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Abstract Introduction The novel Confirm Rx™ implantable cardiac monitor (ICM) with SharpSense™ technology incorporates a new P‐wave discriminator designed to improve AF detection. This study aimed to evaluate the diagnostic performance of the Confirm Rx™ ICM in detecting AF episodes of varying durations. Methods We conducted a multicenter retrospective analysis of consecutive patients implanted with a Confirm Rx™ ICM (v1.2) across nine UK hospitals, all with documented AF lasting at least 6 min. Electrocardiograms (ECGs) were manually adjudicated by cardiologists. To account for intra‐ and inter‐reviewer variability, a random sample of 10% of ECGs underwent additional review. Disagreements were resolved by a third reviewer. Diagnostic performance was determined by calculating the gross and patient‐averaged positive predictive value (PPV) for AF episodes of different duration. The source of false positive (FP) detection was also categorized. Results Overall, 16,230 individual ECGs from 232 patients were included. The median AF episode duration was 14 min. R‐wave amplitude remained stable during follow‐up (0.52 ± 0.27 mV [initial] vs. 0.54 ± 0.29 mV [end of follow‐up], p = .10). The gross and patient‐averaged PPV were 75.0% and 67.0%, respectively. Diagnostic performance (gross) increased with progressively longer AF episodes: 88.0% for ≥1 h, 97.3% for 6 h, and 100% for 24 h. The main source of FP during tachycardia was T‐wave oversensing (54.2%), while in non‐tachycardic episodes it was predominantly ectopy (71.2%). The AF burden precision was excellent (93.3%). Conclusion The Confirm Rx™ ICM diagnostic performance was modest for all AF episodes (75%), with accuracy increasing for longer AF episodes.

Coronary artery disease is caused by the buildup of atherosclerotic plaque in the coronary arteries, affecting the blood supply to the heart, one of the leading causes of death around the world. X-ray coronary angiography is the most common procedure for diagnosing coronary artery disease, which uses contrast material and x-rays to observe vascular lesions. With this type of procedure, blood flow in coronary arteries is viewed in real-time, making it possible to detect stenoses precisely and control percutaneous coronary interventions and stent insertions. Angiograms of coronary arteries are used to plan the necessary revascularisation procedures based on the calculation of occlusions and the affected segments. However, their interpretation in cardiac catheterisation laboratories presently relies on sequentially evaluating multiple 2D image projections, which limits measuring lesion severity, identifying the true shape of vessels, and analysing quantitative data. In silico modelling, which involves computational simulations of patient-specific data, can revolutionise interventional cardiology by providing valuable insights and optimising treatment methods. This paper explores the challenges and future directions associated with applying patient-specific in silico models in catheterisation laboratories. We discuss the implications of the lack of patient-specific in silico models and how their absence hinders the ability to accurately predict and assess the behaviour of individual patients during interventional procedures. Then, we introduce the different components of a typical patient-specific in silico model and explore the potential future directions to bridge this gap and promote the development and utilisation of patient-specific in silico models in the catheterisation laboratories.

Abstract There have been numerous risk tools developed to enable triaging of SARS-CoV-2 positive patients with diverse levels of complexity. Here we presented a simplified risk-tool based on minimal parameters and chest X-ray (CXR) image data that predicts the survival of adult SARS-CoV-2 positive patients at hospital admission. We analysed the NCCID database of patient blood variables and CXR images from 19 hospitals across the UK using multivariable logistic regression. The initial dataset was non-randomly split between development and internal validation dataset with 1434 and 310 SARS-CoV-2 positive patients, respectively. External validation of the final model was conducted on 741 Accident and Emergency (A&E) admissions with suspected SARS-CoV-2 infection from a separate NHS Trust. The LUCAS mortality score included five strongest predictors (Lymphocyte count, Urea, C-reactive protein, Age, Sex), which are available at any point of care with rapid turnaround of results. Our simple multivariable logistic model showed high discrimination for fatal outcome with the area under the receiving operating characteristics curve (AUC-ROC) in development cohort 0.765 (95% confidence interval (CI): 0.738–0.790), in internal validation cohort 0.744 (CI: 0.673–0.808), and in external validation cohort 0.752 (CI: 0.713–0.787). The discriminatory power of LUCAS increased slightly when including the CXR image data. LUCAS can be used to obtain valid predictions of mortality in patients within 60 days of SARS-CoV-2 RT-PCR results into low, moderate, high, or very high risk of fatality.

