Early detection of heart failure using in-patient longitudinal electronic health records.

Heart Failure (HF) is common, with worldwide prevalence of 1%-3% and a lifetime risk of 20% for individuals 40 years or older. Despite its considerable health economic burden, techniques for early detection of HF in the general population are sparse. In this work we tested the hypothesis that a simple Transformer neural network, trained on comprehensive collection of secondary care data across the general population, can be used to prospectively (three-year predictive window) identify patients at an increased risk of first hospitalisation due to HF (HHF).

Phase transitions in random circuit sampling.

Undesired coupling to the surrounding environment destroys long-range correlations in quantum processors and hinders coherent evolution in the nominally available computational space. This noise is an outstanding challenge when leveraging the computation power of near-term quantum processors. It has been shown that benchmarking random circuit sampling with cross-entropy benchmarking can provide an estimate of the effective size of the Hilbert space coherently available.

Evaluation of a multigenomic liquid biopsy (PROSTest) for prostate cancer detection and follow-up in a Caribbean population.

Introduction: The PROSTest is a novel machine learning-based liquid biopsy assay that functions as a diagnostic and prognostic tool in prostate cancer (PCa). The algorithm outcome (scored 0-100) has a cutoff of >50 to indicate PCa. In this study, we evaluated the screening utility of the test in comparison with the commonly used PSA test.

Accurate prediction of all-cause mortality in patients with metabolic dysfunction-associated steatotic liver disease using electronic health records.

Introduction And Objectives: Despite the huge clinical burden of MASLD, validated tools for early risk stratification are lacking, and heterogeneous disease expression and a highly variable rate of progression to clinical outcomes result in prognostic uncertainty. We aimed to investigate longitudinal electronic health record-based outcome prediction in MASLD using a state-of-the-art machine learning model.

Patients And Methods: n = 940 patients with histologically-defined MASLD were used to develop a deep-learning model for all-cause mortality prediction.

Dynamics of magnetization at infinite temperature in a Heisenberg spin chain.

Understanding universal aspects of quantum dynamics is an unresolved problem in statistical mechanics. In particular, the spin dynamics of the one-dimensional Heisenberg model were conjectured as to belong to the Kardar-Parisi-Zhang (KPZ) universality class based on the scaling of the infinite-temperature spin-spin correlation function. In a chain of 46 superconducting qubits, we studied the probability distribution of the magnetization transferred across the chain's center, [Formula: see text].

Development of a multigenomic liquid biopsy (PROSTest) for prostate cancer in whole blood.

Introduction: We describe the development of a molecular assay from publicly available tumor tissue mRNA databases using machine learning and present preliminary evidence of functionality as a diagnostic and monitoring tool for prostate cancer (PCa) in whole blood.

Materials And Methods: We assessed 1055 PCas (public microarray data sets) to identify putative mRNA biomarkers. Specificity was confirmed against 32 different solid and hematological cancers from The Cancer Genome Atlas (n = 10,990).

Stable quantum-correlated many-body states through engineered dissipation.

Engineered dissipative reservoirs have the potential to steer many-body quantum systems toward correlated steady states useful for quantum simulation of high-temperature superconductivity or quantum magnetism. Using up to 49 superconducting qubits, we prepared low-energy states of the transverse-field Ising model through coupling to dissipative auxiliary qubits. In one dimension, we observed long-range quantum correlations and a ground-state fidelity of 0.

A data-driven approach to decode metabolic dysfunction-associated steatotic liver disease.

Metabolic dysfunction-associated steatotic liver disease (MASLD), defined by the presence of liver steatosis together with at least one out of five cardiometabolic factors, is the most common cause of chronic liver disease worldwide, affecting around one in three people. Yet the clinical presentation of MASLD and the risk of progression to cirrhosis and adverse clinical outcomes is highly variable. It, therefore, represents both a global public health threat and a precision medicine challenge.

An Artificial Neural Network for Nasogastric Tube Position Decision Support.

