents and their origin, highlighting the importance of having a standard document, as well as the need to improve training and awareness of health professionals. (AU)

Apparent diffusion coefficient agreement and reliability using different region of interest methods for the evaluation of head and neck cancer post chemo-radiotherapy.

OBJECTIVES: Post chemoradiotherapy (CRT) interval changes in apparent diffusion coefficient (ADC) have prognostic value in head and neck squamous cell cancer (HNSCC). The impact of using different region of interest (ROI) methods on interobserver agreement and their ability to reliably detect the changes in the ADC values was assessed. METHODS: Following ethical approval, 25 patients (mean age 59.5 years, 21 male) with stage 3-4 HNSCC undergoing CRT were recruited for this prospective cohort study. Diffusion weighted MRI (DW-MRI) was performed pre-treatment and at 6 and 12 weeks following CRT. Two radiologists independently delineated ROIs using whole volume (ROIv), largest area (ROIa) or representative area (ROIr) methods at primary tumour (n = 22) and largest nodal (n = 24) locations and recorded the ADCmean. When no clear focus of increased DWI signal was evident at follow-up, a standardised ROI was placed (non-measurable or NM). Bland-Altman plots and interclass correlation coefficient (ICC) were assessed. Paired t-tests evaluated interval changes in pre- and post-treatment ADCmean at each location, which were compared to the smallest detectable difference (SDD). RESULTS: Excellent agreement was obtained for all ROI methods at pre-treatment (ICC 0.94-0.98) and 6-week post-treatment (ICC 0.94-0.98). At 12-week post-treatment, agreement was excellent (ICC 0.91-0.94) apart from ROIr (ICC 0.86) and the NM nodal disease (ICC 0.87). There were significant interval increases in ADCmean between pre-treatment and post-treatment studies, which were greater than the SDD for all ROIs. CONCLUSIONS: ADCmean values can be reproducibly obtained in HNSCC using the different ROI techniques on pre- and post-CRT MRI, and this reliably detects the interval changes.

Kidney function in patients with primary distal renal tubular acidosis.

BACKGROUND: Recent reports indicate that chronic reduction of glomerular filtration rate (GFR) is common in patients with distal renal tubular acidosis (DRTA). Factors responsible for decreased GFR need clarification. METHODS: We reviewed records of 25 patients with genetically confirmed DRTA included in the RenalTube database. Patients < 18 years at diagnosis and having at least one annual follow-up were selected and classified in two groups according to GFR ≥ 90 (normal GFR) or < 90 mL/min/1.73 m2 (low GFR) after median follow-up of 8.8 years. RESULTS: Eighteen and seven patients had normal and low GFR (X ± SEM, 121.16 ± 28.87 and 71.80 ± 10.60 mL/min/1.73 m2, respectively, p < 0.01). At diagnosis, these 2 subgroups did not differ in sex, age, underlying mutated gene, GFR, height SDS, or percentage of ultrasound nephrocalcinosis. Serum creatinine (SCr) was different but likely due to median ages of presentation being 0.6 and 4.0 in normal and low GFR patients, respectively. On the last recorded visit, no differences between both groups were found in serum bicarbonate, serum potassium, or alkali dosage. Height SDS of patients with normal GFR was - 0.15 ± 0.47 whereas it was - 1.06 ± 0.60 in the low GFR group (p = 0.27). Interestingly, 23% of the whole group had low birth weight (LBW; < 2500 g), equating to 20% and 29% in the normal and low GFR patients, respectively (p = 0.65). CONCLUSIONS: Our findings confirm the risk of kidney function reduction in patients with DRTA of pediatric age onset, suggesting that low GFR is related with less favorable growth outcome and discloses the high frequency of LBW in primary DRTA, a hitherto unrecognized feature.

EP-PINNs: Cardiac Electrophysiology Characterisation Using Physics-Informed Neural Networks.

Accurately inferring underlying electrophysiological (EP) tissue properties from action potential recordings is expected to be clinically useful in the diagnosis and treatment of arrhythmias such as atrial fibrillation. It is, however, notoriously difficult to perform. We present EP-PINNs (Physics Informed Neural Networks), a novel tool for accurate action potential simulation and EP parameter estimation from sparse amounts of EP data. We demonstrate, using 1D and 2D in silico data, how EP-PINNs are able to reconstruct the spatio-temporal evolution of action potentials, whilst predicting parameters related to action potential duration (APD), excitability and diffusion coefficients. EP-PINNs are additionally able to identify heterogeneities in EP properties, making them potentially useful for the detection of fibrosis and other localised pathology linked to arrhythmias. Finally, we show EP-PINNs effectiveness on biological in vitro preparations, by characterising the effect of anti-arrhythmic drugs on APD using optical mapping data. EP-PINNs are a promising clinical tool for the characterisation and potential treatment guidance of arrhythmias.