Summary: Parkinson’s disease (PD) is characterized by a progressive deterioration of motor and cognitive functions. Although death of dopamine neurons is the hallmark pathology of PD, this is a late-stage disease process preceded by neuronal dysfunction. Here we describe early physiological perturbations in patient-derived induced pluripotent stem cell (iPSC)-dopamine neurons carrying the GBA-N370S mutation, a strong genetic risk factor for PD. GBA-N370S iPSC-dopamine neurons show an early and persistent calcium dysregulation notably at the mitochondria, followed by reduced mitochondrial membrane potential and oxygen consumption rate, indicating mitochondrial failure. With increased neuronal maturity, we observed decreased synaptic function in PD iPSC-dopamine neurons, consistent with the requirement for ATP and calcium to support the increase in electrophysiological activity over time. Our work demonstrates that calcium dyshomeostasis and mitochondrial failure impair the higher electrophysiological activity of mature neurons and may underlie the vulnerability of dopamine neurons in PD.

Summary: Background: Gallbladder cancer (GBC) is rare but aggressive. The extent of surgical intervention for different GBC stages is non-uniform, ranging from cholecystectomy alone to extended resections including major hepatectomy, resection of adjacent organs and routine extrahepatic bile duct resection (EBDR). Robust evidence here is lacking, however, and survival benefit poorly defined. This study assesses factors associated with recurrence-free survival (RFS), overall survival (OS) and morbidity and mortality following GBC surgery in high income countries (HIC) and low and middle income countries (LMIC). Methods: The multicentre, retrospective Operative Management of Gallbladder Cancer (OMEGA) cohort study included all patients who underwent GBC resection across 133 centres between 1st January 2010 and 31st December 2020. Regression analyses assessed factors associated with OS, RFS and morbidity. Findings: On multivariable analysis of all 3676 patients, wedge resection and segment IVb/V resection failed to improve RFS (HR 1.04 [0.84–1.29], p = 0.711 and HR 1.18 [0.95–1.46], p = 0.13 respectively) or OS (HR 0.96 [0.79–1.17], p = 0.67 and HR 1.48 [1.16–1.88], p = 0.49 respectively), while major hepatectomy was associated with worse RFS (HR 1.33 [1.02–1.74], p = 0.037) and OS (HR 1.26 [1.03–1.53], p = 0.022). Furthermore, EBDR (OR 2.86 [2.3–3.52], p

BackgroundPatients with a history of COVID-19 infection are reported to have cardiac abnormalities on cardiovascular magnetic resonance (CMR) during convalescence. However, it is unclear whether these abnormalities were present during the acute COVID-19 illness and how they may evolve over time.MethodsWe prospectively recruited unvaccinated patients hospitalized with acute COVID-19 (n = 23), and compared them with matched outpatient controls without COVID-19 (n = 19) between May 2020 and May 2021. Only those without a past history of cardiac disease were recruited. We performed in-hospital CMR at a median of 3 days (IQR 1–7 days) after admission, and assessed cardiac function, edema and necrosis/fibrosis, using left and right ventricular ejection fraction (LVEF, RVEF), T1-mapping, T2 signal intensity ratio (T2SI), late gadolinium enhancement (LGE) and extracellular volume (ECV). Acute COVID-19 patients were invited for follow-up CMR and blood tests at 6 months.ResultsThe two cohorts were well matched in baseline clinical characteristics. Both had normal LVEF (62 ± 7 vs. 65 ± 6%), RVEF (60 ± 6 vs. 58 ± 6%), ECV (31 ± 3 vs. 31 ± 4%), and similar frequency of LGE abnormalities (16 vs. 14%; all p > 0.05). However, measures of acute myocardial edema (T1 and T2SI) were significantly higher in patients with acute COVID-19 when compared to controls (T1 = 1,217 ± 41 ms vs. 1,183 ± 22 ms; p = 0.002; T2SI = 1.48 ± 0.36 vs. 1.13 ± 0.09; p