Purpose: To develop and validate a deep learning model for detection of nasogastric tube (NGT) malposition on chest radiographs and assess model impact as a clinical decision support tool for junior physicians to help determine whether feeding can be safely performed in patients (feed/do not feed).

Materials And Methods: A neural network ensemble was pretrained on 1 132 142 retrospectively collected (June 2007-August 2019) frontal chest radiographs and further fine-tuned on 7081 chest radiographs labeled by three radiologists. Clinical relevance was assessed on an independent set of 335 images.

Single-photon detection using high-temperature superconductors.

The detection of individual quanta of light is important for quantum communication, fluorescence lifetime imaging, remote sensing and more. Due to their high detection efficiency, exceptional signal-to-noise ratio and fast recovery times, superconducting-nanowire single-photon detectors (SNSPDs) have become a critical component in these applications. However, the operation of conventional SNSPDs requires costly cryocoolers.

Micrometre-scale single-crystalline borophene on a square-lattice Cu(100) surface.

Borophene, a crystalline monolayer boron sheet, has been predicted to adopt a variety of structures-owing to its high polymorphism-that may possess physical properties that could serve in flexible electronics, energy storage and catalysis. Several borophene polymorphs have been synthesized on noble metal surfaces but for device fabrication larger single-crystal domains are typically needed with, ideally, weak borophene-substrate interactions. Here we report the synthesis of borophene on a square-lattice Cu(100) surface and show that incommensurate coordination reduces the borophene-substrate interactions and also leads to a borophene polymorph different from those previous reported.

Visualizing the unusual spectral weight transfer in DyBaCuO thin film.

We report a Spectroscopic Imaging Scanning Tunneling Microscopy (SI-STM) study of a DyBaCuO (DBCO) thin film (T ~ 79 K) synthesized by the molecular beam epitaxy (MBE). We observed an unusual transfer of spectral weight in the local density of states (LDOS) spectra occurring only within the superconducting gap. By a systematic control of the tip-sample distance and the junction resistance, we demonstrate that the spectral weight transfer can be switched at a nano-meter length scale.

C-type lectin receptor CLEC4A2 promotes tissue adaptation of macrophages and protects against atherosclerosis.

Macrophages are integral to the pathogenesis of atherosclerosis, but the contribution of distinct macrophage subsets to disease remains poorly defined. Using single cell technologies and conditional ablation via a LysM Clec4a2 mouse strain, we demonstrate that the expression of the C-type lectin receptor CLEC4A2 is a distinguishing feature of vascular resident macrophages endowed with athero-protective properties. Through genetic deletion and competitive bone marrow chimera experiments, we identify CLEC4A2 as an intrinsic regulator of macrophage tissue adaptation by promoting a bias in monocyte-to-macrophage in situ differentiation towards colony stimulating factor 1 (CSF1) in vascular health and disease.

Development and prospective validation of COVID-19 chest X-ray screening model for patients attending emergency departments.

Chest X-rays (CXRs) are the first-line investigation in patients presenting to emergency departments (EDs) with dyspnoea and are a valuable adjunct to clinical management of COVID-19 associated lung disease. Artificial intelligence (AI) has the potential to facilitate rapid triage of CXRs for further patient testing and/or isolation. In this work we develop an AI algorithm, CovIx, to differentiate normal, abnormal, non-COVID-19 pneumonia, and COVID-19 CXRs using a multicentre cohort of 293,143 CXRs.

Early Identification of Residual Disease After Neuroendocrine Tumor Resection Using a Liquid Biopsy Multigenomic mRNA Signature (NETest).

Introduction: Surgery is the only cure for neuroendocrine tumors (NETs), with R0 resection being critical for successful tumor removal. Early detection of residual disease is key for optimal management, but both imaging and current biomarkers are ineffective post-surgery. NETest, a multigene blood biomarker, identifies NETs with >90% accuracy.

Neuroendocrine Tumor Omic Gene Cluster Analysis Amplifies the Prognostic Accuracy of the NETest.