Cardiac anatomy and function vary considerably across the human population with important implications for clinical diagnosis and treatment planning. Consequently, many computer-based approaches have been developed to capture this variability for a wide range of applications, including explainable cardiac disease detection and prediction, dimensionality reduction, cardiac shape analysis, and the generation of virtual heart populations. In this work, we propose a variational mesh autoencoder (mesh VAE) as a novel geometric deep learning approach to model such population-wide variations in cardiac shapes. It embeds multi-scale graph convolutions and mesh pooling layers in a hierarchical VAE framework to enable direct processing of surface mesh representations of the cardiac anatomy in an efficient manner. The proposed mesh VAE achieves low reconstruction errors on a dataset of 3D cardiac meshes from over 1,000 patients with acute myocardial infarction, with mean surface distances between input and reconstructed meshes below the underlying image resolution. We also find that it outperforms a voxelgrid-based deep learning benchmark in terms of both mean surface distance and Hausdorff distance while requiring considerably less memory. Furthermore, we explore the quality and interpretability of the mesh VAE's latent space and showcase its ability to improve the prediction of major adverse cardiac events over a clinical benchmark. Finally, we investigate the method's ability to generate realistic virtual populations of cardiac anatomies and find good alignment between the synthesized and gold standard mesh populations in terms of multiple clinical metrics.

IntroductionMyelitis is the least common neuropsychiatric manifestation in systemic lupus erythematosus (SLE). Magnetic resonance imaging (MRI)-negative myelitis is even rarer. Here, we present the largest cohort of MRI-negative lupus myelitis cases to assess their clinical and immunological profiles and outcome.MethodA single-center, observational study conducted over a period of 5 years (2017–2021) was undertaken to evaluate patients with MRI-negative lupus myelitis for the epidemiological, clinical, immunological, and radiological features at baseline and followed up at monthly intervals for a year, and the outcomes were documented. Among the 22 patients that presented with MRI-negative myelopathy (clinical features suggestive of myelopathy without signal changes on spinal-cord MRI [3Tesla], performed serially at the time of presentation and 7 days, 6 weeks, and 3 months after the onset of symptoms), 8 patients had SLE and were included as the study population.ResultsIn 8 of 22 patients presenting with MRI-negative myelopathy, the etiology was SLE. MRI-negative lupus myelitis had a female preponderance (male: female ratio, 1:7). Mean age at onset of myelopathy was 30.0 ± 8.93 years, reaching nadir at 4.9 ± 4.39 weeks (Median, 3.0; range, 1.25–9.75). Clinically, cervical cord involvement was observed in 75% of patients, and 62.5% had selective tract involvement. The mean double stranded deoxyribonucleic acid, C3, and C4 titers at onset of myelopathy were 376.0 ± 342.88 IU/ml (median, 247.0), 46.1 ± 17.98 mg/dL (median, 47.5), and 7.3 ± 3.55 mg/dL (median, 9.0), respectively, with high SLE disease activity index 2,000 score of 20.6 ± 5.9. Anti-ribosomal P protein, anti-Smith antibody, and anti-ribonuclear protein positivity was observed in 87.5, 75, and 75% of the patients, respectively. On follow-up, improvement of myelopathic features with no or minimal deficit was observed in 5 of the 8 patients (62.5%). None of the patients had recurrence or new neurological deficit over 1-year follow-up.ConclusionPersistently “MRI-negative” lupus myelitis presents with white matter dysfunction, often with selective tract involvement, in light of high disease activity, which follows a monophasic course with good responsiveness to immunosuppressive therapy. A meticulous clinical evaluation and a low index of suspicion can greatly aid in the diagnosis of this rare clinical condition in lupus.