Background: The NETest is a multigene assay comprising 51 circulating neuroendocrine tumor (NET)-specific transcripts. The quotient of the 51-gene assay is based upon an ensemble of machine learning algorithms. Eight cancer hallmarks or "omes" (apoptome, epigenome, growth factor signalome, metabolome, proliferome, plurome, secretome, SSTRome) represent 29 genes.

Microbiota-Derived Metabolites Suppress Arthritis by Amplifying Aryl-Hydrocarbon Receptor Activation in Regulatory B Cells.

The differentiation of IL-10-producing regulatory B cells (Bregs) in response to gut-microbiota-derived signals supports the maintenance of tolerance. However, whether microbiota-derived metabolites can modulate Breg suppressive function remains unknown. Here, we demonstrate that rheumatoid arthritis (RA) patients and arthritic mice have a reduction in microbial-derived short-chain fatty acids (SCFAs) compared to healthy controls and that in mice, supplementation with the SCFA butyrate reduces arthritis severity.

Supervised and unsupervised language modelling in Chest X-Ray radiological reports.

Chest radiography (CXR) is the most commonly used imaging modality and deep neural network (DNN) algorithms have shown promise in effective triage of normal and abnormal radiograms. Typically, DNNs require large quantities of expertly labelled training exemplars, which in clinical contexts is a major bottleneck to effective modelling, as both considerable clinical skill and time is required to produce high-quality ground truths. In this work we evaluate thirteen supervised classifiers using two large free-text corpora and demonstrate that bi-directional long short-term memory (BiLSTM) networks with attention mechanism effectively identify Normal, Abnormal, and Unclear CXR reports in internal (n = 965 manually-labelled reports, f1-score = 0.

Disappearance of superconductivity due to vanishing coupling in the overdoped Bi[Formula: see text]Sr[Formula: see text]CaCu[Formula: see text]O[Formula: see text].

In cuprate superconductors, superconductivity is accompanied by a plethora of orders and phenomena that complicate our understanding of superconductivity in these materials. Prominent in the underdoped regime, these orders weaken or vanish with overdoping. Here, we approach the superconducting phase from the more conventional overdoped side.

The clinical applications of a multigene liquid biopsy (NETest) in neuroendocrine tumors.

Purpose: There are few effective biomarkers for neuroendocrine tumors. Precision oncology strategies have provided liquid biopsies for real-time and tailored decision-making. This has led to the development of the first neuroendocrine tumor liquid biopsy (the NETest).

PRRT neuroendocrine tumor response monitored using circulating transcript analysis: the NETest.

Purpose: Peptide receptor radionuclide therapy (PRRT) is effective for metastatic/inoperable neuroendocrine tumors (NETs). Imaging response assessment is usually efficient subsequent to treatment completion. Blood biomarkers such as PRRT Predictive Quotient (PPQ) and NETest are effective in real-time.

Aryl Hydrocarbon Receptor Contributes to the Transcriptional Program of IL-10-Producing Regulatory B Cells.

Regulatory B cells (Bregs) play a critical role in the control of autoimmunity and inflammation. IL-10 production is the hallmark for the identification of Bregs. However, the molecular determinants that regulate the transcription of IL-10 and control the Breg developmental program remain unknown.

Utility of a ready-to-use PCR system for neuroendocrine tumor diagnosis.

Background: Multigene-based PCR tests are time-consuming and limiting aspects of the protocol include increased risk of operator-based variation. In addition, such protocols are complex to transfer and reproduce between laboratories.

Aims: Evaluate the clinical utility of a pre-spotted PCR plate (PSP) for a novel multigene (n = 51) blood-based gene expression diagnostic assay for neuroendocrine tumors (NETs).

Structure-preserving visualisation of high dimensional single-cell datasets.

Single-cell technologies offer an unprecedented opportunity to effectively characterize cellular heterogeneity in health and disease. Nevertheless, visualisation and interpretation of these multi-dimensional datasets remains a challenge. We present a novel framework, ivis, for dimensionality reduction of single-cell expression data.