Extreme events are defined as events that largely deviate from the nominal state of the system as observed in a time series. Due to the rarity and uncertainty of their occurrence, predicting extreme events has been challenging. In real life, some variables (passive variables) often encode significant information about the occurrence of extreme events manifested in another variable (active variable). For example, observables such as temperature, pressure, etc., act as passive variables in case of extreme precipitation events. These passive variables do not show any large excursion from the nominal condition yet carry the fingerprint of the extreme events. In this study, we propose a reservoir computation-based framework that can predict the preceding structure or pattern in the time evolution of the active variable that leads to an extreme event using information from the passive variable. An appropriate threshold height of events is a prerequisite for detecting extreme events and improving the skill of their prediction. We demonstrate that the magnitude of extreme events and the appearance of a coherent pattern before the arrival of the extreme event in a time series affect the prediction skill. Quantitatively, we confirm this using a metric describing the mean phase difference between the input time signals, which decreases when the magnitude of the extreme event is relatively higher, thereby increasing the predictability skill.

Human cardiac function is characterized by a complex interplay of mechanical deformation and electrophysiological conduction. Similar to the underlying cardiac anatomy, these interconnected physiological patterns vary considerably across the human population with important implications for the effectiveness of clinical decision-making and the accuracy of computerized heart models. While many previous works have investigated this variability separately for either cardiac anatomy or physiology, this work aims to combine both aspects in a single data-driven approach and capture their intricate interdependencies in a multi-domain setting. To this end, we propose a novel multi-domain Variational Autoencoder (VAE) network to capture combined Electrocardiogram (ECG) and Magnetic Resonance Imaging (MRI)-based 3D anatomy information in a single model. Each VAE branch is specifically designed to address the particular challenges of the respective input domain, enabling efficient encoding, reconstruction, and synthesis of multi-domain cardiac signals. Our method achieves high reconstruction accuracy on a United Kingdom Biobank dataset, with Chamfer Distances between reconstructed and input anatomies below the underlying image resolution and ECG reconstructions outperforming multiple single-domain benchmarks by a considerable margin. The proposed VAE is capable of generating realistic virtual populations of arbitrary size with good alignment in clinical metrics between the synthesized and gold standard anatomies and Maximum Mean Discrepancy (MMD) scores of generated ECGs below those of comparable single-domain approaches. Furthermore, we observe the latent space of our VAE to be highly interpretable with separate components encoding different aspects of anatomical and ECG variability. Finally, we demonstrate that the combined anatomy and ECG representation improves the performance in a cardiac disease classification task by 3.9% in terms of Area Under the Receiver Operating Characteristic (AUROC) curve over the best corresponding single-domain modeling approach.

Cystic lesions of the liver are common and a major proportion is formed by parasitic cysts and simple cysts. Biliary cystic tumors (BCTs), namely biliary cystadenoma (BCA) and biliary cystadenocarcinoma (BCAC), are rare tumors which usually arise from the intrahepatic biliary tree. BCAs have malignant potential and are difficult to differentiate from BCAC pre-operatively on radiological imaging. Here we have presented 4 patients with BCTs and reviewed the literature pertaining to them.The data of four patients with BCA/BCAC diagnosed and treated at our institute were retrieved from our database and records were reviewed for age, sex, history, imaging, surgery, pathology and follow-up. Mean age of the patients was 53.5 years (range 30-71 years). Two male and two female patients presented with abdominal pain, of which one male patient had pancreatitis at diagnosis. Characteristic features were seen on pre-operative imaging (cystic lesions with internal septations) and biliary communication was identified in the patient with pancreatitis. Three patients were diagnosed with a BCA on final histology, while one patient had a BCAC. Following surgical resection, all the patients are asymptomatic and disease free with a mean follow-up of 24 months (range 10-40 months). In conclusion, BCTs should be suspected in the presence of a well-encapsulated, cystic hepatic lesion with internal septations. Although pre-operative distinction between BCA and BCAC is difficult, the lesion, whenever possible, should be completely resected as long-term outcomes are good, especially with BCA.